diff --git "a/medEvidence.csv" "b/medEvidence.csv"
--- "a/medEvidence.csv"
+++ "b/medEvidence.csv"
@@ -17,8 +17,8 @@ question_id,question,answer,evidence_certainty,fulltext_required,relevant_source
 14,"Is length gain higher, lower, or the same when comparing moderate protein concentration to low protein concentration?",higher,low,no,"['27801753', '10838460', '8906139']",33215474,2020,"{'27801753': {'article_id': '27801753', 'content': 'The aim of the study was to determine whether higher enteral protein intake leads to improved head growth at 40 weeks postmenstrual age (PMA) in preterm infants <32 weeks or 1500 g.\nRandomized controlled trial in which 120 infants were assigned to either group A with higher enteral protein intake achieved by fortification with higher protein containing fortifier (1 g/100 mL expressed breast milk) or to group B with lower enteral protein intake where fortification was done with standard available protein fortifier (0.4 g /100 mL expressed breast milk).\nThe mean (standard deviation) protein intake was higher in group A as compared to group B; 4.2 (0.47) compared with 3.6 (0.37) g\u200a·\u200akg\u200a·\u200aday, P\u200a<\u200a0.001. At 40 weeks PMA, the mean (standard deviation) weekly occipitofrontal circumference gain was significantly higher in group A as compared to group B; 0.66 (0.16) compared with 0.60 (0.15) cm/week (mean difference 0.064, 95% confidence interval [0.004-0.123], [P\u200a=\u200a0.04]). Weight growth velocity in group A was 11.95 (2.2) g\u200a·\u200akg\u200a·\u200aday as compared to 10.78 (2.6) g\u200a·\u200akg\u200a·\u200aday in group B (mean difference 1.10, 95% confidence interval [0.25-2.07], [P\u200a=\u200a0.01]). No difference was observed in the length between the 2 groups. There was no difference in growth indices and neurodevelopmental outcomes at 12 to 18 months corrected age in the 2 groups.\nFortification of expressed human milk with fortifier containing higher protein results in better head growth and weight gain at 40 weeks PMA in preterm infants <32 weeks or 1500 g without any benefits on long-term growth and neurodevelopment at 12 to 18 months corrected age (CTRI/2014/06/004661).', 'title': 'Effect of Differential Enteral Protein on Growth and Neurodevelopment in Infants <1500 g: A Randomized Controlled Trial.', 'date': '2016-11-02'}, '10838460': {'article_id': '10838460', 'content': ""Human milk fortification has been advocated to enhance premature infants' growth. We, therefore, undertook this study of a new human milk fortifier containing more protein than a reference one.\nOpen, randomized, controlled, multiclinic trial, with weekly growth parameters and safety evaluations in premature infants <1,500 g.\nThe 2 groups did not differ in demographic and baseline characteristics. The adjusted daily milk intake was significantly higher in the infants fed reference human milk fortifier (n = 29; 154.2 +/- 2.1 vs. 144.4 +/- 2.5 ml/kg/day, mean +/- SE; p < 0.05). Both human milk fortifiers produced increases over baseline in weight, length, and head circumference, with greater gains observed in the new human milk fortifier-fed infants for the former two parameters (weight gain 26.8 +/- 1.3 and 20.4 +/- 1.2 g/day, p < 0.05; head circumference 1.0 +/- 0.1 and 0.8 +/- 0.1 cm/week; length 0.9 +/- 0.1 and 0.8 +/- 0.1 cm/week, respectively). Serum chemistries were normal and acceptable for age. Study events were typical for premature infants and similar in both groups.\nThis new human milk fortifier had comparable safety to the reference human milk fortifier and promoted faster weight gain and head circumference growth."", 'title': 'Growth in human milk-Fed very low birth weight infants receiving a new human milk fortifier.', 'date': '2000-06-06'}, '8906139': {'article_id': '8906139', 'content': ""To evaluate the added nutritional value of the two commercially available human breast milk fortifiers: Similac Natural Care (NC) and Enfamil Powder (EP).\nA randomized controlled evaluation in healthy preterm neonates.\nNeonatal Intensive Care Unit, Royal University Hospital, Saskatoon, Saskatchewan, and Neonatal Intensive Care Unit, Jewish General Hospital, Montreal, Quebec, Canada.\nHealthy preterm infants admitted to and cared for in the aforementioned neonatal intensive care units.\nHealthy preterm neonates who were receiving expressed breast milk from their own mothers were supplemented with human milk fortifiers (NC and EP) per manufacturer's recommendations.\nGestational age and birth weight, gender, and race. At entry to and exit from the study, serum concentrations of albumin, protein, calcium, phosphorus, and alkaline phosphatase. The age at which the supplements were added and the number of days the infant remained in the hospital. Daily weight gain, head circumference, length, and height were also measured.\nStudent's t test was used to test the differences between the groups and within the groups at entry to and exit from the study. Fisher's exact test was used to determine differences in race, size, and gestational age in each group. When necessary, a chi 2 test was used to analyze the preponderance of either sex in each group. A Wilcoxon rank test was applied to the true exit date to determine whether the bias was comparable in each group.\nThe mean (+/- standard error) gestational age and birth weight were similar in both groups: 30 +/- 0.3 weeks and 1,314 +/- 40 g, respectively, for NC vs 29.6 +/- 0.35 weeks and 1,262 +/- 45 g, respectively, for EP. At entry to the study, values for the NC group (N = 29) were albumin 31 +/- 1.2 g/L, serum protein 48 +/- 1.4 g/L, calcium 2.4 +/- 0.03 mmol/L, phosphorus 1.85 +/- 0.08 mmol/L, alkaline phosphatase 347 +/- 27 IU/L. The values for the EP group (N = 30) were albumin 32 +/- 0.9 g/L, serum protein 49 +/- 1.4 g/L, calcium 2.4 +/- 0.4 mmol/L, phosphorus 1.9 +/- 0.1 mmol/L, alkaline phosphatase 420 +/- 34 IU/L. At the study exit, the values for the NC group were albumin 30 +/- 0.7 g/L, serum protein 45 +/- 0.9 g/L, calcium 2.4 +/- 0.3 mmol/L, phosphorus 1.96 +/- 0.07 mmol/L, and alkaline phosphatase 371 +/- 23 IU/L. The values for the EP group were albumin 32 +/- 1.0 g/L, serum protein 46.0 +/- 1.4 g/L, calcium 2.5 +/- 0.03 mmol/L, serum phosphorus 2.2 +/- 0.1, and alkaline phosphatase 367 +/- 27 IU/L. No significant differences were observed between groups at entry to and exit from the study. However, in the EP group the alkaline phosphatase decreased significantly (P = .02) from entry to exit and calcium increased significantly during the same period compared with the NC group (P = .003). The mean daily weight gain was 33 +/- 0.7 g for the NC group and 31 +/- 1 g for the EP group. The weekly gain in head circumference and body length were also similar in both groups: approximately 1 cm/week. Both groups tolerated the fortifiers well.\nThese findings suggest that both products provide the additional nutritional support necessary for optimal overall postnatal growth in healthy preterm infants. The differences in calcium and alkaline phosphatase may be due to the differences in vitamin D content in fortifiers 88 IU/100 mL in mixed NC vs 270 IU/100 mL in mixed EP. This observation calls for careful monitoring of calcium and alkaline phosphatase values and possible adjustments of vitamin D intake when fortifiers are used for extended periods."", 'title': 'A randomized, controlled evaluation of two commercially available human breast milk fortifiers in healthy preterm neonates.', 'date': '1996-11-01'}}",0.333333333,Pediatrics & Neonatology
 15,"Is head circumference gain higher, lower, or the same when comparing moderate protein concentration to low protein concentration?",higher,very low,no,"['27801753', '10838460', '8906139']",33215474,2020,"{'27801753': {'article_id': '27801753', 'content': 'The aim of the study was to determine whether higher enteral protein intake leads to improved head growth at 40 weeks postmenstrual age (PMA) in preterm infants <32 weeks or 1500 g.\nRandomized controlled trial in which 120 infants were assigned to either group A with higher enteral protein intake achieved by fortification with higher protein containing fortifier (1 g/100 mL expressed breast milk) or to group B with lower enteral protein intake where fortification was done with standard available protein fortifier (0.4 g /100 mL expressed breast milk).\nThe mean (standard deviation) protein intake was higher in group A as compared to group B; 4.2 (0.47) compared with 3.6 (0.37) g\u200a·\u200akg\u200a·\u200aday, P\u200a<\u200a0.001. At 40 weeks PMA, the mean (standard deviation) weekly occipitofrontal circumference gain was significantly higher in group A as compared to group B; 0.66 (0.16) compared with 0.60 (0.15) cm/week (mean difference 0.064, 95% confidence interval [0.004-0.123], [P\u200a=\u200a0.04]). Weight growth velocity in group A was 11.95 (2.2) g\u200a·\u200akg\u200a·\u200aday as compared to 10.78 (2.6) g\u200a·\u200akg\u200a·\u200aday in group B (mean difference 1.10, 95% confidence interval [0.25-2.07], [P\u200a=\u200a0.01]). No difference was observed in the length between the 2 groups. There was no difference in growth indices and neurodevelopmental outcomes at 12 to 18 months corrected age in the 2 groups.\nFortification of expressed human milk with fortifier containing higher protein results in better head growth and weight gain at 40 weeks PMA in preterm infants <32 weeks or 1500 g without any benefits on long-term growth and neurodevelopment at 12 to 18 months corrected age (CTRI/2014/06/004661).', 'title': 'Effect of Differential Enteral Protein on Growth and Neurodevelopment in Infants <1500 g: A Randomized Controlled Trial.', 'date': '2016-11-02'}, '10838460': {'article_id': '10838460', 'content': ""Human milk fortification has been advocated to enhance premature infants' growth. We, therefore, undertook this study of a new human milk fortifier containing more protein than a reference one.\nOpen, randomized, controlled, multiclinic trial, with weekly growth parameters and safety evaluations in premature infants <1,500 g.\nThe 2 groups did not differ in demographic and baseline characteristics. The adjusted daily milk intake was significantly higher in the infants fed reference human milk fortifier (n = 29; 154.2 +/- 2.1 vs. 144.4 +/- 2.5 ml/kg/day, mean +/- SE; p < 0.05). Both human milk fortifiers produced increases over baseline in weight, length, and head circumference, with greater gains observed in the new human milk fortifier-fed infants for the former two parameters (weight gain 26.8 +/- 1.3 and 20.4 +/- 1.2 g/day, p < 0.05; head circumference 1.0 +/- 0.1 and 0.8 +/- 0.1 cm/week; length 0.9 +/- 0.1 and 0.8 +/- 0.1 cm/week, respectively). Serum chemistries were normal and acceptable for age. Study events were typical for premature infants and similar in both groups.\nThis new human milk fortifier had comparable safety to the reference human milk fortifier and promoted faster weight gain and head circumference growth."", 'title': 'Growth in human milk-Fed very low birth weight infants receiving a new human milk fortifier.', 'date': '2000-06-06'}, '8906139': {'article_id': '8906139', 'content': ""To evaluate the added nutritional value of the two commercially available human breast milk fortifiers: Similac Natural Care (NC) and Enfamil Powder (EP).\nA randomized controlled evaluation in healthy preterm neonates.\nNeonatal Intensive Care Unit, Royal University Hospital, Saskatoon, Saskatchewan, and Neonatal Intensive Care Unit, Jewish General Hospital, Montreal, Quebec, Canada.\nHealthy preterm infants admitted to and cared for in the aforementioned neonatal intensive care units.\nHealthy preterm neonates who were receiving expressed breast milk from their own mothers were supplemented with human milk fortifiers (NC and EP) per manufacturer's recommendations.\nGestational age and birth weight, gender, and race. At entry to and exit from the study, serum concentrations of albumin, protein, calcium, phosphorus, and alkaline phosphatase. The age at which the supplements were added and the number of days the infant remained in the hospital. Daily weight gain, head circumference, length, and height were also measured.\nStudent's t test was used to test the differences between the groups and within the groups at entry to and exit from the study. Fisher's exact test was used to determine differences in race, size, and gestational age in each group. When necessary, a chi 2 test was used to analyze the preponderance of either sex in each group. A Wilcoxon rank test was applied to the true exit date to determine whether the bias was comparable in each group.\nThe mean (+/- standard error) gestational age and birth weight were similar in both groups: 30 +/- 0.3 weeks and 1,314 +/- 40 g, respectively, for NC vs 29.6 +/- 0.35 weeks and 1,262 +/- 45 g, respectively, for EP. At entry to the study, values for the NC group (N = 29) were albumin 31 +/- 1.2 g/L, serum protein 48 +/- 1.4 g/L, calcium 2.4 +/- 0.03 mmol/L, phosphorus 1.85 +/- 0.08 mmol/L, alkaline phosphatase 347 +/- 27 IU/L. The values for the EP group (N = 30) were albumin 32 +/- 0.9 g/L, serum protein 49 +/- 1.4 g/L, calcium 2.4 +/- 0.4 mmol/L, phosphorus 1.9 +/- 0.1 mmol/L, alkaline phosphatase 420 +/- 34 IU/L. At the study exit, the values for the NC group were albumin 30 +/- 0.7 g/L, serum protein 45 +/- 0.9 g/L, calcium 2.4 +/- 0.3 mmol/L, phosphorus 1.96 +/- 0.07 mmol/L, and alkaline phosphatase 371 +/- 23 IU/L. The values for the EP group were albumin 32 +/- 1.0 g/L, serum protein 46.0 +/- 1.4 g/L, calcium 2.5 +/- 0.03 mmol/L, serum phosphorus 2.2 +/- 0.1, and alkaline phosphatase 367 +/- 27 IU/L. No significant differences were observed between groups at entry to and exit from the study. However, in the EP group the alkaline phosphatase decreased significantly (P = .02) from entry to exit and calcium increased significantly during the same period compared with the NC group (P = .003). The mean daily weight gain was 33 +/- 0.7 g for the NC group and 31 +/- 1 g for the EP group. The weekly gain in head circumference and body length were also similar in both groups: approximately 1 cm/week. Both groups tolerated the fortifiers well.\nThese findings suggest that both products provide the additional nutritional support necessary for optimal overall postnatal growth in healthy preterm infants. The differences in calcium and alkaline phosphatase may be due to the differences in vitamin D content in fortifiers 88 IU/100 mL in mixed NC vs 270 IU/100 mL in mixed EP. This observation calls for careful monitoring of calcium and alkaline phosphatase values and possible adjustments of vitamin D intake when fortifiers are used for extended periods."", 'title': 'A randomized, controlled evaluation of two commercially available human breast milk fortifiers in healthy preterm neonates.', 'date': '1996-11-01'}}",0.666666667,Pediatrics & Neonatology
 16,"Is weight gain higher, lower, or the same when comparing moderate protein concentration to low protein concentration?",higher,very low,no,"['27801753', '10838460']",33215474,2020,"{'27801753': {'article_id': '27801753', 'content': 'The aim of the study was to determine whether higher enteral protein intake leads to improved head growth at 40 weeks postmenstrual age (PMA) in preterm infants <32 weeks or 1500 g.\nRandomized controlled trial in which 120 infants were assigned to either group A with higher enteral protein intake achieved by fortification with higher protein containing fortifier (1 g/100 mL expressed breast milk) or to group B with lower enteral protein intake where fortification was done with standard available protein fortifier (0.4 g /100 mL expressed breast milk).\nThe mean (standard deviation) protein intake was higher in group A as compared to group B; 4.2 (0.47) compared with 3.6 (0.37) g\u200a·\u200akg\u200a·\u200aday, P\u200a<\u200a0.001. At 40 weeks PMA, the mean (standard deviation) weekly occipitofrontal circumference gain was significantly higher in group A as compared to group B; 0.66 (0.16) compared with 0.60 (0.15) cm/week (mean difference 0.064, 95% confidence interval [0.004-0.123], [P\u200a=\u200a0.04]). Weight growth velocity in group A was 11.95 (2.2) g\u200a·\u200akg\u200a·\u200aday as compared to 10.78 (2.6) g\u200a·\u200akg\u200a·\u200aday in group B (mean difference 1.10, 95% confidence interval [0.25-2.07], [P\u200a=\u200a0.01]). No difference was observed in the length between the 2 groups. There was no difference in growth indices and neurodevelopmental outcomes at 12 to 18 months corrected age in the 2 groups.\nFortification of expressed human milk with fortifier containing higher protein results in better head growth and weight gain at 40 weeks PMA in preterm infants <32 weeks or 1500 g without any benefits on long-term growth and neurodevelopment at 12 to 18 months corrected age (CTRI/2014/06/004661).', 'title': 'Effect of Differential Enteral Protein on Growth and Neurodevelopment in Infants <1500 g: A Randomized Controlled Trial.', 'date': '2016-11-02'}, '10838460': {'article_id': '10838460', 'content': ""Human milk fortification has been advocated to enhance premature infants' growth. We, therefore, undertook this study of a new human milk fortifier containing more protein than a reference one.\nOpen, randomized, controlled, multiclinic trial, with weekly growth parameters and safety evaluations in premature infants <1,500 g.\nThe 2 groups did not differ in demographic and baseline characteristics. The adjusted daily milk intake was significantly higher in the infants fed reference human milk fortifier (n = 29; 154.2 +/- 2.1 vs. 144.4 +/- 2.5 ml/kg/day, mean +/- SE; p < 0.05). Both human milk fortifiers produced increases over baseline in weight, length, and head circumference, with greater gains observed in the new human milk fortifier-fed infants for the former two parameters (weight gain 26.8 +/- 1.3 and 20.4 +/- 1.2 g/day, p < 0.05; head circumference 1.0 +/- 0.1 and 0.8 +/- 0.1 cm/week; length 0.9 +/- 0.1 and 0.8 +/- 0.1 cm/week, respectively). Serum chemistries were normal and acceptable for age. Study events were typical for premature infants and similar in both groups.\nThis new human milk fortifier had comparable safety to the reference human milk fortifier and promoted faster weight gain and head circumference growth."", 'title': 'Growth in human milk-Fed very low birth weight infants receiving a new human milk fortifier.', 'date': '2000-06-06'}}",1.0,Pediatrics & Neonatology
-17,"Is head circumference gain higher, lower, or the same when comparing high protein concentration low protein concentration?",uncertain effect,very low,no,"['26488118', '22301933', '22987877', '29772833', '28727654']",33215474,2020,"{'26488118': {'article_id': '26488118', 'content': ""J Pediatr Gastroenterol NutrJ. Pediatr. Gastroenterol. NutrJPGAJournal of Pediatric Gastroenterology and Nutrition0277-21161536-4801Lippincott Williams & Wilkins26488118464595610.1097/MPG.000000000000101000012Original Articles: NutritionGrowth and Tolerance of Preterm Infants Fed a New Extensively Hydrolyzed Liquid Human Milk FortifierKimJae H.∗ChanGary†SchanlerRichard‡Groh-WargoSharon§BloomBarry||DimmitReed¶WilliamsLarry#BaggsGeraldine#Barrett-ReisBridget#∗University of California, San Diego-Rady Children's Hospital of San Diego, San Diego†University of Utah, Salt Lake City‡Cohen Children's Medical Center of New York, New Hyde Park§Case Western Reserve University, MetroHealth Medical Center, Cleveland, OH||Wesley Medical Center, Wichita, KS¶University of Alabama, Birmingham#Abbott Nutrition, Columbus, OH.Address correspondence and reprint requests to Jae H. Kim, MD, PhD, University of California, San Diego, 200 W Arbor Dr, MPF 1140, San Diego, CA 92103 (e-mail: neojae@ucsd.edu).12201524112015616665671212201512102015Copyright 2015 by ESPGHAN and NASPGHAN. Unauthorized reproduction of this article is prohibited.2015This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License, where it is permissible to download and share the work, provided it is properly cited. The work cannot be changed in any way or used commercially. http://creativecommons.org/licenses/by-nc-nd/4.0ABSTRACTObjectives:This study was a comparison of growth and tolerance in premature infants fed either standard powdered human milk fortifier (HMF) or a newly formulated concentrated liquid that contained extensively hydrolyzed protein.Methods:This was an unblinded randomized controlled multicenter noninferiority study on preterm infants receiving human milk (HM) supplemented with 2 randomly assigned HMFs, either concentrated liquid HMF containing extensively hydrolyzed protein (LE-HMF) or a powdered intact protein HMF (PI-HMF) as the control. The study population consisted of preterm infants ≤33 weeks who were enterally fed HM. Infants were studied from the first day of HM fortification until day 29 or hospital discharge, whichever came first.Results:A total of 147 preterm infants were enrolled. Noninferiority was observed in weight gain reported in the intent-to-treat (ITT) analysis was 18.2 and 17.5 g · kg−1 · day−1 for the LE-HMF and PI-HMF groups, respectively. In an a priori defined subgroup of strict protocol followers (n\u200a=\u200a75), the infants fed LE-HMF achieved greater weight over time than those fed PI-HMF (P\u200a=\u200a0.036). The LE-HMF group achieved greater linear growth over time compared to the PI-HMF (P\u200a=\u200a0.029). The protein intake from fortified HM was significantly higher in the LE-HMF group compared with the PI-HMF group (3.9 vs 3.3 g · kg−1 · day−1, P\u200a<\u200a0.0001). Both fortifiers were well tolerated with no significant differences in overall morbidity.Conclusions:Both fortifiers showed excellent weight gain (grams per kilograms per day), tolerance, and low incidence of morbidity outcomes with the infants who were strict protocol followers fed LE-HMF having improved growth during the study. These data point to the safety and suitability of this new concentrated liquid HMF (LE-HMF) in preterm infants. Growth with this fortifier closely matches the recent recommendations for a weight gain of >18 g · kg−1 · day−1.Keywordsbreast-feedinggrowthhuman milk fortifierpreterm infantsOPEN-ACCESSTRUEWhat Is KnownPowdered infant milk products cannot be sterilized and is a source of bacterial infection.Very-low-birth-weight infants often require more protein than presently can be provided with conventional human milk fortifiers.A liquid fortifier with higher protein than conventional fortifiers is desirable to increase safety and improved growth.What Is NewA liquid human milk fortifier that is based on extensively hydrolyzed bovine casein with higher amounts of total protein than powder human milk fortifiers confers equal to improved growth to very-low-birth-weight infantsUse of this new liquid fortifier provides sterility without any increase in feeding intolerance or short-term adverse effects.Human milk (HM) is a source of essential nutrients and immunologic factors for the preterm infant, but alone it is not sufficient nutritionally to meet the high demands of the rapidly growing infant. Human milk fortifiers (HMFs) are nutritional supplements designed to increase total energy, protein, and micronutrient delivery to preterm infants. The primary benefits of HM fortification have been improved growth, bone mineralization, and protein status such as blood urea nitrogen (BUN) (1–5).Increasing survival and improving growth of the preterm infant to avoid extrauterine growth restriction have resulted in demands for protein that present powdered HMF may not achieve. Although some of these infants may compensate with higher volume intake, many are unable to consume a sufficient volume because of pulmonary or other clinical issues and therefore require further concentration of protein and energy. Higher intake of protein between 3 and 4 g · kg−1 · day−1 has been associated with improved growth without complications compared with a lower consumption of protein (<3 g · kg−1 · day−1) (6). Poor weight gain has been associated with a higher risk for retinopathy of prematurity and poor neurodevelopmental outcomes (7,8). It is common practice in the neonatal intensive care units (NICUs) to add protein modular (powder or liquid) to the feeding to better meet the protein needs of the smaller preterm infant. In fact, 42% of the respondents to a recent survey on nutritional practices in the NICU reported adding protein to HM (9).There has been a gradual transition to sterile liquid nutritionals in the neonatal environment during the last decade because of concerns about powder-based transmission of pathogens such as Cronobacteria sakasakii(10). The recent development of a liquid HM–based HMF and a partially hydrolyzed whey-acidified liquid HMF respond to these concerns (11,12). Unlike powder nutritionals, a liquid HMF may have the advantage of sterility and simpler liquid-liquid mixing with human milk (HM). One disadvantage of a liquid fortifier is volume displacement of HM.In this study, we evaluated a novel liquid HMF containing extensively hydrolyzed protein source to determine efficacy and safety in very-low-birth-weight preterm infants.METHODSStudy PopulationA total of 14 NICUs from across the United States participated in this study, including Tampa, Florida; Wichita, Kansas; Toledo, Ohio; Salt Lake City, Utah; Birmingham, Alabama; Cleveland, Ohio; Allentown, Pennsylvania; San Diego, California; Valhalla, New York; Manhasset, New York; Portland, Oregon; Cleveland, Ohio; South Bend, India; and Brooklyn, New York. The study population consisted of preterm infants born at ≤33 weeks’ gestational age with birth weights ranging from 700 to 1500 g who were enterally fed HM in the NICU. Infants identified as eligible for randomization and for whom consent was obtained were randomly assigned to one of the 2 study regimens. Sealed envelopes containing the subject treatment group assignment were prepared from randomization schedules that were computer-generated using a pseudorandom permuted blocks algorithm. A separate computer-generated randomization schedule was produced for twins to ensure that eligible twins were both assigned to the same product. The randomization was block stratified by birth weight (700–1000 g and 1000–1500\u200ag) and sex.Eligibility criteria included appropriate intrauterine growth and maternal intent to provide breast milk during the study. The use of donor HM was not permitted during the study period unless indicated by the clinical staff or PI but could have been used in the first week of life before study initiation. Infants were excluded for enteral feeds not started within 21 days of life, severe congenital anomalies, expectant transfer to another facility, 5-minute Apgar <5, severe intraventricular hemorrhage (grade 3 or 4), mechanical ventilation, major abdominal surgery, severe asphyxia, and necrotizing enterocolitis (NEC). Use of probiotics or postnatal corticosteroids was not permitted.Study DesignThis was an unblinded randomized controlled multicenter study conducted on preterm infants receiving HM supplemented with 2 randomly assigned HMFs, either a newly formulated concentrated liquid HMF containing extensively hydrolyzed protein (Abbott Nutrition, Columbus, OH; LE-HMF) or a conventional powdered intact protein HMF (Similac Human Milk Fortifier, PI-HMF, Abbott Nutrition) as control. For every 25 mL of HM, HMF was added as a 5-mL dose of LE-HMF or 1 single packet of PI-HMF. Study Day (SDAY) 1 was defined as the first day of HM fortification and occurred within 72 hours after the subject had reached an intake of at least 100 mL · kg−1 · day−1 of HM. The primary study period was from SDAY 1 until SDAY 29 or hospital discharge, whichever came first. This study was approved by institutional research ethics board as appropriate at each study sites. Table 1 shows the key study fortifier differences.Anthropometric indices (weight, length, and head circumference [HC]), tolerance, serum biochemistries, intake, and morbidity data were assessed. Anthropometric variables and tolerance outcomes were collected after SDAY 29 if the infant remained on study HMF.Weight, length, and HC of infants were measured according to standardized procedures from SDAY 1 to SDAY 29 or hospital discharge, whichever came first. Weight measures were taken daily using the hospital scales (incubator or bedside). Documentation of scale calibration was reviewed during routine visits. The other anthropometric measurements were performed weekly. Recumbent length was obtained with a fixed headboard and moveable footboard and HC using a nonstretchable tape.Feeding tolerance was assessed by variables such as stool characteristics (bloody, hard, black, and/or watery) and the incidence of feedings withheld because of abdominal distention, gastric residuals, and vomiting. Any nil per os periods were also collected.Enteral intake was collected from enrollment to SDAY 29. Intake of HM (including donor/banked HM) or other enteral feeding (including supplements such as protein modulars) were recorded. Although the LE-HMF contained the same amount of energy as the PI-HMF, it contained higher protein and a different source of protein. It also contained added lutein, docosahexaenoic acid, and arachidonic acid.Blood samples were drawn from each infant by venipuncture or, if necessary, by heelstick on SDAYs 1, 15, and 29. Serum electrolytes, bicarbonate, calcium, phosphorus, magnesium, alkaline phosphatase, BUN, and prealbumin were analyzed at the hospital site. Confirmed NEC (determined by using modified Bell staging criteria) and sepsis were recorded. The occurrence of these and other serious adverse events was documented throughout the study.Statistical AnalysisStudy data were analyzed on an intent-to-treat (ITT) basis including all enrolled infants who received study fortifier. Based on anticipated protocol deviations in this high-risk population, a subgroup analysis was prospectively planned to analyze data from infants who strictly adhered to the assigned HMF. The strict protocol followers (SPFs) were defined a priori as those infants who received <20% of total energy from sources other than the assigned study HMF; and <3 consecutive days on modular supplements (eg, protein supplements, another study HMF, nonstudy formula, or donor milk) for at least 2 weeks from SDAY 1 to SDAY 29.Sample size was calculated to test the hypothesis that LE-HMF was noninferior to PI-HMF using an equivalence limit of 1.6 g · kg−1 · day−1 in weight gain per day. With a noninferiority hypothesis and assuming that the expected difference in means is zero and the common standard deviation is 2.56 g · kg−1 · day−1, the total sample size required to have 80% power was 66 subjects who are SPF (33 per group). The power for this unbalanced sample size distribution is 83%. Assuming an attrition rate of approximately 46%, the target number for enrollment was 124 subjects (62 per group). A study designed for noninferiority does not preclude testing for superiority (13). Weight gain (grams per kilogram per day) for each subject was calculated by an exponential model that involved a regression line fit on loge (wt), where wt is weight (in grams) on each day (13). Weight gain (grams per kilogram per day) was analyzed using analysis of variance with factors for center and feeding (primary). Analyses were also made adjusting for sex, birth weight, and average fortified HM intake (milliliters per kilogram per day) diluted full strength during the study period. A 95% 1-sided confidence interval for the difference in means between groups was used for noninferiority evaluation.Length (centimeters per week) and HC gains (centimeters per week) were analyzed using the same models. Weight, length, and HC collected at 1-week intervals were analyzed with repeated measures analysis of covariance (ANCOVA) testing effects of center, feeding, sex, study day, interaction of feeding with sex, feeding with study day, and covariate birth weight. By time point analyses of weight, length, and HC using ANCOVA were made post-hoc using 1-sided tests consistent with a noninferiority design.Average daily volume enteral intake (milliliters per kilogram per day) was analyzed using analysis of variance. Complete blood cell counts with differential and serum blood biochemistries were analyzed using repeated measures ANCOVA with covariate SDAY 1 measure.Outcomes expressed as percent of infants (tolerance, morbidity, and respiratory variables) were analyzed using the Cochran-Mantel-Haenzsel test stratified by center. The frequencies of occurrence of adverse events by system organ class and preferred terms using MedDRA codes were tabulated and analyzed using Fisher exact test. Hypothesis testing for this study was done using 2-sided, 0.05 level tests. All analyses were made using SAS version 9.2 (SAS Institute, Cary, NC) on a computer.RESULTSStudy PopulationA total of 147 subjects were randomized into the study. Of the 147 subjects, 129 were included in the ITT group, that is, all randomized subjects who received study HMF. Of those subjects in the ITT group, 75% completed the study duration (45 PI-HMF, 52 LE-HMF). More than half the infants in the ITT group met the definition for the SPFs (Fig. 1). The number of days on the assigned study fortifier was 25 and 29 for the PI-HMF (n\u200a=\u200a63) and LE-HMF (n\u200a=\u200a66) groups, respectively. The median number of days on the assigned study fortifier for SPF was 29 days for both the PI-HMF and LE-HMF groups as some extended their use beyond the study period. Of note, some SPF subjects did not complete the study duration because they were discharged from the hospital.FIGURE 1Disposition of subjects.Demographic and Other Baseline CharacteristicsCharacteristics of the study patients are summarized in Table 2. There were no statistically significant differences among study subjects randomized to the PI-HMF or the LE-HMF group in gestational age, sex, race, mode of delivery and multiple birth status. There were, however, more Hispanic infants in the PI-HMF as compared to the LE-HMF group (28% vs 13%, P\u200a=\u200a0.041). In addition, there were no statistical differences between groups at birth or SDAY 1 for weight, length, and HC. Furthermore, there were no differences in clinical history and progression of enteral feeds. Infants in the 2 feeding groups who were SPF reflect comparable demographic and baseline characteristics patterns.GrowthThere were no statistical differences in the primary outcome of weight gain (grams per kilogram per day) during the study period regardless of whether the statistical analysis was performed on the ITT group or SPFs. Hence, noninferiority was achieved. Respective weight gains were 17.5 and 18.2 g · kg−1 · day−1 for PI-HMF and LE-HMF (Table 3). Likewise in the subgroup (SPF) analysis weight gains were 18.2 and 18.4 g · kg−1 · day−1 for PI-HMF and LE-HMF. There was, however, a main feeding effect that was the infants fed LE-HMF compared with infants fed PI-HMF had increased weight during the study among SPFs as depicted in Fig. 2A (P\u200a=\u200a0.036). When analyzing the data at separate time points the weight at SDAY 29 was significantly higher in LE-HMF group versus the PI-HMF group (P\u200a=\u200a0.024). Likewise, infants in the ITT group fed LE-HMF had higher weights at SDAYs 15, 22, and 29 than infants fed PI-HMF whether or not adjusted for differences in ethnicity. The SPF infants receiving LE-HMF reached 1800 g 7 days sooner than the infants fed PI-HMF (19 vs 26 days, respectively, P\u200a=\u200a0.049).FIGURE 2Evaluable analysis: A, weight (in grams); B, length (in centimeters); C, head circumference (in centimeters). A, Weight (in grams). Repeated measures analysis main effect, P\u200a=\u200a0.036; post-hoc per time point analysis: SDAY 29, P\u200a=\u200a0.024. B, Length (in centimeters). Repeated measures analysis main effect, P\u200a=\u200a0.029; post-hoc per time point analysis: SDAY 22, P\u200a=\u200a0.006, SDAY 29, P\u200a=\u200a0.037. C, Head circumference (in centimeters).The length and HC gains (centimeters per week) during the study period revealed no statistical differences between the groups and met growth targets (Table 3). The infants fed LE-HMF compared with infants fed PI-HMF had increased linear growth during the study among SPFs as depicted in Fig. 2B (P\u200a=\u200a0.029). When analyzing the data at separate time points adjusted for birth length, the length at SDAY 22 and SDAY 29 were significantly higher in LE-HMF group versus the PI-HMF group (P\u200a<\u200a0.05). HC was not different between the fortifier groups (Fig. 2C).Feeding Tolerance and Stool CharacteristicsIn both the ITT and SPF groups, both fortifiers were well tolerated with similar number and percentage of infants having feedings withheld because of abdominal distention, gastric residuals and/or vomiting. There was no difference in the percentage of infants who were nil per os between the groups (22.7 LE-HMF, 19 PI-HMF). The stool characteristics in both groups were similar with no differences in bloody stools, hard stools or black stools. Loose stools were commonly reported—56% in the PI-HMF group and 53% in the LE-HMF group—and were considered normal for infants who are receiving HM as their primary feeding.Enteral NutritionThe mean caloric and protein intakes are reported for both HMF groups. For the SPFs, the average percentage of calories from fortified HM was ∼96% in both the PI-HMF and LE-HMF groups. The mean intake of fortified HM was 116 and 114 kcal · kg−1 · day−1 in the PI-HMF and LE-HMF groups, respectively. The calculated protein intake from fortified HM was significantly higher in the LE-HMF group as compared to the PI-HMF group (3.9 vs 3.3\u200a g · kg−1 · day−1, P\u200a<\u200a0.0001). This difference was expected as LE-HMF contains more protein than PI-HMF. Energy intakes were not different between the groups.Blood ChemistriesThe blood chemistries reported in Table 4 include bicarbonate, BUN, prealbumin, calcium, phosphorus, magnesium, alkaline phosphatase, and electrolytes. In general, the blood biochemistries at SDAYs 1, 15, and 29 were within the normal reference ranges for preterm infants for both the ITT and SPF groups fed milk fortified with either fortifier (14,15). There were significant differences between groups in both the ITT and SPF analyses for BUN (P\u200a<\u200a0.001) and prealbumin (P\u200a<\u200a0.01), with both being higher in the LE-HMF group. Both groups were well within reference ranges for these parameters. Bicarbonate was significantly higher in the LE-HMF group only at SDAY1 in the ITT analysis.Safety and Morbidity DataIn the ITT group, fewer infants discontinued fortifier because of feeding intolerance in the LE-HMF group as compared to the PI-HMF group (2% vs 10%, P\u200a=\u200a0.048). There was a low incidence of confirmed NEC (1.5% in the LE-HMF group and 3.2% in the PI-HMF group) and confirmed sepsis (4.5% vs 3.2%, respectively)DISCUSSIONThe purpose of developing LE-HMF was to provide a concentrated liquid fortifier that would be superior to conventional powder HMF by virtue of sterility, higher protein concentration, and absence of intact cow's-milk protein. An extensively hydrolyzed protein source is included to promote feeding tolerance in preterm infants. The extensively hydrolyzed protein may be tolerated better for infants who are sensitive to the intact cow's-milk protein.The primary purpose of the present clinical trial was to assess whether the new HMF would promote targeted weight gain, with good tolerance and without association with specific comorbidities in a noninferiority comparison with a commercially available powder HMF that has demonstrated safety and efficacy in preterm infants (13).Weight gain and linear growth approaching intrauterine rates are important goals in the management of premature infants. The mean weight gain for both groups (PI-HMF and LE-HMF) exceeded the intrauterine growth rate of 15 g · kg−1 · day−1 and closely matched recent recommendations for a weight gain of >18 g · kg−1 · day−1(7). The mean HC gain for both groups also closely matched recent recommendations for a HC gain of >0.9 cm/wk (7). This result was not surprising given the excellent weight, length, and HC gains previously reported in infants fed PI-HMF powder (13).Ehrenkranz et al (7) have reported that as the rate of weight gain increased in hospitalized preterm infants, the incidence of cerebral palsy, neurodevelopmental impairment, and need for re-hospitalization decreased significantly. A weight gain rate of >18 g · kg−1 · day−1 and a HC growth rate of >0.9 cm/wk were associated with better neurodevelopmental and growth outcomes. Lower quartile growth was associated with the poorest neurodevelopmental outcomes.Weight and length differed between the groups. Although there were no significant differences in mean weight at birth or SDAY 1, infants receiving LE-HMF had ∼½ lb greater mean weight than the infants in the PI-HMF group at the end of the study period. Although the rate of linear growth was not statistically different, infants in the LE-HMF group had greater achieved linear growth during the study period. It is possible that the greater weight and length in the LE-HMF infants was because of the higher number of infants in this group that adhered to the assigned study feeding.New expert recommendations suggest that extremely-low-birth-weight infants (<1000 g birth weight) have higher protein requirements (3.5–4.5 g/100 kcal) (16). HMFs provide an important strategy to overcoming nutrient deficits for preterm and low-birth-weight infants. Differences in the level and ingredient sources of the macronutrients, especially the protein quantity, in PI-HMF versus LE-HMF may have contributed to the overall performance of the LE-HMF group. The higher protein intake in infants receiving LE-HMF (∼3.6 g/100 kcal) as compared to PI-HMF (∼3.0 g/100 kcal) was likely one of the reasons for the improved growth observed in these infants. Although infants in the LE-HMF group had higher protein intakes, energy intakes were not different between the groups.Preterm infants fed fortified HM have variable rates of growth at least partly because of differences in intake of calories, carbohydrates, electrolytes, calcium, phosphate, and protein. The acid-base status of the preterm infant also, however, affects growth. In preterm infants the kidney may not tolerate an acid load, leading to the development of metabolic acidosis. In a recent study, a liquid acidified HMF caused metabolic acidosis and poor growth in preterm infants in the NICU (17,18). In another study, Rochow et al (19) described a commercially available fortifier in Europe that had to be reformulated because of the development of metabolic acidosis from an imbalance of electrolytes. The authors recorded a mean weight gain of only 9.7 g · kg−1 · day−1 and decreased bone mineralization with metabolic acidosis. No infants in our study developed metabolic acidosis.The LE-HMF protein source may be beneficial for this population because it was extensively hydrolyzed casein formulation without any intact cow's-milk protein. It has been suggested that a combination of free amino acids and short chain peptides (di- and tri peptides) may allow more optimal nitrogen absorption (20,21). Intact bovine protein powder HMF has an excellent safety record; however, a recent study by Sullivan et al (11) suggested the possibility that even in the presence of a HM base diet, the addition of intact bovine protein powder HMF is associated with higher rates of total and surgical NEC. The mechanism for the higher NEC risk is not known yet. Although this study was not powdered for NEC there was no difference in the NEC or sepsis rates between the infants fed an intact bovine protein and the extensively hydrolyzed protein. Both groups had rates lower than previously reported (22–24).Intact bovine protein has higher associated long-term risk for allergy and atopy compared with HM-fed infants. Protein intolerance is seen in premature infants and in term infants (25). Because preterm infants have a similar risk for allergy and atopy compared with term infants and in the NICU have presented with symptoms suggestive of allergic colitis, avoiding intact bovine protein may be a desirable objective. For preterm infants fed HM the use of an extensively hydrolyzed protein-based HMF is an appropriate option.In general, blood chemistries were within normal reference ranges for preterm infants. The higher BUN and prealbumin seen in the LE-HMF group can be attributed to the higher protein content of LE-HMF. These higher values may be indicative of improved protein nutriture. It should be noted that although BUN is influenced by renal function and hydration state, all other influences being equal, it is proportional to protein intake and responds rapidly to changes in protein intake (4,5,26,27).Postnatal growth failure remains common in premature infants. Nearly 25 years ago Kashyap et al showed that even a small deficit in protein intake impairs both growth in lean body mass and linear growth (28). In recent years, Arslanoglu et al reported that addition of protein to preterm feedings of recovering VLBW infants resulted in significantly improved linear growth (4,5). This was accomplished by monitoring the BUN level so that when it was less than 9\u200amg/dL, increased protein was added to their feedings. It was observed in the present study that the mean BUN level fell <9 mg/dL by week 2 in infants receiving PI-HMF; however, in infants receiving LE-HMF it never fell <9 mg/dL during the entire study period. Our results, in part, agree with other investigators that an increased protein-to-calorie ratio in the feeds of preterm infants will improve linear growth (4,5,9,28). It is becoming increasingly evident that promoting catch-up growth in the NICU may have implications for long-term development and health (7,29).Our study did have several limitations. The study examined the combined effects of changing both protein content and type (hydrolyzed vs intact). Future studies may want to capture effects of changing one of these variables. A number of subjects in this study did not complete the protocol to SDAY 29. This partially diluted the effects seen in the ITT groups but still permitted demonstration of differential effects seen in the SPF subgroup. A larger study design may improve this in the future. Infants <700 g birth weight were excluded from this study and therefore the study findings cannot be readily extrapolated to this vulnerable group. It is expected however that this group would have higher protein demands than infants in this study and therefore would be as likely or more to have a favorable response to higher protein. Although no differences were seen between both groups for NEC and sepsis the study size was too small to discern true differences for these outcomes.CONCLUSIONSBoth fortifiers showed excellent tolerance and a low rate of morbidity outcomes, with the infants who were SPFs fed LE-HMF having improved growth. These data confirm the safety and suitability of this new concentrated liquid HMF for preterm infants.AcknowledgmentsThe authors thank the following individuals for their hard work and dedication: Coryn Commare, MS, RD; Christy Saulters, BS; Debra Lee-Butcher, BSN, RN; Holy Boyko, BSN, RN; Angela Worley; Carolyn Richardson; Sue Zhang, MS, MAS; Mustafa Vurma, PhD; Maggie Hroncich, BS; Aimee Diley; Kristen Fithian; Sue Nicholson, MS, RD; and Jennifer Teran, BS, RD. The authors also thank study investigators and their staff for their cooperation: Terri Ashmeade, MD; Anthony Killian, MD; Lance Parton, MD; Robert Schelonka, MD; Robert White, MD; Ivan Hand, MD, FAAP; Michelle Walsh, MD; Jeffrey Blumer, PhD, MD; Paula Delmore, RN; Carrie Rau, RN; Renee Bridge, RN; Lisa Lepis, RN; Judy Zaritt, RN; Claire Roane, RN, MSN; Julie Gualtier, RN; Diane Fierst, RN; Christina Gogal; Natalie Dweck; Debra Potak, RN; Barbara Wilkens, RN; Nakia Clay, BS; Mashelle Monhaut, NNP-BC; Rickey Taing, NPL; Susan Bergant, RN, CCRP; and Bonnie Rosolowski, RPT.www.clinicaltrials.gov registration number: NCT01373073.This study was funded by Abbott Nutrition.J.H.K., B.B., G.C., R.S. and S.G.-W. received research funds from the study sponsor, Abbott Nutrition, to conduct the study. J.H.K. is on the speakers’ bureaus for Abbott Nutrition, Mead Johnson Nutrition, Nestle Nutrition, Nutricia, and Medela. J.H.K. and R.S. are on the medical advisory board for Medela. J.H.K. owns shares in PediaSolutions and has provided medical expert testimony. B.B. received a grant from the Wichita Medical Research and Education Foundation. G.C. received a research grant from the University of Utah and has provided medical expert testimony. S.G.-W. is on the speakers’ bureau of Abbott Nutrition. B.B.-R., L.W., and G.B. are employees of Abbott Nutrition.The authors report no conflicts of interest.REFERENCES1.SchanlerRJ\nSuitability of human milk for the low-birthweight infant. Clin Perinatol\n1995; 22:207–222.77812532.SchanlerRJAbramsSA\nPostnatal attainment of intrauterine macromineral accretion rates in low birth weight infants fed fortified human milk. J Pediatr\n1995; 126:441–447.78692083.KuschelCAHardingJE\nMulticomponent fortified human milk for promoting growth in preterm infants. Cochrane Database Syst Rev\n2004; 1:CD000343.149739534.ArslanogluSBertinoECosciaA\nUpdate of adjustable fortification regimen for preterm infants: a new protocol. J Biol Regul Homeost Agents\n2012; 26\n(3 suppl):65–67.231585175.ArslanogluSMoroGEZieglerEE\nAdjustable fortification of human milk fed to preterm infants: does it make a difference?\nJ Perinatol\n2006; 26:614–621.168859896.PremjiSSFentonTRSauveRS\nHigher versus lower protein intake in formula-fed low birth weight infants. Cochrane Database Syst Rev\n2006; 1:CD003959.164374687.EhrenkranzRADusickAMVohrBR\nGrowth in the neonatal intensive care unit influences neurodevelopmental and growth outcomes of extremely low birth weight infants. Pediatrics\n2006; 117:1253–1261.165853228.VanderveenDKMartinCRMehendaleR\nEarly nutrition and weight gain in preterm newborns and the risk of retinopathy of prematurity. PLoS One\n2013; 8: e64325.9.WhitfieldJPunjabi-GuptaSHendriksonH\nImproved linear growth in VLBW infants at discharge: impact of increasing the protein/kcal ratio (PCR) of feeds. E-PAS Abstract\n2012; 4510:122.10.TaylorC\nHealth Professionals Letter on Enterobacter sakazakii Infections Associated With the Use of Powdered (Dry) Infant Formulas in Neonatal Intensive Care Units. Bethesda, MD: US Food and Drug Administration, Center for Food Safety and Applied Nutrition, Office of Nutritional Products, Labeling and Dietary Supplements; 2002.11.SullivanSSchanlerRJKimJH\nAn exclusively human milk-based diet is associated with a lower rate of necrotizing enterocolitis than a diet of human milk and bovine milk-based products. J Pediatr\n2010; 156:562–567.2003637812.MoyaFSiskPMWalshKR\nA new liquid human milk fortifier and linear growth in preterm infants. Pediatrics\n2012; 130:e928–e935.2298787713.Barrett-ReisBHallRTSchanlerRJ\nEnhanced growth of preterm infants fed a new powdered human milk fortifier: a randomized, controlled trial. Pediatrics\n2000; 106:581–588.1096910614.The Harriet Lane Handbook (The Johns Hopkins Hospital). 19th ed. New York: Elsevier Health Sciences; 2011: chap 27.15.RamelSEGeorgieffMK\nNutrition. In: Avery's Neonatology—Pathophysiology and Management of the Newborn. 7th ed. Philadelphia: Lippincott Williams & Wilkins; 2015.16.KoletzkoBPoindexterBUauyR\nRecommended nutrient intake levels for stable, fully enterally fed very low birth weight infants. World Rev Nutr Diet\n2014; 110:297–299.2475163817.ThoeneMHansonCLydenE\nComparison of the effect of two human milk fortifiers on clinical outcomes in premature infants. Nutrients\n2014; 6:261–275.2439453818.CibulskisCCArmbrechtES\nAssociation of metabolic acidosis with bovine milk-based human milk fortifiers. J Perinatol\n2015; 35:115–119.2510232119.RochowNJochumFRedlichA\nFortification of breast milk in VLBW infants: metabolic acidosis is linked to the composition of fortifiers and alters weight gain and bone mineralization. Clin Nutr\n2011; 30:99–105.2072762620.GrimbleGKKeohanePPHigginsBE\nEffect of peptide chain length on amino acid and nitrogen absorption from two lactalbumin hydrolysates in the normal human jejunum. Clin Sci (Lond)\n1986; 71:65–69.370907621.BozaJJMartinez-AugustinOBaroL\nProtein v. enzymic protein hydrolysates. Nitrogen utilization in starved rats. Br J Nutr\n1995; 73:65–71.785791622.PatoleS\nPrevention and treatment of necrotising enterocolitis in preterm neonates. Early Hum Dev\n2007; 83:635–642.1782600923.FanaroffAAStollBJWrightLL\nTrends in neonatal morbidity and mortality for very low birthweight infants. Am J Obstet Gynecol\n2007; 196:147e1-8.1730665924.StollBJHansenNIBellEF\nNeonatal outcomes of extremely preterm infants from the NICHD Neonatal Research Network. Pediatrics\n2010; 126:443–456.2073294525.D’NettoMAHersonVCHussainN\nAllergic gastroenteropathy in preterm infants. J Pediatr\n2000; 137:480–486.1103582526.ZieglerEE\nBreast-milk fortification. Acta Paediatr\n2001; 90:720–723.1151997227.PolbergerSKAxelssonIERaihaNC\nUrinary and serum urea as indicators of protein metabolism in very low birthweight infants fed varying human milk protein intakes. Acta Paediatr Scand\n1990; 79:737–742.223926628.KashyapSSchulzeKFForsythM\nGrowth, nutrient retention, and metabolic response in low birth weight infants fed varying intakes of protein and energy. J Pediatr\n1988; 113:713–721.313985629.HansonCSundermeierJDugickL\nImplementation, process, and outcomes of nutrition best practices for infants <1500\u200ag. Nutr Clin Pract\n2011; 26:614–624.2194764530.EhrenkranzRAYounesNLemonsJA\nLongitudinal growth of hospitalized very low birth weight infants. Pediatrics\n1999; 104\n(2 Pt 1):280–289.10429008TABLE 1Approximate nutrient composition of PI-HMF or LE-HMF added to HMNutrient PI-HMFLE-HMFEnergy, cal100100Fat, g5.25.1CHO, g10.410.1Protein, g33.6Source/type of proteinIntact whey protein concentrateExtensively hydrolyzed caseinDHA, mg1224Vitamin D, IU150150Calcium, mg175153Phosphorus, mg9886Osmolality, mOsm/kg water385450Lutein, μg*23Values per 100 calories mixed at a ratio of 1 pkt or 5 mL:25 mL HM (as fed). CHO\u2009=\u2009carbohydrate; DHA\u2009=\u2009docosahexaenoic acid; HM\u2009=\u2009human milk; LE-HMF \u2009=\u2009liquid HMF containing extensively hydrolyzed protein; PI-HMF\u2009=\u2009powdered intact protein HMF.*Lutein not added to product but available in varying amounts from HM.TABLE 2Neonatal and perinatal characteristics of preterm infantsTreatment group*PI-HMF (n\u2009=\u200963)LE-HMF (n\u2009=\u200966)Gestational age, wk28.7\u2009±\u20090.228.8\u2009±\u20090.2Birth weight, g1156\u2009±\u2009241193\u2009±\u200926Birth length, cm37.4\u2009±\u20090.337.7\u2009±\u20090.3Birth HC, cm26.1\u2009±\u20090.226.5\u2009±\u20090.2Male sex, n (%)35 (56)36 (55)Ethnicity: Hispanic, n (%)17 (28)8 (13)†Race, n (%)\u2003White42 (67)43 (65)\u2003Black13 (21)17 (26)\u2003Asian1 (2)1 (2)\u2003Other7 (11)3 (5)\u2003White/other0 (0)2 (3)C-section, n (%)38 (60)42 (64)Twin, n (%)16 (25)12 (18)Age at study day 1, d12.3\u2009±\u20090.712.8\u2009±\u20090.6Birth class, n (%)\u2003≤1000\u2009g16 (24)12 (19)\u2003>1000\u2009g66 (76)63 (81)LE-HMF\u2009=\u2009liquid HMF containing extensively hydrolyzed protein; PI-HMF\u2009=\u2009powdered intact protein HMF.*Mean\u2009±\u2009SEM.†P\u2009=\u20090.0407.TABLE 3Anthropometric gainsTreatment group*Targeted growth†,‡PI-HMF (n\u2009=\u200963)LE-HMF (n\u2009=\u200966)Weight gain, g kg−1 day−117.5\u2009±\u20090.618.2\u2009±\u20090.3>18Length gain, cm/wk1.2\u2009±\u20090.071.2\u2009±\u20090.06>0.9HC gain, cm/wk1.0\u2009±\u20090.041.0\u2009±\u20090.05>0.9LE-HMF\u2009=\u2009liquid HMF containing extensively hydrolyzed protein; PI-HMF\u2009=\u2009powdered intact protein HMF; HC\u2009=\u2009head circumference.*Intent-to-treat group, n\u2009=\u2009129.†Ehrenkranz et al (7).‡Ehrenkranz et al (30).TABLE 4Blood chemistry dataCharacteristicsReference rangesStudy dayTreatment group*PI-HMFLE-HMFBicarbonate, mEq/L†17–24123.27\u2009±\u20090.45 (59)25.05\u2009±\u20090.45 (62)1524.32\u2009±\u20090.50 (49)25.40\u2009±\u20090.39 (58)2925.04\u2009±\u20090.43 (40)25.54\u2009±\u20090.44 (50)BUN, mg/dL‡2.5–31.4111.47\u2009±\u20090.78 (56)11.89\u2009±\u20091.03 (61)158.30\u2009±\u20091.15 (50)11.72\u2009±\u20090.68 (58)295.81\u2009±\u20090.38 (40)9.31\u2009±\u20090.53 (49)Prealbumin, mg/dL§7.0–39.0110.05\u2009±\u20090.37 (58)9.69\u2009±\u20090.33 (54)1510.11\u2009±\u20090.37 (47)11.40\u2009±\u20090.41 (46)299.08\u2009±\u20090.35 (36)10.01\u2009±\u20090.35 (37)Calcium, mg/dL8.0–11.0110.10\u2009±\u20090.08 (56)9.93\u2009±\u20090.08 (60)159.93\u2009±\u20090.10 (50)9.95\u2009±\u20090.07 (57)299.89\u2009±\u20090.09 (40)9.82\u2009±\u20090.06 (49)Phosphorus, mg/dL4.2–8.716.41\u2009±\u20090.17 (54)6.20\u2009±\u20090.13 (58)156.71\u2009±\u20090.13 (46)6.50\u2009±\u20090.12 (56)296.66\u2009±\u20090.10 (40)6.46\u2009±\u20090.12 (47)Magnesium, mg/dL1.5–2.111.90\u2009±\u20090.03 (54)1.88\u2009±\u20090.02 (59)151.80\u2009±\u20090.03 (47)1.86\u2009±\u20090.03 (55)291.81\u2009±\u20090.02 (40)1.82\u2009±\u20090.03 (46)Alkaline phosphatase, U/L150–4001443.89\u2009±\u200924.50 (55)415.40\u2009±\u200915.78 (60)15366.13\u2009±\u200921.80 (48)332.68\u2009±\u200910.87 (57)29335.28\u2009±\u200921.84 (40)342.36\u2009±\u200913.10 (47)Sodium, mEq/L129–1431137.49\u2009±\u20090.49 (61)138.42\u2009±\u20090.34 (65)15137.46\u2009±\u20090.55 (52)137.56\u2009±\u20090.29 (59)29139.07\u2009±\u20090.41 (41)138.70\u2009±\u20090.40 (50)Potassium, mEq/L4.5–7.115.39\u2009±\u20090.11 (61)5.20\u2009±\u20090.09 (65)155.25\u2009±\u20090.09 (52)5.23\u2009±\u20090.09 (59)295.25\u2009±\u20090.10 (41)5.06\u2009±\u20090.07 (50)Chloride, mEq/L100–1171104.16\u2009±\u20090.60 (58)104.03\u2009±\u20090.55 (63)15104.10\u2009±\u20090.72 (49)103.88\u2009±\u20090.43 (57)29106.00\u2009±\u20090.57 (40)106.14\u2009±\u20090.37 (49)BUN\u2009=\u2009blood urea nitrogen; LE-HMF\u2009=\u2009liquid HMF containing extensively hydrolyzed protein; PI-HMF\u2009=\u2009powdered intact protein HMF; HC\u2009=\u2009head circumference.*Values are mean\u2009±\u2009SEM (n).†Bicarbonate (mEq/L): (SDAY 1) LE-HMF > PI-HMF, P\u2009=\u20090.0419, LSM\u2009±\u2009SE: LE-HMF\u2009=\u200924.71\u2009±\u20090.56, PI-HMF\u2009=\u200923.33\u2009±\u20090.62.‡BUN (mg/dL): Feeding main effect: LE-HMF > PI-HMF, P\u2009=\u20090.0013, LSM\u2009±\u2009SE: LE-HMF\u2009=\u200911.99\u2009±\u20090.73, PI-HMF\u2009=\u20098.99\u2009±\u20090.83.§Prealbumin (mg/dL): Feeding main effect: LE-HMF > PI-HMF, P\u2009=\u20090.0049, LSM\u2009±\u2009SE: LE-HMF\u2009=\u200910.61\u2009±\u20090.35, PI-HMF\u2009=\u20099.32\u2009±\u20090.38."", 'title': 'Growth and Tolerance of Preterm Infants Fed a New Extensively Hydrolyzed Liquid Human Milk Fortifier.', 'date': '2015-10-22'}, '22301933': {'article_id': '22301933', 'content': 'Preterm human milk-fed infants often experience suboptimal growth despite the use of human milk fortifier (HMF). The extra protein supplied in fortifiers may be inadequate to meet dietary protein requirements for preterm infants.\nWe assessed the effect of human milk fortified with a higher-protein HMF on growth in preterm infants.\nThis is a randomized controlled trial in 92 preterm infants born at <31 wk gestation who received maternal breast milk that was fortified with HMF containing 1.4 g protein/100 mL (higher-protein group) or 1.0 g protein/100 mL (current practice) until discharge or estimated due date, whichever came first. The HMFs used were isocaloric and differed only in the amount of protein or carbohydrate. Length, weight, and head-circumference gains were assessed over the study duration.\nLength gains did not differ between the higher- and standard-protein groups (mean difference: 0.06 cm/wk; 95% CI: -0.01, 0.12 cm/wk; P = 0.08). Infants in the higher-protein group achieved a greater weight at study end (mean difference: 220 g; 95% CI: 23, 419 g; P = 0.03). Secondary analyses showed a significant reduction in the proportion of infants who were less than the 10th percentile for length at the study end in the higher-protein group (risk difference: 0.186; 95% CI: 0.370, 0.003; P = 0.047).\nA higher protein intake results in less growth faltering in human milk-fed preterm infants. It is possible that a higher-protein fortifier than used in this study is needed. This trial was registered with the Australian New Zealand Clinical Trials Registry (http://www.anzctr.org.au/) as ACTRN12606000525583.', 'title': 'Effect of increasing protein content of human milk fortifier on growth in preterm infants born at <31 wk gestation: a randomized controlled trial.', 'date': '2012-02-04'}, '22987877': {'article_id': '22987877', 'content': 'To evaluate the growth, tolerance, and safety of a new ultraconcentrated liquid human milk fortifier (LHMF) designed to provide optimal nutrients for preterm infants receiving human breast milk in a safe, nonpowder formulation.\nPreterm infants with a body weight ≤ 1250 g fed expressed and/or donor breast milk were randomized to receive a control powder human milk fortifier (HMF) or a new LHMF for 28 days. When added to breast milk, the LHMF provided ∼20% more protein than the control HMF. Weight, length, head circumference, and serum prealbumin, albumin, blood urea nitrogen, electrolytes, and blood gases were measured. The occurrence of sepsis, necrotizing enterocolitis, and serious adverse events were monitored.\nThis multicenter, third party-blinded, randomized controlled, prospective study enrolled 150 infants. Achieved weight and linear growth rate were significantly higher in the LHMF versus control groups (P = .04 and 0.03, respectively). Among infants who adhered closely to the protocol, the LHMF had a significantly higher achieved weight, length, head circumference, and linear growth rate than the control HMF (P = .004, P = .003, P = .04, and P = .01, respectively). There were no differences in measures of feeding tolerance or days to achieve full feeding volumes. Prealbumin, albumin, and blood urea nitrogen were higher in the LHMF group versus the control group (all P < .05). There was no difference in the incidence of confirmed sepsis or necrotizing enterocolitis.\nUse of a new LHMF in preterm infants instead of powder HMF is safe. Benefits of LHMF include improvements in growth and avoidance of the use of powder products in the NICU.', 'title': 'A new liquid human milk fortifier and linear growth in preterm infants.', 'date': '2012-09-19'}, '29772833': {'article_id': '29772833', 'content': ""NutrientsNutrientsnutrientsNutrients2072-6643MDPI29772833598651310.3390/nu10050634nutrients-10-00634ArticleThe Effect of Increasing the Protein Content of Human Milk Fortifier to 1.8 g/100 mL on Growth in Preterm Infants: A Randomised Controlled TrialReidJessica1MakridesMaria12McPheeAndrew J.13StarkMichael J.34https://orcid.org/0000-0002-6474-0505MillerJacqueline15CollinsCarmel T.12*1Healthy Mothers, Babies and Children, South Australian Health and Medical Research Institute, Adelaide, SA 5006, Australia; jessica.reid@adelaide.edu.au (J.R.); maria.makrides@sahmri.com (M.M.); andrew.mcphee@sa.gov.au (A.J.M.); jacqueline.miller@sahmri.com (J.M.)2Adelaide Medical School, Discipline of Paediatrics, The University of Adelaide, Adelaide, SA 5006, Australia3Neonatal Medicine, Women’s and Children’s Hospital, Adelaide, SA 5006, Australia; michael.stark@adelaide.edu.au4The Robinson Research Institute, The University of Adelaide, Adelaide, SA 5006, Australia5Nutrition and Dietetics, Flinders University, Adelaide, SA 5006, Australia*Correspondence: carmel.collins@sahmri.com; Tel.: +61-8-8128-440917520185201810563426420181552018© 2018 by the authors.2018Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).The aim of this study was to assess the effect of feeding high protein human milk fortifier (HMF) on growth in preterm infants. In this single-centre randomised trial, 60 infants born 28–32 weeks’ gestation were randomised to receive a higher protein HMF providing 1.8 g protein (n = 31) or standard HMF providing 1 g protein per 100 mL expressed breast milk (EBM) (n = 29). The primary outcome was rate of weight gain. Baseline characteristics were similar between groups. There was no difference between high and standard HMF groups for weight gain (mean difference (MD) −14 g/week; 95% CI −32, 4; p = 0.12), length gain (MD −0.01 cm/week; 95% CI −0.06, 0.03; p = 0.45) or head circumference gain (MD 0.007 cm/week; 95% CI −0.05, 0.06; p = 0.79), despite achieving a 0.7 g/kg/day increase in protein intake in the high protein group. Infants in the high protein group had a higher proportion of lean body mass at trial entry; however, there was no group by time effect on lean mass gains over the study. Increasing HMF protein content to 1.8 g per 100 mL EBM does not improve growth in preterm infants born 28–32 weeks’ gestation.human milkgrowthlow birth weighthuman milk fortifier1. IntroductionIt is well established that fortified human milk improves growth rates in preterm infants [1,2,3]. However, the optimal amount of protein in the fortifier is yet to be determined, partly due to the variability in the protein content of human milk, both within mothers and over time. Too little protein results in a failure to meet protein requirements, estimated to be 4.0–4.5 g/kg/day for infants born <1000 g and 3.5–4.0 g/kg/day for those born 1000–1800 g [4]. Consequently, growth failure in the neonatal period is common in infants fed fortified human milk compared with infants fed preterm formula [5,6,7]. Conversely, too much protein may result in metabolic acidosis [8]. Individualized fortification, based on either the metabolic response of the infant [9,10,11], or the macronutrient content of mother’s milk [12] has been investigated and provides evidence in support of inadequate protein concentration of human milk fortifiers (HMFs) when used in a standardised approach. However, individualised approaches are time consuming and not easily translated to the clinical environment [13]. We previously investigated a fortifier providing 1.4 g compared with 1 g protein per 100 mL human milk in preterm infants <31 weeks’ gestation [14]. While we found no difference in the rate of weight and length gain between groups, there were fewer infants with length <10th percentile at discharge in the high protein group, suggesting a higher protein concentration fortifier may be needed to improve growth. We therefore aimed to determine the effect of further increasing the protein content of HMF to 1.8 g/100 mL compared with 1 g/100 mL, on growth in preterm infants born 28–32 weeks’ gestation.2. Materials and Methods2.1. Study DesignThe study was a single centre (Women’s and Children’s Hospital, North Adelaide, South Australia), parallel group randomised controlled trial conducted between February 2012 and May 2013.2.2. ParticipantsInfants born 28–32 completed weeks’ gestation whose mothers intended to provide breast milk were eligible to participate. Multiple births were eligible and were randomised individually. Infants with a major congenital or chromosomal abnormality likely to affect growth, or where protein therapy was contraindicated (e.g., major heart defects, cystic fibrosis, phenylketonuria, disorders of the urea cycle) were ineligible. Infants likely to transfer to remote locations and infants who had received standard practice HMF for more than four days were also excluded.2.3. Randomisation and BlindingInfants were randomised to one of two groups: the higher protein intervention group or the standard protein control group. An independent researcher created the randomisation schedule using a computer generated variable block design of 4 and 6. Stratification occurred for sex and gestational age 28–29 weeks and 30–32 weeks. Parents of eligible infants were approached by a neonatologist and followed-up for consent by a research nurse who was not involved in clinical care. Upon consent, infants were randomised by telephoning an independent researcher who held the randomisation schedule and assigned a unique study identification number. Participants, clinicians, outcome assessors and data analysts were blinded to randomisation group.2.4. InterventionsThe base HMF used for both trial groups was FM85 Human Milk Supplement (Nestlé Nutrition, Gland, Switzerland) which provides 1.0 g protein and 17.5 kcal when 5 g HMF is added to 100 mL expressed breast milk (EBM). The high protein fortifier was prepared by adding 0.9 g Protifar (Nutricia, Zoetermeer, The Netherlands), a bovine casein-based powder, to the FM 85. This resulted in an additional 0.8 g protein and 3.5 kcal per 100 mL EBM providing 1.8 g protein and 21 kcal when added to 100 mL of EBM. To ensure both fortifiers were isocaloric, thereby eliminating the effect of different energy intakes on growth, 0.9 g Polyjoule (Nutricia, Zoetermeer, The Netherlands), a glucose polymer, was added to the standard fortifier providing an additional 3.5 kcal but no extra protein, giving a total of 1.0 g protein and 21 kcal when added to 100 mL of EBM. The Polyjoule and Protifar supplements were packaged into identical 400-g containers each with a tamper proof seal (Pharmaceutical Packaging Professionals Pty Ltd., Thebarton, Australia). The containers were differentiated by four colour-coded labels to facilitate blinding, with each trial group separately color-coded into two groups. Infant nutrition attendants, under the direction of the Nutrition and Food Services Department, were trained in the preparation of the HMF. Trial fortifier was mixed at the rate of 5 g FM 85 plus either 0.9 g Protifar, or 0.9 g Polyjoule, for the high and standard protein groups respectively, with 4 mL of sterile water, to give a total volume of 8 mL for use with each 100 mL of EBM.2.5. Intervention AdministrationThe fortifier intervention and control fortifiers were delivered via the enteral tube, immediately prior to a feed (tube, bottle or breast). Trial HMFs were delivered at 8 mL HMF/100 mL EBM with the volume of HMF for each feed ordered daily by the medical or neonatal nurse practitioners. In cases where a mix of EBM and preterm formula was to be given, the trial HMF was only given if EBM was >50% of the total feed. When the infant received a direct breast feed, the timing of administration of the trial product (before, during or after the feed) was at the discretion of the primary care nurse in consultation with the mother. For each day, the trial HMFs were decanted into syringes and labelled with infant identification, volume of HMF and trial details. Syringes were stored refrigerated in the neonatal unit in each infant’s individually labelled container. Any syringes not administered in the 24-h period were recorded and discarded. Fluid balance records were audited daily for compliance with the trial protocol. Administration of trial HMF began as soon as practical after randomisation (within one to two days) and continued until study end, defined as the removal of the naso-gastric tube or estimated date of delivery, whichever came first.2.6. Nutritional IntakeMeasured protein and fat content of a weekly sample of unfortified EBM (MilkoScan Minor, Foss, Denmark) were used to represent the weekly composition of EBM [14]. The lactose concentration was assumed to be 6.8 g/100 mL. EBM was only sampled when the supply was surplus to the infant’s requirements. Missing values were substituted with the average macronutrient composition of all available samples (32 of the 45 mothers involved in the study were able to provide breast milk samples). Macronutrient intakes for the study fortifiers, EBM and formula were calculated from the volume ingested, the protein and fat concentration of EBM, and the manufacturer’s information on the study fortifiers and formula. The protein content of the preterm formula in use at the time of the study was 2.2 g/100 mL. Energy content was calculated by using the Atwater factors of 4, 4, and 9 kcal/g for protein, carbohydrate, and fat respectively.2.7. Outcome Assessments2.7.1. Primary outcomeThe primary outcome was rate of weight gain (g/week) from trial start (day of randomisation) to trial end. In addition to routine clinical measurements, a research nurse and J.R. weighed infants on randomisation, weekly and at study end; duplicate weight measurements were taken using electronic balance scales accurate to 5 g. Measurements were repeated if there was a discrepancy ≥10 g, with the average of the two closest measurements used.2.7.2. Secondary Efficacy and Safety OutcomesSecondary efficacy outcomes included length and head circumference gain (cm/week), infant weight at study end, small for gestational age (SGA) at study end and body composition (fat-free mass). Length measurements were taken weekly with the infant in the supine position and measured to the nearest 0.1 cm using a recumbent length board. Head circumference was measured weekly using a non-stretching tape placed around the largest occipito-frontal circumference. Duplicate measurements were done and repeated if there was a discrepancy ≥0.5 cm, with the average of the 2 closest measures taken. SGA was defined as below the 10th percentile for infants of the same sex and gestational age, as determined from Australian birth reference data [15]. Fat free (lean) mass was measured weekly by bioelectrical impedance spectroscopy (BIS) using the Imp™ SFB7 (ImpediMed Limited, Queensland, Australia) with the first measurement taken during the first week of the study.Secondary safety outcomes included feeding tolerance (days feeds interrupted and days to reach enteral intake ≥150 mL/kg/day). A protocol was developed for discontinuation of the trial fortifier based on uraemia (blood urea nitrogen (BUN) concentration >8.0 mmol/L) and/or a metabolic acidosis (base excess <−6 mmol/L) persisting for more than 48 hours. However, no infant met these criteria. Similarly, criteria were defined for the addition of protein to feeds if an infant had poor weight gain defined as <15 g/kg/day over the preceding 7-day period associated with a BUN of <2 mmol/L when feed volumes reached 170 to 180 mL/kg/day. In this case, Protifar could be added at the discretion of the attending neonatologist, in addition to the allocated intervention fortifier. Additional protein was ceased when weight gain of 15 g/kg/day and a BUN >2 mmol/L were achieved.2.7.3. Biochemical AnalysesWeekly blood samples were taken and BUN, plasma albumin, plasma creatinine, pH and base deficit measured. Blood spots were collected weekly on filter paper and amino acids measured using tandem mass spectrometry (SA Pathology, Neonatal Screening Centre, Adelaide, Australia).2.7.4. Sample Size and Statistical AnalysisA sample size of 60 (30 per group) would detect a difference in weight gain of 3.31 g per day between the high protein and standard protein groups (80% power, p = 0.05). Consultation with the neonatal medical team agreed that this was a clinically important difference on which clinical practice could be changed. Mean weight, length, head circumference and lean mass gains over the trial period, were calculated for each infant using a linear effects model with a random intercept and slope. Using the slope, a linear regression model was fitted for each infant. Clustering (multiple births) was accounted for by using a generalised estimating equation with an independent working correlation matrix. All analyses were intention-to-treat. All models were adjusted for sex and gestational age category (28–29 and 30–32 weeks’ gestation). A per protocol analysis was specified a priori for infants who consumed ≥70% of their prescribed trial fortifier.2.7.5. EthicsEthical approval was granted by the Women’s and Children’s Health Network Human Research Ethics Committee (REC2401/10/14). This trial was registered with the Australian New Zealand Clinical Trials Registry (http://www.anzctr.org.au/) as ACTRN12611001275954.3. Results3.1. Study PopulationSixty infants were enrolled in the trial with 31 infants randomised to the high protein group and 29 infants to the standard protein group (Figure 1). There were 31 infants born from multiple births (14 sets of twins, 1 set of triplets). In all multiple births, apart from two sets of twins, the infants were randomly allocated to different interventions. For the triplets, two were randomised to the high protein group and one to the standard protein group. Four infants, two from each group, were withdrawn from the study after randomisation but before the first dose of trial fortifier was administered after parents changed their minds about involvement. A further two infants (twins) in the high protein group did not have any available breast milk and withdrew before the commencement of fortifier. One set of twins and one singleton were withdrawn by the parents midway through the trial due to perceived feeding intolerance and another infant was withdrawn by the clinical team after developing necrotising enterocolitis. In all cases of withdrawal, parents consented to the ongoing collection of data and all were included in intention-to-treat analyses. Baseline infant and maternal demographic, clinical and nutritional characteristics at randomisation were comparable between groups except that there were more male infants in the high protein group, n = 16 (52%) than the standard protein group, n = 12 (41%), the mean ± SD birth weight was lower in the higher protein group (1483 ± 423 g versus 1551 ± 407 g in the high and standard groups, respectively) and there were more infants classified as SGA for weight in the high protein group, n = 5 (16%) than the standard protein group, n = 1 (3%) (Table 1).3.2. Nutritional ManagementForty infants received standard ward HMF, S-26 SMA HMF (Wyeth Nutrition) while waiting for consent, 18 in the high and 22 in the standard protein group (Table 1). The remaining twenty trial infants started immediately on their allocated trial intervention.Nutritional intake of the infants for the first 28 days of the study did not differ between the groups except that the high protein group received more protein (mean ± SD 4.2 ± 1.3 vs. 3.5 ± 0.93 g/kg/day in the high and standard protein groups respectively). The protein concentration of the EBM was not different between groups (mean ± SD 1.43 ± 0.27 and 1.45 ± 0.28 g protein/100 mL in the high and standard groups, respectively) and the difference in protein intake was due to more protein derived from the HMF (mean ± SD 1.9 ± 1.2 and 1.2 ± 0.6 g/kg/day, in the high and standard groups, respectively. Energy intakes and fluid volume were similar between the groups (energy: mean ± SD 124 ± 34 and 126 ± 27 kcal/kg/day and fluid: mean ± SD 154 ± 39 and 157 ± 32 mL/kg/day in the high and standard groups, respectively). The high protein group received 83% (±32) of their total enteral intake as EBM compared with the control group who received 90% (±23).3.3. Primary OutcomeThere was no difference in the rate of weight gain between groups (Table 2) (mean (95% CI) high protein 245 (230, 260) g/week and standard protein 258 (244, 272) g/week, adjusted mean difference −14 (−32, 4) p = 0.12). Results were similar when analysed per protocol (Table 2).3.4. Secondary Outcomes3.4.1. GrowthThere were no differences in rate of length or head circumference gain (Table 2). High protein HMF infants weighed less at study end but this was not statistically significant (Table 2) and is consistent with the difference in birth weight between the groups (Table 1). There were no differences in length or head circumference at study end between the groups (Table 2). There was no difference in SGA status for weight between high and standard protein HMF groups at the end of the study (n = 8, 25%, and n = 3, 10% SGA infants in the high and standard protein groups, respectively, adjusted Relative Risk (95% CI); 2.5 (0.8, 7.9), p = 0.11).Over the first four weeks of the trial, when >75% of participants were still in hospital, fat free (lean) mass was measured with the week one measurement taken a mean of 8 ± SD 2 days after randomisation. Fat free mass as a proportion of body weight (Figure 2) from weeks one to four was greater in high protein group infants than standard protein group infants (p = 0.03). However, there was no significant group by time interaction (p = 0.84). At week three alone, there was a significant increase for fat free mass as a proportion of body weight in the high protein group (p = 0.04).3.4.2. BiochemistryDue to the variable nature of blood chemistry data and length of hospital stay (to discharge), only the first three trial weeks could be accurately analysed using a linear mixed effects model.There was a significant group by time interaction for BUN levels (p < 0.001) with BUN levels significantly increased in the high protein group (Figure 3). This difference continued for the duration of the trial (p < 0.001). There were 12 occurrences in nine separate infants where BUN levels were measured over the pre-specified safety threshold of 8 mmol/L. Seven of these occurred during baseline blood tests taken at randomisation and were therefore not a result of the intervention. Six of these infants had BUN measurements in the normal range at their next weekly blood test. One infant had a BUN measurement >8 mmol/L at week one; the infant did not have another BUN measurement over 8 mmol/L for the rest of the trial. Two other infants, both in the high protein group, recorded BUN concentrations >8.0 mmol/L, peaking at 8.8 mmol/L, on five occasions, however the base excess remained above −6 mmol/L with no other abnormal biochemistry. There was one occurrence of an infant in the standard protein group requiring additional protein due to poor weight gain and BUN <2 mmol/L.There were no group by time interactions or group differences for albumin, creatinine, glucose, pH (results not shown). Phenylalanine (Phe) and tyrosine (Tyr), amino acids associated with increased protein intake, were both increased in the high protein group compared to the standard group at study week 3 (Phe median (IQR) μmol/L: 33 (28–42) vs. 25 (23–30), p <0.001 and Tyr median (IQR) μmol/L: 196 (151–267) vs. 128 (99–172) μmol/L, p <0.003 in the high and standard groups respectively.3.4.3. Clinical OutcomesHigh protein HMF infants were significantly more likely to have feeds interrupted (11 (35%) vs. 6 (21%), p = 0.01, in the high and standard protein groups, respectively) Table 3. There was no significant difference in the number of days spent on parenteral nutrition, days of intravenous lipid or the days taken to reach full enteral feeds. Likewise, there was no significant difference between the groups for any other clinical outcome (Table 3).4. DiscussionThe aim of this study was to assess the effect of a higher protein HMF on preterm infant growth. Our trial interventions resulted in the high protein group infants receiving 0.7 g/kg/day more protein than infants in the standard protein group, with mean protein intakes within recommended ranges for both groups. Despite this, there were no differences in growth between the two groups. The accumulation of fat free mass and fat mass, also did not differ between groups. While the higher protein group had a greater proportion of fat free mass from week one, the absence of a baseline measurement makes the interpretation of this difficult. It is unlikely that the intervention would have had an effect in the first week of the study, particularly as the change in fat free mass over time did not differ between groups. A significant difference between groups was noted at week three only and the implication of this is unclear. It is possible that this is a chance finding of no clinical significance.These results are confirmed by a recent study by Maas et al. [16] who compared 1 and 1.8 g protein concentration in powdered HMFs in a similar population to ours and found no difference in growth. Their trial interventions achieved a 0.6 g/kg/day median greater intake of protein, similar to our study, and protein intakes were within recommendations. Growth rates in both studies approximated foetal growth rates. A further two studies compared two different, newly formulated liquid HMFs with higher protein concentrations, with standard powdered HMFs. Moya et al. [17] compared Mead Johnson Nutrition products: a liquid fortifier with an Enfamil powdered fortifier, which when mixed with EBM provided 3.2 and 2.6 g protein/100 mL, respectively, equating to an additional 1.8 and 1.1 g protein. Kim et al. [18], in a non-inferiority trial, compared the Abbott Nutrition products of Similac HMF liquid, providing 3.6 g protein/100 kcal when mixed with EBM, with Similac HMF powder providing 3 g protein/100 kcal when mixed. These comparisons equate to an additional 1.6 and 1 g protein added to 100 mL EBM in the liquid and powder, respectively. The populations were similar between studies [17,18] except that Moya et al. [17] inclusion criteria (≤30 weeks’ gestation, birth weight ≤1250 g) resulted in a slightly less mature and smaller population than in both Kim et al. [18] study and this current study. Neither study [17,18] showed a difference in weight gain between groups, however, Moya et al. [17] found improved length gain with the higher protein. Both studies found infants in the high protein group were heavier at study end. Almost half the participants in Moya’s study were <1000 g at birth; hence their protein requirements of 4 to 4.5 g/kg would have been met by the high, but not the control, protein fortifier at volumes of 150 mL/kg. This may explain the effect seen on length gain. Two other studies have compared fortifiers containing 1 and 1.4 g protein added to 100 mL EBM with mixed results. Our previous trial [14] showed no effect of increased protein on growth, although did show a reduction in the number of infants SGA for length at discharge. However, Rigo et al. [19], in a non-inferiority trial, found improved weight gain of 2.3 g/day with the higher protein fortifier. The trial products in both these studies were similar, as were the population. It is possible that the smallest infants, with the highest protein needs, are the ones to benefit most from increased protein and that the larger sample size in Rigo (n = 153) compared to that in Miller (n = 92) elucidated the differences. Taken collectively, these results and ours suggest that protein concentrations in HMFs of 1.8 g provide no additional benefit in the population studied, but smaller infants are worthy of further investigation.The significantly elevated BUN levels seen at weeks 1, 2 and 3 were expected and have occurred in other high protein nutritional intervention studies [9,14,17]. Assuming adequate renal function, BUN is proportional to protein intake [20] and is often used as a crude marker of protein sufficiency. Low BUN levels suggest inadequate protein intake and high levels indicate possible excessive intake [9]. Blood phenylalanine and tyrosine concentrations were also significantly increased in the higher protein group, in week 3 only, and this is unlikely to be clinically significant. There were no differences in creatinine, albumin or other biochemical markers suggesting the intervention did not harm the infants.A strength of this study is the rigour with which dietary intake and growth were assessed. The protein and fat concentrations of EBM were measured, rather than assumed, resulting in accurate reporting of dietary intake and confirmation that, despite the variability of protein in EBM, we achieved a mean intake difference of 0.7 g/kg/day of protein between groups. Similarly, we measured both growth and body composition in an attempt to discern differences in weight gain arising from extra protein. This trial also has some limitations. Although all infants were included in the analyses, there were 10 who either did not receive, or ceased the intervention, which may have impacted results. In addition, the pragmatic nature of this trial may have influenced results as clinicians may have adjusted feed regimes if poor weight gain was identified. There was one instance of extra Protifar prescribed to an infant in the standard protein group and subtle increases in feed volume may also have occurred although volume of intake was not different between groups. This may have made it more difficult to detect differences between intervention groups. We used BIS to determine fat and fat free mass. BIS is the only cot-side technique available where infants requiring respiratory support can be assessed. While accuracy of BIS at the individual level is poor, BIS provides a useful means of determining differences in body composition between population means [21].Many of the recent trials discussed have already achieved mean growth rates approaching intra-uterine growth, with similar growth rates between groups. Findings from this current study are only generalisable to a similar population (infants born 28–32 week’s gestation). Therefore, to explicate the subtle effects of increasing protein on growth, future trials may need to focus on birth weight categories as they relate to protein requirements (i.e., <1000 g and 1000–1800 g). Due to the small proportion of infants born <1000 g, large multi-centre trials will be needed to tease out the effect.5. ConclusionsIncreasing the protein concentration of HMF from 1.0 to 1.8 g protein added per 100 mL EBM does not improve growth in preterm infants born 28–32 weeks’ gestation.AcknowledgmentsWe thank the families who participated in this study.Author ContributionsConceptualization, J.R., M.M., A.J.M. and C.T.C.; Formal analysis, J.R., M.M., A.J.M., M.J.S., J.M. and C.T.C.; Funding acquisition, J.R., M.M., A.J.M., M.J.S. and C.T.C.; Investigation, J.R., M.M., A.J.M., M.J.S. and C.T.C.; Supervision, M.M., A.J.M., M.J.S. and C.T.C.; Writing: original draft, J.R., J.M. and C.T.C.; Writing: review and editing, J.R., M.M., A.J.M., M.J.S., J.M. and C.T.C.FundingThis research was funded by a Women’s and Children’s Hospital Foundation Grant. Research Fellowships were provided by the National Health and Medical Research Council of Australia (M.M. Principal Research Fellow APP1061704) and the MS McLeod Research Fellowship, MS McLeod Research Fund, Women's and Children’s Hospital Research Foundation (C.T.C). The contents of the published material are solely the responsibility of the authors and do not reflect the views of the National Health and Medical Research Council of Australia.Conflicts of InterestOutside the submitted work, Maria Makrides serves on scientific advisory boards for Fonterra and Nestle. Honoraria are paid to her institution for continuing education of early career researchers. Maria Makrides also holds a Principal Research Fellowship from the NHMRC (APP1061704). Other authors declare no conflict of interest. Nestlé Nutrition donated half of the human milk fortifier used in the trial and Nutricia donated the Polyjoule and Protifar supplements. However, these sponsors had no role in the design of the study, in the collection, analyses or interpretation of data; in writing of the manuscript, and the decision to publish the results.References1.AdamkinD.H.RadmacherP.G.Fortification of human milk in very low birth weight infants (VLBW <1500 g birth weight)Clin. Perinatol.20144140542110.1016/j.clp.2014.02.010248738402.MoroG.E.ArslanogluS.BertinoE.CorvagliaL.MontirossoR.PicaudJ.C.PolbergerS.SchanlerR.J.SteelC.van GoudoeverJ.Human milk in feeding premature infants: Consensus statementJ. Pediatr. Gastroenterol. Nutr.201561Suppl. 1S16S1910.1097/01.mpg.0000471460.08792.4d262959993.BrownJ.V.E.EmbletonN.D.HardingJ.E.McGuireW.Multi-nutrient fortification of human milk for preterm infantsCochrane Database Syst. Rev.201610.1002/14651858.CD000343.pub3271558884.AgostoniC.BuonocoreG.CarnielliV.P.De CurtisM.DarmaunD.DecsiT.DomellofM.EmbletonN.D.FuschC.Genzel-BoroviczenyO.Enteral nutrient supply for preterm infants: Commentary from the European Society of Paediatric Gastroenterology, Hepatology and Nutrition committee on nutritionJ. Pediatr. Gastroenterol. Nutr.201050859110.1097/MPG.0b013e3181adaee0198813905.ColaizyT.T.CarlsonS.SaftlasA.F.MorrissF.H.Jr.Growth in vlbw infants fed predominantly fortified maternal and donor human milk diets: A retrospective cohort studyBMC Pediatr.20121212410.1186/1471-2431-12-124229005906.EmbletonN.E.PangN.CookeR.J.Postnatal malnutrition and growth retardation: An inevitable consequence of current recommendations in preterm infants?Pediatrics200110727027310.1542/peds.107.2.270111584577.MaasC.WiechersC.BernhardW.PoetsC.F.FranzA.R.Early feeding of fortified breast milk and in-hospital-growth in very premature infants: A retrospective cohort analysisBMC Pediatr.20131317810.1186/1471-2431-13-178241802398.CibulskisC.C.ArmbrechtE.S.Association of metabolic acidosis with bovine milk-based human milk fortifiersJ. Perinatol.20153511511910.1038/jp.2014.143251023219.ArslanogluS.MoroG.E.ZieglerE.E.Adjustable fortification of human milk fed to preterm infants: Does it make a difference?J. Perinatol.20062661462110.1038/sj.jp.72115711688598910.AlanS.AtasayB.CakirU.YildizD.KilicA.KahveciogluD.ErdeveO.ArsanS.An intention to achieve better postnatal in-hospital-growth for preterm infants: Adjustable protein fortification of human milkEarly Hum. Dev.2013891017102310.1016/j.earlhumdev.2013.08.0152403503911.BiasiniA.MarvulliL.NeriE.ChinaM.StellaM.MontiF.Growth and neurological outcome in ELBW preterms fed with human milk and extra-protein supplementation as routine practice: Do we need further evidence?J. Matern. Fetal Neonatal Med.201225Suppl. 4727410.3109/14767058.2012.7150322295802412.RochowN.FuschG.ChoiA.ChessellL.ElliottL.McDonaldK.KuiperE.PurchaM.TurnerS.ChanE.Target fortification of breast milk with fat, protein, and carbohydrates for preterm infantsJ. Pediatr.20131631001100710.1016/j.jpeds.2013.04.0522376949813.McLeodG.SherriffJ.HartmannP.E.NathanE.GeddesD.SimmerK.Comparing different methods of human breast milk fortification using measured v. Assumed macronutrient composition to target reference growth: A randomised controlled trialBr. J. Nutr.201611543143910.1017/S00071145150046142662789914.MillerJ.MakridesM.GibsonR.A.McPheeA.J.StanfordT.E.MorrisS.RyanP.CollinsC.T.Effect of increasing protein content of human milk fortifier on growth in preterm infants born at <31 wk gestation: A randomized controlled trialAm. J. Clin. Nutr.20129564865510.3945/ajcn.111.0263512230193315.BeebyP.J.BhutapT.TaylorL.K.New South Wales population-based birthweight percentile chartsJ. Paediatr. Child Health19963251251810.1111/j.1440-1754.1996.tb00965.x900778216.MaasC.MathesM.BleekerC.VekJ.BernhardW.WiechersC.PeterA.PoetsC.F.FranzA.R.Effect of increased enteral protein intake on growth in human milk–fed preterm infants: A randomized clinical trialJAMA Pediatr.2017171162210.1001/jamapediatrics.2016.26812789306417.MoyaF.SiskP.M.WalshK.R.BersethC.L.A new liquid human milk fortifier and linear growth in preterm infantsPediatrics2012130e928e93510.1542/peds.2011-31202298787718.KimJ.H.ChanG.SchanlerR.Groh-WargoS.BloomB.DimmitR.WilliamsL.BaggsG.Barrett-ReisB.Growth and tolerance of preterm infants fed a new extensively hydrolyzed liquid human milk fortifierJ. Pediatr. Gastroenterol. Nutr.20156166567110.1097/MPG.00000000000010102648811819.RigoJ.HascoetJ.M.BilleaudC.PicaudJ.C.MoscaF.RubioA.SalibaE.RadkeM.SimeoniU.GuilloisB.Growth and nutritional biomarkers of preterm infants fed a new powdered human milk fortifier: A randomized trialJ. Pediatr. Gastroenterol. Nutr.201765e83e9310.1097/MPG.00000000000016862872765420.PolbergerS.K.AxelssonI.E.RaihaN.C.Urinary and serum urea as indicators of protein metabolism in very low birthweight infants fed varying human milk protein intakesActa Paediatr. Scand.19907973774210.1111/j.1651-2227.1990.tb11548.x223926621.CollinsC.T.ReidJ.MakridesM.LingwoodB.E.McPheeA.J.MorrisS.A.GibsonR.A.WardL.C.Prediction of body water compartments in preterm infants by bioelectrical impedance spectroscopyEur. J. Clin. Nutr.201367Suppl. 1S47S5310.1038/ejcn.2012.16423299871Figure 1Participant flow through the trial. 1 from rural locations (n = 52), insufficient milk supply (n = 36), required interpreter (n = 6); congenital abnormality (n = 3); 2 did not want to take part (n = 25), did not want twins to be randomized individually (n = 8), parent not visiting (n = 1), immediately transferred to another centre (n = 1).Figure 2Fat free mass as a proportion of body weight for the first four weeks of the trial. Values are means, error bars are 95% CI. High protein n = 30, 30, 27, 26 and standard protein 29, 27, 26, 23 in weeks 1, 2, 3, 4 respectively. Adjusted for sex and gestational age, group interaction, p = 0.03, time interaction, p = 0.01. group × time interaction p = 0.84; * p = 0.04.Figure 3BUN from randomisation to week 3. Values are mean, error bars are 95% CI. High protein: n = 31, 28, 26, 25; Standard protein: n = 29, 26, 24, 24 for weeks baseline, 1, 2, 3. Adjusted for sex and GA, overall group effect <0.001, group * week interaction, p <0.001, * p = 0.04; ** p <0.001.nutrients-10-00634-t001_Table 1Table 1Baseline infant and maternal characteristics.CharacteristicHigh Protein (n = 31)Standard Protein (n = 29)\nInfant characteristics\n\n\nSingleton15 (48)16 (55)Twin15 (48)12 (41)Triplet2 (7)1 (3)Gestational age (week)30.5 ± 1.530.1 ± 1.428–29 weeks’ gestation10 (32)9 (31)30–32 weeks’ gestation21 (68)20 (69)Male infants16 (52)12 (41)Birth weight (g)1483 ± 4231551 ± 407SGA for weight at birth5 (16)1 (3)Birth length (cm)40.0 ± 3.340.2 ± 2.8Head circumference (cm)28.5 ± 328.5 ± 1.8Infants received standard ward HMF before randomisation18 (58)22 (76)Length of standard ward fortification before trial HMF start (day)1.3 ± 1.72.0 ± 1.5Time between birth and trial HMF start (day)8.9 ± 3.29.0 ± 2.5\nMaternal characteristics\n\n\nMaternal age (years)29.9 ± 6.331.7 ± 5.3Mother smoked during pregnancy5 (16.1)3 (10.3)Caucasian27 (96)23 (82)Primiparous19 (61.3)12 (41.4)Previous preterm birth4 (33.3)6 (35.3)Data are presented as n (%) or mean ± SD.nutrients-10-00634-t002_Table 2Table 2Anthropometric changes over the study.\nIntention to Treat AnalysesPer Protocol Analyses 1High Protein (n = 31)Standard Protein (n = 29)Adjusted Mean Difference 2\np\n2\nHigh Protein (n = 21)Standard Protein (n = 23)Adjusted Mean Difference 2\np\n2\nWeight gain (g/week)245 (230, 260)258 (244, 272)−14 (−32, 4)0.12245 (228, 262)262 (247, 277)−15 (−36, 5)0.14Length gain (cm/week)1.1 (1.1, 1.2)1.1 (1.1, 1.2)−0.01 (−0.06, 0.03)0.451.1 (1.1, 1.2)1.2 (1.1, 1.2)−0.01 (−0.06, 0.04)0.62Head circumference gain (cm/week)1.1 (1.0, 1.1)1.1 (1.0,1.1)0.007 (−0.05, 0.06)0.791.1 (1.1, 1.1)1.1 (1.1, 1.1)−0.004 (−0.06, 0.05)0.88Weight at study end (g) 32658 (2544, 2771)2757 (2632, 2883)−100 (−251, 50)0.192646 (2489, 2805)2815 (2675, 2955)−157 (−341, 28) 0.1Length at study end (cm)45.2 (44.5, 45.9)45.8 (45.0, 46.6)−0.5 (−1.3, 0.3)0.1945.2 (44.4, 46.0)46.3 (45.6, 47)−0.86 (−1.85, 0.12)0.09Head circumference at study end (cm)33.1 (32.5, 33.6)33.0 (32.4, 33.7)0.03 (−0.6, 0.7)0.9233.3 (32.7, 33.9)33.6 (33.0, 34.1)−0.16 (−0.90, 0.57)0.66Data are presented as mean, (95% CI); 1 For inclusion in ‘per protocol’ analysis, infants must have consumed 70% or more of their trial group HMF; 2 adjusted for sex and gestational age; 3 study end defined as removal of naso-gastric tube or term equivalent, whichever came first.nutrients-10-00634-t003_Table 3Table 3Feeding and clinical management.VariableHigh Protein (n = 31)Standard Protein (n = 29)\np\nInfant required enteral protein supplementation 101 (3.4)0.48Feeding interrupted 211 (35)6 (21)0.01Days receiving parenteral nutrition10 (7, 13)9 (7, 11)0.34Days of intravenous lipid4 (3, 7)4 (3, 6)0.72Days to full enteral feeds 38 (6, 10)8 (7, 10)0.72Confirmed necrotizing enterocolitis1 (3.2)0>0.99Oxygen at discharge2 (6.5)1 (3.4)0.15Late onset sepsis1 (3.2)0>0.99Data are reported as n (%) or mean (95% CI).1 One infant in the standard protein group was prescribed a protein supplement (Protifar) 2 Feeding interrupted was defined as one of more feeds not given in a day; 3 Full enteral feeds was defined as 150 mL/kg/day)."", 'title': 'The Effect of Increasing the Protein Content of Human Milk Fortifier to 1.8 g/100 mL on Growth in Preterm Infants: A Randomised Controlled Trial.', 'date': '2018-05-19'}, '28727654': {'article_id': '28727654', 'content': ""J Pediatr Gastroenterol NutrJ. Pediatr. Gastroenterol. NutrJPGAJournal of Pediatric Gastroenterology and Nutrition0277-21161536-4801Lippincott Williams & Wilkins287276545625962JPGN-16-82510.1097/MPG.000000000000168600025Original Articles: NutritionGrowth and Nutritional Biomarkers of Preterm Infants Fed a New Powdered Human Milk Fortifier: A Randomized TrialRigoJacques∗HascoëtJean-Michel†BilleaudClaude‡PicaudJean-Charles§MoscaFabio||RubioAmandine¶SalibaElie#RadkëMichaël∗∗SimeoniUmberto††GuilloisBernard‡‡de HalleuxVirginie∗JaegerJonathan§§AmeyeLaurent||||HaysNicholas P.¶¶SpalingerJohannes##∗Department of Neonatology, University of Liège, CHR Citadelle, Liège, Belgium†Maternité Régionale Universitaire A. Pinard, Nancy‡CIC Pédiatrique 1401 INSERM-CHU, Bordeaux§Service de Neonatologie, Hôpital de la Croix Rousse, Lyon, France||Neonatal Intensive Care Unit, Department of Clinical Science and Community Health, Fondazione IRCCS “Ca’ Granda” Ospedale Maggiore Policlinico, University of Milan, Milan, Italy¶Hôpital Couple Enfant, CHU de Grenoble, Grenoble#Hôpital Clocheville, CHU de Tours, Tours, France∗∗Klinikum Westbrandenburg GmbH, Potsdam, Germany††Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland‡‡Hôpital Clemenceau, CHU de Caen, Caen, France§§Nestlé Clinical Development Unit, Lausanne, Switzerland||||Nestlé Nutrition R&D, Vevey, Switzerland¶¶Nestlé Nutrition R&D, King of Prussia, PA##Children's Hospital of Lucerne, Lucerne, Switzerland.Address correspondence to Jacques Rigo, MD, PhD, Service Universitaire de Néonatologie, CHR de la Citadelle, Boulevard du Douzième de Ligne, 1 4000 Liège, Belgium (e-mail: J.Rigo@ulg.ac.be); Address reprint or protocol requests to: Nicholas P. Hays, PhD, 3000 Horizon Dr., Suite 100, King of Prussia, PA 19406 (e-mail: Nicholas.Hays@rd.nestle.com).1020172292017654e83e93231120162952017Copyright © The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the European Society for Pediatric Gastroenterology, Hepatology, and Nutrition and the North American Society for Pediatric Gastroenterology, Hepatology, and Nutrition2017This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc-nd/4.0ABSTRACTObjectives:The aim of this study was to assess growth and nutritional biomarkers of preterm infants fed human milk (HM) supplemented with a new powdered HM fortifier (nHMF) or a control HM fortifier (cHMF). The nHMF provides similar energy content, 16% more protein (partially hydrolyzed whey), and higher micronutrient levels than the cHMF, along with medium-chain triglycerides and docosahexaenoic acid.Methods:In this controlled, multicenter, double-blind study, a sample of preterm infants ≤32 weeks or ≤1500\u200ag were randomized to receive nHMF (n\u200a=\u200a77) or cHMF (n\u200a=\u200a76) for a minimum of 21 days. Weight gain was evaluated for noninferiority (margin\u200a=\u200a–1\u200ag/day) and superiority (margin\u200a=\u200a0\u200ag/day). Nutritional status and gut inflammation were assessed by blood, urine, and fecal biochemistries. Adverse events were monitored.Results:Adjusted mean weight gain (analysis of covariance) was 2.3\u200ag/day greater in nHMF versus cHMF; the lower limit of the 95% CI (0.4\u200ag/day) exceeded both noninferiority (P\u200a<\u200a0.001) and superiority margins (P\u200a=\u200a0.01). Weight gain rate (unadjusted) was 18.3 (nHMF) and 16.8\u200ag\u200a·\u200akg−1\u200a·\u200aday−1 (cHMF) between study days 1 and 21 (D1–D21). Length and head circumference (HC) gains between D1 and D21 were not different. Adjusted weight-for-age z score at D21 and HC-for-age z score at week 40 corrected age were greater in nHMF versus cHMF (P\u200a=\u200a0.013, P\u200a=\u200a0.003 respectively). nHMF had higher serum blood urea nitrogen, pre-albumin, alkaline phosphatase, and calcium (all within normal ranges; all P\u200a≤\u200a0.019) at D21 versus cHMF. Both HMFs were well tolerated with similar incidence of gastrointestinal adverse events.Conclusions:nHMF providing more protein and fat compared to a control fortifier is safe, well-tolerated, and improves the weight gain of preterm infants.Keywordsgrowthhuman milklow birth weightSTATUSONLINE-ONLYOPEN-ACCESSTRUEWhat Is KnownDue in part to variability in human milk composition, incidence of postnatal growth restriction is more frequently reported in very-low-birth-weight infants fed fortified human milk compared to those fed preterm formulas.The optimal composition of human milk fortifier and nutritional recommendations for preterm infants fed fortified human milk are still debated.What Is NewA new human milk fortifier containing partially hydrolyzed protein, fat, and carbohydrate provides a higher protein:energy ratio while achieving lower osmolality versus a current fortifier.In preterm infants, the new fortifier improves weight gain and reduces postnatal growth restriction compared to the current fortifier.Feeding of human milk (HM) rather than preterm formulas provides many benefits to preterm infants (eg, accelerated gut maturation (1); protection against infections (2), sepsis (3), necrotizing enterocolitis (2), and retinopathy of prematurity (4); possible protective effect on neurodevelopment (5)) that are mediated by protective biomolecules and trophic factors in HM. HM, however, provides inadequate protein and micronutrients to support the rapid growth and bone mineralization of preterm infants. These deficits are particularly acute in the smallest infants (birthweight <1500\u200ag) who have the highest protein and mineral needs (6). Fortification of mother's own milk or banked HM is therefore recommended for all preterm infants with birthweight <1800\u200ag to improve nutrient accretion and in-hospital growth (7,8).Feeding fortified HM helps support adequate growth and bone mineralization (9), and is associated with favorable neurodevelopmental outcomes (10), although evidence for improved outcomes other than in-hospital growth is limited (11). The nutritional content, however, of some currently available fortifiers may be inadequate for many preterm infants. Incidence of postnatal growth restriction is more frequently reported in very-low-birth-weight infants fed fortified HM compared to those fed preterm formulas (12,13). In addition, the nutritional profile of HM from mothers of premature infants varies greatly (14) and may differ from published reference compositional data, which may lead to less-than-recommended intakes of protein and energy (15,16). These nutritional inadequacies may worsen with use of donor HM, which is often from mothers of term infants >1-month postpartum (17).A new powdered HM fortifier has been developed with a higher protein:energy ratio (protein provided as partially hydrolyzed whey), non-protein energy from lipids and carbohydrate, and higher electrolyte and vitamin levels (enriching HM in line with ESPGHAN (18) and expert group (19) recommendations) versus a control fortifier. When mixed with HM containing 1.5\u200ag protein/100\u200amL (2–4 week milk) (20–22), it provides 3.6\u200ag protein/100 kcal (within the ESPGHAN-recommended ranges (18) for protein and energy intakes for a minimal intake volume of 140\u200amL/kg/day in very-low-birth-weight infants up to 1.8\u200akg body weight), with osmolality below the recommended threshold of 450\u200amOsm/kg (23,24).This study evaluated growth and nutritional biomarkers during a 21-day interval in clinically stable preterm infants receiving the new HM fortifier (nHMF) compared to infants fed a control fortifier (cHMF). The primary objective was to assess weight gain velocity (grams per day); evaluations of other growth parameters (including weight gain velocity in gram per kilograms per day) and intervals (eg, to 40 weeks corrected age [W40CA]), feeding tolerance, adverse events, time to full fortification/full enteral feeding, and markers of protein-energy, electrolytes, bone metabolic status, gut inflammation, and maturity of gastrointestinal (GI) function were also conducted as secondary outcomes. We hypothesized that weight gain of infants fed nHMF would be both noninferior (lower limit of 95% confidence interval [CI] of mean difference >–1\u200ag/day) and superior (lower limit of 95% CI of mean difference >0\u200ag/day) to that of infants fed cHMF.METHODSStudy design and participantsThis was a controlled, double-blind, randomized, parallel-group study conducted in neonatal intensive care units (NICUs) at 11 metropolitan hospitals in France, Belgium, Germany, Switzerland, and Italy. NICU size ranged from 25 to 45 beds. Clinically stable male and female preterm infants with gestational age ≤32 weeks or birthweight ≤1500\u200ag and born to mothers who had agreed to provide expressed or donor breastmilk for the entire 21-day study duration were enrolled in the study from April 2011 to March 2014. Infants were excluded if they had a history of or current systemic, metabolic, or chromosomic disease, any congenital anomalies of the GI tract, were small for gestational age (defined in this study as bodyweight ≤5th percentile (25)), or were receiving steroids or preterm formula during the study period. For multiple births, the first sibling was randomized and other siblings were allocated to the same group. The study was reviewed and approved by an institutional review board/independent Ethics Committee at each study site. Each subject's parent/legal representative provided written informed consent before participating in the study.Infants tolerating ≥100\u200amL\u200a·\u200akg−1\u200a·\u200aday−1 of HM for >24\u200ahours were randomized to receive either nHMF or cHMF for a minimum of 21 days; infants continued to receive their allocated study fortifier (or were transitioned to a routine/standard fortifier) until NICU discharge or medical decision to stop fortification, and fortification was stopped after discharge. The fortifiers were both cow's milk-based and provided similar energy supplementation (17\u200akcal/100\u200amL of HM). For every 100\u200amL of HM, nHMF provided 1.4\u200ag partially hydrolyzed whey protein, 0.7\u200ag lipids (primarily medium chain triglycerides and docosahexaenoic acid), 1.3\u200ag carbohydrate (maltodextrin), with a blend of micronutrients. cHMF (FM85 Human Milk Fortifier, Nestlé, Switzerland) provided 1.0\u200ag extensively hydrolyzed whey protein, no lipids, 3.3\u200ag carbohydrate (lactose and maltodextrin), with a blend of micronutrients. nHMF contained higher concentrations of some vitamins and electrolytes compared to cHMF, but both contained similar levels of minerals, including calcium (as calcium glycerophosphate and calcium phosphate) and phosphorus. Table 1 presents the estimated composition and osmolality of preterm HM (22) fortified with each fortifier. Fortifiers were fed beginning at half-strength (Fortification Strength Increase day 1; FSI1), then advanced per hospital practice, with full-strength fortification occurring once infants could maintain intakes of 150 to 180\u200amL\u200a·\u200akg−1\u200a·\u200aday−1 (ie, full enteral feeds; study day 1 [D1]). A study plan schematic is presented in Figure 1.FIGURE 1Study design. cHMF\u200a=\u200acontrol human milk fortifier; D1\u200a=\u200astudy day 1; D7\u200a=\u200astudy day 7; D10/11\u200a=\u200astudy day 10/11; D14\u200a=\u200astudy day 14; D21\u200a=\u200astudy day 21; DC\u200a=\u200adischarge (note that infants continued to receive their allocated study fortifier [or were transitioned to a routine/standard fortifier] until neonatal unit discharge or medical decision to stop fortification if length of stay was >21 days, and fortification was stopped after discharge) ; FSI1\u200a=\u200afortification strength increase day 1; HC\u200a=\u200ahead circumference; HM\u200a=\u200ahuman milk; nHMF\u200a=\u200anew human milk fortifier; W40CA\u200a=\u200aweek 40 corrected age.Study ProceduresGrowthInfant nude weight (to the nearest 1\u200ag) was measured daily by trained nursery personnel using a calibrated electronic scale (Baby Scale 717, Seca, Semur-en-Auxois, France). Recumbent length and head circumference (HC; both to the nearest 0.1\u200acm) were measured at FSI1, D1, and weekly thereafter. At least 2 trained examiners measured recumbent length using a length board (Mobile Measuring Board 417, Seca, Semur-en-Auxois, France) while maintaining proper body alignment and full body extension with feet flexed. HC was measured using a nonelastic measuring tape (Measuring Tape 212 or 218, Seca, Semur-en-Auxois, France) placed over the largest circumference of the skull (above the supraorbital ridges while covering the most prominent part of the frontal bulge anteriorly). The same calibrated equipment was used for anthropometric measures for each infant at all sites. Weight-for-age, length-for-age, and HC-for-age z scores were calculated using Fenton (25). Weight gain velocity (grams per kilograms per day) was calculated using the average of the start and end weights as the denominator.Markers of Protein-energy, Electrolyte, and Bone Metabolic StatusBlood and urine samples were collected at D1, D10/11, and D21 and analyzed for serum creatinine and prealbumin, blood urea nitrogen (BUN), urinary urea, hemoglobin, hematocrit, electrolyte status, and bone metabolic status. All blood and urine parameters were analyzed as part of routine clinical assessments at each NICU. Since 24-hour urine collections were not performed in this study owing to logistical infeasibility, urinary markers were corrected for 24-hour creatinine excretion (26) assuming a standard urinary excretion in preterm infants of 10\u200amg\u200a·\u200akg−1\u200a·\u200aday−1(27).Feeding Tolerance and Adverse EventsFeeding tolerance was evaluated by trained nursery staff who recorded daily milk intake (milliliters), stool pattern (defecation frequency and stool consistency [5\u200a=\u200ahard, 4\u200a=\u200aformed, 3\u200a=\u200asoft, 2\u200a=\u200aliquid, or 1\u200a=\u200awatery]), presence of abdominal distention, and incidence of spitting-up (defined as return of a small amount of swallowed food, usually a mouthful, and usually occurring during or shortly after feeding) and vomiting (defined as return of a larger amount of food with more complete emptying of the stomach, and usually occurring sometime after feeding). In addition, frequency, type, and attribution to fortifier intake of adverse events (AEs; including clinical and laboratory) were evaluated using physician-reported information recorded using standardized forms from enrollment to W40CA. AEs were categorized by the reporting investigator as “serious” in accordance with International Conference on Harmonization criteria (28) and as “related to the intervention” based on detailed, standardized criteria provided in the protocol.Statistical AnalysisSample size was based on a previous study (29), which investigated growth and zinc status in preterm infants fed fortified HM. In the present trial, a group-sequential design was chosen (Wang and Tsiatis) (30) with 1 interim analysis. To detect a noninferior weight gain in infants fed with nHMF versus cHMF from D1 to D21 (noninferiority margin –1\u200ag/day, expected weight gain difference 2\u200ag/day, standard deviation 4.73\u200ag/day, type I error 5%, power 80%) (29), 192 subjects (males and females combined) were needed. A computer-generated list of random numbers was used to allocate group assignments. Minimization algorithm with allocation ratio 1:1 and second best probability of 15% was used. Stratification factors were center, sex, and birthweight (100g intervals). Group coding was used with 2 nonspeaking codes per group; fortifier packaging was coded accordingly but otherwise identical in appearance. Infants were enrolled and assigned to their intervention by the study investigators or trained delegates. All study personnel (both site- and sponsor-based) and participants (infants’ families) were blind to group assignment. Noninferiority was demonstrated if the lower limit of the 2-sided 95% CI of the difference in weight gain from D1 to D21 was larger than the noninferiority margin. Superiority was evaluated if noninferiority was demonstrated. Weight gain was analyzed in the intent-to-treat (ITT) and per-protocol populations by analysis of covariance (ANCOVA) adjusting for D1 postmenstrual age and weight, sex, and center (random effect). Sensitivity analyses were conducted using ANCOVA models that adjusted for covariates that were determined post hoc to be significantly different between groups and which may have confounded the primary results (eg, mother smoking status). Secondary endpoints were analyzed in the ITT population only. For noninferiority and superiority tests, 1-sided P values are provided and should be compared to a reference value of 0.025. For other tests, 2-sided P values are provided and should be compared to a reference value of 0.05. 95% CIs provide estimates for feeding effects on all endpoints. Based on prespecified guidelines in the independent Data Monitoring Committee's (DMC) charter, a single interim analysis was conducted when 134 subjects had completed their D21 visit. The interim analysis was planned to occur when the first 100 infants completed at least 21 days of full fortification; however, the analysis was conducted using data from 134 infants owing to unforeseen delays in conducting the analysis (eg, performing statistical programming, data cleaning, and query resolution) while recruitment continued. The type 1 error rate was adjusted to account for the analysis being conducted at ∼70% enrollment rather than the planned 52%. The DMC consisted of independent experts (2 clinicians, 1 biostatistician) who reviewed growth, formula intake, and key biochemical data as well as AEs. The purpose of the interim analysis was to examine unblinded growth velocity results and determine whether the trial could be stopped early for success or futility, or whether the targeted sample size required adjustment (the interim statistical analysis plan was finalized before unblinding, and the analysis was unblinded only to the DMC to facilitate ethical decision-making) (31). On April 2, 2014, the DMC recommended to stop the trial, as noninferiority and superiority in regard to the primary outcome had been demonstrated. The sponsor was notified of this decision on April 3, 2014, and the final study population included infants enrolled through March 31, 2014.RESULTSA total of 274 infants were screened, with 153 enrolled and randomized to either nHMF (n\u200a=\u200a77) or cHMF (n\u200a=\u200a76) (Fig. 2). Demographic and baseline anthropometry data are summarized in Table 2. There was no evidence of imbalance between the 2 groups with respect to infant characteristics. A significantly lower percentage of mothers and fathers of infants in the nHMF group, however, smoked during pregnancy. Number of twins was similar in each group.FIGURE 2Flow of study participants. AE\u200a=\u200aadverse event; cHMF\u200a=\u200acontrol human milk fortifier; D21\u200a=\u200astudy day 21; ITT\u200a=\u200aintent-to-treat; NEC\u200a=\u200anecrotizing enterocolitis; nHMF\u200a=\u200anew human milk fortifier; NICU\u200a=\u200aneonatal intensive care unit; PP\u200a=\u200aper-protocol; SAE\u200a=\u200aserious adverse event. ∗Although screening procedures were standardized across sites, some variability in prescreening procedures did occur. Based on the typical clinical characteristics of infants who were admitted to each NICU during the study interval, the total number of infants who would have been theoretically considered eligible for the study was higher than the number shown here.The majority (84% and 87% by volume in nHMF and cHMF, respectively) of milk provided to infants was pasteurized. Donor milk was always pasteurized and accounted for 49% and 51% of the fortified HM volume provided in the nHMF and cHMF groups, respectively. There was no significant difference in mean volume of fortified milk intake between groups (152.7\u200a±\u200a13.0 and 152.6\u200a±\u200a17.2\u200amL\u200a·\u200akg−1\u200a·\u200aday−1 in nHMF and cHMF, respectively). Protein intake estimated using standard values for preterm HM composition per 100\u200amL (22) was significantly greater in nHMF compared to cHMF (4.48\u200a±\u200a0.38 vs 3.81\u200a±\u200a0.43\u200ag\u200a·\u200akg−1\u200a·\u200aday−1, respectively; P\u200a<\u200a0.001) because of higher protein content of the nHMF. Estimated energy intake was not significantly different between groups (125\u200akcal\u200a·\u200akg−1\u200a·\u200aday−1 in both groups). There was no significant difference in number of days between FSI1 and D1, but adjusted time between birth and D1 was significantly shorter in nHMF (16.8\u200a±\u200a5.4 vs 18.7\u200a±\u200a8.8 days; −8.5% [95% CI: −15.0%, −1.0%]).GrowthIn the ITT population, adjusted weight gain from D1 to D21 was 2.3\u200ag/day higher in nHMF, with the 95% CI ranging from 0.4 to 4.2\u200ag/day, demonstrating noninferiority (P\u200a<\u200a0.001) and superiority (P\u200a=\u200a0.01) of nHMF. Per-protocol results were similar. Weight gain from D1 to D21 remained significantly higher in nHMF when expressed in grams per kilogram per day (Table 3). Weight-for-age z scores (Fig. 3) remained stable from FSI1 to D21 in nHMF, but continued to decrease in cHMF (P\u200a=\u200a0.007 vs D1). At D21, weight-for-age z score was significantly higher in nHMF compared to cHMF (0.12 [95% CI: 0.03, 0.22]). Length and HC gains during the D1 to D21 period were not significantly different between groups (Table 3), with comparable results observed from analyses of unadjusted means (Table 4). Length-for-age z scores at D21 (Fig. 3) were significantly lower than D1 values in cHMF (P\u200a=\u200a0.041). Additionally, at W40CA, adjusted HC-for-age z scores were significantly higher in nHMF compared to cHMF (0.41 [95% CI: 0.14, 0.68]). Mean weight, length, and HC at D1, D21, and W40CA are summarized in Table 5.FIGURE 3Mean\u200a±\u200aSD weight-for-age (panel A), length-for-age (panel B), and head circumference-for-age (panel C) z scores for the overall ITT population. Circle symbols/solid line represents nHMF. Triangle symbols/dashed line represents cHMF. FSI1\u200a=\u200afortification strength increase day 1; ITT\u200a=\u200aintent-to-treat; SD\u200a=\u200astandard deviation; W40CA\u200a=\u200aweek 40 corrected age; z scores calculated using Fenton preterm growth chart (25). ∗P\u200a=\u200a0.013 vs cHMF (by analysis of covariance, adjusting for value at D1, sex, and center); †P\u200a=\u200a0.007 vs day 1 (by t test); ‡P\u200a=\u200a0.041 vs day 1 (by t test); ∗∗P\u200a=\u200a0.003 vs cHMF (by analysis of covariance, adjusting for value at D1, sex, and center).Protein-Energy StatusBUN decreased progressively in cHMF (P\u200a=\u200a0.004 for D21 vs D1), whereas it increased in nHMF (P\u200a<\u200a0.001 for D10/11 vs D1 [data not shown]) and remained stable up to D21 (Table 6). Prealbumin levels were similar at D1 and increased in both groups during the study (Table 6). The increase from D1 to D21, however, was only significant in nHMF (P\u200a=\u200a0.004). At D21, adjusted mean prealbumin in nHMF was significantly higher (+11.8% [95%CI: +2.3%, +22.2%]) than in cHMF. Urinary urea excretion (corrected for creatinine excretion) at D1 was similar in the 2 groups (Table 6). Urea excretion remained steady in cHMF but increased sharply in nHMF (P\u200a<\u200a0.001 for D10/11 vs D1 [data not shown]), after which it remained stable (to D21). At D21, urea excretion was significantly higher in nHMF versus cHMF (+108.7% [95% CI: +66.0%, +162.5%]).Bone Metabolic StatusSerum calcium concentrations were generally stable during the study (Table 6), with mean values for both groups within the normal range (32). Nevertheless, adjusted mean serum calcium concentration in nHMF was minimally but significantly higher than in cHMF at D21 (+1.9% [95% CI: +0.3%, +3.5%]). Serum phosphorus increased slightly in the 2 groups (Table 6). At D1, relative hypophosphatemia (<1.55\u200ammol/L) was observed in 13 infants in both groups; this was corrected in 11 infants by D10/11 and 12 infants by D21. At D1, serum alkaline phosphatase was not significantly different in nHMF versus cHMF (P\u200a=\u200a0.208). Thereafter, serum alkaline phosphatase decreased significantly in both groups (D21 vs D1: P\u200a=\u200a0.005 for nHMF, P\u200a<\u200a0.001 for cHMF), with mean values significantly higher in nHMF versus cHMF at D10/11 (+8.6% [95% CI: +1.0%, +16.8%]; data not shown) and D21 (+12.1% [95% CI: +2.8%, +22.3%]) (Table 6). Declines from baseline were significantly greater in cHMF versus nHMF at D10/11 (P\u200a<\u200a0.001; data not shown) and D21 (P\u200a=\u200a0.035). At D1, spot urinary excretions of calcium and phosphorus corrected for urinary creatinine excretion were similar in the 2 groups (Table 6). Calcium excretion tended to increase slowly during the study in both groups, with mean concentration significantly lower in nHMF compared to cHMF at D21 (P\u200a=\u200a0.011). Phosphorus excretion increased in both groups, resulting in a decreased median urinary calcium:phosphorus molar ratio in both groups (Table 6).ElectrolytesSerum electrolyte concentrations were stable during the study and similar in both groups (Table 6). Urinary sodium and potassium concentrations were significantly higher (sodium: +31.1% [95% CI: +1.7%, +68.9%], potassium: +22.5% [95% CI: +1.0%, +48.6%]) in nHMF compared to cHMF at D21 (Table 7).Stool Characteristics and Feeding ToleranceStool frequency from D1 to D21 was not significantly different in nHMF and cHMF (3.9\u200a±\u200a1.05 vs 3.6\u200a±\u200a0.93\u200astools/day; 0.29 [95% CI: −0.05, 0.63]). Stool consistency was slightly more “formed” in nHMF compared to cHMF during this interval (3.1\u200a±\u200a0.26 vs 3.0\u200a±\u200a0.27; 0.12 [95% CI: 0.02, 0.21]). Most infants (>90%) had stool consistency scores of “soft.” There were no significant differences between groups in frequencies of spitting-up, vomiting, or abdominal distention. There also were no group differences in incidence of AEs indicative of feeding intolerance (all P\u200a≥\u200a0.25).Adverse EventsThe overall incidence of AEs was significantly larger in nHMF (103 events in 56 infants, including 26 events categorized as GI disorders, 18 as infections or infestations, and 5 as metabolism and nutrition disorders) compared to cHMF (78 events in 41 infants, including 21 events categorized as GI disorders, 18 as infections or infestations, and 1 as metabolism and nutrition disorder; odds ratio: 2.26 [95% CI: 1.10, 4.47]). Other AEs that occurred more frequently in nHMF included several that were classified by study investigators as unlikely to be related to consumption of milk fortifiers (eg, cardiac disorders [16 events in nHMF vs 5 in cHMF], eye disorders [10 events in nHMF vs 3 in cHMF]). The number of AEs considered related to study product intake as determined by physician report was low (3 events in nHMF [2 events of hyponatremia, 1 of vomiting] and 0 events in cHMF). No significant difference was demonstrated in overall incidence of serious AEs between the 2 groups (7 events in 7 infants [including 2 events of necrotizing enterocolitis, 0 events of bronchopulmonary dysplasia, 0 events of sepsis, 0 events of retinopathy] in nHMF and 12 events in 11 subjects [including 4 events of necrotizing enterocolitis, 1 event of bronchopulmonary dysplasia, 0 events of sepsis, 0 events of retinopathy] in cHMF; odds ratio: 0.54 [95% CI: 0.17, 1.58]).DISCUSSIONThis study demonstrated that weight gain from D1 of full fortification until D21 in preterm infants fed HM fortified with a new fortifier designed to add 1.4\u200ag partially hydrolyzed protein and 0.7\u200ag fat to 100\u200amL of HM was significantly greater than weight gain in infants fed HM fortified with an isocaloric control fortifier designed to add 1.0\u200ag extensively hydrolyzed protein and no fat. The mean difference was 2.3\u200ag/day or 1.2\u200ag\u200a·\u200akg−1\u200a·\u200aday−1, consistent with our hypothesized difference of 2\u200ag/day, and which indicates the superiority of the new fortifier compared to the control with regard to weight gain. In addition, the weight gain benefit tended to persist until discharge, with a significantly higher adjusted weight gain difference in the nHMF group compared to cHMF from FSI1 to W40CA (2.01\u200ag/day; P\u200a=\u200a0.009). In the nHMF group, weight-for-age z scores were stable from FSI1 to D21 and average weight gain exceeded 18\u200ag\u200a·\u200akg−1\u200a·\u200aday−1, matching recommended rates of postnatal weight gain to mimic intrauterine growth (33,34). Consistent with the increased protein content of the new fortifier, the nHMF group had significantly higher serum prealbumin concentrations, suggesting an increase in nitrogen retention compared to cHMF. The lack of difference, however, in length gain during the study may be in part the result of the relatively limited period of protein supplementation (only 21 days) or because mean length gains in both groups were already quite high (ie, ≥1.1\u200acm/week), whereas the significantly higher HC-for-age z score at W40CA in the nHMF group may be because of the increased protein and lipid content of the new fortifier. In contrast, the absence of a significant difference at earlier timepoints could be attributable to the relatively high variability of HC gain (31% and 27% for nHMF and cHMF, respectively, from D1 to D21) induced by the natural dolichocephalic evolution of the skull that occurs in preterm infants (35). Feeding tolerance and stool patterns were similar in each group, and AEs related to feeding were low and not significantly different between groups, consistent with fortified HM osmolality values slightly lower in nHMF versus cHMF and below the recommended cutoff (23,24) in both groups.Although there was no evidence of imbalance between the 2 fortifier groups with respect to infant baseline characteristics, significant differences in maternal weight gain, smoking, and alcohol usage during pregnancy were observed. As these may be confounding factors in the analysis of weight gain, post hoc ANCOVAs including these parameters were performed. The post hoc results were essentially the same as the main results, indicating that differences in maternal baseline characteristics did not confound the results. Additionally, to determine the possible impact of including clustered data from twins in the analyses, a sensitivity analysis on weight gain (grams per day) from D1 to D21 accounting for the correlated multiple-birth data was performed. Again, these results were similar to those of the main analysis (weight gain 3.2\u200ag/day higher in nHMF [95% CI: 0.5, 5.9\u200ag/day]).Our results are consistent with those of previous studies (36–42). A recent meta-analysis of 5 studies (comprising 352 infants with birthweight ≤1750\u200ag and gestational age ≤34 weeks) compared growth of infants fed HM fortified with either lower-protein or higher-protein fortifier (43). Infants receiving higher-protein fortifier had significantly greater weight (mean difference 1.77\u200ag/kg/day), length (0.21\u200acm/week), and HC gains (0.19\u200acm/week) compared to those receiving lower-protein fortifier (43). Miller et al (39) used a higher-protein fortifier similar in protein content to the one used in the present study, and reported a higher bodyweight at study end among infants in the higher-protein HMF group (mean difference 220\u200ag), but no significant differences in length or HC. In contrast, Moya et al (40) observed a significantly higher achieved weight, length, and HC in the experimental group compared to controls, but their fortifier had a slightly higher protein content (3.2\u200ag/100\u200amL) versus the one used in the present study (3.04\u200ag/100\u200amL), plus the intervention lasted 28 rather than 21 days.Energy and protein content of HM samples were not analyzed in this study but estimated according to Tsang et al (22). Variability of protein, fat, and energy content of HM fed to preterm infants in the NICU is high (15,21). In addition, fat content may be reduced during processing of HM from expression to administration (44), which could be exacerbated with the use of continuous tube feeding (45). In our study, percentage of intake from mother's own milk, donor milk, and pasteurized HM was assessed. Pasteurized donor milk accounted for 51% of the fortified HM provided during the study, whereas 56% of mother's own milk was also pasteurized. Considering that protein content of donor HM is lower than that of mother's own milk (46) and that all the required processing steps (eg, collection, transfer, refrigeration, pasteurization, tube feeding) may significantly decrease fat and energy content (47), the characteristics of the HM used in the present study suggests that protein and energy content could be overestimated when based on a theoretical composition of preterm HM.In the present study, the mean increase in protein supplementation provided by nHMF compared to cHMF was 0.65\u200ag\u200a·\u200akg−1\u200a·\u200aday−1 or 7.4\u200ammol\u200a·\u200akg−1\u200a·\u200aday−1 of nitrogen, from which approximately 6.14\u200ammol\u200a·\u200akg−1\u200a·\u200aday−1 of nitrogen (83%) is absorbed (based on data from balance studies) (48). During the study, urea production increased significantly in the nHMF group leading to an increase in BUN of 1.7\u200ammol/L at D21 and in urea excretion of 2.3\u200ammol\u200a·\u200akg−1\u200a·\u200aday−1 (2.3\u200ammol/10\u200amg creatinine). These data suggest that the nitrogen balance was improved to ∼3.8\u200ammol nitrogen (52% of nitrogen intake) in preterm infants fed nHMF compared to control. This relatively limited protein utilization could result from reduced energy bioavailability of HM, and an increase in energy supply could improve protein utilization in preterm infants fed fortified HM. These data also suggest that specific nutritional recommendations should be formulated for infants fed fortified HM. Nevertheless, the increase in nitrogen retention (∼3.8\u200ammol\u200a·\u200akg−1\u200a·\u200aday−1) appears to be higher than the nitrogen content of the higher weight gain observed with the nHMF (12% of the 1.5\u200ag\u200a·\u200akg−1\u200a·\u200aday−1 corresponding to 2\u200ammol\u200a·\u200akg−1\u200a·\u200aday−1 of nitrogen), suggesting an increase in lean body mass accretion and a moderate reduction in fat mass gain as previously demonstrated in preterm infants fed protein-fortified HM (49).Indices of bone metabolism were satisfactory in both groups, with a significant decrease in serum alkaline phosphatase observed in both groups and 98% of the infants having normal serum phosphorus concentrations at D21. Adequate postnatal bone mineralization is difficult to obtain in preterm infants owing to the interruption of mineral transplacental transfer (50). Although elevated alkaline phosphatase activity may be associated with reduced bone mineralization when mineral intake is deficient (51), the decrease in enzyme levels observed in the presence of normal serum phosphorus values, as well as the low urinary calcium and moderate urinary phosphorus excretion observed in both groups in this study, suggest that intakes were adequate to promote bone mineralization and limit postnatal osteopenia. Mean serum creatinine concentration decreased significantly in both groups suggesting a similar maturation of renal function during this period. Urinary electrolyte concentrations were higher in nHMF versus cHMF at D21, likely in parallel with the higher electrolyte content of nHMF.A lack of HM composition data (allowing estimation of nutritional balance) is a limitation of our study, although standardized accurate techniques are still not available in the NICU. Additionally, the composition of the faster weight gain can only be estimated as lean body mass and/or bone mineralization were not determined. As a result, nutrient absorption and metabolism can only be estimated from serum and urinary metabolite concentrations. Lastly, the results need to be confirmed in a broader population of preterm infants commonly admitted to the NICU including SGA infants and partially breast-fed infants, as these infants were excluded by design. Strengths of this study include the size and multiple sites (11 pediatric hospitals in 4 European countries), which enhances external validity.In conclusion, these results indicate that the new HM fortifier, made with partially hydrolyzed whey protein and a higher protein:energy ratio is safe, well-tolerated, and improves weight gain of preterm infants compared to control fortifier. Providing some energy as fat and replacing extensively hydrolyzed with partially hydrolyzed protein in the new HM fortifier allows a reduction in osmolality <400\u200amOsm/kg immediately after fortification. Protein intakes from HM supplemented with the new fortifier are within the range of the most recent nutritional recommendations for preterm infants.AcknowledgmentsThe authors thank the families of the infants who participated in the study, as well as the research staff at each participating institution. The authors also thank Christelle Perdrieu and Samir Dahbane from the Clinical Development Unit at the Nestlé Research Center for assistance with trial management and Philippe Steenhout, Medical Director at Nestlé Nutrition, for input on study design and assistance with trial supervision.This study was sponsored by Nestlé Nutrition. J.J., L.A., and N.P.H. are employees of Nestlé SA. J.R., J.M.H., C.B., J.C.P., F.M., A.R., E.S., M.R., U.S., B.G., and J.S. received research funding from Nestlé Nutrition. J.R., J.C.P., and C.B. are consultants for Nestlé Nutrition. U.S. has been a speaker, consultant, and expert panel participant for Nestlé, Danone, and Bledina over the past 3 years. V.d.H. has no conflicts of interest to declare.www.clinicaltrials.gov NCT01771588This study was sponsored by Nestlé Nutrition.Portions of these data were presented in abstract form at the 1st Congress of joint European Neonatal Societies, Budapest, Hungary, 15–20 September 2015.REFERENCES1.GarciaCDuanRDBrevaut-MalatyV\nBioactive compounds in human milk and intestinal health and maturity in preterm newborn: an overview. Cell Mol Biol (Noisy-le-grand)\n2013; 59:108–131.253266482.CorpeleijnWEKouwenhovenSMPaapMC\nIntake of own mother's milk during the first days of life is associated with decreased morbidity and mortality in very low birth weight infants during the first 60 days of life. Neonatology\n2012; 102:276–281.229226753.PatelALJohnsonTJEngstromJL\nImpact of early human milk on sepsis and health-care costs in very low birth weight infants. J Perinatol\n2013; 33:514–519.233706064.ManzoniPStolfiIPedicinoR\nHuman milk feeding prevents retinopathy of prematurity (ROP) in preterm VLBW neonates. Early Hum Dev\n2013; 89\nsuppl 1:S64–S68.238093555.KooWTankSMartinS\nHuman milk and neurodevelopment in children with very low birth weight: a systematic review. Nutr J\n2014; 13:94.252313646.CarlsonSWojcikBBarkerA\nGuidelines for the use of human milk fortifier in the neonatal intensive care unit. University of Iowa Neonatology Handbook. 2011. Available at: http://www.uichildrens.org/iowa-neonatology-handbook/feeding/human-milk\nAccessed on January 22, 2017.7.AdamkinDHRadmacherPG\nFortification of human milk in very low birth weight infants (VLBW <1500\u200ag birth weight). Clin Perinatol\n2014; 41:405–421.248738408.MoroGEArslanogluSBertinoE\nXII. Human milk in feeding premature infants: consensus statement. J Pediatr Gastroenterol Nutr\n2015; 61\nsuppl 1:S16–S19.262959999.EinloftPRGarciaPCPivaJP\nSupplemented vs. unsupplemented human milk on bone mineralization in very low birth weight preterm infants: a randomized clinical trial. Osteoporos Int\n2015; 26:2265–2271.2597168610.GibertoniDCorvagliaLVandiniS\nPositive effect of human milk feeding during NICU hospitalization on 24 month neurodevelopment of very low birth weight infants: an Italian cohort study. PLoS ONE\n2015; 10:e0116552.2559063011.BrownJVEmbletonNDHardingJE\nMulti-nutrient fortification of human milk for preterm infants. Cochrane Database Syst Rev\n2016; 5:CD000343.12.SchanlerRJShulmanRJLauC\nFeeding strategies for premature infants: beneficial outcomes of feeding fortified human milk versus preterm formula. Pediatrics\n1999; 103\n(6 pt 1):1150–1157.1035392213.O’ConnorDLJacobsJHallR\nGrowth and development of premature infants fed predominantly human milk, predominantly premature infant formula, or a combination of human milk and premature formula. J Pediatr Gastroenterol Nutr\n2003; 37:437–446.1450821414.WeberALouiAJochumF\nBreast milk from mothers of very low birthweight infants: variability in fat and protein content. Acta Paediatr\n2001; 90:772–775.1151998015.CorvagliaLAcetiAPaolettiV\nStandard fortification of preterm human milk fails to meet recommended protein intake: bedside evaluation by near-infrared-reflectance-analysis. Early Hum Dev\n2010; 86:237–240.2044777916.ArslanogluSMoroGEZieglerEE\nPreterm infants fed fortified human milk receive less protein than they need. J Perinatol\n2009; 29:489–492.1944423717.ArslanogluSCorpeleijnWMoroG\nDonor human milk for preterm infants: current evidence and research directions. J Pediatr Gastroenterol Nutr\n2013; 57:535–542.2408437318.AgostoniCBuonocoreGCarnielliVP\nEnteral nutrient supply for preterm infants: commentary from the European Society for Paediatric Gastroenterology, Hepatology, and Nutrition Committee on Nutrition. J Pediatr Gastroenterol Nutr\n2010; 50:85–91.1988139019.KoletzkoBPoindexterBUauyR\nRecommended nutrient intake levels for stable, fully enterally fed very low birth weight infants. World Rev Nutr Diet\n2014; 110:297–299.2475163820.GidrewiczDAFentonTR\nA systematic review and meta-analysis of the nutrient content of preterm and term breast milk. BMC Pediatr\n2014; 14:216.2517443521.de HalleuxVRigoJ\nVariability in human milk composition: benefit of individualized fortification in very-low-birth-weight infants. Am J Clin Nutr\n2013; 98\nsuppl:529S–535S.2382472522.TsangRCUauyRKoletzkoB\nNutrition of the Preterm Infant, Scientific Basis and Practical Guidelines. Cincinnati: Digital Educational Publishing, Inc; 2005.23.KreisslAZwiauerVRepaA\nEffect of fortifiers and additional protein on the osmolarity of human milk: is it still safe for the premature infant?\nJ Pediatr Gastroenterol Nutr\n2013; 57:432–437.2385734024.BilleaudCSenterreJRigoJ\nOsmolality of the gastric and duodenal contents in low birth weight infants fed human milk or various formulae. Acta Paediatr Scand\n1982; 71:799–803.718044925.FentonTRKimJH\nA systematic review and meta-analysis to revise the Fenton growth chart for preterm infants. BMC Pediatr\n2013; 13:59.2360119026.NewmanDJPugiaMJLottJA\nUrinary protein and albumin excretion corrected by creatinine and specific gravity. Clin Chim Acta\n2000; 294:139–155.1072768027.Al-DahhanJStimmlerLChantlerC\nUrinary creatinine excretion in the newborn. Arch Dis Child\n1988; 63:398–402.336500928.ICH Expert Working Group. Guideline for good clinical practice E6(R1). 1996\nAvailable at: http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Efficacy/E6/E6_R1_Guideline.pdf\nAccessed on January 22, 2017.29.SpalingerJHSchmidtMBergerTM\nComparison of two human milk fortifiers: effects on growth and zinc status in premature infants. J Pediatr Gastroenterol Nutr\n2004; 39\nsuppl 1:1126.30.WangSKTsiatisAA\nApproximately optimal one-parameter boundaries for group sequential trials. Biometrics\n1987; 43:193–199.356730431.KnottnerusJASpigtMG\nWhen should an interim analysis be unblinded to the data monitoring committee?\nJ Clin Epidemiol\n2010; 63:350–352.1976221032.NicholsonJFPesceMA\nNelsonWEBehrmanREKliegmanRArvinAM\nLaboratory Testing and Reference Values (Table 670-2) in Infants and Children. Nelson Textbook of Pediatrics. Philadelphia: W.B. Saunders; 1996\n2031–2084.33.FentonTRNasserREliasziwM\nValidating the weight gain of preterm infants between the reference growth curve of the fetus and the term infant. BMC Pediatr\n2013; 13:92.2375880834.EhrenkranzRADusickAMVohrBR\nGrowth in the neonatal intensive care unit influences neurodevelopmental and growth outcomes of extremely low birth weight infants. Pediatrics\n2006; 117:1253–1261.1658532235.McCartyDBPeatJRMalcolmWF\nDolichocephaly in preterm infants: prevalence, risk factors, and early motor outcomes. Am J Perinatol\n2016; 34:372–378.2758893336.PorcelliPSchanlerRGreerF\nGrowth in human milk-fed very low birth weight infants receiving a new human milk fortifier. Ann Nutr Metab\n2000; 44:2–10.1083846037.ReisBBHallRTSchanlerRJ\nEnhanced growth of preterm infants fed a new powdered human milk fortifier: a randomized, controlled trial. Pediatrics\n2000; 106:581–588.1096910638.BersethCLVan AerdeJEGrossS\nGrowth, efficacy, and safety of feeding an iron-fortified human milk fortifier. Pediatrics\n2004; 114:e699–e706.1554561639.MillerJMakridesMGibsonRA\nEffect of increasing protein content of human milk fortifier on growth in preterm infants born at <31 wk gestation: a randomized controlled trial. Am J Clin Nutr\n2012; 95:648–655.2230193340.MoyaFSiskPMWalshKR\nA new liquid human milk fortifier and linear growth in preterm infants. Pediatrics\n2012; 130:e928–e935.2298787741.AlanSAtasayBCakirU\nAn intention to achieve better postnatal in-hospital-growth for preterm infants: adjustable protein fortification of human milk. Early Hum Dev\n2013; 89:1017–1023.2403503942.ThoeneMHansonCLydenE\nComparison of the effect of two human milk fortifiers on clinical outcomes in premature infants. Nutrients\n2014; 6:261–275.2439453843.LiuTTDangDLvXM\nHuman milk fortifier with high versus standard protein content for promoting growth of preterm infants: A meta-analysis. J Int Med Res\n2015; 43:279–289.2595615644.VieiraAASoaresFVPimentaHP\nAnalysis of the influence of pasteurization, freezing/thawing, and offer processes on human milk's macronutrient concentrations. Early Hum Dev\n2011; 87:577–580.2159268845.IgawaMMuraseMMizunoK\nIs fat content of human milk decreased by infusion?\nPediatr Int\n2014; 56:230–233.2484751446.WojcikKYRechtmanDJLeeML\nMacronutrient analysis of a nationwide sample of donor breast milk. J Am Diet Assoc\n2009; 109:137–140.1910333547.de HalleuxVPeiltainCSanterreT\nUse of donor milk in the neonatal intensive care unit. Semin Fetal Neonatal Med\n2017; 22:23–29.2764999548.PicaudJCPutetGRigoJ\nMetabolic and energy balance in small- and appropriate-for-gestational-age, very low-birth-weight infants. Acta Paediatr Suppl\n1994; 405:54–59.773479249.PutetGRigoJSalleB\nSupplementation of pooled human milk with casein hydrolysate: energy and nitrogen balance and weight gain composition in very low birth weight infants. Pediatr Res\n1987; 21:458–461.358808250.PieltainCde HalleuxVSenterreT\nPrematurity and bone health. World Rev Nutr Diet\n2013; 106:181–188.2342869951.RuskC\nRickets screening in the preterm infant. Neonatal Netw\n1998; 17:55–57.TABLE 1Calculated∗ nutrient composition of fortified preterm human milkPreterm HM\u2009+\u2009nHMFPreterm HM\u2009+\u2009cHMF4\u2009g fortifier alone4\u2009g fortifier per 100\u2009kcal milk4\u2009g fortifier per 100\u2009mL milk5\u2009g fortifier alone5\u2009g fortifier per 100\u2009kcal milk5\u2009g fortifier per 100\u2009mL milkRecommended intake range (per 100\u2009kcal)†NutrientEnergy, kcal17.410084.617.410084.5Protein, g1.423.63.041.03.102.623.2–4.1Protein sourcePartially hydrolyzed wheyExtensively hydrolyzed wheyFat, g0.725.004.230.024.163.524.4–6MCT, g0.500.590.50000DHA, mg6.319.316.3011.810.0(16.4–) 50–55Carbohydrate, g1.3010.178.603.3012.5310.6010.5–12Carbohydrate sourceMaltodextrinLactose and maltodextrinCalcium, mg7611910175118100109–182Phosphorus, mg44695845705955–127Magnesium, mg4.08.67.32.46.75.77.3–13.6Sodium, mg36.776.564.720.056.848.063–105Potassium, mg48.4116.498.442.0108.892.071–177Chloride, mg32.1106.690.117.088.775.095–161Iron, mg1.802.231.891.301.641.391.8–2.7Zinc, mg0.941.551.310.801.381.171.3–2.3Manganese, μg8.089.988.445.006.345.360.9–13.6Copper, mg0.050.110.090.040.090.080.09–0.21Iodine, μg16.936.630.915.034.329.09–50Selenium, μg3.77.26.11.54.63.94.5–9Vitamin A, IU1183175414835009468001217–3333Vitamin D, IU150187158100128108100–350Vitamin E, IU4.45.64.72.23.02.52.2–11.1Vitamin K, μg8.09.88.34.05.14.34–25Thiamin, mg0.150.190.160.050.070.060.13–0.27Riboflavin, mg0.200.270.230.100.150.130.18–0.36Vitamin B6, mg0.130.160.140.050.070.060.05–0.27Vitamin B12, μg0.200.260.220.100.140.120.09–0.73Niacin, mg1.502.021.710.801.191.010.9–5Folic acid, μg40.051.043.140.051.043.132–91Pantothenic acid, mg0.701.100.930.400.740.630.45–1.9Biotin, μg3.504.784.043.004.193.541.5–15Vitamin C, mg20.028.924.410.017.014.418–50Osmolality‡, mOsm/kg390441cHMF\u2009=\u2009control human milk fortifier; DHA\u2009=\u2009docosahexaenoic acid; HM\u2009=\u2009human milk; nHMF\u2009=\u2009new human milk fortifier; MCT\u2009=\u2009medium chain triglycerides.*Calculated based on preterm human milk composition from Tsang et al, 2005 (22).†Recommended nutrient intakes for fully enterally fed preterm very low birth weight infants (19).‡Measured immediately after fortification at room temperature (25°C).TABLE 2Demographic and baseline characteristics of infants and parentsnHMF (n\u2009=\u200976)cHMF (n\u2009=\u200974)Infant characteristicsSex\u2003Boys38 (50)35 (47)Delivery type\u2003Vaginal24 (32)20 (27)Twin18 (24)16 (22)Birth weight, g1147\u2009±\u20092581156\u2009±\u2009289Birth weight by birth weight category\u2003<1000\u2009g\u2003\u2003n (%)24 (32)26 (35)\u2003\u2003Birth weight, g850.5\u2009±\u2009118.9847.3\u2009±\u2009105.1\u2003≥1000\u2009g\u2003\u2003Birth weight, g1283.6\u2009±\u2009175.41323.9\u2009±\u2009206.2Birth length, cm37.1\u2009±\u20092.737.1\u2009±\u20093.1Birth head circumference, cm26.5\u2009±\u20092.726.7\u2009±\u20092.5Gestational age at birth, weeks28.8\u2009±\u20092.128.7\u2009±\u20091.8Postnatal age at study time points, days*\u2003FSI113 (11, 18)14 (10, 20)\u2003Day 116 (13, 20)17 (13, 23)\u2003Day 2136 (33, 40)37 (33, 43)\u2003Week 40 corrected age76 (66, 91)76 (67, 83)Apgar score\u20031 min5.8\u2009±\u20092.55.8\u2009±\u20092.3\u20035 min8.0\u2009±\u20091.87.7\u2009±\u20091.9Parent characteristicsSmoking status\u2003Mother smoker during pregnancy6 (9)18 (29)\u2003Father smoker3 (5)12 (21)\u2003Mother drank alcohol during pregnancy0 (0)4 (6)Mother's age, y31.1\u2009±\u20095.130.8\u2009±\u20095.5Mother's BMI before pregnancy, kg/m2*23.2 (20.6, 27.2)21.3 (19.7, 26.1)Mother's weight gain during pregnancy, kg11.2\u2009±\u20096.89.2\u2009±\u20095.2BMI\u2009=\u2009body mass index; cHMF\u2009=\u2009control human milk fortifier; FSI1\u2009=\u2009fortification strength increase day 1; nHMF\u2009=\u2009new human milk fortifier . Data are presented as n (%) for categorical variables and mean\u2009±\u2009SD for continuous variables except where noted.*Data are presented as median (Q1, Q3).TABLE 3Anthropometric gains from D1 to D21Treatment groupnnHMFncHMFP*Weight gain, g\u2009·\u2009kg−1\u2009·\u2009day−16418.3\u2009±\u20093.76716.8\u2009±\u20093.70.013†Length gain, cm/wk551.23\u2009±\u20090.62651.18\u2009±\u20090.490.842HC gain, cm/wk571.04\u2009±\u20090.32650.96\u2009±\u20090.260.125cHMF\u2009=\u2009control human milk fortifier; D1\u2009=\u2009study day 1 (first day of full-strength fortification); D21\u2009=\u2009study day 21; HC\u2009=\u2009head circumference; nHMF\u2009=\u2009new human milk fortifier. Data are presented as unadjusted mean\u2009±\u2009SD.*One-sided superiority P value based on analysis of covariance model adjusted for postmenstrual age and relevant anthropometric measure at D1, sex, and center.†Adjusted difference in weight gain (nHMF–cHMF): mean difference\u2009=\u20091.18\u2009g\u2009·\u2009kg−1\u2009·\u2009day−1; 95% CI\u2009=\u20090.14, 2.21.TABLE 4Body length and head circumference gains between study days 1 and 21, by infant sex and by birth weight categoryUnadjusted length gain, cm/wk*Unadjusted head circumference gain, cm/wk*nHMFcHMFnHMFcHMFnMean\u2009±\u2009SDnMean\u2009±\u2009SDP†nMean\u2009±\u2009SDnMean\u2009±\u2009SDP†Overall551.23\u2009±\u20090.62651.18\u2009±\u20090.490.842571.04\u2009±\u20090.32650.96\u2009±\u20090.260.126Boys271.40\u2009±\u20090.65281.18\u2009±\u20090.490.364281.12\u2009±\u20090.28280.99\u2009±\u20090.220.062Girls281.08\u2009±\u20090.56371.17\u2009±\u20090.500.510290.97\u2009±\u20090.35370.93\u2009±\u20090.290.598<1000\u2009g191.07\u2009±\u20090.52211.27\u2009±\u20090.520.563191.04\u2009±\u20090.34210.94\u2009±\u20090.280.223≥1000\u2009g361.32\u2009±\u20090.66441.13\u2009±\u20090.480.499381.05\u2009±\u20090.32440.96\u2009±\u20090.260.270cHMF\u2009=\u2009control human milk fortifier; nHMF\u2009=\u2009new human milk fortifier.*Data are presented as unadjusted mean\u2009±\u2009SD.†Superiority P value for gain differences adjusted for postmenstrual age and the relevant anthropometric measure at D1, sex, and center by analysis of covariance.TABLE 5Weight, length, and head circumference at selected study time pointsnHMFcHMFVariablenMeanSDnMeanSDWeight, g\u2003D1721346271741347270\u2003D21641884336671863328\u2003W40CA603076519632897416Length, cm\u2003D16738.72.57438.72.8\u2003D215841.82.46542.02.7\u2003W40CA6047.62.66247.32.5Head circumference, cm\u2003D16827.72.57327.61.9\u2003D215930.22.26630.32.0\u2003W40CA5935.31.46434.61.5cHMF\u2009=\u2009control human milk fortifier; D1\u2009=\u2009study day 1; D21\u2009=\u2009study day 21; nHMF\u2009=\u2009new human milk fortifier; SD\u2009=\u2009standard deviation; W40CA\u2009=\u2009week 40 corrected age.TABLE 6Markers of protein-energy status, electrolytes, and bone metabolic status at study days 1 and 21nHMFcHMFVariablenMedianIQRGeometric meannMedianIQRGeometric meanP*Serum creatinine, μmol/L\u2003D16944.036.2–48.041.57044.138.0–51.843.50.303\u2003D216328.023.5–32.026.76530.025.0–35.029.50.001BUN, mmol/L\u2003D1703.101.70–4.562.89712.501.65–4.672.730.585\u2003D21633.903.05–4.653.89642.151.50–2.632.15<0.001Serum prealbumin, mg/L\u2003D15110080–12096.8469080–10087.80.073\u2003D214611691.3–140113.84110090–12098.10.015Urinary urea†, mmol/10\u2009mg creatinine\u2003D1472.72.0–4.72.8532.51.9–3.32.50.302\u2003D21425.84.6–6.85.1402.82.0–3.32.7<0.001Serum calcium, mmol/L\u2003D1502.442.31–2.532.41542.472.38–2.562.440.445\u2003D21502.472.40–2.542.46482.432.34–2.532.430.019Serum phosphorus, mmol/L\u2003D1681.991.85–2.221.96711.941.76–2.251.940.816\u2003D21622.101.93–2.232.05642.121.93–2.262.080.681Alkaline phosphatase, U/L\u2003D167353.0298.5–459.5377.963333.0250.0–438.5343.80.208\u2003D2162320.5273.3–405.5337.562270.5233.0–354.3297.50.010Urinary calcium †, mmol/10\u2009mg creatinine\u2003D1600.110.07–0.190.12690.140.09–0.200.120.985\u2003D21550.140.09–0.230.15540.210.13–0.320.190.011Urinary phosphorus†, mmol/10\u2009mg creatinine\u2003D1590.410.12–0.660.22650.340.14–0.650.230.867\u2003D21520.680.44–1.100.53520.710.40–0.920.580.896Urinary calcium:phosphorus molar ratio\u2003D1590.390.15–0.900.50640.410.16–1.340.470.824\u2003D21530.220.12–0.480.28530.310.19–0.600.340.054Serum sodium, mmol/L\u2003D171138.0137.0–140.0138.672138.6136.6–140.0138.50.891\u2003D2165138.0136.4–140.0138.064138.0137.0–139.9138.30.449Serum potassium, mmol/L\u2003D1714.734.30–5.324.83724.774.40–5.104.780.685\u2003D21644.744.29–5.104.72644.514.14–4.884.540.091Serum chloride, mmol/L\u2003D171106.0104.0–109.0106.172105.0102.8–108.0105.20.148\u2003D2163105.0103.0–107.0104.662105.0104.0–107.0105.30.111BUN\u2009=\u2009blood urea nitrogen; cHMF\u2009=\u2009control human milk fortifier; D1\u2009=\u2009study day 1; D21\u2009=\u2009study day 21; IQR\u2009=\u2009interquartile range; nHMF\u2009=\u2009new human milk fortifier.*D1 geometric mean values were log-transformed and analyzed using t test; D21 geometric mean values were log-transformed and analyzed using analysis of covariance (adjusting for the relevant biochemical parameter at D1, sex, and center).†Corrected for urinary creatinine excretion of 10\u2009mg/kg body weight/day.TABLE 7Markers of kidney function, blood count, and urinary electrolyte status at study days 1 and 21nHMFcHMFVariablenMedianIQRGeometric meannMedianIQRGeometric meanP*Urinary creatinine, μmol/L\u2003D1631300.0785.5–1685.51224.7691105.0900.0–1500.01182.3\u2003D21571030.0660.0–1609.01000.355854.0618.0–1273.0900.80.447Serum hemoglobin, mmol/L\u2003D1682.081.84–2.292.14722.021.84–2.262.18\u2003D21631.711.56–1.911.83661.691.50–1.981.760.936Serum hematocrit, %\u2003D1680.400.35–0.430.39720.390.35–0.430.38\u2003D21630.320.29–0.380.33660.330.28–0.380.330.805Urinary sodium, mmol/L\u2003D16637.023.3–57.337.56932.019.4–54.031.2\u2003D215934.021.1–48.033.35623.014.3–36.424.00.037Urinary potassium, mmol/L\u2003D16625.913.6–37.023.66921.815.0–32.220.0\u2003D215930.016.9–45.027.65722.916.9–30.422.80.040Urinary chloride, mmol/L\u2003D16037.026.3–60.040.26733.020.5–55.034.2\u2003D215431.017.8–43.830.75526.018.0–39.527.80.558cHMF\u2009=\u2009control human milk fortifier; D1\u2009=\u2009study day 1; D21\u2009=\u2009study day 21; IQR\u2009=\u2009interquartile range; nHMF\u2009=\u2009new human milk fortifier .*D21 geometric mean values were log-transformed and analyzed using analysis of covariance (adjusting for the relevant biochemical measure at D1, sex, and center)."", 'title': 'Growth and Nutritional Biomarkers of Preterm Infants Fed a New Powdered Human Milk Fortifier: A Randomized Trial.', 'date': '2017-07-21'}}",0.0,Pediatrics & Neonatology
-18,"Is length gain higher, lower, or the same when comparing high protein concentration low protein concentration?",uncertain effect,very low,no,"['26488118', '22301933', '22987877', '29772833', '28727654']",33215474,2020,"{'26488118': {'article_id': '26488118', 'content': ""J Pediatr Gastroenterol NutrJ. Pediatr. Gastroenterol. NutrJPGAJournal of Pediatric Gastroenterology and Nutrition0277-21161536-4801Lippincott Williams & Wilkins26488118464595610.1097/MPG.000000000000101000012Original Articles: NutritionGrowth and Tolerance of Preterm Infants Fed a New Extensively Hydrolyzed Liquid Human Milk FortifierKimJae H.∗ChanGary†SchanlerRichard‡Groh-WargoSharon§BloomBarry||DimmitReed¶WilliamsLarry#BaggsGeraldine#Barrett-ReisBridget#∗University of California, San Diego-Rady Children's Hospital of San Diego, San Diego†University of Utah, Salt Lake City‡Cohen Children's Medical Center of New York, New Hyde Park§Case Western Reserve University, MetroHealth Medical Center, Cleveland, OH||Wesley Medical Center, Wichita, KS¶University of Alabama, Birmingham#Abbott Nutrition, Columbus, OH.Address correspondence and reprint requests to Jae H. Kim, MD, PhD, University of California, San Diego, 200 W Arbor Dr, MPF 1140, San Diego, CA 92103 (e-mail: neojae@ucsd.edu).12201524112015616665671212201512102015Copyright 2015 by ESPGHAN and NASPGHAN. Unauthorized reproduction of this article is prohibited.2015This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License, where it is permissible to download and share the work, provided it is properly cited. The work cannot be changed in any way or used commercially. http://creativecommons.org/licenses/by-nc-nd/4.0ABSTRACTObjectives:This study was a comparison of growth and tolerance in premature infants fed either standard powdered human milk fortifier (HMF) or a newly formulated concentrated liquid that contained extensively hydrolyzed protein.Methods:This was an unblinded randomized controlled multicenter noninferiority study on preterm infants receiving human milk (HM) supplemented with 2 randomly assigned HMFs, either concentrated liquid HMF containing extensively hydrolyzed protein (LE-HMF) or a powdered intact protein HMF (PI-HMF) as the control. The study population consisted of preterm infants ≤33 weeks who were enterally fed HM. Infants were studied from the first day of HM fortification until day 29 or hospital discharge, whichever came first.Results:A total of 147 preterm infants were enrolled. Noninferiority was observed in weight gain reported in the intent-to-treat (ITT) analysis was 18.2 and 17.5 g · kg−1 · day−1 for the LE-HMF and PI-HMF groups, respectively. In an a priori defined subgroup of strict protocol followers (n\u200a=\u200a75), the infants fed LE-HMF achieved greater weight over time than those fed PI-HMF (P\u200a=\u200a0.036). The LE-HMF group achieved greater linear growth over time compared to the PI-HMF (P\u200a=\u200a0.029). The protein intake from fortified HM was significantly higher in the LE-HMF group compared with the PI-HMF group (3.9 vs 3.3 g · kg−1 · day−1, P\u200a<\u200a0.0001). Both fortifiers were well tolerated with no significant differences in overall morbidity.Conclusions:Both fortifiers showed excellent weight gain (grams per kilograms per day), tolerance, and low incidence of morbidity outcomes with the infants who were strict protocol followers fed LE-HMF having improved growth during the study. These data point to the safety and suitability of this new concentrated liquid HMF (LE-HMF) in preterm infants. Growth with this fortifier closely matches the recent recommendations for a weight gain of >18 g · kg−1 · day−1.Keywordsbreast-feedinggrowthhuman milk fortifierpreterm infantsOPEN-ACCESSTRUEWhat Is KnownPowdered infant milk products cannot be sterilized and is a source of bacterial infection.Very-low-birth-weight infants often require more protein than presently can be provided with conventional human milk fortifiers.A liquid fortifier with higher protein than conventional fortifiers is desirable to increase safety and improved growth.What Is NewA liquid human milk fortifier that is based on extensively hydrolyzed bovine casein with higher amounts of total protein than powder human milk fortifiers confers equal to improved growth to very-low-birth-weight infantsUse of this new liquid fortifier provides sterility without any increase in feeding intolerance or short-term adverse effects.Human milk (HM) is a source of essential nutrients and immunologic factors for the preterm infant, but alone it is not sufficient nutritionally to meet the high demands of the rapidly growing infant. Human milk fortifiers (HMFs) are nutritional supplements designed to increase total energy, protein, and micronutrient delivery to preterm infants. The primary benefits of HM fortification have been improved growth, bone mineralization, and protein status such as blood urea nitrogen (BUN) (1–5).Increasing survival and improving growth of the preterm infant to avoid extrauterine growth restriction have resulted in demands for protein that present powdered HMF may not achieve. Although some of these infants may compensate with higher volume intake, many are unable to consume a sufficient volume because of pulmonary or other clinical issues and therefore require further concentration of protein and energy. Higher intake of protein between 3 and 4 g · kg−1 · day−1 has been associated with improved growth without complications compared with a lower consumption of protein (<3 g · kg−1 · day−1) (6). Poor weight gain has been associated with a higher risk for retinopathy of prematurity and poor neurodevelopmental outcomes (7,8). It is common practice in the neonatal intensive care units (NICUs) to add protein modular (powder or liquid) to the feeding to better meet the protein needs of the smaller preterm infant. In fact, 42% of the respondents to a recent survey on nutritional practices in the NICU reported adding protein to HM (9).There has been a gradual transition to sterile liquid nutritionals in the neonatal environment during the last decade because of concerns about powder-based transmission of pathogens such as Cronobacteria sakasakii(10). The recent development of a liquid HM–based HMF and a partially hydrolyzed whey-acidified liquid HMF respond to these concerns (11,12). Unlike powder nutritionals, a liquid HMF may have the advantage of sterility and simpler liquid-liquid mixing with human milk (HM). One disadvantage of a liquid fortifier is volume displacement of HM.In this study, we evaluated a novel liquid HMF containing extensively hydrolyzed protein source to determine efficacy and safety in very-low-birth-weight preterm infants.METHODSStudy PopulationA total of 14 NICUs from across the United States participated in this study, including Tampa, Florida; Wichita, Kansas; Toledo, Ohio; Salt Lake City, Utah; Birmingham, Alabama; Cleveland, Ohio; Allentown, Pennsylvania; San Diego, California; Valhalla, New York; Manhasset, New York; Portland, Oregon; Cleveland, Ohio; South Bend, India; and Brooklyn, New York. The study population consisted of preterm infants born at ≤33 weeks’ gestational age with birth weights ranging from 700 to 1500 g who were enterally fed HM in the NICU. Infants identified as eligible for randomization and for whom consent was obtained were randomly assigned to one of the 2 study regimens. Sealed envelopes containing the subject treatment group assignment were prepared from randomization schedules that were computer-generated using a pseudorandom permuted blocks algorithm. A separate computer-generated randomization schedule was produced for twins to ensure that eligible twins were both assigned to the same product. The randomization was block stratified by birth weight (700–1000 g and 1000–1500\u200ag) and sex.Eligibility criteria included appropriate intrauterine growth and maternal intent to provide breast milk during the study. The use of donor HM was not permitted during the study period unless indicated by the clinical staff or PI but could have been used in the first week of life before study initiation. Infants were excluded for enteral feeds not started within 21 days of life, severe congenital anomalies, expectant transfer to another facility, 5-minute Apgar <5, severe intraventricular hemorrhage (grade 3 or 4), mechanical ventilation, major abdominal surgery, severe asphyxia, and necrotizing enterocolitis (NEC). Use of probiotics or postnatal corticosteroids was not permitted.Study DesignThis was an unblinded randomized controlled multicenter study conducted on preterm infants receiving HM supplemented with 2 randomly assigned HMFs, either a newly formulated concentrated liquid HMF containing extensively hydrolyzed protein (Abbott Nutrition, Columbus, OH; LE-HMF) or a conventional powdered intact protein HMF (Similac Human Milk Fortifier, PI-HMF, Abbott Nutrition) as control. For every 25 mL of HM, HMF was added as a 5-mL dose of LE-HMF or 1 single packet of PI-HMF. Study Day (SDAY) 1 was defined as the first day of HM fortification and occurred within 72 hours after the subject had reached an intake of at least 100 mL · kg−1 · day−1 of HM. The primary study period was from SDAY 1 until SDAY 29 or hospital discharge, whichever came first. This study was approved by institutional research ethics board as appropriate at each study sites. Table 1 shows the key study fortifier differences.Anthropometric indices (weight, length, and head circumference [HC]), tolerance, serum biochemistries, intake, and morbidity data were assessed. Anthropometric variables and tolerance outcomes were collected after SDAY 29 if the infant remained on study HMF.Weight, length, and HC of infants were measured according to standardized procedures from SDAY 1 to SDAY 29 or hospital discharge, whichever came first. Weight measures were taken daily using the hospital scales (incubator or bedside). Documentation of scale calibration was reviewed during routine visits. The other anthropometric measurements were performed weekly. Recumbent length was obtained with a fixed headboard and moveable footboard and HC using a nonstretchable tape.Feeding tolerance was assessed by variables such as stool characteristics (bloody, hard, black, and/or watery) and the incidence of feedings withheld because of abdominal distention, gastric residuals, and vomiting. Any nil per os periods were also collected.Enteral intake was collected from enrollment to SDAY 29. Intake of HM (including donor/banked HM) or other enteral feeding (including supplements such as protein modulars) were recorded. Although the LE-HMF contained the same amount of energy as the PI-HMF, it contained higher protein and a different source of protein. It also contained added lutein, docosahexaenoic acid, and arachidonic acid.Blood samples were drawn from each infant by venipuncture or, if necessary, by heelstick on SDAYs 1, 15, and 29. Serum electrolytes, bicarbonate, calcium, phosphorus, magnesium, alkaline phosphatase, BUN, and prealbumin were analyzed at the hospital site. Confirmed NEC (determined by using modified Bell staging criteria) and sepsis were recorded. The occurrence of these and other serious adverse events was documented throughout the study.Statistical AnalysisStudy data were analyzed on an intent-to-treat (ITT) basis including all enrolled infants who received study fortifier. Based on anticipated protocol deviations in this high-risk population, a subgroup analysis was prospectively planned to analyze data from infants who strictly adhered to the assigned HMF. The strict protocol followers (SPFs) were defined a priori as those infants who received <20% of total energy from sources other than the assigned study HMF; and <3 consecutive days on modular supplements (eg, protein supplements, another study HMF, nonstudy formula, or donor milk) for at least 2 weeks from SDAY 1 to SDAY 29.Sample size was calculated to test the hypothesis that LE-HMF was noninferior to PI-HMF using an equivalence limit of 1.6 g · kg−1 · day−1 in weight gain per day. With a noninferiority hypothesis and assuming that the expected difference in means is zero and the common standard deviation is 2.56 g · kg−1 · day−1, the total sample size required to have 80% power was 66 subjects who are SPF (33 per group). The power for this unbalanced sample size distribution is 83%. Assuming an attrition rate of approximately 46%, the target number for enrollment was 124 subjects (62 per group). A study designed for noninferiority does not preclude testing for superiority (13). Weight gain (grams per kilogram per day) for each subject was calculated by an exponential model that involved a regression line fit on loge (wt), where wt is weight (in grams) on each day (13). Weight gain (grams per kilogram per day) was analyzed using analysis of variance with factors for center and feeding (primary). Analyses were also made adjusting for sex, birth weight, and average fortified HM intake (milliliters per kilogram per day) diluted full strength during the study period. A 95% 1-sided confidence interval for the difference in means between groups was used for noninferiority evaluation.Length (centimeters per week) and HC gains (centimeters per week) were analyzed using the same models. Weight, length, and HC collected at 1-week intervals were analyzed with repeated measures analysis of covariance (ANCOVA) testing effects of center, feeding, sex, study day, interaction of feeding with sex, feeding with study day, and covariate birth weight. By time point analyses of weight, length, and HC using ANCOVA were made post-hoc using 1-sided tests consistent with a noninferiority design.Average daily volume enteral intake (milliliters per kilogram per day) was analyzed using analysis of variance. Complete blood cell counts with differential and serum blood biochemistries were analyzed using repeated measures ANCOVA with covariate SDAY 1 measure.Outcomes expressed as percent of infants (tolerance, morbidity, and respiratory variables) were analyzed using the Cochran-Mantel-Haenzsel test stratified by center. The frequencies of occurrence of adverse events by system organ class and preferred terms using MedDRA codes were tabulated and analyzed using Fisher exact test. Hypothesis testing for this study was done using 2-sided, 0.05 level tests. All analyses were made using SAS version 9.2 (SAS Institute, Cary, NC) on a computer.RESULTSStudy PopulationA total of 147 subjects were randomized into the study. Of the 147 subjects, 129 were included in the ITT group, that is, all randomized subjects who received study HMF. Of those subjects in the ITT group, 75% completed the study duration (45 PI-HMF, 52 LE-HMF). More than half the infants in the ITT group met the definition for the SPFs (Fig. 1). The number of days on the assigned study fortifier was 25 and 29 for the PI-HMF (n\u200a=\u200a63) and LE-HMF (n\u200a=\u200a66) groups, respectively. The median number of days on the assigned study fortifier for SPF was 29 days for both the PI-HMF and LE-HMF groups as some extended their use beyond the study period. Of note, some SPF subjects did not complete the study duration because they were discharged from the hospital.FIGURE 1Disposition of subjects.Demographic and Other Baseline CharacteristicsCharacteristics of the study patients are summarized in Table 2. There were no statistically significant differences among study subjects randomized to the PI-HMF or the LE-HMF group in gestational age, sex, race, mode of delivery and multiple birth status. There were, however, more Hispanic infants in the PI-HMF as compared to the LE-HMF group (28% vs 13%, P\u200a=\u200a0.041). In addition, there were no statistical differences between groups at birth or SDAY 1 for weight, length, and HC. Furthermore, there were no differences in clinical history and progression of enteral feeds. Infants in the 2 feeding groups who were SPF reflect comparable demographic and baseline characteristics patterns.GrowthThere were no statistical differences in the primary outcome of weight gain (grams per kilogram per day) during the study period regardless of whether the statistical analysis was performed on the ITT group or SPFs. Hence, noninferiority was achieved. Respective weight gains were 17.5 and 18.2 g · kg−1 · day−1 for PI-HMF and LE-HMF (Table 3). Likewise in the subgroup (SPF) analysis weight gains were 18.2 and 18.4 g · kg−1 · day−1 for PI-HMF and LE-HMF. There was, however, a main feeding effect that was the infants fed LE-HMF compared with infants fed PI-HMF had increased weight during the study among SPFs as depicted in Fig. 2A (P\u200a=\u200a0.036). When analyzing the data at separate time points the weight at SDAY 29 was significantly higher in LE-HMF group versus the PI-HMF group (P\u200a=\u200a0.024). Likewise, infants in the ITT group fed LE-HMF had higher weights at SDAYs 15, 22, and 29 than infants fed PI-HMF whether or not adjusted for differences in ethnicity. The SPF infants receiving LE-HMF reached 1800 g 7 days sooner than the infants fed PI-HMF (19 vs 26 days, respectively, P\u200a=\u200a0.049).FIGURE 2Evaluable analysis: A, weight (in grams); B, length (in centimeters); C, head circumference (in centimeters). A, Weight (in grams). Repeated measures analysis main effect, P\u200a=\u200a0.036; post-hoc per time point analysis: SDAY 29, P\u200a=\u200a0.024. B, Length (in centimeters). Repeated measures analysis main effect, P\u200a=\u200a0.029; post-hoc per time point analysis: SDAY 22, P\u200a=\u200a0.006, SDAY 29, P\u200a=\u200a0.037. C, Head circumference (in centimeters).The length and HC gains (centimeters per week) during the study period revealed no statistical differences between the groups and met growth targets (Table 3). The infants fed LE-HMF compared with infants fed PI-HMF had increased linear growth during the study among SPFs as depicted in Fig. 2B (P\u200a=\u200a0.029). When analyzing the data at separate time points adjusted for birth length, the length at SDAY 22 and SDAY 29 were significantly higher in LE-HMF group versus the PI-HMF group (P\u200a<\u200a0.05). HC was not different between the fortifier groups (Fig. 2C).Feeding Tolerance and Stool CharacteristicsIn both the ITT and SPF groups, both fortifiers were well tolerated with similar number and percentage of infants having feedings withheld because of abdominal distention, gastric residuals and/or vomiting. There was no difference in the percentage of infants who were nil per os between the groups (22.7 LE-HMF, 19 PI-HMF). The stool characteristics in both groups were similar with no differences in bloody stools, hard stools or black stools. Loose stools were commonly reported—56% in the PI-HMF group and 53% in the LE-HMF group—and were considered normal for infants who are receiving HM as their primary feeding.Enteral NutritionThe mean caloric and protein intakes are reported for both HMF groups. For the SPFs, the average percentage of calories from fortified HM was ∼96% in both the PI-HMF and LE-HMF groups. The mean intake of fortified HM was 116 and 114 kcal · kg−1 · day−1 in the PI-HMF and LE-HMF groups, respectively. The calculated protein intake from fortified HM was significantly higher in the LE-HMF group as compared to the PI-HMF group (3.9 vs 3.3\u200a g · kg−1 · day−1, P\u200a<\u200a0.0001). This difference was expected as LE-HMF contains more protein than PI-HMF. Energy intakes were not different between the groups.Blood ChemistriesThe blood chemistries reported in Table 4 include bicarbonate, BUN, prealbumin, calcium, phosphorus, magnesium, alkaline phosphatase, and electrolytes. In general, the blood biochemistries at SDAYs 1, 15, and 29 were within the normal reference ranges for preterm infants for both the ITT and SPF groups fed milk fortified with either fortifier (14,15). There were significant differences between groups in both the ITT and SPF analyses for BUN (P\u200a<\u200a0.001) and prealbumin (P\u200a<\u200a0.01), with both being higher in the LE-HMF group. Both groups were well within reference ranges for these parameters. Bicarbonate was significantly higher in the LE-HMF group only at SDAY1 in the ITT analysis.Safety and Morbidity DataIn the ITT group, fewer infants discontinued fortifier because of feeding intolerance in the LE-HMF group as compared to the PI-HMF group (2% vs 10%, P\u200a=\u200a0.048). There was a low incidence of confirmed NEC (1.5% in the LE-HMF group and 3.2% in the PI-HMF group) and confirmed sepsis (4.5% vs 3.2%, respectively)DISCUSSIONThe purpose of developing LE-HMF was to provide a concentrated liquid fortifier that would be superior to conventional powder HMF by virtue of sterility, higher protein concentration, and absence of intact cow's-milk protein. An extensively hydrolyzed protein source is included to promote feeding tolerance in preterm infants. The extensively hydrolyzed protein may be tolerated better for infants who are sensitive to the intact cow's-milk protein.The primary purpose of the present clinical trial was to assess whether the new HMF would promote targeted weight gain, with good tolerance and without association with specific comorbidities in a noninferiority comparison with a commercially available powder HMF that has demonstrated safety and efficacy in preterm infants (13).Weight gain and linear growth approaching intrauterine rates are important goals in the management of premature infants. The mean weight gain for both groups (PI-HMF and LE-HMF) exceeded the intrauterine growth rate of 15 g · kg−1 · day−1 and closely matched recent recommendations for a weight gain of >18 g · kg−1 · day−1(7). The mean HC gain for both groups also closely matched recent recommendations for a HC gain of >0.9 cm/wk (7). This result was not surprising given the excellent weight, length, and HC gains previously reported in infants fed PI-HMF powder (13).Ehrenkranz et al (7) have reported that as the rate of weight gain increased in hospitalized preterm infants, the incidence of cerebral palsy, neurodevelopmental impairment, and need for re-hospitalization decreased significantly. A weight gain rate of >18 g · kg−1 · day−1 and a HC growth rate of >0.9 cm/wk were associated with better neurodevelopmental and growth outcomes. Lower quartile growth was associated with the poorest neurodevelopmental outcomes.Weight and length differed between the groups. Although there were no significant differences in mean weight at birth or SDAY 1, infants receiving LE-HMF had ∼½ lb greater mean weight than the infants in the PI-HMF group at the end of the study period. Although the rate of linear growth was not statistically different, infants in the LE-HMF group had greater achieved linear growth during the study period. It is possible that the greater weight and length in the LE-HMF infants was because of the higher number of infants in this group that adhered to the assigned study feeding.New expert recommendations suggest that extremely-low-birth-weight infants (<1000 g birth weight) have higher protein requirements (3.5–4.5 g/100 kcal) (16). HMFs provide an important strategy to overcoming nutrient deficits for preterm and low-birth-weight infants. Differences in the level and ingredient sources of the macronutrients, especially the protein quantity, in PI-HMF versus LE-HMF may have contributed to the overall performance of the LE-HMF group. The higher protein intake in infants receiving LE-HMF (∼3.6 g/100 kcal) as compared to PI-HMF (∼3.0 g/100 kcal) was likely one of the reasons for the improved growth observed in these infants. Although infants in the LE-HMF group had higher protein intakes, energy intakes were not different between the groups.Preterm infants fed fortified HM have variable rates of growth at least partly because of differences in intake of calories, carbohydrates, electrolytes, calcium, phosphate, and protein. The acid-base status of the preterm infant also, however, affects growth. In preterm infants the kidney may not tolerate an acid load, leading to the development of metabolic acidosis. In a recent study, a liquid acidified HMF caused metabolic acidosis and poor growth in preterm infants in the NICU (17,18). In another study, Rochow et al (19) described a commercially available fortifier in Europe that had to be reformulated because of the development of metabolic acidosis from an imbalance of electrolytes. The authors recorded a mean weight gain of only 9.7 g · kg−1 · day−1 and decreased bone mineralization with metabolic acidosis. No infants in our study developed metabolic acidosis.The LE-HMF protein source may be beneficial for this population because it was extensively hydrolyzed casein formulation without any intact cow's-milk protein. It has been suggested that a combination of free amino acids and short chain peptides (di- and tri peptides) may allow more optimal nitrogen absorption (20,21). Intact bovine protein powder HMF has an excellent safety record; however, a recent study by Sullivan et al (11) suggested the possibility that even in the presence of a HM base diet, the addition of intact bovine protein powder HMF is associated with higher rates of total and surgical NEC. The mechanism for the higher NEC risk is not known yet. Although this study was not powdered for NEC there was no difference in the NEC or sepsis rates between the infants fed an intact bovine protein and the extensively hydrolyzed protein. Both groups had rates lower than previously reported (22–24).Intact bovine protein has higher associated long-term risk for allergy and atopy compared with HM-fed infants. Protein intolerance is seen in premature infants and in term infants (25). Because preterm infants have a similar risk for allergy and atopy compared with term infants and in the NICU have presented with symptoms suggestive of allergic colitis, avoiding intact bovine protein may be a desirable objective. For preterm infants fed HM the use of an extensively hydrolyzed protein-based HMF is an appropriate option.In general, blood chemistries were within normal reference ranges for preterm infants. The higher BUN and prealbumin seen in the LE-HMF group can be attributed to the higher protein content of LE-HMF. These higher values may be indicative of improved protein nutriture. It should be noted that although BUN is influenced by renal function and hydration state, all other influences being equal, it is proportional to protein intake and responds rapidly to changes in protein intake (4,5,26,27).Postnatal growth failure remains common in premature infants. Nearly 25 years ago Kashyap et al showed that even a small deficit in protein intake impairs both growth in lean body mass and linear growth (28). In recent years, Arslanoglu et al reported that addition of protein to preterm feedings of recovering VLBW infants resulted in significantly improved linear growth (4,5). This was accomplished by monitoring the BUN level so that when it was less than 9\u200amg/dL, increased protein was added to their feedings. It was observed in the present study that the mean BUN level fell <9 mg/dL by week 2 in infants receiving PI-HMF; however, in infants receiving LE-HMF it never fell <9 mg/dL during the entire study period. Our results, in part, agree with other investigators that an increased protein-to-calorie ratio in the feeds of preterm infants will improve linear growth (4,5,9,28). It is becoming increasingly evident that promoting catch-up growth in the NICU may have implications for long-term development and health (7,29).Our study did have several limitations. The study examined the combined effects of changing both protein content and type (hydrolyzed vs intact). Future studies may want to capture effects of changing one of these variables. A number of subjects in this study did not complete the protocol to SDAY 29. This partially diluted the effects seen in the ITT groups but still permitted demonstration of differential effects seen in the SPF subgroup. A larger study design may improve this in the future. Infants <700 g birth weight were excluded from this study and therefore the study findings cannot be readily extrapolated to this vulnerable group. It is expected however that this group would have higher protein demands than infants in this study and therefore would be as likely or more to have a favorable response to higher protein. Although no differences were seen between both groups for NEC and sepsis the study size was too small to discern true differences for these outcomes.CONCLUSIONSBoth fortifiers showed excellent tolerance and a low rate of morbidity outcomes, with the infants who were SPFs fed LE-HMF having improved growth. These data confirm the safety and suitability of this new concentrated liquid HMF for preterm infants.AcknowledgmentsThe authors thank the following individuals for their hard work and dedication: Coryn Commare, MS, RD; Christy Saulters, BS; Debra Lee-Butcher, BSN, RN; Holy Boyko, BSN, RN; Angela Worley; Carolyn Richardson; Sue Zhang, MS, MAS; Mustafa Vurma, PhD; Maggie Hroncich, BS; Aimee Diley; Kristen Fithian; Sue Nicholson, MS, RD; and Jennifer Teran, BS, RD. The authors also thank study investigators and their staff for their cooperation: Terri Ashmeade, MD; Anthony Killian, MD; Lance Parton, MD; Robert Schelonka, MD; Robert White, MD; Ivan Hand, MD, FAAP; Michelle Walsh, MD; Jeffrey Blumer, PhD, MD; Paula Delmore, RN; Carrie Rau, RN; Renee Bridge, RN; Lisa Lepis, RN; Judy Zaritt, RN; Claire Roane, RN, MSN; Julie Gualtier, RN; Diane Fierst, RN; Christina Gogal; Natalie Dweck; Debra Potak, RN; Barbara Wilkens, RN; Nakia Clay, BS; Mashelle Monhaut, NNP-BC; Rickey Taing, NPL; Susan Bergant, RN, CCRP; and Bonnie Rosolowski, RPT.www.clinicaltrials.gov registration number: NCT01373073.This study was funded by Abbott Nutrition.J.H.K., B.B., G.C., R.S. and S.G.-W. received research funds from the study sponsor, Abbott Nutrition, to conduct the study. J.H.K. is on the speakers’ bureaus for Abbott Nutrition, Mead Johnson Nutrition, Nestle Nutrition, Nutricia, and Medela. J.H.K. and R.S. are on the medical advisory board for Medela. J.H.K. owns shares in PediaSolutions and has provided medical expert testimony. B.B. received a grant from the Wichita Medical Research and Education Foundation. G.C. received a research grant from the University of Utah and has provided medical expert testimony. S.G.-W. is on the speakers’ bureau of Abbott Nutrition. B.B.-R., L.W., and G.B. are employees of Abbott Nutrition.The authors report no conflicts of interest.REFERENCES1.SchanlerRJ\nSuitability of human milk for the low-birthweight infant. Clin Perinatol\n1995; 22:207–222.77812532.SchanlerRJAbramsSA\nPostnatal attainment of intrauterine macromineral accretion rates in low birth weight infants fed fortified human milk. J Pediatr\n1995; 126:441–447.78692083.KuschelCAHardingJE\nMulticomponent fortified human milk for promoting growth in preterm infants. Cochrane Database Syst Rev\n2004; 1:CD000343.149739534.ArslanogluSBertinoECosciaA\nUpdate of adjustable fortification regimen for preterm infants: a new protocol. J Biol Regul Homeost Agents\n2012; 26\n(3 suppl):65–67.231585175.ArslanogluSMoroGEZieglerEE\nAdjustable fortification of human milk fed to preterm infants: does it make a difference?\nJ Perinatol\n2006; 26:614–621.168859896.PremjiSSFentonTRSauveRS\nHigher versus lower protein intake in formula-fed low birth weight infants. Cochrane Database Syst Rev\n2006; 1:CD003959.164374687.EhrenkranzRADusickAMVohrBR\nGrowth in the neonatal intensive care unit influences neurodevelopmental and growth outcomes of extremely low birth weight infants. Pediatrics\n2006; 117:1253–1261.165853228.VanderveenDKMartinCRMehendaleR\nEarly nutrition and weight gain in preterm newborns and the risk of retinopathy of prematurity. PLoS One\n2013; 8: e64325.9.WhitfieldJPunjabi-GuptaSHendriksonH\nImproved linear growth in VLBW infants at discharge: impact of increasing the protein/kcal ratio (PCR) of feeds. E-PAS Abstract\n2012; 4510:122.10.TaylorC\nHealth Professionals Letter on Enterobacter sakazakii Infections Associated With the Use of Powdered (Dry) Infant Formulas in Neonatal Intensive Care Units. Bethesda, MD: US Food and Drug Administration, Center for Food Safety and Applied Nutrition, Office of Nutritional Products, Labeling and Dietary Supplements; 2002.11.SullivanSSchanlerRJKimJH\nAn exclusively human milk-based diet is associated with a lower rate of necrotizing enterocolitis than a diet of human milk and bovine milk-based products. J Pediatr\n2010; 156:562–567.2003637812.MoyaFSiskPMWalshKR\nA new liquid human milk fortifier and linear growth in preterm infants. Pediatrics\n2012; 130:e928–e935.2298787713.Barrett-ReisBHallRTSchanlerRJ\nEnhanced growth of preterm infants fed a new powdered human milk fortifier: a randomized, controlled trial. Pediatrics\n2000; 106:581–588.1096910614.The Harriet Lane Handbook (The Johns Hopkins Hospital). 19th ed. New York: Elsevier Health Sciences; 2011: chap 27.15.RamelSEGeorgieffMK\nNutrition. In: Avery's Neonatology—Pathophysiology and Management of the Newborn. 7th ed. Philadelphia: Lippincott Williams & Wilkins; 2015.16.KoletzkoBPoindexterBUauyR\nRecommended nutrient intake levels for stable, fully enterally fed very low birth weight infants. World Rev Nutr Diet\n2014; 110:297–299.2475163817.ThoeneMHansonCLydenE\nComparison of the effect of two human milk fortifiers on clinical outcomes in premature infants. Nutrients\n2014; 6:261–275.2439453818.CibulskisCCArmbrechtES\nAssociation of metabolic acidosis with bovine milk-based human milk fortifiers. J Perinatol\n2015; 35:115–119.2510232119.RochowNJochumFRedlichA\nFortification of breast milk in VLBW infants: metabolic acidosis is linked to the composition of fortifiers and alters weight gain and bone mineralization. Clin Nutr\n2011; 30:99–105.2072762620.GrimbleGKKeohanePPHigginsBE\nEffect of peptide chain length on amino acid and nitrogen absorption from two lactalbumin hydrolysates in the normal human jejunum. Clin Sci (Lond)\n1986; 71:65–69.370907621.BozaJJMartinez-AugustinOBaroL\nProtein v. enzymic protein hydrolysates. Nitrogen utilization in starved rats. Br J Nutr\n1995; 73:65–71.785791622.PatoleS\nPrevention and treatment of necrotising enterocolitis in preterm neonates. Early Hum Dev\n2007; 83:635–642.1782600923.FanaroffAAStollBJWrightLL\nTrends in neonatal morbidity and mortality for very low birthweight infants. Am J Obstet Gynecol\n2007; 196:147e1-8.1730665924.StollBJHansenNIBellEF\nNeonatal outcomes of extremely preterm infants from the NICHD Neonatal Research Network. Pediatrics\n2010; 126:443–456.2073294525.D’NettoMAHersonVCHussainN\nAllergic gastroenteropathy in preterm infants. J Pediatr\n2000; 137:480–486.1103582526.ZieglerEE\nBreast-milk fortification. Acta Paediatr\n2001; 90:720–723.1151997227.PolbergerSKAxelssonIERaihaNC\nUrinary and serum urea as indicators of protein metabolism in very low birthweight infants fed varying human milk protein intakes. Acta Paediatr Scand\n1990; 79:737–742.223926628.KashyapSSchulzeKFForsythM\nGrowth, nutrient retention, and metabolic response in low birth weight infants fed varying intakes of protein and energy. J Pediatr\n1988; 113:713–721.313985629.HansonCSundermeierJDugickL\nImplementation, process, and outcomes of nutrition best practices for infants <1500\u200ag. Nutr Clin Pract\n2011; 26:614–624.2194764530.EhrenkranzRAYounesNLemonsJA\nLongitudinal growth of hospitalized very low birth weight infants. Pediatrics\n1999; 104\n(2 Pt 1):280–289.10429008TABLE 1Approximate nutrient composition of PI-HMF or LE-HMF added to HMNutrient PI-HMFLE-HMFEnergy, cal100100Fat, g5.25.1CHO, g10.410.1Protein, g33.6Source/type of proteinIntact whey protein concentrateExtensively hydrolyzed caseinDHA, mg1224Vitamin D, IU150150Calcium, mg175153Phosphorus, mg9886Osmolality, mOsm/kg water385450Lutein, μg*23Values per 100 calories mixed at a ratio of 1 pkt or 5 mL:25 mL HM (as fed). CHO\u2009=\u2009carbohydrate; DHA\u2009=\u2009docosahexaenoic acid; HM\u2009=\u2009human milk; LE-HMF \u2009=\u2009liquid HMF containing extensively hydrolyzed protein; PI-HMF\u2009=\u2009powdered intact protein HMF.*Lutein not added to product but available in varying amounts from HM.TABLE 2Neonatal and perinatal characteristics of preterm infantsTreatment group*PI-HMF (n\u2009=\u200963)LE-HMF (n\u2009=\u200966)Gestational age, wk28.7\u2009±\u20090.228.8\u2009±\u20090.2Birth weight, g1156\u2009±\u2009241193\u2009±\u200926Birth length, cm37.4\u2009±\u20090.337.7\u2009±\u20090.3Birth HC, cm26.1\u2009±\u20090.226.5\u2009±\u20090.2Male sex, n (%)35 (56)36 (55)Ethnicity: Hispanic, n (%)17 (28)8 (13)†Race, n (%)\u2003White42 (67)43 (65)\u2003Black13 (21)17 (26)\u2003Asian1 (2)1 (2)\u2003Other7 (11)3 (5)\u2003White/other0 (0)2 (3)C-section, n (%)38 (60)42 (64)Twin, n (%)16 (25)12 (18)Age at study day 1, d12.3\u2009±\u20090.712.8\u2009±\u20090.6Birth class, n (%)\u2003≤1000\u2009g16 (24)12 (19)\u2003>1000\u2009g66 (76)63 (81)LE-HMF\u2009=\u2009liquid HMF containing extensively hydrolyzed protein; PI-HMF\u2009=\u2009powdered intact protein HMF.*Mean\u2009±\u2009SEM.†P\u2009=\u20090.0407.TABLE 3Anthropometric gainsTreatment group*Targeted growth†,‡PI-HMF (n\u2009=\u200963)LE-HMF (n\u2009=\u200966)Weight gain, g kg−1 day−117.5\u2009±\u20090.618.2\u2009±\u20090.3>18Length gain, cm/wk1.2\u2009±\u20090.071.2\u2009±\u20090.06>0.9HC gain, cm/wk1.0\u2009±\u20090.041.0\u2009±\u20090.05>0.9LE-HMF\u2009=\u2009liquid HMF containing extensively hydrolyzed protein; PI-HMF\u2009=\u2009powdered intact protein HMF; HC\u2009=\u2009head circumference.*Intent-to-treat group, n\u2009=\u2009129.†Ehrenkranz et al (7).‡Ehrenkranz et al (30).TABLE 4Blood chemistry dataCharacteristicsReference rangesStudy dayTreatment group*PI-HMFLE-HMFBicarbonate, mEq/L†17–24123.27\u2009±\u20090.45 (59)25.05\u2009±\u20090.45 (62)1524.32\u2009±\u20090.50 (49)25.40\u2009±\u20090.39 (58)2925.04\u2009±\u20090.43 (40)25.54\u2009±\u20090.44 (50)BUN, mg/dL‡2.5–31.4111.47\u2009±\u20090.78 (56)11.89\u2009±\u20091.03 (61)158.30\u2009±\u20091.15 (50)11.72\u2009±\u20090.68 (58)295.81\u2009±\u20090.38 (40)9.31\u2009±\u20090.53 (49)Prealbumin, mg/dL§7.0–39.0110.05\u2009±\u20090.37 (58)9.69\u2009±\u20090.33 (54)1510.11\u2009±\u20090.37 (47)11.40\u2009±\u20090.41 (46)299.08\u2009±\u20090.35 (36)10.01\u2009±\u20090.35 (37)Calcium, mg/dL8.0–11.0110.10\u2009±\u20090.08 (56)9.93\u2009±\u20090.08 (60)159.93\u2009±\u20090.10 (50)9.95\u2009±\u20090.07 (57)299.89\u2009±\u20090.09 (40)9.82\u2009±\u20090.06 (49)Phosphorus, mg/dL4.2–8.716.41\u2009±\u20090.17 (54)6.20\u2009±\u20090.13 (58)156.71\u2009±\u20090.13 (46)6.50\u2009±\u20090.12 (56)296.66\u2009±\u20090.10 (40)6.46\u2009±\u20090.12 (47)Magnesium, mg/dL1.5–2.111.90\u2009±\u20090.03 (54)1.88\u2009±\u20090.02 (59)151.80\u2009±\u20090.03 (47)1.86\u2009±\u20090.03 (55)291.81\u2009±\u20090.02 (40)1.82\u2009±\u20090.03 (46)Alkaline phosphatase, U/L150–4001443.89\u2009±\u200924.50 (55)415.40\u2009±\u200915.78 (60)15366.13\u2009±\u200921.80 (48)332.68\u2009±\u200910.87 (57)29335.28\u2009±\u200921.84 (40)342.36\u2009±\u200913.10 (47)Sodium, mEq/L129–1431137.49\u2009±\u20090.49 (61)138.42\u2009±\u20090.34 (65)15137.46\u2009±\u20090.55 (52)137.56\u2009±\u20090.29 (59)29139.07\u2009±\u20090.41 (41)138.70\u2009±\u20090.40 (50)Potassium, mEq/L4.5–7.115.39\u2009±\u20090.11 (61)5.20\u2009±\u20090.09 (65)155.25\u2009±\u20090.09 (52)5.23\u2009±\u20090.09 (59)295.25\u2009±\u20090.10 (41)5.06\u2009±\u20090.07 (50)Chloride, mEq/L100–1171104.16\u2009±\u20090.60 (58)104.03\u2009±\u20090.55 (63)15104.10\u2009±\u20090.72 (49)103.88\u2009±\u20090.43 (57)29106.00\u2009±\u20090.57 (40)106.14\u2009±\u20090.37 (49)BUN\u2009=\u2009blood urea nitrogen; LE-HMF\u2009=\u2009liquid HMF containing extensively hydrolyzed protein; PI-HMF\u2009=\u2009powdered intact protein HMF; HC\u2009=\u2009head circumference.*Values are mean\u2009±\u2009SEM (n).†Bicarbonate (mEq/L): (SDAY 1) LE-HMF > PI-HMF, P\u2009=\u20090.0419, LSM\u2009±\u2009SE: LE-HMF\u2009=\u200924.71\u2009±\u20090.56, PI-HMF\u2009=\u200923.33\u2009±\u20090.62.‡BUN (mg/dL): Feeding main effect: LE-HMF > PI-HMF, P\u2009=\u20090.0013, LSM\u2009±\u2009SE: LE-HMF\u2009=\u200911.99\u2009±\u20090.73, PI-HMF\u2009=\u20098.99\u2009±\u20090.83.§Prealbumin (mg/dL): Feeding main effect: LE-HMF > PI-HMF, P\u2009=\u20090.0049, LSM\u2009±\u2009SE: LE-HMF\u2009=\u200910.61\u2009±\u20090.35, PI-HMF\u2009=\u20099.32\u2009±\u20090.38."", 'title': 'Growth and Tolerance of Preterm Infants Fed a New Extensively Hydrolyzed Liquid Human Milk Fortifier.', 'date': '2015-10-22'}, '22301933': {'article_id': '22301933', 'content': 'Preterm human milk-fed infants often experience suboptimal growth despite the use of human milk fortifier (HMF). The extra protein supplied in fortifiers may be inadequate to meet dietary protein requirements for preterm infants.\nWe assessed the effect of human milk fortified with a higher-protein HMF on growth in preterm infants.\nThis is a randomized controlled trial in 92 preterm infants born at <31 wk gestation who received maternal breast milk that was fortified with HMF containing 1.4 g protein/100 mL (higher-protein group) or 1.0 g protein/100 mL (current practice) until discharge or estimated due date, whichever came first. The HMFs used were isocaloric and differed only in the amount of protein or carbohydrate. Length, weight, and head-circumference gains were assessed over the study duration.\nLength gains did not differ between the higher- and standard-protein groups (mean difference: 0.06 cm/wk; 95% CI: -0.01, 0.12 cm/wk; P = 0.08). Infants in the higher-protein group achieved a greater weight at study end (mean difference: 220 g; 95% CI: 23, 419 g; P = 0.03). Secondary analyses showed a significant reduction in the proportion of infants who were less than the 10th percentile for length at the study end in the higher-protein group (risk difference: 0.186; 95% CI: 0.370, 0.003; P = 0.047).\nA higher protein intake results in less growth faltering in human milk-fed preterm infants. It is possible that a higher-protein fortifier than used in this study is needed. This trial was registered with the Australian New Zealand Clinical Trials Registry (http://www.anzctr.org.au/) as ACTRN12606000525583.', 'title': 'Effect of increasing protein content of human milk fortifier on growth in preterm infants born at <31 wk gestation: a randomized controlled trial.', 'date': '2012-02-04'}, '22987877': {'article_id': '22987877', 'content': 'To evaluate the growth, tolerance, and safety of a new ultraconcentrated liquid human milk fortifier (LHMF) designed to provide optimal nutrients for preterm infants receiving human breast milk in a safe, nonpowder formulation.\nPreterm infants with a body weight ≤ 1250 g fed expressed and/or donor breast milk were randomized to receive a control powder human milk fortifier (HMF) or a new LHMF for 28 days. When added to breast milk, the LHMF provided ∼20% more protein than the control HMF. Weight, length, head circumference, and serum prealbumin, albumin, blood urea nitrogen, electrolytes, and blood gases were measured. The occurrence of sepsis, necrotizing enterocolitis, and serious adverse events were monitored.\nThis multicenter, third party-blinded, randomized controlled, prospective study enrolled 150 infants. Achieved weight and linear growth rate were significantly higher in the LHMF versus control groups (P = .04 and 0.03, respectively). Among infants who adhered closely to the protocol, the LHMF had a significantly higher achieved weight, length, head circumference, and linear growth rate than the control HMF (P = .004, P = .003, P = .04, and P = .01, respectively). There were no differences in measures of feeding tolerance or days to achieve full feeding volumes. Prealbumin, albumin, and blood urea nitrogen were higher in the LHMF group versus the control group (all P < .05). There was no difference in the incidence of confirmed sepsis or necrotizing enterocolitis.\nUse of a new LHMF in preterm infants instead of powder HMF is safe. Benefits of LHMF include improvements in growth and avoidance of the use of powder products in the NICU.', 'title': 'A new liquid human milk fortifier and linear growth in preterm infants.', 'date': '2012-09-19'}, '29772833': {'article_id': '29772833', 'content': ""NutrientsNutrientsnutrientsNutrients2072-6643MDPI29772833598651310.3390/nu10050634nutrients-10-00634ArticleThe Effect of Increasing the Protein Content of Human Milk Fortifier to 1.8 g/100 mL on Growth in Preterm Infants: A Randomised Controlled TrialReidJessica1MakridesMaria12McPheeAndrew J.13StarkMichael J.34https://orcid.org/0000-0002-6474-0505MillerJacqueline15CollinsCarmel T.12*1Healthy Mothers, Babies and Children, South Australian Health and Medical Research Institute, Adelaide, SA 5006, Australia; jessica.reid@adelaide.edu.au (J.R.); maria.makrides@sahmri.com (M.M.); andrew.mcphee@sa.gov.au (A.J.M.); jacqueline.miller@sahmri.com (J.M.)2Adelaide Medical School, Discipline of Paediatrics, The University of Adelaide, Adelaide, SA 5006, Australia3Neonatal Medicine, Women’s and Children’s Hospital, Adelaide, SA 5006, Australia; michael.stark@adelaide.edu.au4The Robinson Research Institute, The University of Adelaide, Adelaide, SA 5006, Australia5Nutrition and Dietetics, Flinders University, Adelaide, SA 5006, Australia*Correspondence: carmel.collins@sahmri.com; Tel.: +61-8-8128-440917520185201810563426420181552018© 2018 by the authors.2018Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).The aim of this study was to assess the effect of feeding high protein human milk fortifier (HMF) on growth in preterm infants. In this single-centre randomised trial, 60 infants born 28–32 weeks’ gestation were randomised to receive a higher protein HMF providing 1.8 g protein (n = 31) or standard HMF providing 1 g protein per 100 mL expressed breast milk (EBM) (n = 29). The primary outcome was rate of weight gain. Baseline characteristics were similar between groups. There was no difference between high and standard HMF groups for weight gain (mean difference (MD) −14 g/week; 95% CI −32, 4; p = 0.12), length gain (MD −0.01 cm/week; 95% CI −0.06, 0.03; p = 0.45) or head circumference gain (MD 0.007 cm/week; 95% CI −0.05, 0.06; p = 0.79), despite achieving a 0.7 g/kg/day increase in protein intake in the high protein group. Infants in the high protein group had a higher proportion of lean body mass at trial entry; however, there was no group by time effect on lean mass gains over the study. Increasing HMF protein content to 1.8 g per 100 mL EBM does not improve growth in preterm infants born 28–32 weeks’ gestation.human milkgrowthlow birth weighthuman milk fortifier1. IntroductionIt is well established that fortified human milk improves growth rates in preterm infants [1,2,3]. However, the optimal amount of protein in the fortifier is yet to be determined, partly due to the variability in the protein content of human milk, both within mothers and over time. Too little protein results in a failure to meet protein requirements, estimated to be 4.0–4.5 g/kg/day for infants born <1000 g and 3.5–4.0 g/kg/day for those born 1000–1800 g [4]. Consequently, growth failure in the neonatal period is common in infants fed fortified human milk compared with infants fed preterm formula [5,6,7]. Conversely, too much protein may result in metabolic acidosis [8]. Individualized fortification, based on either the metabolic response of the infant [9,10,11], or the macronutrient content of mother’s milk [12] has been investigated and provides evidence in support of inadequate protein concentration of human milk fortifiers (HMFs) when used in a standardised approach. However, individualised approaches are time consuming and not easily translated to the clinical environment [13]. We previously investigated a fortifier providing 1.4 g compared with 1 g protein per 100 mL human milk in preterm infants <31 weeks’ gestation [14]. While we found no difference in the rate of weight and length gain between groups, there were fewer infants with length <10th percentile at discharge in the high protein group, suggesting a higher protein concentration fortifier may be needed to improve growth. We therefore aimed to determine the effect of further increasing the protein content of HMF to 1.8 g/100 mL compared with 1 g/100 mL, on growth in preterm infants born 28–32 weeks’ gestation.2. Materials and Methods2.1. Study DesignThe study was a single centre (Women’s and Children’s Hospital, North Adelaide, South Australia), parallel group randomised controlled trial conducted between February 2012 and May 2013.2.2. ParticipantsInfants born 28–32 completed weeks’ gestation whose mothers intended to provide breast milk were eligible to participate. Multiple births were eligible and were randomised individually. Infants with a major congenital or chromosomal abnormality likely to affect growth, or where protein therapy was contraindicated (e.g., major heart defects, cystic fibrosis, phenylketonuria, disorders of the urea cycle) were ineligible. Infants likely to transfer to remote locations and infants who had received standard practice HMF for more than four days were also excluded.2.3. Randomisation and BlindingInfants were randomised to one of two groups: the higher protein intervention group or the standard protein control group. An independent researcher created the randomisation schedule using a computer generated variable block design of 4 and 6. Stratification occurred for sex and gestational age 28–29 weeks and 30–32 weeks. Parents of eligible infants were approached by a neonatologist and followed-up for consent by a research nurse who was not involved in clinical care. Upon consent, infants were randomised by telephoning an independent researcher who held the randomisation schedule and assigned a unique study identification number. Participants, clinicians, outcome assessors and data analysts were blinded to randomisation group.2.4. InterventionsThe base HMF used for both trial groups was FM85 Human Milk Supplement (Nestlé Nutrition, Gland, Switzerland) which provides 1.0 g protein and 17.5 kcal when 5 g HMF is added to 100 mL expressed breast milk (EBM). The high protein fortifier was prepared by adding 0.9 g Protifar (Nutricia, Zoetermeer, The Netherlands), a bovine casein-based powder, to the FM 85. This resulted in an additional 0.8 g protein and 3.5 kcal per 100 mL EBM providing 1.8 g protein and 21 kcal when added to 100 mL of EBM. To ensure both fortifiers were isocaloric, thereby eliminating the effect of different energy intakes on growth, 0.9 g Polyjoule (Nutricia, Zoetermeer, The Netherlands), a glucose polymer, was added to the standard fortifier providing an additional 3.5 kcal but no extra protein, giving a total of 1.0 g protein and 21 kcal when added to 100 mL of EBM. The Polyjoule and Protifar supplements were packaged into identical 400-g containers each with a tamper proof seal (Pharmaceutical Packaging Professionals Pty Ltd., Thebarton, Australia). The containers were differentiated by four colour-coded labels to facilitate blinding, with each trial group separately color-coded into two groups. Infant nutrition attendants, under the direction of the Nutrition and Food Services Department, were trained in the preparation of the HMF. Trial fortifier was mixed at the rate of 5 g FM 85 plus either 0.9 g Protifar, or 0.9 g Polyjoule, for the high and standard protein groups respectively, with 4 mL of sterile water, to give a total volume of 8 mL for use with each 100 mL of EBM.2.5. Intervention AdministrationThe fortifier intervention and control fortifiers were delivered via the enteral tube, immediately prior to a feed (tube, bottle or breast). Trial HMFs were delivered at 8 mL HMF/100 mL EBM with the volume of HMF for each feed ordered daily by the medical or neonatal nurse practitioners. In cases where a mix of EBM and preterm formula was to be given, the trial HMF was only given if EBM was >50% of the total feed. When the infant received a direct breast feed, the timing of administration of the trial product (before, during or after the feed) was at the discretion of the primary care nurse in consultation with the mother. For each day, the trial HMFs were decanted into syringes and labelled with infant identification, volume of HMF and trial details. Syringes were stored refrigerated in the neonatal unit in each infant’s individually labelled container. Any syringes not administered in the 24-h period were recorded and discarded. Fluid balance records were audited daily for compliance with the trial protocol. Administration of trial HMF began as soon as practical after randomisation (within one to two days) and continued until study end, defined as the removal of the naso-gastric tube or estimated date of delivery, whichever came first.2.6. Nutritional IntakeMeasured protein and fat content of a weekly sample of unfortified EBM (MilkoScan Minor, Foss, Denmark) were used to represent the weekly composition of EBM [14]. The lactose concentration was assumed to be 6.8 g/100 mL. EBM was only sampled when the supply was surplus to the infant’s requirements. Missing values were substituted with the average macronutrient composition of all available samples (32 of the 45 mothers involved in the study were able to provide breast milk samples). Macronutrient intakes for the study fortifiers, EBM and formula were calculated from the volume ingested, the protein and fat concentration of EBM, and the manufacturer’s information on the study fortifiers and formula. The protein content of the preterm formula in use at the time of the study was 2.2 g/100 mL. Energy content was calculated by using the Atwater factors of 4, 4, and 9 kcal/g for protein, carbohydrate, and fat respectively.2.7. Outcome Assessments2.7.1. Primary outcomeThe primary outcome was rate of weight gain (g/week) from trial start (day of randomisation) to trial end. In addition to routine clinical measurements, a research nurse and J.R. weighed infants on randomisation, weekly and at study end; duplicate weight measurements were taken using electronic balance scales accurate to 5 g. Measurements were repeated if there was a discrepancy ≥10 g, with the average of the two closest measurements used.2.7.2. Secondary Efficacy and Safety OutcomesSecondary efficacy outcomes included length and head circumference gain (cm/week), infant weight at study end, small for gestational age (SGA) at study end and body composition (fat-free mass). Length measurements were taken weekly with the infant in the supine position and measured to the nearest 0.1 cm using a recumbent length board. Head circumference was measured weekly using a non-stretching tape placed around the largest occipito-frontal circumference. Duplicate measurements were done and repeated if there was a discrepancy ≥0.5 cm, with the average of the 2 closest measures taken. SGA was defined as below the 10th percentile for infants of the same sex and gestational age, as determined from Australian birth reference data [15]. Fat free (lean) mass was measured weekly by bioelectrical impedance spectroscopy (BIS) using the Imp™ SFB7 (ImpediMed Limited, Queensland, Australia) with the first measurement taken during the first week of the study.Secondary safety outcomes included feeding tolerance (days feeds interrupted and days to reach enteral intake ≥150 mL/kg/day). A protocol was developed for discontinuation of the trial fortifier based on uraemia (blood urea nitrogen (BUN) concentration >8.0 mmol/L) and/or a metabolic acidosis (base excess <−6 mmol/L) persisting for more than 48 hours. However, no infant met these criteria. Similarly, criteria were defined for the addition of protein to feeds if an infant had poor weight gain defined as <15 g/kg/day over the preceding 7-day period associated with a BUN of <2 mmol/L when feed volumes reached 170 to 180 mL/kg/day. In this case, Protifar could be added at the discretion of the attending neonatologist, in addition to the allocated intervention fortifier. Additional protein was ceased when weight gain of 15 g/kg/day and a BUN >2 mmol/L were achieved.2.7.3. Biochemical AnalysesWeekly blood samples were taken and BUN, plasma albumin, plasma creatinine, pH and base deficit measured. Blood spots were collected weekly on filter paper and amino acids measured using tandem mass spectrometry (SA Pathology, Neonatal Screening Centre, Adelaide, Australia).2.7.4. Sample Size and Statistical AnalysisA sample size of 60 (30 per group) would detect a difference in weight gain of 3.31 g per day between the high protein and standard protein groups (80% power, p = 0.05). Consultation with the neonatal medical team agreed that this was a clinically important difference on which clinical practice could be changed. Mean weight, length, head circumference and lean mass gains over the trial period, were calculated for each infant using a linear effects model with a random intercept and slope. Using the slope, a linear regression model was fitted for each infant. Clustering (multiple births) was accounted for by using a generalised estimating equation with an independent working correlation matrix. All analyses were intention-to-treat. All models were adjusted for sex and gestational age category (28–29 and 30–32 weeks’ gestation). A per protocol analysis was specified a priori for infants who consumed ≥70% of their prescribed trial fortifier.2.7.5. EthicsEthical approval was granted by the Women’s and Children’s Health Network Human Research Ethics Committee (REC2401/10/14). This trial was registered with the Australian New Zealand Clinical Trials Registry (http://www.anzctr.org.au/) as ACTRN12611001275954.3. Results3.1. Study PopulationSixty infants were enrolled in the trial with 31 infants randomised to the high protein group and 29 infants to the standard protein group (Figure 1). There were 31 infants born from multiple births (14 sets of twins, 1 set of triplets). In all multiple births, apart from two sets of twins, the infants were randomly allocated to different interventions. For the triplets, two were randomised to the high protein group and one to the standard protein group. Four infants, two from each group, were withdrawn from the study after randomisation but before the first dose of trial fortifier was administered after parents changed their minds about involvement. A further two infants (twins) in the high protein group did not have any available breast milk and withdrew before the commencement of fortifier. One set of twins and one singleton were withdrawn by the parents midway through the trial due to perceived feeding intolerance and another infant was withdrawn by the clinical team after developing necrotising enterocolitis. In all cases of withdrawal, parents consented to the ongoing collection of data and all were included in intention-to-treat analyses. Baseline infant and maternal demographic, clinical and nutritional characteristics at randomisation were comparable between groups except that there were more male infants in the high protein group, n = 16 (52%) than the standard protein group, n = 12 (41%), the mean ± SD birth weight was lower in the higher protein group (1483 ± 423 g versus 1551 ± 407 g in the high and standard groups, respectively) and there were more infants classified as SGA for weight in the high protein group, n = 5 (16%) than the standard protein group, n = 1 (3%) (Table 1).3.2. Nutritional ManagementForty infants received standard ward HMF, S-26 SMA HMF (Wyeth Nutrition) while waiting for consent, 18 in the high and 22 in the standard protein group (Table 1). The remaining twenty trial infants started immediately on their allocated trial intervention.Nutritional intake of the infants for the first 28 days of the study did not differ between the groups except that the high protein group received more protein (mean ± SD 4.2 ± 1.3 vs. 3.5 ± 0.93 g/kg/day in the high and standard protein groups respectively). The protein concentration of the EBM was not different between groups (mean ± SD 1.43 ± 0.27 and 1.45 ± 0.28 g protein/100 mL in the high and standard groups, respectively) and the difference in protein intake was due to more protein derived from the HMF (mean ± SD 1.9 ± 1.2 and 1.2 ± 0.6 g/kg/day, in the high and standard groups, respectively. Energy intakes and fluid volume were similar between the groups (energy: mean ± SD 124 ± 34 and 126 ± 27 kcal/kg/day and fluid: mean ± SD 154 ± 39 and 157 ± 32 mL/kg/day in the high and standard groups, respectively). The high protein group received 83% (±32) of their total enteral intake as EBM compared with the control group who received 90% (±23).3.3. Primary OutcomeThere was no difference in the rate of weight gain between groups (Table 2) (mean (95% CI) high protein 245 (230, 260) g/week and standard protein 258 (244, 272) g/week, adjusted mean difference −14 (−32, 4) p = 0.12). Results were similar when analysed per protocol (Table 2).3.4. Secondary Outcomes3.4.1. GrowthThere were no differences in rate of length or head circumference gain (Table 2). High protein HMF infants weighed less at study end but this was not statistically significant (Table 2) and is consistent with the difference in birth weight between the groups (Table 1). There were no differences in length or head circumference at study end between the groups (Table 2). There was no difference in SGA status for weight between high and standard protein HMF groups at the end of the study (n = 8, 25%, and n = 3, 10% SGA infants in the high and standard protein groups, respectively, adjusted Relative Risk (95% CI); 2.5 (0.8, 7.9), p = 0.11).Over the first four weeks of the trial, when >75% of participants were still in hospital, fat free (lean) mass was measured with the week one measurement taken a mean of 8 ± SD 2 days after randomisation. Fat free mass as a proportion of body weight (Figure 2) from weeks one to four was greater in high protein group infants than standard protein group infants (p = 0.03). However, there was no significant group by time interaction (p = 0.84). At week three alone, there was a significant increase for fat free mass as a proportion of body weight in the high protein group (p = 0.04).3.4.2. BiochemistryDue to the variable nature of blood chemistry data and length of hospital stay (to discharge), only the first three trial weeks could be accurately analysed using a linear mixed effects model.There was a significant group by time interaction for BUN levels (p < 0.001) with BUN levels significantly increased in the high protein group (Figure 3). This difference continued for the duration of the trial (p < 0.001). There were 12 occurrences in nine separate infants where BUN levels were measured over the pre-specified safety threshold of 8 mmol/L. Seven of these occurred during baseline blood tests taken at randomisation and were therefore not a result of the intervention. Six of these infants had BUN measurements in the normal range at their next weekly blood test. One infant had a BUN measurement >8 mmol/L at week one; the infant did not have another BUN measurement over 8 mmol/L for the rest of the trial. Two other infants, both in the high protein group, recorded BUN concentrations >8.0 mmol/L, peaking at 8.8 mmol/L, on five occasions, however the base excess remained above −6 mmol/L with no other abnormal biochemistry. There was one occurrence of an infant in the standard protein group requiring additional protein due to poor weight gain and BUN <2 mmol/L.There were no group by time interactions or group differences for albumin, creatinine, glucose, pH (results not shown). Phenylalanine (Phe) and tyrosine (Tyr), amino acids associated with increased protein intake, were both increased in the high protein group compared to the standard group at study week 3 (Phe median (IQR) μmol/L: 33 (28–42) vs. 25 (23–30), p <0.001 and Tyr median (IQR) μmol/L: 196 (151–267) vs. 128 (99–172) μmol/L, p <0.003 in the high and standard groups respectively.3.4.3. Clinical OutcomesHigh protein HMF infants were significantly more likely to have feeds interrupted (11 (35%) vs. 6 (21%), p = 0.01, in the high and standard protein groups, respectively) Table 3. There was no significant difference in the number of days spent on parenteral nutrition, days of intravenous lipid or the days taken to reach full enteral feeds. Likewise, there was no significant difference between the groups for any other clinical outcome (Table 3).4. DiscussionThe aim of this study was to assess the effect of a higher protein HMF on preterm infant growth. Our trial interventions resulted in the high protein group infants receiving 0.7 g/kg/day more protein than infants in the standard protein group, with mean protein intakes within recommended ranges for both groups. Despite this, there were no differences in growth between the two groups. The accumulation of fat free mass and fat mass, also did not differ between groups. While the higher protein group had a greater proportion of fat free mass from week one, the absence of a baseline measurement makes the interpretation of this difficult. It is unlikely that the intervention would have had an effect in the first week of the study, particularly as the change in fat free mass over time did not differ between groups. A significant difference between groups was noted at week three only and the implication of this is unclear. It is possible that this is a chance finding of no clinical significance.These results are confirmed by a recent study by Maas et al. [16] who compared 1 and 1.8 g protein concentration in powdered HMFs in a similar population to ours and found no difference in growth. Their trial interventions achieved a 0.6 g/kg/day median greater intake of protein, similar to our study, and protein intakes were within recommendations. Growth rates in both studies approximated foetal growth rates. A further two studies compared two different, newly formulated liquid HMFs with higher protein concentrations, with standard powdered HMFs. Moya et al. [17] compared Mead Johnson Nutrition products: a liquid fortifier with an Enfamil powdered fortifier, which when mixed with EBM provided 3.2 and 2.6 g protein/100 mL, respectively, equating to an additional 1.8 and 1.1 g protein. Kim et al. [18], in a non-inferiority trial, compared the Abbott Nutrition products of Similac HMF liquid, providing 3.6 g protein/100 kcal when mixed with EBM, with Similac HMF powder providing 3 g protein/100 kcal when mixed. These comparisons equate to an additional 1.6 and 1 g protein added to 100 mL EBM in the liquid and powder, respectively. The populations were similar between studies [17,18] except that Moya et al. [17] inclusion criteria (≤30 weeks’ gestation, birth weight ≤1250 g) resulted in a slightly less mature and smaller population than in both Kim et al. [18] study and this current study. Neither study [17,18] showed a difference in weight gain between groups, however, Moya et al. [17] found improved length gain with the higher protein. Both studies found infants in the high protein group were heavier at study end. Almost half the participants in Moya’s study were <1000 g at birth; hence their protein requirements of 4 to 4.5 g/kg would have been met by the high, but not the control, protein fortifier at volumes of 150 mL/kg. This may explain the effect seen on length gain. Two other studies have compared fortifiers containing 1 and 1.4 g protein added to 100 mL EBM with mixed results. Our previous trial [14] showed no effect of increased protein on growth, although did show a reduction in the number of infants SGA for length at discharge. However, Rigo et al. [19], in a non-inferiority trial, found improved weight gain of 2.3 g/day with the higher protein fortifier. The trial products in both these studies were similar, as were the population. It is possible that the smallest infants, with the highest protein needs, are the ones to benefit most from increased protein and that the larger sample size in Rigo (n = 153) compared to that in Miller (n = 92) elucidated the differences. Taken collectively, these results and ours suggest that protein concentrations in HMFs of 1.8 g provide no additional benefit in the population studied, but smaller infants are worthy of further investigation.The significantly elevated BUN levels seen at weeks 1, 2 and 3 were expected and have occurred in other high protein nutritional intervention studies [9,14,17]. Assuming adequate renal function, BUN is proportional to protein intake [20] and is often used as a crude marker of protein sufficiency. Low BUN levels suggest inadequate protein intake and high levels indicate possible excessive intake [9]. Blood phenylalanine and tyrosine concentrations were also significantly increased in the higher protein group, in week 3 only, and this is unlikely to be clinically significant. There were no differences in creatinine, albumin or other biochemical markers suggesting the intervention did not harm the infants.A strength of this study is the rigour with which dietary intake and growth were assessed. The protein and fat concentrations of EBM were measured, rather than assumed, resulting in accurate reporting of dietary intake and confirmation that, despite the variability of protein in EBM, we achieved a mean intake difference of 0.7 g/kg/day of protein between groups. Similarly, we measured both growth and body composition in an attempt to discern differences in weight gain arising from extra protein. This trial also has some limitations. Although all infants were included in the analyses, there were 10 who either did not receive, or ceased the intervention, which may have impacted results. In addition, the pragmatic nature of this trial may have influenced results as clinicians may have adjusted feed regimes if poor weight gain was identified. There was one instance of extra Protifar prescribed to an infant in the standard protein group and subtle increases in feed volume may also have occurred although volume of intake was not different between groups. This may have made it more difficult to detect differences between intervention groups. We used BIS to determine fat and fat free mass. BIS is the only cot-side technique available where infants requiring respiratory support can be assessed. While accuracy of BIS at the individual level is poor, BIS provides a useful means of determining differences in body composition between population means [21].Many of the recent trials discussed have already achieved mean growth rates approaching intra-uterine growth, with similar growth rates between groups. Findings from this current study are only generalisable to a similar population (infants born 28–32 week’s gestation). Therefore, to explicate the subtle effects of increasing protein on growth, future trials may need to focus on birth weight categories as they relate to protein requirements (i.e., <1000 g and 1000–1800 g). Due to the small proportion of infants born <1000 g, large multi-centre trials will be needed to tease out the effect.5. ConclusionsIncreasing the protein concentration of HMF from 1.0 to 1.8 g protein added per 100 mL EBM does not improve growth in preterm infants born 28–32 weeks’ gestation.AcknowledgmentsWe thank the families who participated in this study.Author ContributionsConceptualization, J.R., M.M., A.J.M. and C.T.C.; Formal analysis, J.R., M.M., A.J.M., M.J.S., J.M. and C.T.C.; Funding acquisition, J.R., M.M., A.J.M., M.J.S. and C.T.C.; Investigation, J.R., M.M., A.J.M., M.J.S. and C.T.C.; Supervision, M.M., A.J.M., M.J.S. and C.T.C.; Writing: original draft, J.R., J.M. and C.T.C.; Writing: review and editing, J.R., M.M., A.J.M., M.J.S., J.M. and C.T.C.FundingThis research was funded by a Women’s and Children’s Hospital Foundation Grant. Research Fellowships were provided by the National Health and Medical Research Council of Australia (M.M. Principal Research Fellow APP1061704) and the MS McLeod Research Fellowship, MS McLeod Research Fund, Women's and Children’s Hospital Research Foundation (C.T.C). The contents of the published material are solely the responsibility of the authors and do not reflect the views of the National Health and Medical Research Council of Australia.Conflicts of InterestOutside the submitted work, Maria Makrides serves on scientific advisory boards for Fonterra and Nestle. Honoraria are paid to her institution for continuing education of early career researchers. Maria Makrides also holds a Principal Research Fellowship from the NHMRC (APP1061704). Other authors declare no conflict of interest. Nestlé Nutrition donated half of the human milk fortifier used in the trial and Nutricia donated the Polyjoule and Protifar supplements. However, these sponsors had no role in the design of the study, in the collection, analyses or interpretation of data; in writing of the manuscript, and the decision to publish the results.References1.AdamkinD.H.RadmacherP.G.Fortification of human milk in very low birth weight infants (VLBW <1500 g birth weight)Clin. Perinatol.20144140542110.1016/j.clp.2014.02.010248738402.MoroG.E.ArslanogluS.BertinoE.CorvagliaL.MontirossoR.PicaudJ.C.PolbergerS.SchanlerR.J.SteelC.van GoudoeverJ.Human milk in feeding premature infants: Consensus statementJ. Pediatr. Gastroenterol. Nutr.201561Suppl. 1S16S1910.1097/01.mpg.0000471460.08792.4d262959993.BrownJ.V.E.EmbletonN.D.HardingJ.E.McGuireW.Multi-nutrient fortification of human milk for preterm infantsCochrane Database Syst. Rev.201610.1002/14651858.CD000343.pub3271558884.AgostoniC.BuonocoreG.CarnielliV.P.De CurtisM.DarmaunD.DecsiT.DomellofM.EmbletonN.D.FuschC.Genzel-BoroviczenyO.Enteral nutrient supply for preterm infants: Commentary from the European Society of Paediatric Gastroenterology, Hepatology and Nutrition committee on nutritionJ. Pediatr. Gastroenterol. Nutr.201050859110.1097/MPG.0b013e3181adaee0198813905.ColaizyT.T.CarlsonS.SaftlasA.F.MorrissF.H.Jr.Growth in vlbw infants fed predominantly fortified maternal and donor human milk diets: A retrospective cohort studyBMC Pediatr.20121212410.1186/1471-2431-12-124229005906.EmbletonN.E.PangN.CookeR.J.Postnatal malnutrition and growth retardation: An inevitable consequence of current recommendations in preterm infants?Pediatrics200110727027310.1542/peds.107.2.270111584577.MaasC.WiechersC.BernhardW.PoetsC.F.FranzA.R.Early feeding of fortified breast milk and in-hospital-growth in very premature infants: A retrospective cohort analysisBMC Pediatr.20131317810.1186/1471-2431-13-178241802398.CibulskisC.C.ArmbrechtE.S.Association of metabolic acidosis with bovine milk-based human milk fortifiersJ. Perinatol.20153511511910.1038/jp.2014.143251023219.ArslanogluS.MoroG.E.ZieglerE.E.Adjustable fortification of human milk fed to preterm infants: Does it make a difference?J. Perinatol.20062661462110.1038/sj.jp.72115711688598910.AlanS.AtasayB.CakirU.YildizD.KilicA.KahveciogluD.ErdeveO.ArsanS.An intention to achieve better postnatal in-hospital-growth for preterm infants: Adjustable protein fortification of human milkEarly Hum. Dev.2013891017102310.1016/j.earlhumdev.2013.08.0152403503911.BiasiniA.MarvulliL.NeriE.ChinaM.StellaM.MontiF.Growth and neurological outcome in ELBW preterms fed with human milk and extra-protein supplementation as routine practice: Do we need further evidence?J. Matern. Fetal Neonatal Med.201225Suppl. 4727410.3109/14767058.2012.7150322295802412.RochowN.FuschG.ChoiA.ChessellL.ElliottL.McDonaldK.KuiperE.PurchaM.TurnerS.ChanE.Target fortification of breast milk with fat, protein, and carbohydrates for preterm infantsJ. Pediatr.20131631001100710.1016/j.jpeds.2013.04.0522376949813.McLeodG.SherriffJ.HartmannP.E.NathanE.GeddesD.SimmerK.Comparing different methods of human breast milk fortification using measured v. Assumed macronutrient composition to target reference growth: A randomised controlled trialBr. J. Nutr.201611543143910.1017/S00071145150046142662789914.MillerJ.MakridesM.GibsonR.A.McPheeA.J.StanfordT.E.MorrisS.RyanP.CollinsC.T.Effect of increasing protein content of human milk fortifier on growth in preterm infants born at <31 wk gestation: A randomized controlled trialAm. J. Clin. Nutr.20129564865510.3945/ajcn.111.0263512230193315.BeebyP.J.BhutapT.TaylorL.K.New South Wales population-based birthweight percentile chartsJ. Paediatr. Child Health19963251251810.1111/j.1440-1754.1996.tb00965.x900778216.MaasC.MathesM.BleekerC.VekJ.BernhardW.WiechersC.PeterA.PoetsC.F.FranzA.R.Effect of increased enteral protein intake on growth in human milk–fed preterm infants: A randomized clinical trialJAMA Pediatr.2017171162210.1001/jamapediatrics.2016.26812789306417.MoyaF.SiskP.M.WalshK.R.BersethC.L.A new liquid human milk fortifier and linear growth in preterm infantsPediatrics2012130e928e93510.1542/peds.2011-31202298787718.KimJ.H.ChanG.SchanlerR.Groh-WargoS.BloomB.DimmitR.WilliamsL.BaggsG.Barrett-ReisB.Growth and tolerance of preterm infants fed a new extensively hydrolyzed liquid human milk fortifierJ. Pediatr. Gastroenterol. Nutr.20156166567110.1097/MPG.00000000000010102648811819.RigoJ.HascoetJ.M.BilleaudC.PicaudJ.C.MoscaF.RubioA.SalibaE.RadkeM.SimeoniU.GuilloisB.Growth and nutritional biomarkers of preterm infants fed a new powdered human milk fortifier: A randomized trialJ. Pediatr. Gastroenterol. Nutr.201765e83e9310.1097/MPG.00000000000016862872765420.PolbergerS.K.AxelssonI.E.RaihaN.C.Urinary and serum urea as indicators of protein metabolism in very low birthweight infants fed varying human milk protein intakesActa Paediatr. Scand.19907973774210.1111/j.1651-2227.1990.tb11548.x223926621.CollinsC.T.ReidJ.MakridesM.LingwoodB.E.McPheeA.J.MorrisS.A.GibsonR.A.WardL.C.Prediction of body water compartments in preterm infants by bioelectrical impedance spectroscopyEur. J. Clin. Nutr.201367Suppl. 1S47S5310.1038/ejcn.2012.16423299871Figure 1Participant flow through the trial. 1 from rural locations (n = 52), insufficient milk supply (n = 36), required interpreter (n = 6); congenital abnormality (n = 3); 2 did not want to take part (n = 25), did not want twins to be randomized individually (n = 8), parent not visiting (n = 1), immediately transferred to another centre (n = 1).Figure 2Fat free mass as a proportion of body weight for the first four weeks of the trial. Values are means, error bars are 95% CI. High protein n = 30, 30, 27, 26 and standard protein 29, 27, 26, 23 in weeks 1, 2, 3, 4 respectively. Adjusted for sex and gestational age, group interaction, p = 0.03, time interaction, p = 0.01. group × time interaction p = 0.84; * p = 0.04.Figure 3BUN from randomisation to week 3. Values are mean, error bars are 95% CI. High protein: n = 31, 28, 26, 25; Standard protein: n = 29, 26, 24, 24 for weeks baseline, 1, 2, 3. Adjusted for sex and GA, overall group effect <0.001, group * week interaction, p <0.001, * p = 0.04; ** p <0.001.nutrients-10-00634-t001_Table 1Table 1Baseline infant and maternal characteristics.CharacteristicHigh Protein (n = 31)Standard Protein (n = 29)\nInfant characteristics\n\n\nSingleton15 (48)16 (55)Twin15 (48)12 (41)Triplet2 (7)1 (3)Gestational age (week)30.5 ± 1.530.1 ± 1.428–29 weeks’ gestation10 (32)9 (31)30–32 weeks’ gestation21 (68)20 (69)Male infants16 (52)12 (41)Birth weight (g)1483 ± 4231551 ± 407SGA for weight at birth5 (16)1 (3)Birth length (cm)40.0 ± 3.340.2 ± 2.8Head circumference (cm)28.5 ± 328.5 ± 1.8Infants received standard ward HMF before randomisation18 (58)22 (76)Length of standard ward fortification before trial HMF start (day)1.3 ± 1.72.0 ± 1.5Time between birth and trial HMF start (day)8.9 ± 3.29.0 ± 2.5\nMaternal characteristics\n\n\nMaternal age (years)29.9 ± 6.331.7 ± 5.3Mother smoked during pregnancy5 (16.1)3 (10.3)Caucasian27 (96)23 (82)Primiparous19 (61.3)12 (41.4)Previous preterm birth4 (33.3)6 (35.3)Data are presented as n (%) or mean ± SD.nutrients-10-00634-t002_Table 2Table 2Anthropometric changes over the study.\nIntention to Treat AnalysesPer Protocol Analyses 1High Protein (n = 31)Standard Protein (n = 29)Adjusted Mean Difference 2\np\n2\nHigh Protein (n = 21)Standard Protein (n = 23)Adjusted Mean Difference 2\np\n2\nWeight gain (g/week)245 (230, 260)258 (244, 272)−14 (−32, 4)0.12245 (228, 262)262 (247, 277)−15 (−36, 5)0.14Length gain (cm/week)1.1 (1.1, 1.2)1.1 (1.1, 1.2)−0.01 (−0.06, 0.03)0.451.1 (1.1, 1.2)1.2 (1.1, 1.2)−0.01 (−0.06, 0.04)0.62Head circumference gain (cm/week)1.1 (1.0, 1.1)1.1 (1.0,1.1)0.007 (−0.05, 0.06)0.791.1 (1.1, 1.1)1.1 (1.1, 1.1)−0.004 (−0.06, 0.05)0.88Weight at study end (g) 32658 (2544, 2771)2757 (2632, 2883)−100 (−251, 50)0.192646 (2489, 2805)2815 (2675, 2955)−157 (−341, 28) 0.1Length at study end (cm)45.2 (44.5, 45.9)45.8 (45.0, 46.6)−0.5 (−1.3, 0.3)0.1945.2 (44.4, 46.0)46.3 (45.6, 47)−0.86 (−1.85, 0.12)0.09Head circumference at study end (cm)33.1 (32.5, 33.6)33.0 (32.4, 33.7)0.03 (−0.6, 0.7)0.9233.3 (32.7, 33.9)33.6 (33.0, 34.1)−0.16 (−0.90, 0.57)0.66Data are presented as mean, (95% CI); 1 For inclusion in ‘per protocol’ analysis, infants must have consumed 70% or more of their trial group HMF; 2 adjusted for sex and gestational age; 3 study end defined as removal of naso-gastric tube or term equivalent, whichever came first.nutrients-10-00634-t003_Table 3Table 3Feeding and clinical management.VariableHigh Protein (n = 31)Standard Protein (n = 29)\np\nInfant required enteral protein supplementation 101 (3.4)0.48Feeding interrupted 211 (35)6 (21)0.01Days receiving parenteral nutrition10 (7, 13)9 (7, 11)0.34Days of intravenous lipid4 (3, 7)4 (3, 6)0.72Days to full enteral feeds 38 (6, 10)8 (7, 10)0.72Confirmed necrotizing enterocolitis1 (3.2)0>0.99Oxygen at discharge2 (6.5)1 (3.4)0.15Late onset sepsis1 (3.2)0>0.99Data are reported as n (%) or mean (95% CI).1 One infant in the standard protein group was prescribed a protein supplement (Protifar) 2 Feeding interrupted was defined as one of more feeds not given in a day; 3 Full enteral feeds was defined as 150 mL/kg/day)."", 'title': 'The Effect of Increasing the Protein Content of Human Milk Fortifier to 1.8 g/100 mL on Growth in Preterm Infants: A Randomised Controlled Trial.', 'date': '2018-05-19'}, '28727654': {'article_id': '28727654', 'content': ""J Pediatr Gastroenterol NutrJ. Pediatr. Gastroenterol. NutrJPGAJournal of Pediatric Gastroenterology and Nutrition0277-21161536-4801Lippincott Williams & Wilkins287276545625962JPGN-16-82510.1097/MPG.000000000000168600025Original Articles: NutritionGrowth and Nutritional Biomarkers of Preterm Infants Fed a New Powdered Human Milk Fortifier: A Randomized TrialRigoJacques∗HascoëtJean-Michel†BilleaudClaude‡PicaudJean-Charles§MoscaFabio||RubioAmandine¶SalibaElie#RadkëMichaël∗∗SimeoniUmberto††GuilloisBernard‡‡de HalleuxVirginie∗JaegerJonathan§§AmeyeLaurent||||HaysNicholas P.¶¶SpalingerJohannes##∗Department of Neonatology, University of Liège, CHR Citadelle, Liège, Belgium†Maternité Régionale Universitaire A. Pinard, Nancy‡CIC Pédiatrique 1401 INSERM-CHU, Bordeaux§Service de Neonatologie, Hôpital de la Croix Rousse, Lyon, France||Neonatal Intensive Care Unit, Department of Clinical Science and Community Health, Fondazione IRCCS “Ca’ Granda” Ospedale Maggiore Policlinico, University of Milan, Milan, Italy¶Hôpital Couple Enfant, CHU de Grenoble, Grenoble#Hôpital Clocheville, CHU de Tours, Tours, France∗∗Klinikum Westbrandenburg GmbH, Potsdam, Germany††Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland‡‡Hôpital Clemenceau, CHU de Caen, Caen, France§§Nestlé Clinical Development Unit, Lausanne, Switzerland||||Nestlé Nutrition R&D, Vevey, Switzerland¶¶Nestlé Nutrition R&D, King of Prussia, PA##Children's Hospital of Lucerne, Lucerne, Switzerland.Address correspondence to Jacques Rigo, MD, PhD, Service Universitaire de Néonatologie, CHR de la Citadelle, Boulevard du Douzième de Ligne, 1 4000 Liège, Belgium (e-mail: J.Rigo@ulg.ac.be); Address reprint or protocol requests to: Nicholas P. Hays, PhD, 3000 Horizon Dr., Suite 100, King of Prussia, PA 19406 (e-mail: Nicholas.Hays@rd.nestle.com).1020172292017654e83e93231120162952017Copyright © The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the European Society for Pediatric Gastroenterology, Hepatology, and Nutrition and the North American Society for Pediatric Gastroenterology, Hepatology, and Nutrition2017This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc-nd/4.0ABSTRACTObjectives:The aim of this study was to assess growth and nutritional biomarkers of preterm infants fed human milk (HM) supplemented with a new powdered HM fortifier (nHMF) or a control HM fortifier (cHMF). The nHMF provides similar energy content, 16% more protein (partially hydrolyzed whey), and higher micronutrient levels than the cHMF, along with medium-chain triglycerides and docosahexaenoic acid.Methods:In this controlled, multicenter, double-blind study, a sample of preterm infants ≤32 weeks or ≤1500\u200ag were randomized to receive nHMF (n\u200a=\u200a77) or cHMF (n\u200a=\u200a76) for a minimum of 21 days. Weight gain was evaluated for noninferiority (margin\u200a=\u200a–1\u200ag/day) and superiority (margin\u200a=\u200a0\u200ag/day). Nutritional status and gut inflammation were assessed by blood, urine, and fecal biochemistries. Adverse events were monitored.Results:Adjusted mean weight gain (analysis of covariance) was 2.3\u200ag/day greater in nHMF versus cHMF; the lower limit of the 95% CI (0.4\u200ag/day) exceeded both noninferiority (P\u200a<\u200a0.001) and superiority margins (P\u200a=\u200a0.01). Weight gain rate (unadjusted) was 18.3 (nHMF) and 16.8\u200ag\u200a·\u200akg−1\u200a·\u200aday−1 (cHMF) between study days 1 and 21 (D1–D21). Length and head circumference (HC) gains between D1 and D21 were not different. Adjusted weight-for-age z score at D21 and HC-for-age z score at week 40 corrected age were greater in nHMF versus cHMF (P\u200a=\u200a0.013, P\u200a=\u200a0.003 respectively). nHMF had higher serum blood urea nitrogen, pre-albumin, alkaline phosphatase, and calcium (all within normal ranges; all P\u200a≤\u200a0.019) at D21 versus cHMF. Both HMFs were well tolerated with similar incidence of gastrointestinal adverse events.Conclusions:nHMF providing more protein and fat compared to a control fortifier is safe, well-tolerated, and improves the weight gain of preterm infants.Keywordsgrowthhuman milklow birth weightSTATUSONLINE-ONLYOPEN-ACCESSTRUEWhat Is KnownDue in part to variability in human milk composition, incidence of postnatal growth restriction is more frequently reported in very-low-birth-weight infants fed fortified human milk compared to those fed preterm formulas.The optimal composition of human milk fortifier and nutritional recommendations for preterm infants fed fortified human milk are still debated.What Is NewA new human milk fortifier containing partially hydrolyzed protein, fat, and carbohydrate provides a higher protein:energy ratio while achieving lower osmolality versus a current fortifier.In preterm infants, the new fortifier improves weight gain and reduces postnatal growth restriction compared to the current fortifier.Feeding of human milk (HM) rather than preterm formulas provides many benefits to preterm infants (eg, accelerated gut maturation (1); protection against infections (2), sepsis (3), necrotizing enterocolitis (2), and retinopathy of prematurity (4); possible protective effect on neurodevelopment (5)) that are mediated by protective biomolecules and trophic factors in HM. HM, however, provides inadequate protein and micronutrients to support the rapid growth and bone mineralization of preterm infants. These deficits are particularly acute in the smallest infants (birthweight <1500\u200ag) who have the highest protein and mineral needs (6). Fortification of mother's own milk or banked HM is therefore recommended for all preterm infants with birthweight <1800\u200ag to improve nutrient accretion and in-hospital growth (7,8).Feeding fortified HM helps support adequate growth and bone mineralization (9), and is associated with favorable neurodevelopmental outcomes (10), although evidence for improved outcomes other than in-hospital growth is limited (11). The nutritional content, however, of some currently available fortifiers may be inadequate for many preterm infants. Incidence of postnatal growth restriction is more frequently reported in very-low-birth-weight infants fed fortified HM compared to those fed preterm formulas (12,13). In addition, the nutritional profile of HM from mothers of premature infants varies greatly (14) and may differ from published reference compositional data, which may lead to less-than-recommended intakes of protein and energy (15,16). These nutritional inadequacies may worsen with use of donor HM, which is often from mothers of term infants >1-month postpartum (17).A new powdered HM fortifier has been developed with a higher protein:energy ratio (protein provided as partially hydrolyzed whey), non-protein energy from lipids and carbohydrate, and higher electrolyte and vitamin levels (enriching HM in line with ESPGHAN (18) and expert group (19) recommendations) versus a control fortifier. When mixed with HM containing 1.5\u200ag protein/100\u200amL (2–4 week milk) (20–22), it provides 3.6\u200ag protein/100 kcal (within the ESPGHAN-recommended ranges (18) for protein and energy intakes for a minimal intake volume of 140\u200amL/kg/day in very-low-birth-weight infants up to 1.8\u200akg body weight), with osmolality below the recommended threshold of 450\u200amOsm/kg (23,24).This study evaluated growth and nutritional biomarkers during a 21-day interval in clinically stable preterm infants receiving the new HM fortifier (nHMF) compared to infants fed a control fortifier (cHMF). The primary objective was to assess weight gain velocity (grams per day); evaluations of other growth parameters (including weight gain velocity in gram per kilograms per day) and intervals (eg, to 40 weeks corrected age [W40CA]), feeding tolerance, adverse events, time to full fortification/full enteral feeding, and markers of protein-energy, electrolytes, bone metabolic status, gut inflammation, and maturity of gastrointestinal (GI) function were also conducted as secondary outcomes. We hypothesized that weight gain of infants fed nHMF would be both noninferior (lower limit of 95% confidence interval [CI] of mean difference >–1\u200ag/day) and superior (lower limit of 95% CI of mean difference >0\u200ag/day) to that of infants fed cHMF.METHODSStudy design and participantsThis was a controlled, double-blind, randomized, parallel-group study conducted in neonatal intensive care units (NICUs) at 11 metropolitan hospitals in France, Belgium, Germany, Switzerland, and Italy. NICU size ranged from 25 to 45 beds. Clinically stable male and female preterm infants with gestational age ≤32 weeks or birthweight ≤1500\u200ag and born to mothers who had agreed to provide expressed or donor breastmilk for the entire 21-day study duration were enrolled in the study from April 2011 to March 2014. Infants were excluded if they had a history of or current systemic, metabolic, or chromosomic disease, any congenital anomalies of the GI tract, were small for gestational age (defined in this study as bodyweight ≤5th percentile (25)), or were receiving steroids or preterm formula during the study period. For multiple births, the first sibling was randomized and other siblings were allocated to the same group. The study was reviewed and approved by an institutional review board/independent Ethics Committee at each study site. Each subject's parent/legal representative provided written informed consent before participating in the study.Infants tolerating ≥100\u200amL\u200a·\u200akg−1\u200a·\u200aday−1 of HM for >24\u200ahours were randomized to receive either nHMF or cHMF for a minimum of 21 days; infants continued to receive their allocated study fortifier (or were transitioned to a routine/standard fortifier) until NICU discharge or medical decision to stop fortification, and fortification was stopped after discharge. The fortifiers were both cow's milk-based and provided similar energy supplementation (17\u200akcal/100\u200amL of HM). For every 100\u200amL of HM, nHMF provided 1.4\u200ag partially hydrolyzed whey protein, 0.7\u200ag lipids (primarily medium chain triglycerides and docosahexaenoic acid), 1.3\u200ag carbohydrate (maltodextrin), with a blend of micronutrients. cHMF (FM85 Human Milk Fortifier, Nestlé, Switzerland) provided 1.0\u200ag extensively hydrolyzed whey protein, no lipids, 3.3\u200ag carbohydrate (lactose and maltodextrin), with a blend of micronutrients. nHMF contained higher concentrations of some vitamins and electrolytes compared to cHMF, but both contained similar levels of minerals, including calcium (as calcium glycerophosphate and calcium phosphate) and phosphorus. Table 1 presents the estimated composition and osmolality of preterm HM (22) fortified with each fortifier. Fortifiers were fed beginning at half-strength (Fortification Strength Increase day 1; FSI1), then advanced per hospital practice, with full-strength fortification occurring once infants could maintain intakes of 150 to 180\u200amL\u200a·\u200akg−1\u200a·\u200aday−1 (ie, full enteral feeds; study day 1 [D1]). A study plan schematic is presented in Figure 1.FIGURE 1Study design. cHMF\u200a=\u200acontrol human milk fortifier; D1\u200a=\u200astudy day 1; D7\u200a=\u200astudy day 7; D10/11\u200a=\u200astudy day 10/11; D14\u200a=\u200astudy day 14; D21\u200a=\u200astudy day 21; DC\u200a=\u200adischarge (note that infants continued to receive their allocated study fortifier [or were transitioned to a routine/standard fortifier] until neonatal unit discharge or medical decision to stop fortification if length of stay was >21 days, and fortification was stopped after discharge) ; FSI1\u200a=\u200afortification strength increase day 1; HC\u200a=\u200ahead circumference; HM\u200a=\u200ahuman milk; nHMF\u200a=\u200anew human milk fortifier; W40CA\u200a=\u200aweek 40 corrected age.Study ProceduresGrowthInfant nude weight (to the nearest 1\u200ag) was measured daily by trained nursery personnel using a calibrated electronic scale (Baby Scale 717, Seca, Semur-en-Auxois, France). Recumbent length and head circumference (HC; both to the nearest 0.1\u200acm) were measured at FSI1, D1, and weekly thereafter. At least 2 trained examiners measured recumbent length using a length board (Mobile Measuring Board 417, Seca, Semur-en-Auxois, France) while maintaining proper body alignment and full body extension with feet flexed. HC was measured using a nonelastic measuring tape (Measuring Tape 212 or 218, Seca, Semur-en-Auxois, France) placed over the largest circumference of the skull (above the supraorbital ridges while covering the most prominent part of the frontal bulge anteriorly). The same calibrated equipment was used for anthropometric measures for each infant at all sites. Weight-for-age, length-for-age, and HC-for-age z scores were calculated using Fenton (25). Weight gain velocity (grams per kilograms per day) was calculated using the average of the start and end weights as the denominator.Markers of Protein-energy, Electrolyte, and Bone Metabolic StatusBlood and urine samples were collected at D1, D10/11, and D21 and analyzed for serum creatinine and prealbumin, blood urea nitrogen (BUN), urinary urea, hemoglobin, hematocrit, electrolyte status, and bone metabolic status. All blood and urine parameters were analyzed as part of routine clinical assessments at each NICU. Since 24-hour urine collections were not performed in this study owing to logistical infeasibility, urinary markers were corrected for 24-hour creatinine excretion (26) assuming a standard urinary excretion in preterm infants of 10\u200amg\u200a·\u200akg−1\u200a·\u200aday−1(27).Feeding Tolerance and Adverse EventsFeeding tolerance was evaluated by trained nursery staff who recorded daily milk intake (milliliters), stool pattern (defecation frequency and stool consistency [5\u200a=\u200ahard, 4\u200a=\u200aformed, 3\u200a=\u200asoft, 2\u200a=\u200aliquid, or 1\u200a=\u200awatery]), presence of abdominal distention, and incidence of spitting-up (defined as return of a small amount of swallowed food, usually a mouthful, and usually occurring during or shortly after feeding) and vomiting (defined as return of a larger amount of food with more complete emptying of the stomach, and usually occurring sometime after feeding). In addition, frequency, type, and attribution to fortifier intake of adverse events (AEs; including clinical and laboratory) were evaluated using physician-reported information recorded using standardized forms from enrollment to W40CA. AEs were categorized by the reporting investigator as “serious” in accordance with International Conference on Harmonization criteria (28) and as “related to the intervention” based on detailed, standardized criteria provided in the protocol.Statistical AnalysisSample size was based on a previous study (29), which investigated growth and zinc status in preterm infants fed fortified HM. In the present trial, a group-sequential design was chosen (Wang and Tsiatis) (30) with 1 interim analysis. To detect a noninferior weight gain in infants fed with nHMF versus cHMF from D1 to D21 (noninferiority margin –1\u200ag/day, expected weight gain difference 2\u200ag/day, standard deviation 4.73\u200ag/day, type I error 5%, power 80%) (29), 192 subjects (males and females combined) were needed. A computer-generated list of random numbers was used to allocate group assignments. Minimization algorithm with allocation ratio 1:1 and second best probability of 15% was used. Stratification factors were center, sex, and birthweight (100g intervals). Group coding was used with 2 nonspeaking codes per group; fortifier packaging was coded accordingly but otherwise identical in appearance. Infants were enrolled and assigned to their intervention by the study investigators or trained delegates. All study personnel (both site- and sponsor-based) and participants (infants’ families) were blind to group assignment. Noninferiority was demonstrated if the lower limit of the 2-sided 95% CI of the difference in weight gain from D1 to D21 was larger than the noninferiority margin. Superiority was evaluated if noninferiority was demonstrated. Weight gain was analyzed in the intent-to-treat (ITT) and per-protocol populations by analysis of covariance (ANCOVA) adjusting for D1 postmenstrual age and weight, sex, and center (random effect). Sensitivity analyses were conducted using ANCOVA models that adjusted for covariates that were determined post hoc to be significantly different between groups and which may have confounded the primary results (eg, mother smoking status). Secondary endpoints were analyzed in the ITT population only. For noninferiority and superiority tests, 1-sided P values are provided and should be compared to a reference value of 0.025. For other tests, 2-sided P values are provided and should be compared to a reference value of 0.05. 95% CIs provide estimates for feeding effects on all endpoints. Based on prespecified guidelines in the independent Data Monitoring Committee's (DMC) charter, a single interim analysis was conducted when 134 subjects had completed their D21 visit. The interim analysis was planned to occur when the first 100 infants completed at least 21 days of full fortification; however, the analysis was conducted using data from 134 infants owing to unforeseen delays in conducting the analysis (eg, performing statistical programming, data cleaning, and query resolution) while recruitment continued. The type 1 error rate was adjusted to account for the analysis being conducted at ∼70% enrollment rather than the planned 52%. The DMC consisted of independent experts (2 clinicians, 1 biostatistician) who reviewed growth, formula intake, and key biochemical data as well as AEs. The purpose of the interim analysis was to examine unblinded growth velocity results and determine whether the trial could be stopped early for success or futility, or whether the targeted sample size required adjustment (the interim statistical analysis plan was finalized before unblinding, and the analysis was unblinded only to the DMC to facilitate ethical decision-making) (31). On April 2, 2014, the DMC recommended to stop the trial, as noninferiority and superiority in regard to the primary outcome had been demonstrated. The sponsor was notified of this decision on April 3, 2014, and the final study population included infants enrolled through March 31, 2014.RESULTSA total of 274 infants were screened, with 153 enrolled and randomized to either nHMF (n\u200a=\u200a77) or cHMF (n\u200a=\u200a76) (Fig. 2). Demographic and baseline anthropometry data are summarized in Table 2. There was no evidence of imbalance between the 2 groups with respect to infant characteristics. A significantly lower percentage of mothers and fathers of infants in the nHMF group, however, smoked during pregnancy. Number of twins was similar in each group.FIGURE 2Flow of study participants. AE\u200a=\u200aadverse event; cHMF\u200a=\u200acontrol human milk fortifier; D21\u200a=\u200astudy day 21; ITT\u200a=\u200aintent-to-treat; NEC\u200a=\u200anecrotizing enterocolitis; nHMF\u200a=\u200anew human milk fortifier; NICU\u200a=\u200aneonatal intensive care unit; PP\u200a=\u200aper-protocol; SAE\u200a=\u200aserious adverse event. ∗Although screening procedures were standardized across sites, some variability in prescreening procedures did occur. Based on the typical clinical characteristics of infants who were admitted to each NICU during the study interval, the total number of infants who would have been theoretically considered eligible for the study was higher than the number shown here.The majority (84% and 87% by volume in nHMF and cHMF, respectively) of milk provided to infants was pasteurized. Donor milk was always pasteurized and accounted for 49% and 51% of the fortified HM volume provided in the nHMF and cHMF groups, respectively. There was no significant difference in mean volume of fortified milk intake between groups (152.7\u200a±\u200a13.0 and 152.6\u200a±\u200a17.2\u200amL\u200a·\u200akg−1\u200a·\u200aday−1 in nHMF and cHMF, respectively). Protein intake estimated using standard values for preterm HM composition per 100\u200amL (22) was significantly greater in nHMF compared to cHMF (4.48\u200a±\u200a0.38 vs 3.81\u200a±\u200a0.43\u200ag\u200a·\u200akg−1\u200a·\u200aday−1, respectively; P\u200a<\u200a0.001) because of higher protein content of the nHMF. Estimated energy intake was not significantly different between groups (125\u200akcal\u200a·\u200akg−1\u200a·\u200aday−1 in both groups). There was no significant difference in number of days between FSI1 and D1, but adjusted time between birth and D1 was significantly shorter in nHMF (16.8\u200a±\u200a5.4 vs 18.7\u200a±\u200a8.8 days; −8.5% [95% CI: −15.0%, −1.0%]).GrowthIn the ITT population, adjusted weight gain from D1 to D21 was 2.3\u200ag/day higher in nHMF, with the 95% CI ranging from 0.4 to 4.2\u200ag/day, demonstrating noninferiority (P\u200a<\u200a0.001) and superiority (P\u200a=\u200a0.01) of nHMF. Per-protocol results were similar. Weight gain from D1 to D21 remained significantly higher in nHMF when expressed in grams per kilogram per day (Table 3). Weight-for-age z scores (Fig. 3) remained stable from FSI1 to D21 in nHMF, but continued to decrease in cHMF (P\u200a=\u200a0.007 vs D1). At D21, weight-for-age z score was significantly higher in nHMF compared to cHMF (0.12 [95% CI: 0.03, 0.22]). Length and HC gains during the D1 to D21 period were not significantly different between groups (Table 3), with comparable results observed from analyses of unadjusted means (Table 4). Length-for-age z scores at D21 (Fig. 3) were significantly lower than D1 values in cHMF (P\u200a=\u200a0.041). Additionally, at W40CA, adjusted HC-for-age z scores were significantly higher in nHMF compared to cHMF (0.41 [95% CI: 0.14, 0.68]). Mean weight, length, and HC at D1, D21, and W40CA are summarized in Table 5.FIGURE 3Mean\u200a±\u200aSD weight-for-age (panel A), length-for-age (panel B), and head circumference-for-age (panel C) z scores for the overall ITT population. Circle symbols/solid line represents nHMF. Triangle symbols/dashed line represents cHMF. FSI1\u200a=\u200afortification strength increase day 1; ITT\u200a=\u200aintent-to-treat; SD\u200a=\u200astandard deviation; W40CA\u200a=\u200aweek 40 corrected age; z scores calculated using Fenton preterm growth chart (25). ∗P\u200a=\u200a0.013 vs cHMF (by analysis of covariance, adjusting for value at D1, sex, and center); †P\u200a=\u200a0.007 vs day 1 (by t test); ‡P\u200a=\u200a0.041 vs day 1 (by t test); ∗∗P\u200a=\u200a0.003 vs cHMF (by analysis of covariance, adjusting for value at D1, sex, and center).Protein-Energy StatusBUN decreased progressively in cHMF (P\u200a=\u200a0.004 for D21 vs D1), whereas it increased in nHMF (P\u200a<\u200a0.001 for D10/11 vs D1 [data not shown]) and remained stable up to D21 (Table 6). Prealbumin levels were similar at D1 and increased in both groups during the study (Table 6). The increase from D1 to D21, however, was only significant in nHMF (P\u200a=\u200a0.004). At D21, adjusted mean prealbumin in nHMF was significantly higher (+11.8% [95%CI: +2.3%, +22.2%]) than in cHMF. Urinary urea excretion (corrected for creatinine excretion) at D1 was similar in the 2 groups (Table 6). Urea excretion remained steady in cHMF but increased sharply in nHMF (P\u200a<\u200a0.001 for D10/11 vs D1 [data not shown]), after which it remained stable (to D21). At D21, urea excretion was significantly higher in nHMF versus cHMF (+108.7% [95% CI: +66.0%, +162.5%]).Bone Metabolic StatusSerum calcium concentrations were generally stable during the study (Table 6), with mean values for both groups within the normal range (32). Nevertheless, adjusted mean serum calcium concentration in nHMF was minimally but significantly higher than in cHMF at D21 (+1.9% [95% CI: +0.3%, +3.5%]). Serum phosphorus increased slightly in the 2 groups (Table 6). At D1, relative hypophosphatemia (<1.55\u200ammol/L) was observed in 13 infants in both groups; this was corrected in 11 infants by D10/11 and 12 infants by D21. At D1, serum alkaline phosphatase was not significantly different in nHMF versus cHMF (P\u200a=\u200a0.208). Thereafter, serum alkaline phosphatase decreased significantly in both groups (D21 vs D1: P\u200a=\u200a0.005 for nHMF, P\u200a<\u200a0.001 for cHMF), with mean values significantly higher in nHMF versus cHMF at D10/11 (+8.6% [95% CI: +1.0%, +16.8%]; data not shown) and D21 (+12.1% [95% CI: +2.8%, +22.3%]) (Table 6). Declines from baseline were significantly greater in cHMF versus nHMF at D10/11 (P\u200a<\u200a0.001; data not shown) and D21 (P\u200a=\u200a0.035). At D1, spot urinary excretions of calcium and phosphorus corrected for urinary creatinine excretion were similar in the 2 groups (Table 6). Calcium excretion tended to increase slowly during the study in both groups, with mean concentration significantly lower in nHMF compared to cHMF at D21 (P\u200a=\u200a0.011). Phosphorus excretion increased in both groups, resulting in a decreased median urinary calcium:phosphorus molar ratio in both groups (Table 6).ElectrolytesSerum electrolyte concentrations were stable during the study and similar in both groups (Table 6). Urinary sodium and potassium concentrations were significantly higher (sodium: +31.1% [95% CI: +1.7%, +68.9%], potassium: +22.5% [95% CI: +1.0%, +48.6%]) in nHMF compared to cHMF at D21 (Table 7).Stool Characteristics and Feeding ToleranceStool frequency from D1 to D21 was not significantly different in nHMF and cHMF (3.9\u200a±\u200a1.05 vs 3.6\u200a±\u200a0.93\u200astools/day; 0.29 [95% CI: −0.05, 0.63]). Stool consistency was slightly more “formed” in nHMF compared to cHMF during this interval (3.1\u200a±\u200a0.26 vs 3.0\u200a±\u200a0.27; 0.12 [95% CI: 0.02, 0.21]). Most infants (>90%) had stool consistency scores of “soft.” There were no significant differences between groups in frequencies of spitting-up, vomiting, or abdominal distention. There also were no group differences in incidence of AEs indicative of feeding intolerance (all P\u200a≥\u200a0.25).Adverse EventsThe overall incidence of AEs was significantly larger in nHMF (103 events in 56 infants, including 26 events categorized as GI disorders, 18 as infections or infestations, and 5 as metabolism and nutrition disorders) compared to cHMF (78 events in 41 infants, including 21 events categorized as GI disorders, 18 as infections or infestations, and 1 as metabolism and nutrition disorder; odds ratio: 2.26 [95% CI: 1.10, 4.47]). Other AEs that occurred more frequently in nHMF included several that were classified by study investigators as unlikely to be related to consumption of milk fortifiers (eg, cardiac disorders [16 events in nHMF vs 5 in cHMF], eye disorders [10 events in nHMF vs 3 in cHMF]). The number of AEs considered related to study product intake as determined by physician report was low (3 events in nHMF [2 events of hyponatremia, 1 of vomiting] and 0 events in cHMF). No significant difference was demonstrated in overall incidence of serious AEs between the 2 groups (7 events in 7 infants [including 2 events of necrotizing enterocolitis, 0 events of bronchopulmonary dysplasia, 0 events of sepsis, 0 events of retinopathy] in nHMF and 12 events in 11 subjects [including 4 events of necrotizing enterocolitis, 1 event of bronchopulmonary dysplasia, 0 events of sepsis, 0 events of retinopathy] in cHMF; odds ratio: 0.54 [95% CI: 0.17, 1.58]).DISCUSSIONThis study demonstrated that weight gain from D1 of full fortification until D21 in preterm infants fed HM fortified with a new fortifier designed to add 1.4\u200ag partially hydrolyzed protein and 0.7\u200ag fat to 100\u200amL of HM was significantly greater than weight gain in infants fed HM fortified with an isocaloric control fortifier designed to add 1.0\u200ag extensively hydrolyzed protein and no fat. The mean difference was 2.3\u200ag/day or 1.2\u200ag\u200a·\u200akg−1\u200a·\u200aday−1, consistent with our hypothesized difference of 2\u200ag/day, and which indicates the superiority of the new fortifier compared to the control with regard to weight gain. In addition, the weight gain benefit tended to persist until discharge, with a significantly higher adjusted weight gain difference in the nHMF group compared to cHMF from FSI1 to W40CA (2.01\u200ag/day; P\u200a=\u200a0.009). In the nHMF group, weight-for-age z scores were stable from FSI1 to D21 and average weight gain exceeded 18\u200ag\u200a·\u200akg−1\u200a·\u200aday−1, matching recommended rates of postnatal weight gain to mimic intrauterine growth (33,34). Consistent with the increased protein content of the new fortifier, the nHMF group had significantly higher serum prealbumin concentrations, suggesting an increase in nitrogen retention compared to cHMF. The lack of difference, however, in length gain during the study may be in part the result of the relatively limited period of protein supplementation (only 21 days) or because mean length gains in both groups were already quite high (ie, ≥1.1\u200acm/week), whereas the significantly higher HC-for-age z score at W40CA in the nHMF group may be because of the increased protein and lipid content of the new fortifier. In contrast, the absence of a significant difference at earlier timepoints could be attributable to the relatively high variability of HC gain (31% and 27% for nHMF and cHMF, respectively, from D1 to D21) induced by the natural dolichocephalic evolution of the skull that occurs in preterm infants (35). Feeding tolerance and stool patterns were similar in each group, and AEs related to feeding were low and not significantly different between groups, consistent with fortified HM osmolality values slightly lower in nHMF versus cHMF and below the recommended cutoff (23,24) in both groups.Although there was no evidence of imbalance between the 2 fortifier groups with respect to infant baseline characteristics, significant differences in maternal weight gain, smoking, and alcohol usage during pregnancy were observed. As these may be confounding factors in the analysis of weight gain, post hoc ANCOVAs including these parameters were performed. The post hoc results were essentially the same as the main results, indicating that differences in maternal baseline characteristics did not confound the results. Additionally, to determine the possible impact of including clustered data from twins in the analyses, a sensitivity analysis on weight gain (grams per day) from D1 to D21 accounting for the correlated multiple-birth data was performed. Again, these results were similar to those of the main analysis (weight gain 3.2\u200ag/day higher in nHMF [95% CI: 0.5, 5.9\u200ag/day]).Our results are consistent with those of previous studies (36–42). A recent meta-analysis of 5 studies (comprising 352 infants with birthweight ≤1750\u200ag and gestational age ≤34 weeks) compared growth of infants fed HM fortified with either lower-protein or higher-protein fortifier (43). Infants receiving higher-protein fortifier had significantly greater weight (mean difference 1.77\u200ag/kg/day), length (0.21\u200acm/week), and HC gains (0.19\u200acm/week) compared to those receiving lower-protein fortifier (43). Miller et al (39) used a higher-protein fortifier similar in protein content to the one used in the present study, and reported a higher bodyweight at study end among infants in the higher-protein HMF group (mean difference 220\u200ag), but no significant differences in length or HC. In contrast, Moya et al (40) observed a significantly higher achieved weight, length, and HC in the experimental group compared to controls, but their fortifier had a slightly higher protein content (3.2\u200ag/100\u200amL) versus the one used in the present study (3.04\u200ag/100\u200amL), plus the intervention lasted 28 rather than 21 days.Energy and protein content of HM samples were not analyzed in this study but estimated according to Tsang et al (22). Variability of protein, fat, and energy content of HM fed to preterm infants in the NICU is high (15,21). In addition, fat content may be reduced during processing of HM from expression to administration (44), which could be exacerbated with the use of continuous tube feeding (45). In our study, percentage of intake from mother's own milk, donor milk, and pasteurized HM was assessed. Pasteurized donor milk accounted for 51% of the fortified HM provided during the study, whereas 56% of mother's own milk was also pasteurized. Considering that protein content of donor HM is lower than that of mother's own milk (46) and that all the required processing steps (eg, collection, transfer, refrigeration, pasteurization, tube feeding) may significantly decrease fat and energy content (47), the characteristics of the HM used in the present study suggests that protein and energy content could be overestimated when based on a theoretical composition of preterm HM.In the present study, the mean increase in protein supplementation provided by nHMF compared to cHMF was 0.65\u200ag\u200a·\u200akg−1\u200a·\u200aday−1 or 7.4\u200ammol\u200a·\u200akg−1\u200a·\u200aday−1 of nitrogen, from which approximately 6.14\u200ammol\u200a·\u200akg−1\u200a·\u200aday−1 of nitrogen (83%) is absorbed (based on data from balance studies) (48). During the study, urea production increased significantly in the nHMF group leading to an increase in BUN of 1.7\u200ammol/L at D21 and in urea excretion of 2.3\u200ammol\u200a·\u200akg−1\u200a·\u200aday−1 (2.3\u200ammol/10\u200amg creatinine). These data suggest that the nitrogen balance was improved to ∼3.8\u200ammol nitrogen (52% of nitrogen intake) in preterm infants fed nHMF compared to control. This relatively limited protein utilization could result from reduced energy bioavailability of HM, and an increase in energy supply could improve protein utilization in preterm infants fed fortified HM. These data also suggest that specific nutritional recommendations should be formulated for infants fed fortified HM. Nevertheless, the increase in nitrogen retention (∼3.8\u200ammol\u200a·\u200akg−1\u200a·\u200aday−1) appears to be higher than the nitrogen content of the higher weight gain observed with the nHMF (12% of the 1.5\u200ag\u200a·\u200akg−1\u200a·\u200aday−1 corresponding to 2\u200ammol\u200a·\u200akg−1\u200a·\u200aday−1 of nitrogen), suggesting an increase in lean body mass accretion and a moderate reduction in fat mass gain as previously demonstrated in preterm infants fed protein-fortified HM (49).Indices of bone metabolism were satisfactory in both groups, with a significant decrease in serum alkaline phosphatase observed in both groups and 98% of the infants having normal serum phosphorus concentrations at D21. Adequate postnatal bone mineralization is difficult to obtain in preterm infants owing to the interruption of mineral transplacental transfer (50). Although elevated alkaline phosphatase activity may be associated with reduced bone mineralization when mineral intake is deficient (51), the decrease in enzyme levels observed in the presence of normal serum phosphorus values, as well as the low urinary calcium and moderate urinary phosphorus excretion observed in both groups in this study, suggest that intakes were adequate to promote bone mineralization and limit postnatal osteopenia. Mean serum creatinine concentration decreased significantly in both groups suggesting a similar maturation of renal function during this period. Urinary electrolyte concentrations were higher in nHMF versus cHMF at D21, likely in parallel with the higher electrolyte content of nHMF.A lack of HM composition data (allowing estimation of nutritional balance) is a limitation of our study, although standardized accurate techniques are still not available in the NICU. Additionally, the composition of the faster weight gain can only be estimated as lean body mass and/or bone mineralization were not determined. As a result, nutrient absorption and metabolism can only be estimated from serum and urinary metabolite concentrations. Lastly, the results need to be confirmed in a broader population of preterm infants commonly admitted to the NICU including SGA infants and partially breast-fed infants, as these infants were excluded by design. Strengths of this study include the size and multiple sites (11 pediatric hospitals in 4 European countries), which enhances external validity.In conclusion, these results indicate that the new HM fortifier, made with partially hydrolyzed whey protein and a higher protein:energy ratio is safe, well-tolerated, and improves weight gain of preterm infants compared to control fortifier. Providing some energy as fat and replacing extensively hydrolyzed with partially hydrolyzed protein in the new HM fortifier allows a reduction in osmolality <400\u200amOsm/kg immediately after fortification. Protein intakes from HM supplemented with the new fortifier are within the range of the most recent nutritional recommendations for preterm infants.AcknowledgmentsThe authors thank the families of the infants who participated in the study, as well as the research staff at each participating institution. The authors also thank Christelle Perdrieu and Samir Dahbane from the Clinical Development Unit at the Nestlé Research Center for assistance with trial management and Philippe Steenhout, Medical Director at Nestlé Nutrition, for input on study design and assistance with trial supervision.This study was sponsored by Nestlé Nutrition. J.J., L.A., and N.P.H. are employees of Nestlé SA. J.R., J.M.H., C.B., J.C.P., F.M., A.R., E.S., M.R., U.S., B.G., and J.S. received research funding from Nestlé Nutrition. J.R., J.C.P., and C.B. are consultants for Nestlé Nutrition. U.S. has been a speaker, consultant, and expert panel participant for Nestlé, Danone, and Bledina over the past 3 years. V.d.H. has no conflicts of interest to declare.www.clinicaltrials.gov NCT01771588This study was sponsored by Nestlé Nutrition.Portions of these data were presented in abstract form at the 1st Congress of joint European Neonatal Societies, Budapest, Hungary, 15–20 September 2015.REFERENCES1.GarciaCDuanRDBrevaut-MalatyV\nBioactive compounds in human milk and intestinal health and maturity in preterm newborn: an overview. Cell Mol Biol (Noisy-le-grand)\n2013; 59:108–131.253266482.CorpeleijnWEKouwenhovenSMPaapMC\nIntake of own mother's milk during the first days of life is associated with decreased morbidity and mortality in very low birth weight infants during the first 60 days of life. Neonatology\n2012; 102:276–281.229226753.PatelALJohnsonTJEngstromJL\nImpact of early human milk on sepsis and health-care costs in very low birth weight infants. J Perinatol\n2013; 33:514–519.233706064.ManzoniPStolfiIPedicinoR\nHuman milk feeding prevents retinopathy of prematurity (ROP) in preterm VLBW neonates. Early Hum Dev\n2013; 89\nsuppl 1:S64–S68.238093555.KooWTankSMartinS\nHuman milk and neurodevelopment in children with very low birth weight: a systematic review. Nutr J\n2014; 13:94.252313646.CarlsonSWojcikBBarkerA\nGuidelines for the use of human milk fortifier in the neonatal intensive care unit. University of Iowa Neonatology Handbook. 2011. Available at: http://www.uichildrens.org/iowa-neonatology-handbook/feeding/human-milk\nAccessed on January 22, 2017.7.AdamkinDHRadmacherPG\nFortification of human milk in very low birth weight infants (VLBW <1500\u200ag birth weight). Clin Perinatol\n2014; 41:405–421.248738408.MoroGEArslanogluSBertinoE\nXII. Human milk in feeding premature infants: consensus statement. J Pediatr Gastroenterol Nutr\n2015; 61\nsuppl 1:S16–S19.262959999.EinloftPRGarciaPCPivaJP\nSupplemented vs. unsupplemented human milk on bone mineralization in very low birth weight preterm infants: a randomized clinical trial. Osteoporos Int\n2015; 26:2265–2271.2597168610.GibertoniDCorvagliaLVandiniS\nPositive effect of human milk feeding during NICU hospitalization on 24 month neurodevelopment of very low birth weight infants: an Italian cohort study. PLoS ONE\n2015; 10:e0116552.2559063011.BrownJVEmbletonNDHardingJE\nMulti-nutrient fortification of human milk for preterm infants. Cochrane Database Syst Rev\n2016; 5:CD000343.12.SchanlerRJShulmanRJLauC\nFeeding strategies for premature infants: beneficial outcomes of feeding fortified human milk versus preterm formula. Pediatrics\n1999; 103\n(6 pt 1):1150–1157.1035392213.O’ConnorDLJacobsJHallR\nGrowth and development of premature infants fed predominantly human milk, predominantly premature infant formula, or a combination of human milk and premature formula. J Pediatr Gastroenterol Nutr\n2003; 37:437–446.1450821414.WeberALouiAJochumF\nBreast milk from mothers of very low birthweight infants: variability in fat and protein content. Acta Paediatr\n2001; 90:772–775.1151998015.CorvagliaLAcetiAPaolettiV\nStandard fortification of preterm human milk fails to meet recommended protein intake: bedside evaluation by near-infrared-reflectance-analysis. Early Hum Dev\n2010; 86:237–240.2044777916.ArslanogluSMoroGEZieglerEE\nPreterm infants fed fortified human milk receive less protein than they need. J Perinatol\n2009; 29:489–492.1944423717.ArslanogluSCorpeleijnWMoroG\nDonor human milk for preterm infants: current evidence and research directions. J Pediatr Gastroenterol Nutr\n2013; 57:535–542.2408437318.AgostoniCBuonocoreGCarnielliVP\nEnteral nutrient supply for preterm infants: commentary from the European Society for Paediatric Gastroenterology, Hepatology, and Nutrition Committee on Nutrition. J Pediatr Gastroenterol Nutr\n2010; 50:85–91.1988139019.KoletzkoBPoindexterBUauyR\nRecommended nutrient intake levels for stable, fully enterally fed very low birth weight infants. World Rev Nutr Diet\n2014; 110:297–299.2475163820.GidrewiczDAFentonTR\nA systematic review and meta-analysis of the nutrient content of preterm and term breast milk. BMC Pediatr\n2014; 14:216.2517443521.de HalleuxVRigoJ\nVariability in human milk composition: benefit of individualized fortification in very-low-birth-weight infants. Am J Clin Nutr\n2013; 98\nsuppl:529S–535S.2382472522.TsangRCUauyRKoletzkoB\nNutrition of the Preterm Infant, Scientific Basis and Practical Guidelines. Cincinnati: Digital Educational Publishing, Inc; 2005.23.KreisslAZwiauerVRepaA\nEffect of fortifiers and additional protein on the osmolarity of human milk: is it still safe for the premature infant?\nJ Pediatr Gastroenterol Nutr\n2013; 57:432–437.2385734024.BilleaudCSenterreJRigoJ\nOsmolality of the gastric and duodenal contents in low birth weight infants fed human milk or various formulae. Acta Paediatr Scand\n1982; 71:799–803.718044925.FentonTRKimJH\nA systematic review and meta-analysis to revise the Fenton growth chart for preterm infants. BMC Pediatr\n2013; 13:59.2360119026.NewmanDJPugiaMJLottJA\nUrinary protein and albumin excretion corrected by creatinine and specific gravity. Clin Chim Acta\n2000; 294:139–155.1072768027.Al-DahhanJStimmlerLChantlerC\nUrinary creatinine excretion in the newborn. Arch Dis Child\n1988; 63:398–402.336500928.ICH Expert Working Group. Guideline for good clinical practice E6(R1). 1996\nAvailable at: http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Efficacy/E6/E6_R1_Guideline.pdf\nAccessed on January 22, 2017.29.SpalingerJHSchmidtMBergerTM\nComparison of two human milk fortifiers: effects on growth and zinc status in premature infants. J Pediatr Gastroenterol Nutr\n2004; 39\nsuppl 1:1126.30.WangSKTsiatisAA\nApproximately optimal one-parameter boundaries for group sequential trials. Biometrics\n1987; 43:193–199.356730431.KnottnerusJASpigtMG\nWhen should an interim analysis be unblinded to the data monitoring committee?\nJ Clin Epidemiol\n2010; 63:350–352.1976221032.NicholsonJFPesceMA\nNelsonWEBehrmanREKliegmanRArvinAM\nLaboratory Testing and Reference Values (Table 670-2) in Infants and Children. Nelson Textbook of Pediatrics. Philadelphia: W.B. Saunders; 1996\n2031–2084.33.FentonTRNasserREliasziwM\nValidating the weight gain of preterm infants between the reference growth curve of the fetus and the term infant. BMC Pediatr\n2013; 13:92.2375880834.EhrenkranzRADusickAMVohrBR\nGrowth in the neonatal intensive care unit influences neurodevelopmental and growth outcomes of extremely low birth weight infants. Pediatrics\n2006; 117:1253–1261.1658532235.McCartyDBPeatJRMalcolmWF\nDolichocephaly in preterm infants: prevalence, risk factors, and early motor outcomes. Am J Perinatol\n2016; 34:372–378.2758893336.PorcelliPSchanlerRGreerF\nGrowth in human milk-fed very low birth weight infants receiving a new human milk fortifier. Ann Nutr Metab\n2000; 44:2–10.1083846037.ReisBBHallRTSchanlerRJ\nEnhanced growth of preterm infants fed a new powdered human milk fortifier: a randomized, controlled trial. Pediatrics\n2000; 106:581–588.1096910638.BersethCLVan AerdeJEGrossS\nGrowth, efficacy, and safety of feeding an iron-fortified human milk fortifier. Pediatrics\n2004; 114:e699–e706.1554561639.MillerJMakridesMGibsonRA\nEffect of increasing protein content of human milk fortifier on growth in preterm infants born at <31 wk gestation: a randomized controlled trial. Am J Clin Nutr\n2012; 95:648–655.2230193340.MoyaFSiskPMWalshKR\nA new liquid human milk fortifier and linear growth in preterm infants. Pediatrics\n2012; 130:e928–e935.2298787741.AlanSAtasayBCakirU\nAn intention to achieve better postnatal in-hospital-growth for preterm infants: adjustable protein fortification of human milk. Early Hum Dev\n2013; 89:1017–1023.2403503942.ThoeneMHansonCLydenE\nComparison of the effect of two human milk fortifiers on clinical outcomes in premature infants. Nutrients\n2014; 6:261–275.2439453843.LiuTTDangDLvXM\nHuman milk fortifier with high versus standard protein content for promoting growth of preterm infants: A meta-analysis. J Int Med Res\n2015; 43:279–289.2595615644.VieiraAASoaresFVPimentaHP\nAnalysis of the influence of pasteurization, freezing/thawing, and offer processes on human milk's macronutrient concentrations. Early Hum Dev\n2011; 87:577–580.2159268845.IgawaMMuraseMMizunoK\nIs fat content of human milk decreased by infusion?\nPediatr Int\n2014; 56:230–233.2484751446.WojcikKYRechtmanDJLeeML\nMacronutrient analysis of a nationwide sample of donor breast milk. J Am Diet Assoc\n2009; 109:137–140.1910333547.de HalleuxVPeiltainCSanterreT\nUse of donor milk in the neonatal intensive care unit. Semin Fetal Neonatal Med\n2017; 22:23–29.2764999548.PicaudJCPutetGRigoJ\nMetabolic and energy balance in small- and appropriate-for-gestational-age, very low-birth-weight infants. Acta Paediatr Suppl\n1994; 405:54–59.773479249.PutetGRigoJSalleB\nSupplementation of pooled human milk with casein hydrolysate: energy and nitrogen balance and weight gain composition in very low birth weight infants. Pediatr Res\n1987; 21:458–461.358808250.PieltainCde HalleuxVSenterreT\nPrematurity and bone health. World Rev Nutr Diet\n2013; 106:181–188.2342869951.RuskC\nRickets screening in the preterm infant. Neonatal Netw\n1998; 17:55–57.TABLE 1Calculated∗ nutrient composition of fortified preterm human milkPreterm HM\u2009+\u2009nHMFPreterm HM\u2009+\u2009cHMF4\u2009g fortifier alone4\u2009g fortifier per 100\u2009kcal milk4\u2009g fortifier per 100\u2009mL milk5\u2009g fortifier alone5\u2009g fortifier per 100\u2009kcal milk5\u2009g fortifier per 100\u2009mL milkRecommended intake range (per 100\u2009kcal)†NutrientEnergy, kcal17.410084.617.410084.5Protein, g1.423.63.041.03.102.623.2–4.1Protein sourcePartially hydrolyzed wheyExtensively hydrolyzed wheyFat, g0.725.004.230.024.163.524.4–6MCT, g0.500.590.50000DHA, mg6.319.316.3011.810.0(16.4–) 50–55Carbohydrate, g1.3010.178.603.3012.5310.6010.5–12Carbohydrate sourceMaltodextrinLactose and maltodextrinCalcium, mg7611910175118100109–182Phosphorus, mg44695845705955–127Magnesium, mg4.08.67.32.46.75.77.3–13.6Sodium, mg36.776.564.720.056.848.063–105Potassium, mg48.4116.498.442.0108.892.071–177Chloride, mg32.1106.690.117.088.775.095–161Iron, mg1.802.231.891.301.641.391.8–2.7Zinc, mg0.941.551.310.801.381.171.3–2.3Manganese, μg8.089.988.445.006.345.360.9–13.6Copper, mg0.050.110.090.040.090.080.09–0.21Iodine, μg16.936.630.915.034.329.09–50Selenium, μg3.77.26.11.54.63.94.5–9Vitamin A, IU1183175414835009468001217–3333Vitamin D, IU150187158100128108100–350Vitamin E, IU4.45.64.72.23.02.52.2–11.1Vitamin K, μg8.09.88.34.05.14.34–25Thiamin, mg0.150.190.160.050.070.060.13–0.27Riboflavin, mg0.200.270.230.100.150.130.18–0.36Vitamin B6, mg0.130.160.140.050.070.060.05–0.27Vitamin B12, μg0.200.260.220.100.140.120.09–0.73Niacin, mg1.502.021.710.801.191.010.9–5Folic acid, μg40.051.043.140.051.043.132–91Pantothenic acid, mg0.701.100.930.400.740.630.45–1.9Biotin, μg3.504.784.043.004.193.541.5–15Vitamin C, mg20.028.924.410.017.014.418–50Osmolality‡, mOsm/kg390441cHMF\u2009=\u2009control human milk fortifier; DHA\u2009=\u2009docosahexaenoic acid; HM\u2009=\u2009human milk; nHMF\u2009=\u2009new human milk fortifier; MCT\u2009=\u2009medium chain triglycerides.*Calculated based on preterm human milk composition from Tsang et al, 2005 (22).†Recommended nutrient intakes for fully enterally fed preterm very low birth weight infants (19).‡Measured immediately after fortification at room temperature (25°C).TABLE 2Demographic and baseline characteristics of infants and parentsnHMF (n\u2009=\u200976)cHMF (n\u2009=\u200974)Infant characteristicsSex\u2003Boys38 (50)35 (47)Delivery type\u2003Vaginal24 (32)20 (27)Twin18 (24)16 (22)Birth weight, g1147\u2009±\u20092581156\u2009±\u2009289Birth weight by birth weight category\u2003<1000\u2009g\u2003\u2003n (%)24 (32)26 (35)\u2003\u2003Birth weight, g850.5\u2009±\u2009118.9847.3\u2009±\u2009105.1\u2003≥1000\u2009g\u2003\u2003Birth weight, g1283.6\u2009±\u2009175.41323.9\u2009±\u2009206.2Birth length, cm37.1\u2009±\u20092.737.1\u2009±\u20093.1Birth head circumference, cm26.5\u2009±\u20092.726.7\u2009±\u20092.5Gestational age at birth, weeks28.8\u2009±\u20092.128.7\u2009±\u20091.8Postnatal age at study time points, days*\u2003FSI113 (11, 18)14 (10, 20)\u2003Day 116 (13, 20)17 (13, 23)\u2003Day 2136 (33, 40)37 (33, 43)\u2003Week 40 corrected age76 (66, 91)76 (67, 83)Apgar score\u20031 min5.8\u2009±\u20092.55.8\u2009±\u20092.3\u20035 min8.0\u2009±\u20091.87.7\u2009±\u20091.9Parent characteristicsSmoking status\u2003Mother smoker during pregnancy6 (9)18 (29)\u2003Father smoker3 (5)12 (21)\u2003Mother drank alcohol during pregnancy0 (0)4 (6)Mother's age, y31.1\u2009±\u20095.130.8\u2009±\u20095.5Mother's BMI before pregnancy, kg/m2*23.2 (20.6, 27.2)21.3 (19.7, 26.1)Mother's weight gain during pregnancy, kg11.2\u2009±\u20096.89.2\u2009±\u20095.2BMI\u2009=\u2009body mass index; cHMF\u2009=\u2009control human milk fortifier; FSI1\u2009=\u2009fortification strength increase day 1; nHMF\u2009=\u2009new human milk fortifier . Data are presented as n (%) for categorical variables and mean\u2009±\u2009SD for continuous variables except where noted.*Data are presented as median (Q1, Q3).TABLE 3Anthropometric gains from D1 to D21Treatment groupnnHMFncHMFP*Weight gain, g\u2009·\u2009kg−1\u2009·\u2009day−16418.3\u2009±\u20093.76716.8\u2009±\u20093.70.013†Length gain, cm/wk551.23\u2009±\u20090.62651.18\u2009±\u20090.490.842HC gain, cm/wk571.04\u2009±\u20090.32650.96\u2009±\u20090.260.125cHMF\u2009=\u2009control human milk fortifier; D1\u2009=\u2009study day 1 (first day of full-strength fortification); D21\u2009=\u2009study day 21; HC\u2009=\u2009head circumference; nHMF\u2009=\u2009new human milk fortifier. Data are presented as unadjusted mean\u2009±\u2009SD.*One-sided superiority P value based on analysis of covariance model adjusted for postmenstrual age and relevant anthropometric measure at D1, sex, and center.†Adjusted difference in weight gain (nHMF–cHMF): mean difference\u2009=\u20091.18\u2009g\u2009·\u2009kg−1\u2009·\u2009day−1; 95% CI\u2009=\u20090.14, 2.21.TABLE 4Body length and head circumference gains between study days 1 and 21, by infant sex and by birth weight categoryUnadjusted length gain, cm/wk*Unadjusted head circumference gain, cm/wk*nHMFcHMFnHMFcHMFnMean\u2009±\u2009SDnMean\u2009±\u2009SDP†nMean\u2009±\u2009SDnMean\u2009±\u2009SDP†Overall551.23\u2009±\u20090.62651.18\u2009±\u20090.490.842571.04\u2009±\u20090.32650.96\u2009±\u20090.260.126Boys271.40\u2009±\u20090.65281.18\u2009±\u20090.490.364281.12\u2009±\u20090.28280.99\u2009±\u20090.220.062Girls281.08\u2009±\u20090.56371.17\u2009±\u20090.500.510290.97\u2009±\u20090.35370.93\u2009±\u20090.290.598<1000\u2009g191.07\u2009±\u20090.52211.27\u2009±\u20090.520.563191.04\u2009±\u20090.34210.94\u2009±\u20090.280.223≥1000\u2009g361.32\u2009±\u20090.66441.13\u2009±\u20090.480.499381.05\u2009±\u20090.32440.96\u2009±\u20090.260.270cHMF\u2009=\u2009control human milk fortifier; nHMF\u2009=\u2009new human milk fortifier.*Data are presented as unadjusted mean\u2009±\u2009SD.†Superiority P value for gain differences adjusted for postmenstrual age and the relevant anthropometric measure at D1, sex, and center by analysis of covariance.TABLE 5Weight, length, and head circumference at selected study time pointsnHMFcHMFVariablenMeanSDnMeanSDWeight, g\u2003D1721346271741347270\u2003D21641884336671863328\u2003W40CA603076519632897416Length, cm\u2003D16738.72.57438.72.8\u2003D215841.82.46542.02.7\u2003W40CA6047.62.66247.32.5Head circumference, cm\u2003D16827.72.57327.61.9\u2003D215930.22.26630.32.0\u2003W40CA5935.31.46434.61.5cHMF\u2009=\u2009control human milk fortifier; D1\u2009=\u2009study day 1; D21\u2009=\u2009study day 21; nHMF\u2009=\u2009new human milk fortifier; SD\u2009=\u2009standard deviation; W40CA\u2009=\u2009week 40 corrected age.TABLE 6Markers of protein-energy status, electrolytes, and bone metabolic status at study days 1 and 21nHMFcHMFVariablenMedianIQRGeometric meannMedianIQRGeometric meanP*Serum creatinine, μmol/L\u2003D16944.036.2–48.041.57044.138.0–51.843.50.303\u2003D216328.023.5–32.026.76530.025.0–35.029.50.001BUN, mmol/L\u2003D1703.101.70–4.562.89712.501.65–4.672.730.585\u2003D21633.903.05–4.653.89642.151.50–2.632.15<0.001Serum prealbumin, mg/L\u2003D15110080–12096.8469080–10087.80.073\u2003D214611691.3–140113.84110090–12098.10.015Urinary urea†, mmol/10\u2009mg creatinine\u2003D1472.72.0–4.72.8532.51.9–3.32.50.302\u2003D21425.84.6–6.85.1402.82.0–3.32.7<0.001Serum calcium, mmol/L\u2003D1502.442.31–2.532.41542.472.38–2.562.440.445\u2003D21502.472.40–2.542.46482.432.34–2.532.430.019Serum phosphorus, mmol/L\u2003D1681.991.85–2.221.96711.941.76–2.251.940.816\u2003D21622.101.93–2.232.05642.121.93–2.262.080.681Alkaline phosphatase, U/L\u2003D167353.0298.5–459.5377.963333.0250.0–438.5343.80.208\u2003D2162320.5273.3–405.5337.562270.5233.0–354.3297.50.010Urinary calcium †, mmol/10\u2009mg creatinine\u2003D1600.110.07–0.190.12690.140.09–0.200.120.985\u2003D21550.140.09–0.230.15540.210.13–0.320.190.011Urinary phosphorus†, mmol/10\u2009mg creatinine\u2003D1590.410.12–0.660.22650.340.14–0.650.230.867\u2003D21520.680.44–1.100.53520.710.40–0.920.580.896Urinary calcium:phosphorus molar ratio\u2003D1590.390.15–0.900.50640.410.16–1.340.470.824\u2003D21530.220.12–0.480.28530.310.19–0.600.340.054Serum sodium, mmol/L\u2003D171138.0137.0–140.0138.672138.6136.6–140.0138.50.891\u2003D2165138.0136.4–140.0138.064138.0137.0–139.9138.30.449Serum potassium, mmol/L\u2003D1714.734.30–5.324.83724.774.40–5.104.780.685\u2003D21644.744.29–5.104.72644.514.14–4.884.540.091Serum chloride, mmol/L\u2003D171106.0104.0–109.0106.172105.0102.8–108.0105.20.148\u2003D2163105.0103.0–107.0104.662105.0104.0–107.0105.30.111BUN\u2009=\u2009blood urea nitrogen; cHMF\u2009=\u2009control human milk fortifier; D1\u2009=\u2009study day 1; D21\u2009=\u2009study day 21; IQR\u2009=\u2009interquartile range; nHMF\u2009=\u2009new human milk fortifier.*D1 geometric mean values were log-transformed and analyzed using t test; D21 geometric mean values were log-transformed and analyzed using analysis of covariance (adjusting for the relevant biochemical parameter at D1, sex, and center).†Corrected for urinary creatinine excretion of 10\u2009mg/kg body weight/day.TABLE 7Markers of kidney function, blood count, and urinary electrolyte status at study days 1 and 21nHMFcHMFVariablenMedianIQRGeometric meannMedianIQRGeometric meanP*Urinary creatinine, μmol/L\u2003D1631300.0785.5–1685.51224.7691105.0900.0–1500.01182.3\u2003D21571030.0660.0–1609.01000.355854.0618.0–1273.0900.80.447Serum hemoglobin, mmol/L\u2003D1682.081.84–2.292.14722.021.84–2.262.18\u2003D21631.711.56–1.911.83661.691.50–1.981.760.936Serum hematocrit, %\u2003D1680.400.35–0.430.39720.390.35–0.430.38\u2003D21630.320.29–0.380.33660.330.28–0.380.330.805Urinary sodium, mmol/L\u2003D16637.023.3–57.337.56932.019.4–54.031.2\u2003D215934.021.1–48.033.35623.014.3–36.424.00.037Urinary potassium, mmol/L\u2003D16625.913.6–37.023.66921.815.0–32.220.0\u2003D215930.016.9–45.027.65722.916.9–30.422.80.040Urinary chloride, mmol/L\u2003D16037.026.3–60.040.26733.020.5–55.034.2\u2003D215431.017.8–43.830.75526.018.0–39.527.80.558cHMF\u2009=\u2009control human milk fortifier; D1\u2009=\u2009study day 1; D21\u2009=\u2009study day 21; IQR\u2009=\u2009interquartile range; nHMF\u2009=\u2009new human milk fortifier .*D21 geometric mean values were log-transformed and analyzed using analysis of covariance (adjusting for the relevant biochemical measure at D1, sex, and center)."", 'title': 'Growth and Nutritional Biomarkers of Preterm Infants Fed a New Powdered Human Milk Fortifier: A Randomized Trial.', 'date': '2017-07-21'}}",0.0,Pediatrics & Neonatology
+17,"Is head circumference gain higher, lower, or the same when comparing high protein concentration to low protein concentration?",uncertain effect,very low,no,"['26488118', '22301933', '22987877', '29772833', '28727654']",33215474,2020,"{'26488118': {'article_id': '26488118', 'content': ""J Pediatr Gastroenterol NutrJ. Pediatr. Gastroenterol. NutrJPGAJournal of Pediatric Gastroenterology and Nutrition0277-21161536-4801Lippincott Williams & Wilkins26488118464595610.1097/MPG.000000000000101000012Original Articles: NutritionGrowth and Tolerance of Preterm Infants Fed a New Extensively Hydrolyzed Liquid Human Milk FortifierKimJae H.∗ChanGary†SchanlerRichard‡Groh-WargoSharon§BloomBarry||DimmitReed¶WilliamsLarry#BaggsGeraldine#Barrett-ReisBridget#∗University of California, San Diego-Rady Children's Hospital of San Diego, San Diego†University of Utah, Salt Lake City‡Cohen Children's Medical Center of New York, New Hyde Park§Case Western Reserve University, MetroHealth Medical Center, Cleveland, OH||Wesley Medical Center, Wichita, KS¶University of Alabama, Birmingham#Abbott Nutrition, Columbus, OH.Address correspondence and reprint requests to Jae H. Kim, MD, PhD, University of California, San Diego, 200 W Arbor Dr, MPF 1140, San Diego, CA 92103 (e-mail: neojae@ucsd.edu).12201524112015616665671212201512102015Copyright 2015 by ESPGHAN and NASPGHAN. Unauthorized reproduction of this article is prohibited.2015This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License, where it is permissible to download and share the work, provided it is properly cited. The work cannot be changed in any way or used commercially. http://creativecommons.org/licenses/by-nc-nd/4.0ABSTRACTObjectives:This study was a comparison of growth and tolerance in premature infants fed either standard powdered human milk fortifier (HMF) or a newly formulated concentrated liquid that contained extensively hydrolyzed protein.Methods:This was an unblinded randomized controlled multicenter noninferiority study on preterm infants receiving human milk (HM) supplemented with 2 randomly assigned HMFs, either concentrated liquid HMF containing extensively hydrolyzed protein (LE-HMF) or a powdered intact protein HMF (PI-HMF) as the control. The study population consisted of preterm infants ≤33 weeks who were enterally fed HM. Infants were studied from the first day of HM fortification until day 29 or hospital discharge, whichever came first.Results:A total of 147 preterm infants were enrolled. Noninferiority was observed in weight gain reported in the intent-to-treat (ITT) analysis was 18.2 and 17.5 g · kg−1 · day−1 for the LE-HMF and PI-HMF groups, respectively. In an a priori defined subgroup of strict protocol followers (n\u200a=\u200a75), the infants fed LE-HMF achieved greater weight over time than those fed PI-HMF (P\u200a=\u200a0.036). The LE-HMF group achieved greater linear growth over time compared to the PI-HMF (P\u200a=\u200a0.029). The protein intake from fortified HM was significantly higher in the LE-HMF group compared with the PI-HMF group (3.9 vs 3.3 g · kg−1 · day−1, P\u200a<\u200a0.0001). Both fortifiers were well tolerated with no significant differences in overall morbidity.Conclusions:Both fortifiers showed excellent weight gain (grams per kilograms per day), tolerance, and low incidence of morbidity outcomes with the infants who were strict protocol followers fed LE-HMF having improved growth during the study. These data point to the safety and suitability of this new concentrated liquid HMF (LE-HMF) in preterm infants. Growth with this fortifier closely matches the recent recommendations for a weight gain of >18 g · kg−1 · day−1.Keywordsbreast-feedinggrowthhuman milk fortifierpreterm infantsOPEN-ACCESSTRUEWhat Is KnownPowdered infant milk products cannot be sterilized and is a source of bacterial infection.Very-low-birth-weight infants often require more protein than presently can be provided with conventional human milk fortifiers.A liquid fortifier with higher protein than conventional fortifiers is desirable to increase safety and improved growth.What Is NewA liquid human milk fortifier that is based on extensively hydrolyzed bovine casein with higher amounts of total protein than powder human milk fortifiers confers equal to improved growth to very-low-birth-weight infantsUse of this new liquid fortifier provides sterility without any increase in feeding intolerance or short-term adverse effects.Human milk (HM) is a source of essential nutrients and immunologic factors for the preterm infant, but alone it is not sufficient nutritionally to meet the high demands of the rapidly growing infant. Human milk fortifiers (HMFs) are nutritional supplements designed to increase total energy, protein, and micronutrient delivery to preterm infants. The primary benefits of HM fortification have been improved growth, bone mineralization, and protein status such as blood urea nitrogen (BUN) (1–5).Increasing survival and improving growth of the preterm infant to avoid extrauterine growth restriction have resulted in demands for protein that present powdered HMF may not achieve. Although some of these infants may compensate with higher volume intake, many are unable to consume a sufficient volume because of pulmonary or other clinical issues and therefore require further concentration of protein and energy. Higher intake of protein between 3 and 4 g · kg−1 · day−1 has been associated with improved growth without complications compared with a lower consumption of protein (<3 g · kg−1 · day−1) (6). Poor weight gain has been associated with a higher risk for retinopathy of prematurity and poor neurodevelopmental outcomes (7,8). It is common practice in the neonatal intensive care units (NICUs) to add protein modular (powder or liquid) to the feeding to better meet the protein needs of the smaller preterm infant. In fact, 42% of the respondents to a recent survey on nutritional practices in the NICU reported adding protein to HM (9).There has been a gradual transition to sterile liquid nutritionals in the neonatal environment during the last decade because of concerns about powder-based transmission of pathogens such as Cronobacteria sakasakii(10). The recent development of a liquid HM–based HMF and a partially hydrolyzed whey-acidified liquid HMF respond to these concerns (11,12). Unlike powder nutritionals, a liquid HMF may have the advantage of sterility and simpler liquid-liquid mixing with human milk (HM). One disadvantage of a liquid fortifier is volume displacement of HM.In this study, we evaluated a novel liquid HMF containing extensively hydrolyzed protein source to determine efficacy and safety in very-low-birth-weight preterm infants.METHODSStudy PopulationA total of 14 NICUs from across the United States participated in this study, including Tampa, Florida; Wichita, Kansas; Toledo, Ohio; Salt Lake City, Utah; Birmingham, Alabama; Cleveland, Ohio; Allentown, Pennsylvania; San Diego, California; Valhalla, New York; Manhasset, New York; Portland, Oregon; Cleveland, Ohio; South Bend, India; and Brooklyn, New York. The study population consisted of preterm infants born at ≤33 weeks’ gestational age with birth weights ranging from 700 to 1500 g who were enterally fed HM in the NICU. Infants identified as eligible for randomization and for whom consent was obtained were randomly assigned to one of the 2 study regimens. Sealed envelopes containing the subject treatment group assignment were prepared from randomization schedules that were computer-generated using a pseudorandom permuted blocks algorithm. A separate computer-generated randomization schedule was produced for twins to ensure that eligible twins were both assigned to the same product. The randomization was block stratified by birth weight (700–1000 g and 1000–1500\u200ag) and sex.Eligibility criteria included appropriate intrauterine growth and maternal intent to provide breast milk during the study. The use of donor HM was not permitted during the study period unless indicated by the clinical staff or PI but could have been used in the first week of life before study initiation. Infants were excluded for enteral feeds not started within 21 days of life, severe congenital anomalies, expectant transfer to another facility, 5-minute Apgar <5, severe intraventricular hemorrhage (grade 3 or 4), mechanical ventilation, major abdominal surgery, severe asphyxia, and necrotizing enterocolitis (NEC). Use of probiotics or postnatal corticosteroids was not permitted.Study DesignThis was an unblinded randomized controlled multicenter study conducted on preterm infants receiving HM supplemented with 2 randomly assigned HMFs, either a newly formulated concentrated liquid HMF containing extensively hydrolyzed protein (Abbott Nutrition, Columbus, OH; LE-HMF) or a conventional powdered intact protein HMF (Similac Human Milk Fortifier, PI-HMF, Abbott Nutrition) as control. For every 25 mL of HM, HMF was added as a 5-mL dose of LE-HMF or 1 single packet of PI-HMF. Study Day (SDAY) 1 was defined as the first day of HM fortification and occurred within 72 hours after the subject had reached an intake of at least 100 mL · kg−1 · day−1 of HM. The primary study period was from SDAY 1 until SDAY 29 or hospital discharge, whichever came first. This study was approved by institutional research ethics board as appropriate at each study sites. Table 1 shows the key study fortifier differences.Anthropometric indices (weight, length, and head circumference [HC]), tolerance, serum biochemistries, intake, and morbidity data were assessed. Anthropometric variables and tolerance outcomes were collected after SDAY 29 if the infant remained on study HMF.Weight, length, and HC of infants were measured according to standardized procedures from SDAY 1 to SDAY 29 or hospital discharge, whichever came first. Weight measures were taken daily using the hospital scales (incubator or bedside). Documentation of scale calibration was reviewed during routine visits. The other anthropometric measurements were performed weekly. Recumbent length was obtained with a fixed headboard and moveable footboard and HC using a nonstretchable tape.Feeding tolerance was assessed by variables such as stool characteristics (bloody, hard, black, and/or watery) and the incidence of feedings withheld because of abdominal distention, gastric residuals, and vomiting. Any nil per os periods were also collected.Enteral intake was collected from enrollment to SDAY 29. Intake of HM (including donor/banked HM) or other enteral feeding (including supplements such as protein modulars) were recorded. Although the LE-HMF contained the same amount of energy as the PI-HMF, it contained higher protein and a different source of protein. It also contained added lutein, docosahexaenoic acid, and arachidonic acid.Blood samples were drawn from each infant by venipuncture or, if necessary, by heelstick on SDAYs 1, 15, and 29. Serum electrolytes, bicarbonate, calcium, phosphorus, magnesium, alkaline phosphatase, BUN, and prealbumin were analyzed at the hospital site. Confirmed NEC (determined by using modified Bell staging criteria) and sepsis were recorded. The occurrence of these and other serious adverse events was documented throughout the study.Statistical AnalysisStudy data were analyzed on an intent-to-treat (ITT) basis including all enrolled infants who received study fortifier. Based on anticipated protocol deviations in this high-risk population, a subgroup analysis was prospectively planned to analyze data from infants who strictly adhered to the assigned HMF. The strict protocol followers (SPFs) were defined a priori as those infants who received <20% of total energy from sources other than the assigned study HMF; and <3 consecutive days on modular supplements (eg, protein supplements, another study HMF, nonstudy formula, or donor milk) for at least 2 weeks from SDAY 1 to SDAY 29.Sample size was calculated to test the hypothesis that LE-HMF was noninferior to PI-HMF using an equivalence limit of 1.6 g · kg−1 · day−1 in weight gain per day. With a noninferiority hypothesis and assuming that the expected difference in means is zero and the common standard deviation is 2.56 g · kg−1 · day−1, the total sample size required to have 80% power was 66 subjects who are SPF (33 per group). The power for this unbalanced sample size distribution is 83%. Assuming an attrition rate of approximately 46%, the target number for enrollment was 124 subjects (62 per group). A study designed for noninferiority does not preclude testing for superiority (13). Weight gain (grams per kilogram per day) for each subject was calculated by an exponential model that involved a regression line fit on loge (wt), where wt is weight (in grams) on each day (13). Weight gain (grams per kilogram per day) was analyzed using analysis of variance with factors for center and feeding (primary). Analyses were also made adjusting for sex, birth weight, and average fortified HM intake (milliliters per kilogram per day) diluted full strength during the study period. A 95% 1-sided confidence interval for the difference in means between groups was used for noninferiority evaluation.Length (centimeters per week) and HC gains (centimeters per week) were analyzed using the same models. Weight, length, and HC collected at 1-week intervals were analyzed with repeated measures analysis of covariance (ANCOVA) testing effects of center, feeding, sex, study day, interaction of feeding with sex, feeding with study day, and covariate birth weight. By time point analyses of weight, length, and HC using ANCOVA were made post-hoc using 1-sided tests consistent with a noninferiority design.Average daily volume enteral intake (milliliters per kilogram per day) was analyzed using analysis of variance. Complete blood cell counts with differential and serum blood biochemistries were analyzed using repeated measures ANCOVA with covariate SDAY 1 measure.Outcomes expressed as percent of infants (tolerance, morbidity, and respiratory variables) were analyzed using the Cochran-Mantel-Haenzsel test stratified by center. The frequencies of occurrence of adverse events by system organ class and preferred terms using MedDRA codes were tabulated and analyzed using Fisher exact test. Hypothesis testing for this study was done using 2-sided, 0.05 level tests. All analyses were made using SAS version 9.2 (SAS Institute, Cary, NC) on a computer.RESULTSStudy PopulationA total of 147 subjects were randomized into the study. Of the 147 subjects, 129 were included in the ITT group, that is, all randomized subjects who received study HMF. Of those subjects in the ITT group, 75% completed the study duration (45 PI-HMF, 52 LE-HMF). More than half the infants in the ITT group met the definition for the SPFs (Fig. 1). The number of days on the assigned study fortifier was 25 and 29 for the PI-HMF (n\u200a=\u200a63) and LE-HMF (n\u200a=\u200a66) groups, respectively. The median number of days on the assigned study fortifier for SPF was 29 days for both the PI-HMF and LE-HMF groups as some extended their use beyond the study period. Of note, some SPF subjects did not complete the study duration because they were discharged from the hospital.FIGURE 1Disposition of subjects.Demographic and Other Baseline CharacteristicsCharacteristics of the study patients are summarized in Table 2. There were no statistically significant differences among study subjects randomized to the PI-HMF or the LE-HMF group in gestational age, sex, race, mode of delivery and multiple birth status. There were, however, more Hispanic infants in the PI-HMF as compared to the LE-HMF group (28% vs 13%, P\u200a=\u200a0.041). In addition, there were no statistical differences between groups at birth or SDAY 1 for weight, length, and HC. Furthermore, there were no differences in clinical history and progression of enteral feeds. Infants in the 2 feeding groups who were SPF reflect comparable demographic and baseline characteristics patterns.GrowthThere were no statistical differences in the primary outcome of weight gain (grams per kilogram per day) during the study period regardless of whether the statistical analysis was performed on the ITT group or SPFs. Hence, noninferiority was achieved. Respective weight gains were 17.5 and 18.2 g · kg−1 · day−1 for PI-HMF and LE-HMF (Table 3). Likewise in the subgroup (SPF) analysis weight gains were 18.2 and 18.4 g · kg−1 · day−1 for PI-HMF and LE-HMF. There was, however, a main feeding effect that was the infants fed LE-HMF compared with infants fed PI-HMF had increased weight during the study among SPFs as depicted in Fig. 2A (P\u200a=\u200a0.036). When analyzing the data at separate time points the weight at SDAY 29 was significantly higher in LE-HMF group versus the PI-HMF group (P\u200a=\u200a0.024). Likewise, infants in the ITT group fed LE-HMF had higher weights at SDAYs 15, 22, and 29 than infants fed PI-HMF whether or not adjusted for differences in ethnicity. The SPF infants receiving LE-HMF reached 1800 g 7 days sooner than the infants fed PI-HMF (19 vs 26 days, respectively, P\u200a=\u200a0.049).FIGURE 2Evaluable analysis: A, weight (in grams); B, length (in centimeters); C, head circumference (in centimeters). A, Weight (in grams). Repeated measures analysis main effect, P\u200a=\u200a0.036; post-hoc per time point analysis: SDAY 29, P\u200a=\u200a0.024. B, Length (in centimeters). Repeated measures analysis main effect, P\u200a=\u200a0.029; post-hoc per time point analysis: SDAY 22, P\u200a=\u200a0.006, SDAY 29, P\u200a=\u200a0.037. C, Head circumference (in centimeters).The length and HC gains (centimeters per week) during the study period revealed no statistical differences between the groups and met growth targets (Table 3). The infants fed LE-HMF compared with infants fed PI-HMF had increased linear growth during the study among SPFs as depicted in Fig. 2B (P\u200a=\u200a0.029). When analyzing the data at separate time points adjusted for birth length, the length at SDAY 22 and SDAY 29 were significantly higher in LE-HMF group versus the PI-HMF group (P\u200a<\u200a0.05). HC was not different between the fortifier groups (Fig. 2C).Feeding Tolerance and Stool CharacteristicsIn both the ITT and SPF groups, both fortifiers were well tolerated with similar number and percentage of infants having feedings withheld because of abdominal distention, gastric residuals and/or vomiting. There was no difference in the percentage of infants who were nil per os between the groups (22.7 LE-HMF, 19 PI-HMF). The stool characteristics in both groups were similar with no differences in bloody stools, hard stools or black stools. Loose stools were commonly reported—56% in the PI-HMF group and 53% in the LE-HMF group—and were considered normal for infants who are receiving HM as their primary feeding.Enteral NutritionThe mean caloric and protein intakes are reported for both HMF groups. For the SPFs, the average percentage of calories from fortified HM was ∼96% in both the PI-HMF and LE-HMF groups. The mean intake of fortified HM was 116 and 114 kcal · kg−1 · day−1 in the PI-HMF and LE-HMF groups, respectively. The calculated protein intake from fortified HM was significantly higher in the LE-HMF group as compared to the PI-HMF group (3.9 vs 3.3\u200a g · kg−1 · day−1, P\u200a<\u200a0.0001). This difference was expected as LE-HMF contains more protein than PI-HMF. Energy intakes were not different between the groups.Blood ChemistriesThe blood chemistries reported in Table 4 include bicarbonate, BUN, prealbumin, calcium, phosphorus, magnesium, alkaline phosphatase, and electrolytes. In general, the blood biochemistries at SDAYs 1, 15, and 29 were within the normal reference ranges for preterm infants for both the ITT and SPF groups fed milk fortified with either fortifier (14,15). There were significant differences between groups in both the ITT and SPF analyses for BUN (P\u200a<\u200a0.001) and prealbumin (P\u200a<\u200a0.01), with both being higher in the LE-HMF group. Both groups were well within reference ranges for these parameters. Bicarbonate was significantly higher in the LE-HMF group only at SDAY1 in the ITT analysis.Safety and Morbidity DataIn the ITT group, fewer infants discontinued fortifier because of feeding intolerance in the LE-HMF group as compared to the PI-HMF group (2% vs 10%, P\u200a=\u200a0.048). There was a low incidence of confirmed NEC (1.5% in the LE-HMF group and 3.2% in the PI-HMF group) and confirmed sepsis (4.5% vs 3.2%, respectively)DISCUSSIONThe purpose of developing LE-HMF was to provide a concentrated liquid fortifier that would be superior to conventional powder HMF by virtue of sterility, higher protein concentration, and absence of intact cow's-milk protein. An extensively hydrolyzed protein source is included to promote feeding tolerance in preterm infants. The extensively hydrolyzed protein may be tolerated better for infants who are sensitive to the intact cow's-milk protein.The primary purpose of the present clinical trial was to assess whether the new HMF would promote targeted weight gain, with good tolerance and without association with specific comorbidities in a noninferiority comparison with a commercially available powder HMF that has demonstrated safety and efficacy in preterm infants (13).Weight gain and linear growth approaching intrauterine rates are important goals in the management of premature infants. The mean weight gain for both groups (PI-HMF and LE-HMF) exceeded the intrauterine growth rate of 15 g · kg−1 · day−1 and closely matched recent recommendations for a weight gain of >18 g · kg−1 · day−1(7). The mean HC gain for both groups also closely matched recent recommendations for a HC gain of >0.9 cm/wk (7). This result was not surprising given the excellent weight, length, and HC gains previously reported in infants fed PI-HMF powder (13).Ehrenkranz et al (7) have reported that as the rate of weight gain increased in hospitalized preterm infants, the incidence of cerebral palsy, neurodevelopmental impairment, and need for re-hospitalization decreased significantly. A weight gain rate of >18 g · kg−1 · day−1 and a HC growth rate of >0.9 cm/wk were associated with better neurodevelopmental and growth outcomes. Lower quartile growth was associated with the poorest neurodevelopmental outcomes.Weight and length differed between the groups. Although there were no significant differences in mean weight at birth or SDAY 1, infants receiving LE-HMF had ∼½ lb greater mean weight than the infants in the PI-HMF group at the end of the study period. Although the rate of linear growth was not statistically different, infants in the LE-HMF group had greater achieved linear growth during the study period. It is possible that the greater weight and length in the LE-HMF infants was because of the higher number of infants in this group that adhered to the assigned study feeding.New expert recommendations suggest that extremely-low-birth-weight infants (<1000 g birth weight) have higher protein requirements (3.5–4.5 g/100 kcal) (16). HMFs provide an important strategy to overcoming nutrient deficits for preterm and low-birth-weight infants. Differences in the level and ingredient sources of the macronutrients, especially the protein quantity, in PI-HMF versus LE-HMF may have contributed to the overall performance of the LE-HMF group. The higher protein intake in infants receiving LE-HMF (∼3.6 g/100 kcal) as compared to PI-HMF (∼3.0 g/100 kcal) was likely one of the reasons for the improved growth observed in these infants. Although infants in the LE-HMF group had higher protein intakes, energy intakes were not different between the groups.Preterm infants fed fortified HM have variable rates of growth at least partly because of differences in intake of calories, carbohydrates, electrolytes, calcium, phosphate, and protein. The acid-base status of the preterm infant also, however, affects growth. In preterm infants the kidney may not tolerate an acid load, leading to the development of metabolic acidosis. In a recent study, a liquid acidified HMF caused metabolic acidosis and poor growth in preterm infants in the NICU (17,18). In another study, Rochow et al (19) described a commercially available fortifier in Europe that had to be reformulated because of the development of metabolic acidosis from an imbalance of electrolytes. The authors recorded a mean weight gain of only 9.7 g · kg−1 · day−1 and decreased bone mineralization with metabolic acidosis. No infants in our study developed metabolic acidosis.The LE-HMF protein source may be beneficial for this population because it was extensively hydrolyzed casein formulation without any intact cow's-milk protein. It has been suggested that a combination of free amino acids and short chain peptides (di- and tri peptides) may allow more optimal nitrogen absorption (20,21). Intact bovine protein powder HMF has an excellent safety record; however, a recent study by Sullivan et al (11) suggested the possibility that even in the presence of a HM base diet, the addition of intact bovine protein powder HMF is associated with higher rates of total and surgical NEC. The mechanism for the higher NEC risk is not known yet. Although this study was not powdered for NEC there was no difference in the NEC or sepsis rates between the infants fed an intact bovine protein and the extensively hydrolyzed protein. Both groups had rates lower than previously reported (22–24).Intact bovine protein has higher associated long-term risk for allergy and atopy compared with HM-fed infants. Protein intolerance is seen in premature infants and in term infants (25). Because preterm infants have a similar risk for allergy and atopy compared with term infants and in the NICU have presented with symptoms suggestive of allergic colitis, avoiding intact bovine protein may be a desirable objective. For preterm infants fed HM the use of an extensively hydrolyzed protein-based HMF is an appropriate option.In general, blood chemistries were within normal reference ranges for preterm infants. The higher BUN and prealbumin seen in the LE-HMF group can be attributed to the higher protein content of LE-HMF. These higher values may be indicative of improved protein nutriture. It should be noted that although BUN is influenced by renal function and hydration state, all other influences being equal, it is proportional to protein intake and responds rapidly to changes in protein intake (4,5,26,27).Postnatal growth failure remains common in premature infants. Nearly 25 years ago Kashyap et al showed that even a small deficit in protein intake impairs both growth in lean body mass and linear growth (28). In recent years, Arslanoglu et al reported that addition of protein to preterm feedings of recovering VLBW infants resulted in significantly improved linear growth (4,5). This was accomplished by monitoring the BUN level so that when it was less than 9\u200amg/dL, increased protein was added to their feedings. It was observed in the present study that the mean BUN level fell <9 mg/dL by week 2 in infants receiving PI-HMF; however, in infants receiving LE-HMF it never fell <9 mg/dL during the entire study period. Our results, in part, agree with other investigators that an increased protein-to-calorie ratio in the feeds of preterm infants will improve linear growth (4,5,9,28). It is becoming increasingly evident that promoting catch-up growth in the NICU may have implications for long-term development and health (7,29).Our study did have several limitations. The study examined the combined effects of changing both protein content and type (hydrolyzed vs intact). Future studies may want to capture effects of changing one of these variables. A number of subjects in this study did not complete the protocol to SDAY 29. This partially diluted the effects seen in the ITT groups but still permitted demonstration of differential effects seen in the SPF subgroup. A larger study design may improve this in the future. Infants <700 g birth weight were excluded from this study and therefore the study findings cannot be readily extrapolated to this vulnerable group. It is expected however that this group would have higher protein demands than infants in this study and therefore would be as likely or more to have a favorable response to higher protein. Although no differences were seen between both groups for NEC and sepsis the study size was too small to discern true differences for these outcomes.CONCLUSIONSBoth fortifiers showed excellent tolerance and a low rate of morbidity outcomes, with the infants who were SPFs fed LE-HMF having improved growth. These data confirm the safety and suitability of this new concentrated liquid HMF for preterm infants.AcknowledgmentsThe authors thank the following individuals for their hard work and dedication: Coryn Commare, MS, RD; Christy Saulters, BS; Debra Lee-Butcher, BSN, RN; Holy Boyko, BSN, RN; Angela Worley; Carolyn Richardson; Sue Zhang, MS, MAS; Mustafa Vurma, PhD; Maggie Hroncich, BS; Aimee Diley; Kristen Fithian; Sue Nicholson, MS, RD; and Jennifer Teran, BS, RD. The authors also thank study investigators and their staff for their cooperation: Terri Ashmeade, MD; Anthony Killian, MD; Lance Parton, MD; Robert Schelonka, MD; Robert White, MD; Ivan Hand, MD, FAAP; Michelle Walsh, MD; Jeffrey Blumer, PhD, MD; Paula Delmore, RN; Carrie Rau, RN; Renee Bridge, RN; Lisa Lepis, RN; Judy Zaritt, RN; Claire Roane, RN, MSN; Julie Gualtier, RN; Diane Fierst, RN; Christina Gogal; Natalie Dweck; Debra Potak, RN; Barbara Wilkens, RN; Nakia Clay, BS; Mashelle Monhaut, NNP-BC; Rickey Taing, NPL; Susan Bergant, RN, CCRP; and Bonnie Rosolowski, RPT.www.clinicaltrials.gov registration number: NCT01373073.This study was funded by Abbott Nutrition.J.H.K., B.B., G.C., R.S. and S.G.-W. received research funds from the study sponsor, Abbott Nutrition, to conduct the study. J.H.K. is on the speakers’ bureaus for Abbott Nutrition, Mead Johnson Nutrition, Nestle Nutrition, Nutricia, and Medela. J.H.K. and R.S. are on the medical advisory board for Medela. J.H.K. owns shares in PediaSolutions and has provided medical expert testimony. B.B. received a grant from the Wichita Medical Research and Education Foundation. G.C. received a research grant from the University of Utah and has provided medical expert testimony. S.G.-W. is on the speakers’ bureau of Abbott Nutrition. B.B.-R., L.W., and G.B. are employees of Abbott Nutrition.The authors report no conflicts of interest.REFERENCES1.SchanlerRJ\nSuitability of human milk for the low-birthweight infant. Clin Perinatol\n1995; 22:207–222.77812532.SchanlerRJAbramsSA\nPostnatal attainment of intrauterine macromineral accretion rates in low birth weight infants fed fortified human milk. J Pediatr\n1995; 126:441–447.78692083.KuschelCAHardingJE\nMulticomponent fortified human milk for promoting growth in preterm infants. Cochrane Database Syst Rev\n2004; 1:CD000343.149739534.ArslanogluSBertinoECosciaA\nUpdate of adjustable fortification regimen for preterm infants: a new protocol. J Biol Regul Homeost Agents\n2012; 26\n(3 suppl):65–67.231585175.ArslanogluSMoroGEZieglerEE\nAdjustable fortification of human milk fed to preterm infants: does it make a difference?\nJ Perinatol\n2006; 26:614–621.168859896.PremjiSSFentonTRSauveRS\nHigher versus lower protein intake in formula-fed low birth weight infants. Cochrane Database Syst Rev\n2006; 1:CD003959.164374687.EhrenkranzRADusickAMVohrBR\nGrowth in the neonatal intensive care unit influences neurodevelopmental and growth outcomes of extremely low birth weight infants. Pediatrics\n2006; 117:1253–1261.165853228.VanderveenDKMartinCRMehendaleR\nEarly nutrition and weight gain in preterm newborns and the risk of retinopathy of prematurity. PLoS One\n2013; 8: e64325.9.WhitfieldJPunjabi-GuptaSHendriksonH\nImproved linear growth in VLBW infants at discharge: impact of increasing the protein/kcal ratio (PCR) of feeds. E-PAS Abstract\n2012; 4510:122.10.TaylorC\nHealth Professionals Letter on Enterobacter sakazakii Infections Associated With the Use of Powdered (Dry) Infant Formulas in Neonatal Intensive Care Units. Bethesda, MD: US Food and Drug Administration, Center for Food Safety and Applied Nutrition, Office of Nutritional Products, Labeling and Dietary Supplements; 2002.11.SullivanSSchanlerRJKimJH\nAn exclusively human milk-based diet is associated with a lower rate of necrotizing enterocolitis than a diet of human milk and bovine milk-based products. J Pediatr\n2010; 156:562–567.2003637812.MoyaFSiskPMWalshKR\nA new liquid human milk fortifier and linear growth in preterm infants. Pediatrics\n2012; 130:e928–e935.2298787713.Barrett-ReisBHallRTSchanlerRJ\nEnhanced growth of preterm infants fed a new powdered human milk fortifier: a randomized, controlled trial. Pediatrics\n2000; 106:581–588.1096910614.The Harriet Lane Handbook (The Johns Hopkins Hospital). 19th ed. New York: Elsevier Health Sciences; 2011: chap 27.15.RamelSEGeorgieffMK\nNutrition. In: Avery's Neonatology—Pathophysiology and Management of the Newborn. 7th ed. Philadelphia: Lippincott Williams & Wilkins; 2015.16.KoletzkoBPoindexterBUauyR\nRecommended nutrient intake levels for stable, fully enterally fed very low birth weight infants. World Rev Nutr Diet\n2014; 110:297–299.2475163817.ThoeneMHansonCLydenE\nComparison of the effect of two human milk fortifiers on clinical outcomes in premature infants. Nutrients\n2014; 6:261–275.2439453818.CibulskisCCArmbrechtES\nAssociation of metabolic acidosis with bovine milk-based human milk fortifiers. J Perinatol\n2015; 35:115–119.2510232119.RochowNJochumFRedlichA\nFortification of breast milk in VLBW infants: metabolic acidosis is linked to the composition of fortifiers and alters weight gain and bone mineralization. Clin Nutr\n2011; 30:99–105.2072762620.GrimbleGKKeohanePPHigginsBE\nEffect of peptide chain length on amino acid and nitrogen absorption from two lactalbumin hydrolysates in the normal human jejunum. Clin Sci (Lond)\n1986; 71:65–69.370907621.BozaJJMartinez-AugustinOBaroL\nProtein v. enzymic protein hydrolysates. Nitrogen utilization in starved rats. Br J Nutr\n1995; 73:65–71.785791622.PatoleS\nPrevention and treatment of necrotising enterocolitis in preterm neonates. Early Hum Dev\n2007; 83:635–642.1782600923.FanaroffAAStollBJWrightLL\nTrends in neonatal morbidity and mortality for very low birthweight infants. Am J Obstet Gynecol\n2007; 196:147e1-8.1730665924.StollBJHansenNIBellEF\nNeonatal outcomes of extremely preterm infants from the NICHD Neonatal Research Network. Pediatrics\n2010; 126:443–456.2073294525.D’NettoMAHersonVCHussainN\nAllergic gastroenteropathy in preterm infants. J Pediatr\n2000; 137:480–486.1103582526.ZieglerEE\nBreast-milk fortification. Acta Paediatr\n2001; 90:720–723.1151997227.PolbergerSKAxelssonIERaihaNC\nUrinary and serum urea as indicators of protein metabolism in very low birthweight infants fed varying human milk protein intakes. Acta Paediatr Scand\n1990; 79:737–742.223926628.KashyapSSchulzeKFForsythM\nGrowth, nutrient retention, and metabolic response in low birth weight infants fed varying intakes of protein and energy. J Pediatr\n1988; 113:713–721.313985629.HansonCSundermeierJDugickL\nImplementation, process, and outcomes of nutrition best practices for infants <1500\u200ag. Nutr Clin Pract\n2011; 26:614–624.2194764530.EhrenkranzRAYounesNLemonsJA\nLongitudinal growth of hospitalized very low birth weight infants. Pediatrics\n1999; 104\n(2 Pt 1):280–289.10429008TABLE 1Approximate nutrient composition of PI-HMF or LE-HMF added to HMNutrient PI-HMFLE-HMFEnergy, cal100100Fat, g5.25.1CHO, g10.410.1Protein, g33.6Source/type of proteinIntact whey protein concentrateExtensively hydrolyzed caseinDHA, mg1224Vitamin D, IU150150Calcium, mg175153Phosphorus, mg9886Osmolality, mOsm/kg water385450Lutein, μg*23Values per 100 calories mixed at a ratio of 1 pkt or 5 mL:25 mL HM (as fed). CHO\u2009=\u2009carbohydrate; DHA\u2009=\u2009docosahexaenoic acid; HM\u2009=\u2009human milk; LE-HMF \u2009=\u2009liquid HMF containing extensively hydrolyzed protein; PI-HMF\u2009=\u2009powdered intact protein HMF.*Lutein not added to product but available in varying amounts from HM.TABLE 2Neonatal and perinatal characteristics of preterm infantsTreatment group*PI-HMF (n\u2009=\u200963)LE-HMF (n\u2009=\u200966)Gestational age, wk28.7\u2009±\u20090.228.8\u2009±\u20090.2Birth weight, g1156\u2009±\u2009241193\u2009±\u200926Birth length, cm37.4\u2009±\u20090.337.7\u2009±\u20090.3Birth HC, cm26.1\u2009±\u20090.226.5\u2009±\u20090.2Male sex, n (%)35 (56)36 (55)Ethnicity: Hispanic, n (%)17 (28)8 (13)†Race, n (%)\u2003White42 (67)43 (65)\u2003Black13 (21)17 (26)\u2003Asian1 (2)1 (2)\u2003Other7 (11)3 (5)\u2003White/other0 (0)2 (3)C-section, n (%)38 (60)42 (64)Twin, n (%)16 (25)12 (18)Age at study day 1, d12.3\u2009±\u20090.712.8\u2009±\u20090.6Birth class, n (%)\u2003≤1000\u2009g16 (24)12 (19)\u2003>1000\u2009g66 (76)63 (81)LE-HMF\u2009=\u2009liquid HMF containing extensively hydrolyzed protein; PI-HMF\u2009=\u2009powdered intact protein HMF.*Mean\u2009±\u2009SEM.†P\u2009=\u20090.0407.TABLE 3Anthropometric gainsTreatment group*Targeted growth†,‡PI-HMF (n\u2009=\u200963)LE-HMF (n\u2009=\u200966)Weight gain, g kg−1 day−117.5\u2009±\u20090.618.2\u2009±\u20090.3>18Length gain, cm/wk1.2\u2009±\u20090.071.2\u2009±\u20090.06>0.9HC gain, cm/wk1.0\u2009±\u20090.041.0\u2009±\u20090.05>0.9LE-HMF\u2009=\u2009liquid HMF containing extensively hydrolyzed protein; PI-HMF\u2009=\u2009powdered intact protein HMF; HC\u2009=\u2009head circumference.*Intent-to-treat group, n\u2009=\u2009129.†Ehrenkranz et al (7).‡Ehrenkranz et al (30).TABLE 4Blood chemistry dataCharacteristicsReference rangesStudy dayTreatment group*PI-HMFLE-HMFBicarbonate, mEq/L†17–24123.27\u2009±\u20090.45 (59)25.05\u2009±\u20090.45 (62)1524.32\u2009±\u20090.50 (49)25.40\u2009±\u20090.39 (58)2925.04\u2009±\u20090.43 (40)25.54\u2009±\u20090.44 (50)BUN, mg/dL‡2.5–31.4111.47\u2009±\u20090.78 (56)11.89\u2009±\u20091.03 (61)158.30\u2009±\u20091.15 (50)11.72\u2009±\u20090.68 (58)295.81\u2009±\u20090.38 (40)9.31\u2009±\u20090.53 (49)Prealbumin, mg/dL§7.0–39.0110.05\u2009±\u20090.37 (58)9.69\u2009±\u20090.33 (54)1510.11\u2009±\u20090.37 (47)11.40\u2009±\u20090.41 (46)299.08\u2009±\u20090.35 (36)10.01\u2009±\u20090.35 (37)Calcium, mg/dL8.0–11.0110.10\u2009±\u20090.08 (56)9.93\u2009±\u20090.08 (60)159.93\u2009±\u20090.10 (50)9.95\u2009±\u20090.07 (57)299.89\u2009±\u20090.09 (40)9.82\u2009±\u20090.06 (49)Phosphorus, mg/dL4.2–8.716.41\u2009±\u20090.17 (54)6.20\u2009±\u20090.13 (58)156.71\u2009±\u20090.13 (46)6.50\u2009±\u20090.12 (56)296.66\u2009±\u20090.10 (40)6.46\u2009±\u20090.12 (47)Magnesium, mg/dL1.5–2.111.90\u2009±\u20090.03 (54)1.88\u2009±\u20090.02 (59)151.80\u2009±\u20090.03 (47)1.86\u2009±\u20090.03 (55)291.81\u2009±\u20090.02 (40)1.82\u2009±\u20090.03 (46)Alkaline phosphatase, U/L150–4001443.89\u2009±\u200924.50 (55)415.40\u2009±\u200915.78 (60)15366.13\u2009±\u200921.80 (48)332.68\u2009±\u200910.87 (57)29335.28\u2009±\u200921.84 (40)342.36\u2009±\u200913.10 (47)Sodium, mEq/L129–1431137.49\u2009±\u20090.49 (61)138.42\u2009±\u20090.34 (65)15137.46\u2009±\u20090.55 (52)137.56\u2009±\u20090.29 (59)29139.07\u2009±\u20090.41 (41)138.70\u2009±\u20090.40 (50)Potassium, mEq/L4.5–7.115.39\u2009±\u20090.11 (61)5.20\u2009±\u20090.09 (65)155.25\u2009±\u20090.09 (52)5.23\u2009±\u20090.09 (59)295.25\u2009±\u20090.10 (41)5.06\u2009±\u20090.07 (50)Chloride, mEq/L100–1171104.16\u2009±\u20090.60 (58)104.03\u2009±\u20090.55 (63)15104.10\u2009±\u20090.72 (49)103.88\u2009±\u20090.43 (57)29106.00\u2009±\u20090.57 (40)106.14\u2009±\u20090.37 (49)BUN\u2009=\u2009blood urea nitrogen; LE-HMF\u2009=\u2009liquid HMF containing extensively hydrolyzed protein; PI-HMF\u2009=\u2009powdered intact protein HMF; HC\u2009=\u2009head circumference.*Values are mean\u2009±\u2009SEM (n).†Bicarbonate (mEq/L): (SDAY 1) LE-HMF > PI-HMF, P\u2009=\u20090.0419, LSM\u2009±\u2009SE: LE-HMF\u2009=\u200924.71\u2009±\u20090.56, PI-HMF\u2009=\u200923.33\u2009±\u20090.62.‡BUN (mg/dL): Feeding main effect: LE-HMF > PI-HMF, P\u2009=\u20090.0013, LSM\u2009±\u2009SE: LE-HMF\u2009=\u200911.99\u2009±\u20090.73, PI-HMF\u2009=\u20098.99\u2009±\u20090.83.§Prealbumin (mg/dL): Feeding main effect: LE-HMF > PI-HMF, P\u2009=\u20090.0049, LSM\u2009±\u2009SE: LE-HMF\u2009=\u200910.61\u2009±\u20090.35, PI-HMF\u2009=\u20099.32\u2009±\u20090.38."", 'title': 'Growth and Tolerance of Preterm Infants Fed a New Extensively Hydrolyzed Liquid Human Milk Fortifier.', 'date': '2015-10-22'}, '22301933': {'article_id': '22301933', 'content': 'Preterm human milk-fed infants often experience suboptimal growth despite the use of human milk fortifier (HMF). The extra protein supplied in fortifiers may be inadequate to meet dietary protein requirements for preterm infants.\nWe assessed the effect of human milk fortified with a higher-protein HMF on growth in preterm infants.\nThis is a randomized controlled trial in 92 preterm infants born at <31 wk gestation who received maternal breast milk that was fortified with HMF containing 1.4 g protein/100 mL (higher-protein group) or 1.0 g protein/100 mL (current practice) until discharge or estimated due date, whichever came first. The HMFs used were isocaloric and differed only in the amount of protein or carbohydrate. Length, weight, and head-circumference gains were assessed over the study duration.\nLength gains did not differ between the higher- and standard-protein groups (mean difference: 0.06 cm/wk; 95% CI: -0.01, 0.12 cm/wk; P = 0.08). Infants in the higher-protein group achieved a greater weight at study end (mean difference: 220 g; 95% CI: 23, 419 g; P = 0.03). Secondary analyses showed a significant reduction in the proportion of infants who were less than the 10th percentile for length at the study end in the higher-protein group (risk difference: 0.186; 95% CI: 0.370, 0.003; P = 0.047).\nA higher protein intake results in less growth faltering in human milk-fed preterm infants. It is possible that a higher-protein fortifier than used in this study is needed. This trial was registered with the Australian New Zealand Clinical Trials Registry (http://www.anzctr.org.au/) as ACTRN12606000525583.', 'title': 'Effect of increasing protein content of human milk fortifier on growth in preterm infants born at <31 wk gestation: a randomized controlled trial.', 'date': '2012-02-04'}, '22987877': {'article_id': '22987877', 'content': 'To evaluate the growth, tolerance, and safety of a new ultraconcentrated liquid human milk fortifier (LHMF) designed to provide optimal nutrients for preterm infants receiving human breast milk in a safe, nonpowder formulation.\nPreterm infants with a body weight ≤ 1250 g fed expressed and/or donor breast milk were randomized to receive a control powder human milk fortifier (HMF) or a new LHMF for 28 days. When added to breast milk, the LHMF provided ∼20% more protein than the control HMF. Weight, length, head circumference, and serum prealbumin, albumin, blood urea nitrogen, electrolytes, and blood gases were measured. The occurrence of sepsis, necrotizing enterocolitis, and serious adverse events were monitored.\nThis multicenter, third party-blinded, randomized controlled, prospective study enrolled 150 infants. Achieved weight and linear growth rate were significantly higher in the LHMF versus control groups (P = .04 and 0.03, respectively). Among infants who adhered closely to the protocol, the LHMF had a significantly higher achieved weight, length, head circumference, and linear growth rate than the control HMF (P = .004, P = .003, P = .04, and P = .01, respectively). There were no differences in measures of feeding tolerance or days to achieve full feeding volumes. Prealbumin, albumin, and blood urea nitrogen were higher in the LHMF group versus the control group (all P < .05). There was no difference in the incidence of confirmed sepsis or necrotizing enterocolitis.\nUse of a new LHMF in preterm infants instead of powder HMF is safe. Benefits of LHMF include improvements in growth and avoidance of the use of powder products in the NICU.', 'title': 'A new liquid human milk fortifier and linear growth in preterm infants.', 'date': '2012-09-19'}, '29772833': {'article_id': '29772833', 'content': ""NutrientsNutrientsnutrientsNutrients2072-6643MDPI29772833598651310.3390/nu10050634nutrients-10-00634ArticleThe Effect of Increasing the Protein Content of Human Milk Fortifier to 1.8 g/100 mL on Growth in Preterm Infants: A Randomised Controlled TrialReidJessica1MakridesMaria12McPheeAndrew J.13StarkMichael J.34https://orcid.org/0000-0002-6474-0505MillerJacqueline15CollinsCarmel T.12*1Healthy Mothers, Babies and Children, South Australian Health and Medical Research Institute, Adelaide, SA 5006, Australia; jessica.reid@adelaide.edu.au (J.R.); maria.makrides@sahmri.com (M.M.); andrew.mcphee@sa.gov.au (A.J.M.); jacqueline.miller@sahmri.com (J.M.)2Adelaide Medical School, Discipline of Paediatrics, The University of Adelaide, Adelaide, SA 5006, Australia3Neonatal Medicine, Women’s and Children’s Hospital, Adelaide, SA 5006, Australia; michael.stark@adelaide.edu.au4The Robinson Research Institute, The University of Adelaide, Adelaide, SA 5006, Australia5Nutrition and Dietetics, Flinders University, Adelaide, SA 5006, Australia*Correspondence: carmel.collins@sahmri.com; Tel.: +61-8-8128-440917520185201810563426420181552018© 2018 by the authors.2018Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).The aim of this study was to assess the effect of feeding high protein human milk fortifier (HMF) on growth in preterm infants. In this single-centre randomised trial, 60 infants born 28–32 weeks’ gestation were randomised to receive a higher protein HMF providing 1.8 g protein (n = 31) or standard HMF providing 1 g protein per 100 mL expressed breast milk (EBM) (n = 29). The primary outcome was rate of weight gain. Baseline characteristics were similar between groups. There was no difference between high and standard HMF groups for weight gain (mean difference (MD) −14 g/week; 95% CI −32, 4; p = 0.12), length gain (MD −0.01 cm/week; 95% CI −0.06, 0.03; p = 0.45) or head circumference gain (MD 0.007 cm/week; 95% CI −0.05, 0.06; p = 0.79), despite achieving a 0.7 g/kg/day increase in protein intake in the high protein group. Infants in the high protein group had a higher proportion of lean body mass at trial entry; however, there was no group by time effect on lean mass gains over the study. Increasing HMF protein content to 1.8 g per 100 mL EBM does not improve growth in preterm infants born 28–32 weeks’ gestation.human milkgrowthlow birth weighthuman milk fortifier1. IntroductionIt is well established that fortified human milk improves growth rates in preterm infants [1,2,3]. However, the optimal amount of protein in the fortifier is yet to be determined, partly due to the variability in the protein content of human milk, both within mothers and over time. Too little protein results in a failure to meet protein requirements, estimated to be 4.0–4.5 g/kg/day for infants born <1000 g and 3.5–4.0 g/kg/day for those born 1000–1800 g [4]. Consequently, growth failure in the neonatal period is common in infants fed fortified human milk compared with infants fed preterm formula [5,6,7]. Conversely, too much protein may result in metabolic acidosis [8]. Individualized fortification, based on either the metabolic response of the infant [9,10,11], or the macronutrient content of mother’s milk [12] has been investigated and provides evidence in support of inadequate protein concentration of human milk fortifiers (HMFs) when used in a standardised approach. However, individualised approaches are time consuming and not easily translated to the clinical environment [13]. We previously investigated a fortifier providing 1.4 g compared with 1 g protein per 100 mL human milk in preterm infants <31 weeks’ gestation [14]. While we found no difference in the rate of weight and length gain between groups, there were fewer infants with length <10th percentile at discharge in the high protein group, suggesting a higher protein concentration fortifier may be needed to improve growth. We therefore aimed to determine the effect of further increasing the protein content of HMF to 1.8 g/100 mL compared with 1 g/100 mL, on growth in preterm infants born 28–32 weeks’ gestation.2. Materials and Methods2.1. Study DesignThe study was a single centre (Women’s and Children’s Hospital, North Adelaide, South Australia), parallel group randomised controlled trial conducted between February 2012 and May 2013.2.2. ParticipantsInfants born 28–32 completed weeks’ gestation whose mothers intended to provide breast milk were eligible to participate. Multiple births were eligible and were randomised individually. Infants with a major congenital or chromosomal abnormality likely to affect growth, or where protein therapy was contraindicated (e.g., major heart defects, cystic fibrosis, phenylketonuria, disorders of the urea cycle) were ineligible. Infants likely to transfer to remote locations and infants who had received standard practice HMF for more than four days were also excluded.2.3. Randomisation and BlindingInfants were randomised to one of two groups: the higher protein intervention group or the standard protein control group. An independent researcher created the randomisation schedule using a computer generated variable block design of 4 and 6. Stratification occurred for sex and gestational age 28–29 weeks and 30–32 weeks. Parents of eligible infants were approached by a neonatologist and followed-up for consent by a research nurse who was not involved in clinical care. Upon consent, infants were randomised by telephoning an independent researcher who held the randomisation schedule and assigned a unique study identification number. Participants, clinicians, outcome assessors and data analysts were blinded to randomisation group.2.4. InterventionsThe base HMF used for both trial groups was FM85 Human Milk Supplement (Nestlé Nutrition, Gland, Switzerland) which provides 1.0 g protein and 17.5 kcal when 5 g HMF is added to 100 mL expressed breast milk (EBM). The high protein fortifier was prepared by adding 0.9 g Protifar (Nutricia, Zoetermeer, The Netherlands), a bovine casein-based powder, to the FM 85. This resulted in an additional 0.8 g protein and 3.5 kcal per 100 mL EBM providing 1.8 g protein and 21 kcal when added to 100 mL of EBM. To ensure both fortifiers were isocaloric, thereby eliminating the effect of different energy intakes on growth, 0.9 g Polyjoule (Nutricia, Zoetermeer, The Netherlands), a glucose polymer, was added to the standard fortifier providing an additional 3.5 kcal but no extra protein, giving a total of 1.0 g protein and 21 kcal when added to 100 mL of EBM. The Polyjoule and Protifar supplements were packaged into identical 400-g containers each with a tamper proof seal (Pharmaceutical Packaging Professionals Pty Ltd., Thebarton, Australia). The containers were differentiated by four colour-coded labels to facilitate blinding, with each trial group separately color-coded into two groups. Infant nutrition attendants, under the direction of the Nutrition and Food Services Department, were trained in the preparation of the HMF. Trial fortifier was mixed at the rate of 5 g FM 85 plus either 0.9 g Protifar, or 0.9 g Polyjoule, for the high and standard protein groups respectively, with 4 mL of sterile water, to give a total volume of 8 mL for use with each 100 mL of EBM.2.5. Intervention AdministrationThe fortifier intervention and control fortifiers were delivered via the enteral tube, immediately prior to a feed (tube, bottle or breast). Trial HMFs were delivered at 8 mL HMF/100 mL EBM with the volume of HMF for each feed ordered daily by the medical or neonatal nurse practitioners. In cases where a mix of EBM and preterm formula was to be given, the trial HMF was only given if EBM was >50% of the total feed. When the infant received a direct breast feed, the timing of administration of the trial product (before, during or after the feed) was at the discretion of the primary care nurse in consultation with the mother. For each day, the trial HMFs were decanted into syringes and labelled with infant identification, volume of HMF and trial details. Syringes were stored refrigerated in the neonatal unit in each infant’s individually labelled container. Any syringes not administered in the 24-h period were recorded and discarded. Fluid balance records were audited daily for compliance with the trial protocol. Administration of trial HMF began as soon as practical after randomisation (within one to two days) and continued until study end, defined as the removal of the naso-gastric tube or estimated date of delivery, whichever came first.2.6. Nutritional IntakeMeasured protein and fat content of a weekly sample of unfortified EBM (MilkoScan Minor, Foss, Denmark) were used to represent the weekly composition of EBM [14]. The lactose concentration was assumed to be 6.8 g/100 mL. EBM was only sampled when the supply was surplus to the infant’s requirements. Missing values were substituted with the average macronutrient composition of all available samples (32 of the 45 mothers involved in the study were able to provide breast milk samples). Macronutrient intakes for the study fortifiers, EBM and formula were calculated from the volume ingested, the protein and fat concentration of EBM, and the manufacturer’s information on the study fortifiers and formula. The protein content of the preterm formula in use at the time of the study was 2.2 g/100 mL. Energy content was calculated by using the Atwater factors of 4, 4, and 9 kcal/g for protein, carbohydrate, and fat respectively.2.7. Outcome Assessments2.7.1. Primary outcomeThe primary outcome was rate of weight gain (g/week) from trial start (day of randomisation) to trial end. In addition to routine clinical measurements, a research nurse and J.R. weighed infants on randomisation, weekly and at study end; duplicate weight measurements were taken using electronic balance scales accurate to 5 g. Measurements were repeated if there was a discrepancy ≥10 g, with the average of the two closest measurements used.2.7.2. Secondary Efficacy and Safety OutcomesSecondary efficacy outcomes included length and head circumference gain (cm/week), infant weight at study end, small for gestational age (SGA) at study end and body composition (fat-free mass). Length measurements were taken weekly with the infant in the supine position and measured to the nearest 0.1 cm using a recumbent length board. Head circumference was measured weekly using a non-stretching tape placed around the largest occipito-frontal circumference. Duplicate measurements were done and repeated if there was a discrepancy ≥0.5 cm, with the average of the 2 closest measures taken. SGA was defined as below the 10th percentile for infants of the same sex and gestational age, as determined from Australian birth reference data [15]. Fat free (lean) mass was measured weekly by bioelectrical impedance spectroscopy (BIS) using the Imp™ SFB7 (ImpediMed Limited, Queensland, Australia) with the first measurement taken during the first week of the study.Secondary safety outcomes included feeding tolerance (days feeds interrupted and days to reach enteral intake ≥150 mL/kg/day). A protocol was developed for discontinuation of the trial fortifier based on uraemia (blood urea nitrogen (BUN) concentration >8.0 mmol/L) and/or a metabolic acidosis (base excess <−6 mmol/L) persisting for more than 48 hours. However, no infant met these criteria. Similarly, criteria were defined for the addition of protein to feeds if an infant had poor weight gain defined as <15 g/kg/day over the preceding 7-day period associated with a BUN of <2 mmol/L when feed volumes reached 170 to 180 mL/kg/day. In this case, Protifar could be added at the discretion of the attending neonatologist, in addition to the allocated intervention fortifier. Additional protein was ceased when weight gain of 15 g/kg/day and a BUN >2 mmol/L were achieved.2.7.3. Biochemical AnalysesWeekly blood samples were taken and BUN, plasma albumin, plasma creatinine, pH and base deficit measured. Blood spots were collected weekly on filter paper and amino acids measured using tandem mass spectrometry (SA Pathology, Neonatal Screening Centre, Adelaide, Australia).2.7.4. Sample Size and Statistical AnalysisA sample size of 60 (30 per group) would detect a difference in weight gain of 3.31 g per day between the high protein and standard protein groups (80% power, p = 0.05). Consultation with the neonatal medical team agreed that this was a clinically important difference on which clinical practice could be changed. Mean weight, length, head circumference and lean mass gains over the trial period, were calculated for each infant using a linear effects model with a random intercept and slope. Using the slope, a linear regression model was fitted for each infant. Clustering (multiple births) was accounted for by using a generalised estimating equation with an independent working correlation matrix. All analyses were intention-to-treat. All models were adjusted for sex and gestational age category (28–29 and 30–32 weeks’ gestation). A per protocol analysis was specified a priori for infants who consumed ≥70% of their prescribed trial fortifier.2.7.5. EthicsEthical approval was granted by the Women’s and Children’s Health Network Human Research Ethics Committee (REC2401/10/14). This trial was registered with the Australian New Zealand Clinical Trials Registry (http://www.anzctr.org.au/) as ACTRN12611001275954.3. Results3.1. Study PopulationSixty infants were enrolled in the trial with 31 infants randomised to the high protein group and 29 infants to the standard protein group (Figure 1). There were 31 infants born from multiple births (14 sets of twins, 1 set of triplets). In all multiple births, apart from two sets of twins, the infants were randomly allocated to different interventions. For the triplets, two were randomised to the high protein group and one to the standard protein group. Four infants, two from each group, were withdrawn from the study after randomisation but before the first dose of trial fortifier was administered after parents changed their minds about involvement. A further two infants (twins) in the high protein group did not have any available breast milk and withdrew before the commencement of fortifier. One set of twins and one singleton were withdrawn by the parents midway through the trial due to perceived feeding intolerance and another infant was withdrawn by the clinical team after developing necrotising enterocolitis. In all cases of withdrawal, parents consented to the ongoing collection of data and all were included in intention-to-treat analyses. Baseline infant and maternal demographic, clinical and nutritional characteristics at randomisation were comparable between groups except that there were more male infants in the high protein group, n = 16 (52%) than the standard protein group, n = 12 (41%), the mean ± SD birth weight was lower in the higher protein group (1483 ± 423 g versus 1551 ± 407 g in the high and standard groups, respectively) and there were more infants classified as SGA for weight in the high protein group, n = 5 (16%) than the standard protein group, n = 1 (3%) (Table 1).3.2. Nutritional ManagementForty infants received standard ward HMF, S-26 SMA HMF (Wyeth Nutrition) while waiting for consent, 18 in the high and 22 in the standard protein group (Table 1). The remaining twenty trial infants started immediately on their allocated trial intervention.Nutritional intake of the infants for the first 28 days of the study did not differ between the groups except that the high protein group received more protein (mean ± SD 4.2 ± 1.3 vs. 3.5 ± 0.93 g/kg/day in the high and standard protein groups respectively). The protein concentration of the EBM was not different between groups (mean ± SD 1.43 ± 0.27 and 1.45 ± 0.28 g protein/100 mL in the high and standard groups, respectively) and the difference in protein intake was due to more protein derived from the HMF (mean ± SD 1.9 ± 1.2 and 1.2 ± 0.6 g/kg/day, in the high and standard groups, respectively. Energy intakes and fluid volume were similar between the groups (energy: mean ± SD 124 ± 34 and 126 ± 27 kcal/kg/day and fluid: mean ± SD 154 ± 39 and 157 ± 32 mL/kg/day in the high and standard groups, respectively). The high protein group received 83% (±32) of their total enteral intake as EBM compared with the control group who received 90% (±23).3.3. Primary OutcomeThere was no difference in the rate of weight gain between groups (Table 2) (mean (95% CI) high protein 245 (230, 260) g/week and standard protein 258 (244, 272) g/week, adjusted mean difference −14 (−32, 4) p = 0.12). Results were similar when analysed per protocol (Table 2).3.4. Secondary Outcomes3.4.1. GrowthThere were no differences in rate of length or head circumference gain (Table 2). High protein HMF infants weighed less at study end but this was not statistically significant (Table 2) and is consistent with the difference in birth weight between the groups (Table 1). There were no differences in length or head circumference at study end between the groups (Table 2). There was no difference in SGA status for weight between high and standard protein HMF groups at the end of the study (n = 8, 25%, and n = 3, 10% SGA infants in the high and standard protein groups, respectively, adjusted Relative Risk (95% CI); 2.5 (0.8, 7.9), p = 0.11).Over the first four weeks of the trial, when >75% of participants were still in hospital, fat free (lean) mass was measured with the week one measurement taken a mean of 8 ± SD 2 days after randomisation. Fat free mass as a proportion of body weight (Figure 2) from weeks one to four was greater in high protein group infants than standard protein group infants (p = 0.03). However, there was no significant group by time interaction (p = 0.84). At week three alone, there was a significant increase for fat free mass as a proportion of body weight in the high protein group (p = 0.04).3.4.2. BiochemistryDue to the variable nature of blood chemistry data and length of hospital stay (to discharge), only the first three trial weeks could be accurately analysed using a linear mixed effects model.There was a significant group by time interaction for BUN levels (p < 0.001) with BUN levels significantly increased in the high protein group (Figure 3). This difference continued for the duration of the trial (p < 0.001). There were 12 occurrences in nine separate infants where BUN levels were measured over the pre-specified safety threshold of 8 mmol/L. Seven of these occurred during baseline blood tests taken at randomisation and were therefore not a result of the intervention. Six of these infants had BUN measurements in the normal range at their next weekly blood test. One infant had a BUN measurement >8 mmol/L at week one; the infant did not have another BUN measurement over 8 mmol/L for the rest of the trial. Two other infants, both in the high protein group, recorded BUN concentrations >8.0 mmol/L, peaking at 8.8 mmol/L, on five occasions, however the base excess remained above −6 mmol/L with no other abnormal biochemistry. There was one occurrence of an infant in the standard protein group requiring additional protein due to poor weight gain and BUN <2 mmol/L.There were no group by time interactions or group differences for albumin, creatinine, glucose, pH (results not shown). Phenylalanine (Phe) and tyrosine (Tyr), amino acids associated with increased protein intake, were both increased in the high protein group compared to the standard group at study week 3 (Phe median (IQR) μmol/L: 33 (28–42) vs. 25 (23–30), p <0.001 and Tyr median (IQR) μmol/L: 196 (151–267) vs. 128 (99–172) μmol/L, p <0.003 in the high and standard groups respectively.3.4.3. Clinical OutcomesHigh protein HMF infants were significantly more likely to have feeds interrupted (11 (35%) vs. 6 (21%), p = 0.01, in the high and standard protein groups, respectively) Table 3. There was no significant difference in the number of days spent on parenteral nutrition, days of intravenous lipid or the days taken to reach full enteral feeds. Likewise, there was no significant difference between the groups for any other clinical outcome (Table 3).4. DiscussionThe aim of this study was to assess the effect of a higher protein HMF on preterm infant growth. Our trial interventions resulted in the high protein group infants receiving 0.7 g/kg/day more protein than infants in the standard protein group, with mean protein intakes within recommended ranges for both groups. Despite this, there were no differences in growth between the two groups. The accumulation of fat free mass and fat mass, also did not differ between groups. While the higher protein group had a greater proportion of fat free mass from week one, the absence of a baseline measurement makes the interpretation of this difficult. It is unlikely that the intervention would have had an effect in the first week of the study, particularly as the change in fat free mass over time did not differ between groups. A significant difference between groups was noted at week three only and the implication of this is unclear. It is possible that this is a chance finding of no clinical significance.These results are confirmed by a recent study by Maas et al. [16] who compared 1 and 1.8 g protein concentration in powdered HMFs in a similar population to ours and found no difference in growth. Their trial interventions achieved a 0.6 g/kg/day median greater intake of protein, similar to our study, and protein intakes were within recommendations. Growth rates in both studies approximated foetal growth rates. A further two studies compared two different, newly formulated liquid HMFs with higher protein concentrations, with standard powdered HMFs. Moya et al. [17] compared Mead Johnson Nutrition products: a liquid fortifier with an Enfamil powdered fortifier, which when mixed with EBM provided 3.2 and 2.6 g protein/100 mL, respectively, equating to an additional 1.8 and 1.1 g protein. Kim et al. [18], in a non-inferiority trial, compared the Abbott Nutrition products of Similac HMF liquid, providing 3.6 g protein/100 kcal when mixed with EBM, with Similac HMF powder providing 3 g protein/100 kcal when mixed. These comparisons equate to an additional 1.6 and 1 g protein added to 100 mL EBM in the liquid and powder, respectively. The populations were similar between studies [17,18] except that Moya et al. [17] inclusion criteria (≤30 weeks’ gestation, birth weight ≤1250 g) resulted in a slightly less mature and smaller population than in both Kim et al. [18] study and this current study. Neither study [17,18] showed a difference in weight gain between groups, however, Moya et al. [17] found improved length gain with the higher protein. Both studies found infants in the high protein group were heavier at study end. Almost half the participants in Moya’s study were <1000 g at birth; hence their protein requirements of 4 to 4.5 g/kg would have been met by the high, but not the control, protein fortifier at volumes of 150 mL/kg. This may explain the effect seen on length gain. Two other studies have compared fortifiers containing 1 and 1.4 g protein added to 100 mL EBM with mixed results. Our previous trial [14] showed no effect of increased protein on growth, although did show a reduction in the number of infants SGA for length at discharge. However, Rigo et al. [19], in a non-inferiority trial, found improved weight gain of 2.3 g/day with the higher protein fortifier. The trial products in both these studies were similar, as were the population. It is possible that the smallest infants, with the highest protein needs, are the ones to benefit most from increased protein and that the larger sample size in Rigo (n = 153) compared to that in Miller (n = 92) elucidated the differences. Taken collectively, these results and ours suggest that protein concentrations in HMFs of 1.8 g provide no additional benefit in the population studied, but smaller infants are worthy of further investigation.The significantly elevated BUN levels seen at weeks 1, 2 and 3 were expected and have occurred in other high protein nutritional intervention studies [9,14,17]. Assuming adequate renal function, BUN is proportional to protein intake [20] and is often used as a crude marker of protein sufficiency. Low BUN levels suggest inadequate protein intake and high levels indicate possible excessive intake [9]. Blood phenylalanine and tyrosine concentrations were also significantly increased in the higher protein group, in week 3 only, and this is unlikely to be clinically significant. There were no differences in creatinine, albumin or other biochemical markers suggesting the intervention did not harm the infants.A strength of this study is the rigour with which dietary intake and growth were assessed. The protein and fat concentrations of EBM were measured, rather than assumed, resulting in accurate reporting of dietary intake and confirmation that, despite the variability of protein in EBM, we achieved a mean intake difference of 0.7 g/kg/day of protein between groups. Similarly, we measured both growth and body composition in an attempt to discern differences in weight gain arising from extra protein. This trial also has some limitations. Although all infants were included in the analyses, there were 10 who either did not receive, or ceased the intervention, which may have impacted results. In addition, the pragmatic nature of this trial may have influenced results as clinicians may have adjusted feed regimes if poor weight gain was identified. There was one instance of extra Protifar prescribed to an infant in the standard protein group and subtle increases in feed volume may also have occurred although volume of intake was not different between groups. This may have made it more difficult to detect differences between intervention groups. We used BIS to determine fat and fat free mass. BIS is the only cot-side technique available where infants requiring respiratory support can be assessed. While accuracy of BIS at the individual level is poor, BIS provides a useful means of determining differences in body composition between population means [21].Many of the recent trials discussed have already achieved mean growth rates approaching intra-uterine growth, with similar growth rates between groups. Findings from this current study are only generalisable to a similar population (infants born 28–32 week’s gestation). Therefore, to explicate the subtle effects of increasing protein on growth, future trials may need to focus on birth weight categories as they relate to protein requirements (i.e., <1000 g and 1000–1800 g). Due to the small proportion of infants born <1000 g, large multi-centre trials will be needed to tease out the effect.5. ConclusionsIncreasing the protein concentration of HMF from 1.0 to 1.8 g protein added per 100 mL EBM does not improve growth in preterm infants born 28–32 weeks’ gestation.AcknowledgmentsWe thank the families who participated in this study.Author ContributionsConceptualization, J.R., M.M., A.J.M. and C.T.C.; Formal analysis, J.R., M.M., A.J.M., M.J.S., J.M. and C.T.C.; Funding acquisition, J.R., M.M., A.J.M., M.J.S. and C.T.C.; Investigation, J.R., M.M., A.J.M., M.J.S. and C.T.C.; Supervision, M.M., A.J.M., M.J.S. and C.T.C.; Writing: original draft, J.R., J.M. and C.T.C.; Writing: review and editing, J.R., M.M., A.J.M., M.J.S., J.M. and C.T.C.FundingThis research was funded by a Women’s and Children’s Hospital Foundation Grant. Research Fellowships were provided by the National Health and Medical Research Council of Australia (M.M. Principal Research Fellow APP1061704) and the MS McLeod Research Fellowship, MS McLeod Research Fund, Women's and Children’s Hospital Research Foundation (C.T.C). The contents of the published material are solely the responsibility of the authors and do not reflect the views of the National Health and Medical Research Council of Australia.Conflicts of InterestOutside the submitted work, Maria Makrides serves on scientific advisory boards for Fonterra and Nestle. Honoraria are paid to her institution for continuing education of early career researchers. Maria Makrides also holds a Principal Research Fellowship from the NHMRC (APP1061704). Other authors declare no conflict of interest. Nestlé Nutrition donated half of the human milk fortifier used in the trial and Nutricia donated the Polyjoule and Protifar supplements. However, these sponsors had no role in the design of the study, in the collection, analyses or interpretation of data; in writing of the manuscript, and the decision to publish the results.References1.AdamkinD.H.RadmacherP.G.Fortification of human milk in very low birth weight infants (VLBW <1500 g birth weight)Clin. Perinatol.20144140542110.1016/j.clp.2014.02.010248738402.MoroG.E.ArslanogluS.BertinoE.CorvagliaL.MontirossoR.PicaudJ.C.PolbergerS.SchanlerR.J.SteelC.van GoudoeverJ.Human milk in feeding premature infants: Consensus statementJ. Pediatr. Gastroenterol. Nutr.201561Suppl. 1S16S1910.1097/01.mpg.0000471460.08792.4d262959993.BrownJ.V.E.EmbletonN.D.HardingJ.E.McGuireW.Multi-nutrient fortification of human milk for preterm infantsCochrane Database Syst. Rev.201610.1002/14651858.CD000343.pub3271558884.AgostoniC.BuonocoreG.CarnielliV.P.De CurtisM.DarmaunD.DecsiT.DomellofM.EmbletonN.D.FuschC.Genzel-BoroviczenyO.Enteral nutrient supply for preterm infants: Commentary from the European Society of Paediatric Gastroenterology, Hepatology and Nutrition committee on nutritionJ. Pediatr. Gastroenterol. Nutr.201050859110.1097/MPG.0b013e3181adaee0198813905.ColaizyT.T.CarlsonS.SaftlasA.F.MorrissF.H.Jr.Growth in vlbw infants fed predominantly fortified maternal and donor human milk diets: A retrospective cohort studyBMC Pediatr.20121212410.1186/1471-2431-12-124229005906.EmbletonN.E.PangN.CookeR.J.Postnatal malnutrition and growth retardation: An inevitable consequence of current recommendations in preterm infants?Pediatrics200110727027310.1542/peds.107.2.270111584577.MaasC.WiechersC.BernhardW.PoetsC.F.FranzA.R.Early feeding of fortified breast milk and in-hospital-growth in very premature infants: A retrospective cohort analysisBMC Pediatr.20131317810.1186/1471-2431-13-178241802398.CibulskisC.C.ArmbrechtE.S.Association of metabolic acidosis with bovine milk-based human milk fortifiersJ. Perinatol.20153511511910.1038/jp.2014.143251023219.ArslanogluS.MoroG.E.ZieglerE.E.Adjustable fortification of human milk fed to preterm infants: Does it make a difference?J. Perinatol.20062661462110.1038/sj.jp.72115711688598910.AlanS.AtasayB.CakirU.YildizD.KilicA.KahveciogluD.ErdeveO.ArsanS.An intention to achieve better postnatal in-hospital-growth for preterm infants: Adjustable protein fortification of human milkEarly Hum. Dev.2013891017102310.1016/j.earlhumdev.2013.08.0152403503911.BiasiniA.MarvulliL.NeriE.ChinaM.StellaM.MontiF.Growth and neurological outcome in ELBW preterms fed with human milk and extra-protein supplementation as routine practice: Do we need further evidence?J. Matern. Fetal Neonatal Med.201225Suppl. 4727410.3109/14767058.2012.7150322295802412.RochowN.FuschG.ChoiA.ChessellL.ElliottL.McDonaldK.KuiperE.PurchaM.TurnerS.ChanE.Target fortification of breast milk with fat, protein, and carbohydrates for preterm infantsJ. Pediatr.20131631001100710.1016/j.jpeds.2013.04.0522376949813.McLeodG.SherriffJ.HartmannP.E.NathanE.GeddesD.SimmerK.Comparing different methods of human breast milk fortification using measured v. Assumed macronutrient composition to target reference growth: A randomised controlled trialBr. J. Nutr.201611543143910.1017/S00071145150046142662789914.MillerJ.MakridesM.GibsonR.A.McPheeA.J.StanfordT.E.MorrisS.RyanP.CollinsC.T.Effect of increasing protein content of human milk fortifier on growth in preterm infants born at <31 wk gestation: A randomized controlled trialAm. J. Clin. Nutr.20129564865510.3945/ajcn.111.0263512230193315.BeebyP.J.BhutapT.TaylorL.K.New South Wales population-based birthweight percentile chartsJ. Paediatr. Child Health19963251251810.1111/j.1440-1754.1996.tb00965.x900778216.MaasC.MathesM.BleekerC.VekJ.BernhardW.WiechersC.PeterA.PoetsC.F.FranzA.R.Effect of increased enteral protein intake on growth in human milk–fed preterm infants: A randomized clinical trialJAMA Pediatr.2017171162210.1001/jamapediatrics.2016.26812789306417.MoyaF.SiskP.M.WalshK.R.BersethC.L.A new liquid human milk fortifier and linear growth in preterm infantsPediatrics2012130e928e93510.1542/peds.2011-31202298787718.KimJ.H.ChanG.SchanlerR.Groh-WargoS.BloomB.DimmitR.WilliamsL.BaggsG.Barrett-ReisB.Growth and tolerance of preterm infants fed a new extensively hydrolyzed liquid human milk fortifierJ. Pediatr. Gastroenterol. Nutr.20156166567110.1097/MPG.00000000000010102648811819.RigoJ.HascoetJ.M.BilleaudC.PicaudJ.C.MoscaF.RubioA.SalibaE.RadkeM.SimeoniU.GuilloisB.Growth and nutritional biomarkers of preterm infants fed a new powdered human milk fortifier: A randomized trialJ. Pediatr. Gastroenterol. Nutr.201765e83e9310.1097/MPG.00000000000016862872765420.PolbergerS.K.AxelssonI.E.RaihaN.C.Urinary and serum urea as indicators of protein metabolism in very low birthweight infants fed varying human milk protein intakesActa Paediatr. Scand.19907973774210.1111/j.1651-2227.1990.tb11548.x223926621.CollinsC.T.ReidJ.MakridesM.LingwoodB.E.McPheeA.J.MorrisS.A.GibsonR.A.WardL.C.Prediction of body water compartments in preterm infants by bioelectrical impedance spectroscopyEur. J. Clin. Nutr.201367Suppl. 1S47S5310.1038/ejcn.2012.16423299871Figure 1Participant flow through the trial. 1 from rural locations (n = 52), insufficient milk supply (n = 36), required interpreter (n = 6); congenital abnormality (n = 3); 2 did not want to take part (n = 25), did not want twins to be randomized individually (n = 8), parent not visiting (n = 1), immediately transferred to another centre (n = 1).Figure 2Fat free mass as a proportion of body weight for the first four weeks of the trial. Values are means, error bars are 95% CI. High protein n = 30, 30, 27, 26 and standard protein 29, 27, 26, 23 in weeks 1, 2, 3, 4 respectively. Adjusted for sex and gestational age, group interaction, p = 0.03, time interaction, p = 0.01. group × time interaction p = 0.84; * p = 0.04.Figure 3BUN from randomisation to week 3. Values are mean, error bars are 95% CI. High protein: n = 31, 28, 26, 25; Standard protein: n = 29, 26, 24, 24 for weeks baseline, 1, 2, 3. Adjusted for sex and GA, overall group effect <0.001, group * week interaction, p <0.001, * p = 0.04; ** p <0.001.nutrients-10-00634-t001_Table 1Table 1Baseline infant and maternal characteristics.CharacteristicHigh Protein (n = 31)Standard Protein (n = 29)\nInfant characteristics\n\n\nSingleton15 (48)16 (55)Twin15 (48)12 (41)Triplet2 (7)1 (3)Gestational age (week)30.5 ± 1.530.1 ± 1.428–29 weeks’ gestation10 (32)9 (31)30–32 weeks’ gestation21 (68)20 (69)Male infants16 (52)12 (41)Birth weight (g)1483 ± 4231551 ± 407SGA for weight at birth5 (16)1 (3)Birth length (cm)40.0 ± 3.340.2 ± 2.8Head circumference (cm)28.5 ± 328.5 ± 1.8Infants received standard ward HMF before randomisation18 (58)22 (76)Length of standard ward fortification before trial HMF start (day)1.3 ± 1.72.0 ± 1.5Time between birth and trial HMF start (day)8.9 ± 3.29.0 ± 2.5\nMaternal characteristics\n\n\nMaternal age (years)29.9 ± 6.331.7 ± 5.3Mother smoked during pregnancy5 (16.1)3 (10.3)Caucasian27 (96)23 (82)Primiparous19 (61.3)12 (41.4)Previous preterm birth4 (33.3)6 (35.3)Data are presented as n (%) or mean ± SD.nutrients-10-00634-t002_Table 2Table 2Anthropometric changes over the study.\nIntention to Treat AnalysesPer Protocol Analyses 1High Protein (n = 31)Standard Protein (n = 29)Adjusted Mean Difference 2\np\n2\nHigh Protein (n = 21)Standard Protein (n = 23)Adjusted Mean Difference 2\np\n2\nWeight gain (g/week)245 (230, 260)258 (244, 272)−14 (−32, 4)0.12245 (228, 262)262 (247, 277)−15 (−36, 5)0.14Length gain (cm/week)1.1 (1.1, 1.2)1.1 (1.1, 1.2)−0.01 (−0.06, 0.03)0.451.1 (1.1, 1.2)1.2 (1.1, 1.2)−0.01 (−0.06, 0.04)0.62Head circumference gain (cm/week)1.1 (1.0, 1.1)1.1 (1.0,1.1)0.007 (−0.05, 0.06)0.791.1 (1.1, 1.1)1.1 (1.1, 1.1)−0.004 (−0.06, 0.05)0.88Weight at study end (g) 32658 (2544, 2771)2757 (2632, 2883)−100 (−251, 50)0.192646 (2489, 2805)2815 (2675, 2955)−157 (−341, 28) 0.1Length at study end (cm)45.2 (44.5, 45.9)45.8 (45.0, 46.6)−0.5 (−1.3, 0.3)0.1945.2 (44.4, 46.0)46.3 (45.6, 47)−0.86 (−1.85, 0.12)0.09Head circumference at study end (cm)33.1 (32.5, 33.6)33.0 (32.4, 33.7)0.03 (−0.6, 0.7)0.9233.3 (32.7, 33.9)33.6 (33.0, 34.1)−0.16 (−0.90, 0.57)0.66Data are presented as mean, (95% CI); 1 For inclusion in ‘per protocol’ analysis, infants must have consumed 70% or more of their trial group HMF; 2 adjusted for sex and gestational age; 3 study end defined as removal of naso-gastric tube or term equivalent, whichever came first.nutrients-10-00634-t003_Table 3Table 3Feeding and clinical management.VariableHigh Protein (n = 31)Standard Protein (n = 29)\np\nInfant required enteral protein supplementation 101 (3.4)0.48Feeding interrupted 211 (35)6 (21)0.01Days receiving parenteral nutrition10 (7, 13)9 (7, 11)0.34Days of intravenous lipid4 (3, 7)4 (3, 6)0.72Days to full enteral feeds 38 (6, 10)8 (7, 10)0.72Confirmed necrotizing enterocolitis1 (3.2)0>0.99Oxygen at discharge2 (6.5)1 (3.4)0.15Late onset sepsis1 (3.2)0>0.99Data are reported as n (%) or mean (95% CI).1 One infant in the standard protein group was prescribed a protein supplement (Protifar) 2 Feeding interrupted was defined as one of more feeds not given in a day; 3 Full enteral feeds was defined as 150 mL/kg/day)."", 'title': 'The Effect of Increasing the Protein Content of Human Milk Fortifier to 1.8 g/100 mL on Growth in Preterm Infants: A Randomised Controlled Trial.', 'date': '2018-05-19'}, '28727654': {'article_id': '28727654', 'content': ""J Pediatr Gastroenterol NutrJ. Pediatr. Gastroenterol. NutrJPGAJournal of Pediatric Gastroenterology and Nutrition0277-21161536-4801Lippincott Williams & Wilkins287276545625962JPGN-16-82510.1097/MPG.000000000000168600025Original Articles: NutritionGrowth and Nutritional Biomarkers of Preterm Infants Fed a New Powdered Human Milk Fortifier: A Randomized TrialRigoJacques∗HascoëtJean-Michel†BilleaudClaude‡PicaudJean-Charles§MoscaFabio||RubioAmandine¶SalibaElie#RadkëMichaël∗∗SimeoniUmberto††GuilloisBernard‡‡de HalleuxVirginie∗JaegerJonathan§§AmeyeLaurent||||HaysNicholas P.¶¶SpalingerJohannes##∗Department of Neonatology, University of Liège, CHR Citadelle, Liège, Belgium†Maternité Régionale Universitaire A. Pinard, Nancy‡CIC Pédiatrique 1401 INSERM-CHU, Bordeaux§Service de Neonatologie, Hôpital de la Croix Rousse, Lyon, France||Neonatal Intensive Care Unit, Department of Clinical Science and Community Health, Fondazione IRCCS “Ca’ Granda” Ospedale Maggiore Policlinico, University of Milan, Milan, Italy¶Hôpital Couple Enfant, CHU de Grenoble, Grenoble#Hôpital Clocheville, CHU de Tours, Tours, France∗∗Klinikum Westbrandenburg GmbH, Potsdam, Germany††Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland‡‡Hôpital Clemenceau, CHU de Caen, Caen, France§§Nestlé Clinical Development Unit, Lausanne, Switzerland||||Nestlé Nutrition R&D, Vevey, Switzerland¶¶Nestlé Nutrition R&D, King of Prussia, PA##Children's Hospital of Lucerne, Lucerne, Switzerland.Address correspondence to Jacques Rigo, MD, PhD, Service Universitaire de Néonatologie, CHR de la Citadelle, Boulevard du Douzième de Ligne, 1 4000 Liège, Belgium (e-mail: J.Rigo@ulg.ac.be); Address reprint or protocol requests to: Nicholas P. Hays, PhD, 3000 Horizon Dr., Suite 100, King of Prussia, PA 19406 (e-mail: Nicholas.Hays@rd.nestle.com).1020172292017654e83e93231120162952017Copyright © The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the European Society for Pediatric Gastroenterology, Hepatology, and Nutrition and the North American Society for Pediatric Gastroenterology, Hepatology, and Nutrition2017This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc-nd/4.0ABSTRACTObjectives:The aim of this study was to assess growth and nutritional biomarkers of preterm infants fed human milk (HM) supplemented with a new powdered HM fortifier (nHMF) or a control HM fortifier (cHMF). The nHMF provides similar energy content, 16% more protein (partially hydrolyzed whey), and higher micronutrient levels than the cHMF, along with medium-chain triglycerides and docosahexaenoic acid.Methods:In this controlled, multicenter, double-blind study, a sample of preterm infants ≤32 weeks or ≤1500\u200ag were randomized to receive nHMF (n\u200a=\u200a77) or cHMF (n\u200a=\u200a76) for a minimum of 21 days. Weight gain was evaluated for noninferiority (margin\u200a=\u200a–1\u200ag/day) and superiority (margin\u200a=\u200a0\u200ag/day). Nutritional status and gut inflammation were assessed by blood, urine, and fecal biochemistries. Adverse events were monitored.Results:Adjusted mean weight gain (analysis of covariance) was 2.3\u200ag/day greater in nHMF versus cHMF; the lower limit of the 95% CI (0.4\u200ag/day) exceeded both noninferiority (P\u200a<\u200a0.001) and superiority margins (P\u200a=\u200a0.01). Weight gain rate (unadjusted) was 18.3 (nHMF) and 16.8\u200ag\u200a·\u200akg−1\u200a·\u200aday−1 (cHMF) between study days 1 and 21 (D1–D21). Length and head circumference (HC) gains between D1 and D21 were not different. Adjusted weight-for-age z score at D21 and HC-for-age z score at week 40 corrected age were greater in nHMF versus cHMF (P\u200a=\u200a0.013, P\u200a=\u200a0.003 respectively). nHMF had higher serum blood urea nitrogen, pre-albumin, alkaline phosphatase, and calcium (all within normal ranges; all P\u200a≤\u200a0.019) at D21 versus cHMF. Both HMFs were well tolerated with similar incidence of gastrointestinal adverse events.Conclusions:nHMF providing more protein and fat compared to a control fortifier is safe, well-tolerated, and improves the weight gain of preterm infants.Keywordsgrowthhuman milklow birth weightSTATUSONLINE-ONLYOPEN-ACCESSTRUEWhat Is KnownDue in part to variability in human milk composition, incidence of postnatal growth restriction is more frequently reported in very-low-birth-weight infants fed fortified human milk compared to those fed preterm formulas.The optimal composition of human milk fortifier and nutritional recommendations for preterm infants fed fortified human milk are still debated.What Is NewA new human milk fortifier containing partially hydrolyzed protein, fat, and carbohydrate provides a higher protein:energy ratio while achieving lower osmolality versus a current fortifier.In preterm infants, the new fortifier improves weight gain and reduces postnatal growth restriction compared to the current fortifier.Feeding of human milk (HM) rather than preterm formulas provides many benefits to preterm infants (eg, accelerated gut maturation (1); protection against infections (2), sepsis (3), necrotizing enterocolitis (2), and retinopathy of prematurity (4); possible protective effect on neurodevelopment (5)) that are mediated by protective biomolecules and trophic factors in HM. HM, however, provides inadequate protein and micronutrients to support the rapid growth and bone mineralization of preterm infants. These deficits are particularly acute in the smallest infants (birthweight <1500\u200ag) who have the highest protein and mineral needs (6). Fortification of mother's own milk or banked HM is therefore recommended for all preterm infants with birthweight <1800\u200ag to improve nutrient accretion and in-hospital growth (7,8).Feeding fortified HM helps support adequate growth and bone mineralization (9), and is associated with favorable neurodevelopmental outcomes (10), although evidence for improved outcomes other than in-hospital growth is limited (11). The nutritional content, however, of some currently available fortifiers may be inadequate for many preterm infants. Incidence of postnatal growth restriction is more frequently reported in very-low-birth-weight infants fed fortified HM compared to those fed preterm formulas (12,13). In addition, the nutritional profile of HM from mothers of premature infants varies greatly (14) and may differ from published reference compositional data, which may lead to less-than-recommended intakes of protein and energy (15,16). These nutritional inadequacies may worsen with use of donor HM, which is often from mothers of term infants >1-month postpartum (17).A new powdered HM fortifier has been developed with a higher protein:energy ratio (protein provided as partially hydrolyzed whey), non-protein energy from lipids and carbohydrate, and higher electrolyte and vitamin levels (enriching HM in line with ESPGHAN (18) and expert group (19) recommendations) versus a control fortifier. When mixed with HM containing 1.5\u200ag protein/100\u200amL (2–4 week milk) (20–22), it provides 3.6\u200ag protein/100 kcal (within the ESPGHAN-recommended ranges (18) for protein and energy intakes for a minimal intake volume of 140\u200amL/kg/day in very-low-birth-weight infants up to 1.8\u200akg body weight), with osmolality below the recommended threshold of 450\u200amOsm/kg (23,24).This study evaluated growth and nutritional biomarkers during a 21-day interval in clinically stable preterm infants receiving the new HM fortifier (nHMF) compared to infants fed a control fortifier (cHMF). The primary objective was to assess weight gain velocity (grams per day); evaluations of other growth parameters (including weight gain velocity in gram per kilograms per day) and intervals (eg, to 40 weeks corrected age [W40CA]), feeding tolerance, adverse events, time to full fortification/full enteral feeding, and markers of protein-energy, electrolytes, bone metabolic status, gut inflammation, and maturity of gastrointestinal (GI) function were also conducted as secondary outcomes. We hypothesized that weight gain of infants fed nHMF would be both noninferior (lower limit of 95% confidence interval [CI] of mean difference >–1\u200ag/day) and superior (lower limit of 95% CI of mean difference >0\u200ag/day) to that of infants fed cHMF.METHODSStudy design and participantsThis was a controlled, double-blind, randomized, parallel-group study conducted in neonatal intensive care units (NICUs) at 11 metropolitan hospitals in France, Belgium, Germany, Switzerland, and Italy. NICU size ranged from 25 to 45 beds. Clinically stable male and female preterm infants with gestational age ≤32 weeks or birthweight ≤1500\u200ag and born to mothers who had agreed to provide expressed or donor breastmilk for the entire 21-day study duration were enrolled in the study from April 2011 to March 2014. Infants were excluded if they had a history of or current systemic, metabolic, or chromosomic disease, any congenital anomalies of the GI tract, were small for gestational age (defined in this study as bodyweight ≤5th percentile (25)), or were receiving steroids or preterm formula during the study period. For multiple births, the first sibling was randomized and other siblings were allocated to the same group. The study was reviewed and approved by an institutional review board/independent Ethics Committee at each study site. Each subject's parent/legal representative provided written informed consent before participating in the study.Infants tolerating ≥100\u200amL\u200a·\u200akg−1\u200a·\u200aday−1 of HM for >24\u200ahours were randomized to receive either nHMF or cHMF for a minimum of 21 days; infants continued to receive their allocated study fortifier (or were transitioned to a routine/standard fortifier) until NICU discharge or medical decision to stop fortification, and fortification was stopped after discharge. The fortifiers were both cow's milk-based and provided similar energy supplementation (17\u200akcal/100\u200amL of HM). For every 100\u200amL of HM, nHMF provided 1.4\u200ag partially hydrolyzed whey protein, 0.7\u200ag lipids (primarily medium chain triglycerides and docosahexaenoic acid), 1.3\u200ag carbohydrate (maltodextrin), with a blend of micronutrients. cHMF (FM85 Human Milk Fortifier, Nestlé, Switzerland) provided 1.0\u200ag extensively hydrolyzed whey protein, no lipids, 3.3\u200ag carbohydrate (lactose and maltodextrin), with a blend of micronutrients. nHMF contained higher concentrations of some vitamins and electrolytes compared to cHMF, but both contained similar levels of minerals, including calcium (as calcium glycerophosphate and calcium phosphate) and phosphorus. Table 1 presents the estimated composition and osmolality of preterm HM (22) fortified with each fortifier. Fortifiers were fed beginning at half-strength (Fortification Strength Increase day 1; FSI1), then advanced per hospital practice, with full-strength fortification occurring once infants could maintain intakes of 150 to 180\u200amL\u200a·\u200akg−1\u200a·\u200aday−1 (ie, full enteral feeds; study day 1 [D1]). A study plan schematic is presented in Figure 1.FIGURE 1Study design. cHMF\u200a=\u200acontrol human milk fortifier; D1\u200a=\u200astudy day 1; D7\u200a=\u200astudy day 7; D10/11\u200a=\u200astudy day 10/11; D14\u200a=\u200astudy day 14; D21\u200a=\u200astudy day 21; DC\u200a=\u200adischarge (note that infants continued to receive their allocated study fortifier [or were transitioned to a routine/standard fortifier] until neonatal unit discharge or medical decision to stop fortification if length of stay was >21 days, and fortification was stopped after discharge) ; FSI1\u200a=\u200afortification strength increase day 1; HC\u200a=\u200ahead circumference; HM\u200a=\u200ahuman milk; nHMF\u200a=\u200anew human milk fortifier; W40CA\u200a=\u200aweek 40 corrected age.Study ProceduresGrowthInfant nude weight (to the nearest 1\u200ag) was measured daily by trained nursery personnel using a calibrated electronic scale (Baby Scale 717, Seca, Semur-en-Auxois, France). Recumbent length and head circumference (HC; both to the nearest 0.1\u200acm) were measured at FSI1, D1, and weekly thereafter. At least 2 trained examiners measured recumbent length using a length board (Mobile Measuring Board 417, Seca, Semur-en-Auxois, France) while maintaining proper body alignment and full body extension with feet flexed. HC was measured using a nonelastic measuring tape (Measuring Tape 212 or 218, Seca, Semur-en-Auxois, France) placed over the largest circumference of the skull (above the supraorbital ridges while covering the most prominent part of the frontal bulge anteriorly). The same calibrated equipment was used for anthropometric measures for each infant at all sites. Weight-for-age, length-for-age, and HC-for-age z scores were calculated using Fenton (25). Weight gain velocity (grams per kilograms per day) was calculated using the average of the start and end weights as the denominator.Markers of Protein-energy, Electrolyte, and Bone Metabolic StatusBlood and urine samples were collected at D1, D10/11, and D21 and analyzed for serum creatinine and prealbumin, blood urea nitrogen (BUN), urinary urea, hemoglobin, hematocrit, electrolyte status, and bone metabolic status. All blood and urine parameters were analyzed as part of routine clinical assessments at each NICU. Since 24-hour urine collections were not performed in this study owing to logistical infeasibility, urinary markers were corrected for 24-hour creatinine excretion (26) assuming a standard urinary excretion in preterm infants of 10\u200amg\u200a·\u200akg−1\u200a·\u200aday−1(27).Feeding Tolerance and Adverse EventsFeeding tolerance was evaluated by trained nursery staff who recorded daily milk intake (milliliters), stool pattern (defecation frequency and stool consistency [5\u200a=\u200ahard, 4\u200a=\u200aformed, 3\u200a=\u200asoft, 2\u200a=\u200aliquid, or 1\u200a=\u200awatery]), presence of abdominal distention, and incidence of spitting-up (defined as return of a small amount of swallowed food, usually a mouthful, and usually occurring during or shortly after feeding) and vomiting (defined as return of a larger amount of food with more complete emptying of the stomach, and usually occurring sometime after feeding). In addition, frequency, type, and attribution to fortifier intake of adverse events (AEs; including clinical and laboratory) were evaluated using physician-reported information recorded using standardized forms from enrollment to W40CA. AEs were categorized by the reporting investigator as “serious” in accordance with International Conference on Harmonization criteria (28) and as “related to the intervention” based on detailed, standardized criteria provided in the protocol.Statistical AnalysisSample size was based on a previous study (29), which investigated growth and zinc status in preterm infants fed fortified HM. In the present trial, a group-sequential design was chosen (Wang and Tsiatis) (30) with 1 interim analysis. To detect a noninferior weight gain in infants fed with nHMF versus cHMF from D1 to D21 (noninferiority margin –1\u200ag/day, expected weight gain difference 2\u200ag/day, standard deviation 4.73\u200ag/day, type I error 5%, power 80%) (29), 192 subjects (males and females combined) were needed. A computer-generated list of random numbers was used to allocate group assignments. Minimization algorithm with allocation ratio 1:1 and second best probability of 15% was used. Stratification factors were center, sex, and birthweight (100g intervals). Group coding was used with 2 nonspeaking codes per group; fortifier packaging was coded accordingly but otherwise identical in appearance. Infants were enrolled and assigned to their intervention by the study investigators or trained delegates. All study personnel (both site- and sponsor-based) and participants (infants’ families) were blind to group assignment. Noninferiority was demonstrated if the lower limit of the 2-sided 95% CI of the difference in weight gain from D1 to D21 was larger than the noninferiority margin. Superiority was evaluated if noninferiority was demonstrated. Weight gain was analyzed in the intent-to-treat (ITT) and per-protocol populations by analysis of covariance (ANCOVA) adjusting for D1 postmenstrual age and weight, sex, and center (random effect). Sensitivity analyses were conducted using ANCOVA models that adjusted for covariates that were determined post hoc to be significantly different between groups and which may have confounded the primary results (eg, mother smoking status). Secondary endpoints were analyzed in the ITT population only. For noninferiority and superiority tests, 1-sided P values are provided and should be compared to a reference value of 0.025. For other tests, 2-sided P values are provided and should be compared to a reference value of 0.05. 95% CIs provide estimates for feeding effects on all endpoints. Based on prespecified guidelines in the independent Data Monitoring Committee's (DMC) charter, a single interim analysis was conducted when 134 subjects had completed their D21 visit. The interim analysis was planned to occur when the first 100 infants completed at least 21 days of full fortification; however, the analysis was conducted using data from 134 infants owing to unforeseen delays in conducting the analysis (eg, performing statistical programming, data cleaning, and query resolution) while recruitment continued. The type 1 error rate was adjusted to account for the analysis being conducted at ∼70% enrollment rather than the planned 52%. The DMC consisted of independent experts (2 clinicians, 1 biostatistician) who reviewed growth, formula intake, and key biochemical data as well as AEs. The purpose of the interim analysis was to examine unblinded growth velocity results and determine whether the trial could be stopped early for success or futility, or whether the targeted sample size required adjustment (the interim statistical analysis plan was finalized before unblinding, and the analysis was unblinded only to the DMC to facilitate ethical decision-making) (31). On April 2, 2014, the DMC recommended to stop the trial, as noninferiority and superiority in regard to the primary outcome had been demonstrated. The sponsor was notified of this decision on April 3, 2014, and the final study population included infants enrolled through March 31, 2014.RESULTSA total of 274 infants were screened, with 153 enrolled and randomized to either nHMF (n\u200a=\u200a77) or cHMF (n\u200a=\u200a76) (Fig. 2). Demographic and baseline anthropometry data are summarized in Table 2. There was no evidence of imbalance between the 2 groups with respect to infant characteristics. A significantly lower percentage of mothers and fathers of infants in the nHMF group, however, smoked during pregnancy. Number of twins was similar in each group.FIGURE 2Flow of study participants. AE\u200a=\u200aadverse event; cHMF\u200a=\u200acontrol human milk fortifier; D21\u200a=\u200astudy day 21; ITT\u200a=\u200aintent-to-treat; NEC\u200a=\u200anecrotizing enterocolitis; nHMF\u200a=\u200anew human milk fortifier; NICU\u200a=\u200aneonatal intensive care unit; PP\u200a=\u200aper-protocol; SAE\u200a=\u200aserious adverse event. ∗Although screening procedures were standardized across sites, some variability in prescreening procedures did occur. Based on the typical clinical characteristics of infants who were admitted to each NICU during the study interval, the total number of infants who would have been theoretically considered eligible for the study was higher than the number shown here.The majority (84% and 87% by volume in nHMF and cHMF, respectively) of milk provided to infants was pasteurized. Donor milk was always pasteurized and accounted for 49% and 51% of the fortified HM volume provided in the nHMF and cHMF groups, respectively. There was no significant difference in mean volume of fortified milk intake between groups (152.7\u200a±\u200a13.0 and 152.6\u200a±\u200a17.2\u200amL\u200a·\u200akg−1\u200a·\u200aday−1 in nHMF and cHMF, respectively). Protein intake estimated using standard values for preterm HM composition per 100\u200amL (22) was significantly greater in nHMF compared to cHMF (4.48\u200a±\u200a0.38 vs 3.81\u200a±\u200a0.43\u200ag\u200a·\u200akg−1\u200a·\u200aday−1, respectively; P\u200a<\u200a0.001) because of higher protein content of the nHMF. Estimated energy intake was not significantly different between groups (125\u200akcal\u200a·\u200akg−1\u200a·\u200aday−1 in both groups). There was no significant difference in number of days between FSI1 and D1, but adjusted time between birth and D1 was significantly shorter in nHMF (16.8\u200a±\u200a5.4 vs 18.7\u200a±\u200a8.8 days; −8.5% [95% CI: −15.0%, −1.0%]).GrowthIn the ITT population, adjusted weight gain from D1 to D21 was 2.3\u200ag/day higher in nHMF, with the 95% CI ranging from 0.4 to 4.2\u200ag/day, demonstrating noninferiority (P\u200a<\u200a0.001) and superiority (P\u200a=\u200a0.01) of nHMF. Per-protocol results were similar. Weight gain from D1 to D21 remained significantly higher in nHMF when expressed in grams per kilogram per day (Table 3). Weight-for-age z scores (Fig. 3) remained stable from FSI1 to D21 in nHMF, but continued to decrease in cHMF (P\u200a=\u200a0.007 vs D1). At D21, weight-for-age z score was significantly higher in nHMF compared to cHMF (0.12 [95% CI: 0.03, 0.22]). Length and HC gains during the D1 to D21 period were not significantly different between groups (Table 3), with comparable results observed from analyses of unadjusted means (Table 4). Length-for-age z scores at D21 (Fig. 3) were significantly lower than D1 values in cHMF (P\u200a=\u200a0.041). Additionally, at W40CA, adjusted HC-for-age z scores were significantly higher in nHMF compared to cHMF (0.41 [95% CI: 0.14, 0.68]). Mean weight, length, and HC at D1, D21, and W40CA are summarized in Table 5.FIGURE 3Mean\u200a±\u200aSD weight-for-age (panel A), length-for-age (panel B), and head circumference-for-age (panel C) z scores for the overall ITT population. Circle symbols/solid line represents nHMF. Triangle symbols/dashed line represents cHMF. FSI1\u200a=\u200afortification strength increase day 1; ITT\u200a=\u200aintent-to-treat; SD\u200a=\u200astandard deviation; W40CA\u200a=\u200aweek 40 corrected age; z scores calculated using Fenton preterm growth chart (25). ∗P\u200a=\u200a0.013 vs cHMF (by analysis of covariance, adjusting for value at D1, sex, and center); †P\u200a=\u200a0.007 vs day 1 (by t test); ‡P\u200a=\u200a0.041 vs day 1 (by t test); ∗∗P\u200a=\u200a0.003 vs cHMF (by analysis of covariance, adjusting for value at D1, sex, and center).Protein-Energy StatusBUN decreased progressively in cHMF (P\u200a=\u200a0.004 for D21 vs D1), whereas it increased in nHMF (P\u200a<\u200a0.001 for D10/11 vs D1 [data not shown]) and remained stable up to D21 (Table 6). Prealbumin levels were similar at D1 and increased in both groups during the study (Table 6). The increase from D1 to D21, however, was only significant in nHMF (P\u200a=\u200a0.004). At D21, adjusted mean prealbumin in nHMF was significantly higher (+11.8% [95%CI: +2.3%, +22.2%]) than in cHMF. Urinary urea excretion (corrected for creatinine excretion) at D1 was similar in the 2 groups (Table 6). Urea excretion remained steady in cHMF but increased sharply in nHMF (P\u200a<\u200a0.001 for D10/11 vs D1 [data not shown]), after which it remained stable (to D21). At D21, urea excretion was significantly higher in nHMF versus cHMF (+108.7% [95% CI: +66.0%, +162.5%]).Bone Metabolic StatusSerum calcium concentrations were generally stable during the study (Table 6), with mean values for both groups within the normal range (32). Nevertheless, adjusted mean serum calcium concentration in nHMF was minimally but significantly higher than in cHMF at D21 (+1.9% [95% CI: +0.3%, +3.5%]). Serum phosphorus increased slightly in the 2 groups (Table 6). At D1, relative hypophosphatemia (<1.55\u200ammol/L) was observed in 13 infants in both groups; this was corrected in 11 infants by D10/11 and 12 infants by D21. At D1, serum alkaline phosphatase was not significantly different in nHMF versus cHMF (P\u200a=\u200a0.208). Thereafter, serum alkaline phosphatase decreased significantly in both groups (D21 vs D1: P\u200a=\u200a0.005 for nHMF, P\u200a<\u200a0.001 for cHMF), with mean values significantly higher in nHMF versus cHMF at D10/11 (+8.6% [95% CI: +1.0%, +16.8%]; data not shown) and D21 (+12.1% [95% CI: +2.8%, +22.3%]) (Table 6). Declines from baseline were significantly greater in cHMF versus nHMF at D10/11 (P\u200a<\u200a0.001; data not shown) and D21 (P\u200a=\u200a0.035). At D1, spot urinary excretions of calcium and phosphorus corrected for urinary creatinine excretion were similar in the 2 groups (Table 6). Calcium excretion tended to increase slowly during the study in both groups, with mean concentration significantly lower in nHMF compared to cHMF at D21 (P\u200a=\u200a0.011). Phosphorus excretion increased in both groups, resulting in a decreased median urinary calcium:phosphorus molar ratio in both groups (Table 6).ElectrolytesSerum electrolyte concentrations were stable during the study and similar in both groups (Table 6). Urinary sodium and potassium concentrations were significantly higher (sodium: +31.1% [95% CI: +1.7%, +68.9%], potassium: +22.5% [95% CI: +1.0%, +48.6%]) in nHMF compared to cHMF at D21 (Table 7).Stool Characteristics and Feeding ToleranceStool frequency from D1 to D21 was not significantly different in nHMF and cHMF (3.9\u200a±\u200a1.05 vs 3.6\u200a±\u200a0.93\u200astools/day; 0.29 [95% CI: −0.05, 0.63]). Stool consistency was slightly more “formed” in nHMF compared to cHMF during this interval (3.1\u200a±\u200a0.26 vs 3.0\u200a±\u200a0.27; 0.12 [95% CI: 0.02, 0.21]). Most infants (>90%) had stool consistency scores of “soft.” There were no significant differences between groups in frequencies of spitting-up, vomiting, or abdominal distention. There also were no group differences in incidence of AEs indicative of feeding intolerance (all P\u200a≥\u200a0.25).Adverse EventsThe overall incidence of AEs was significantly larger in nHMF (103 events in 56 infants, including 26 events categorized as GI disorders, 18 as infections or infestations, and 5 as metabolism and nutrition disorders) compared to cHMF (78 events in 41 infants, including 21 events categorized as GI disorders, 18 as infections or infestations, and 1 as metabolism and nutrition disorder; odds ratio: 2.26 [95% CI: 1.10, 4.47]). Other AEs that occurred more frequently in nHMF included several that were classified by study investigators as unlikely to be related to consumption of milk fortifiers (eg, cardiac disorders [16 events in nHMF vs 5 in cHMF], eye disorders [10 events in nHMF vs 3 in cHMF]). The number of AEs considered related to study product intake as determined by physician report was low (3 events in nHMF [2 events of hyponatremia, 1 of vomiting] and 0 events in cHMF). No significant difference was demonstrated in overall incidence of serious AEs between the 2 groups (7 events in 7 infants [including 2 events of necrotizing enterocolitis, 0 events of bronchopulmonary dysplasia, 0 events of sepsis, 0 events of retinopathy] in nHMF and 12 events in 11 subjects [including 4 events of necrotizing enterocolitis, 1 event of bronchopulmonary dysplasia, 0 events of sepsis, 0 events of retinopathy] in cHMF; odds ratio: 0.54 [95% CI: 0.17, 1.58]).DISCUSSIONThis study demonstrated that weight gain from D1 of full fortification until D21 in preterm infants fed HM fortified with a new fortifier designed to add 1.4\u200ag partially hydrolyzed protein and 0.7\u200ag fat to 100\u200amL of HM was significantly greater than weight gain in infants fed HM fortified with an isocaloric control fortifier designed to add 1.0\u200ag extensively hydrolyzed protein and no fat. The mean difference was 2.3\u200ag/day or 1.2\u200ag\u200a·\u200akg−1\u200a·\u200aday−1, consistent with our hypothesized difference of 2\u200ag/day, and which indicates the superiority of the new fortifier compared to the control with regard to weight gain. In addition, the weight gain benefit tended to persist until discharge, with a significantly higher adjusted weight gain difference in the nHMF group compared to cHMF from FSI1 to W40CA (2.01\u200ag/day; P\u200a=\u200a0.009). In the nHMF group, weight-for-age z scores were stable from FSI1 to D21 and average weight gain exceeded 18\u200ag\u200a·\u200akg−1\u200a·\u200aday−1, matching recommended rates of postnatal weight gain to mimic intrauterine growth (33,34). Consistent with the increased protein content of the new fortifier, the nHMF group had significantly higher serum prealbumin concentrations, suggesting an increase in nitrogen retention compared to cHMF. The lack of difference, however, in length gain during the study may be in part the result of the relatively limited period of protein supplementation (only 21 days) or because mean length gains in both groups were already quite high (ie, ≥1.1\u200acm/week), whereas the significantly higher HC-for-age z score at W40CA in the nHMF group may be because of the increased protein and lipid content of the new fortifier. In contrast, the absence of a significant difference at earlier timepoints could be attributable to the relatively high variability of HC gain (31% and 27% for nHMF and cHMF, respectively, from D1 to D21) induced by the natural dolichocephalic evolution of the skull that occurs in preterm infants (35). Feeding tolerance and stool patterns were similar in each group, and AEs related to feeding were low and not significantly different between groups, consistent with fortified HM osmolality values slightly lower in nHMF versus cHMF and below the recommended cutoff (23,24) in both groups.Although there was no evidence of imbalance between the 2 fortifier groups with respect to infant baseline characteristics, significant differences in maternal weight gain, smoking, and alcohol usage during pregnancy were observed. As these may be confounding factors in the analysis of weight gain, post hoc ANCOVAs including these parameters were performed. The post hoc results were essentially the same as the main results, indicating that differences in maternal baseline characteristics did not confound the results. Additionally, to determine the possible impact of including clustered data from twins in the analyses, a sensitivity analysis on weight gain (grams per day) from D1 to D21 accounting for the correlated multiple-birth data was performed. Again, these results were similar to those of the main analysis (weight gain 3.2\u200ag/day higher in nHMF [95% CI: 0.5, 5.9\u200ag/day]).Our results are consistent with those of previous studies (36–42). A recent meta-analysis of 5 studies (comprising 352 infants with birthweight ≤1750\u200ag and gestational age ≤34 weeks) compared growth of infants fed HM fortified with either lower-protein or higher-protein fortifier (43). Infants receiving higher-protein fortifier had significantly greater weight (mean difference 1.77\u200ag/kg/day), length (0.21\u200acm/week), and HC gains (0.19\u200acm/week) compared to those receiving lower-protein fortifier (43). Miller et al (39) used a higher-protein fortifier similar in protein content to the one used in the present study, and reported a higher bodyweight at study end among infants in the higher-protein HMF group (mean difference 220\u200ag), but no significant differences in length or HC. In contrast, Moya et al (40) observed a significantly higher achieved weight, length, and HC in the experimental group compared to controls, but their fortifier had a slightly higher protein content (3.2\u200ag/100\u200amL) versus the one used in the present study (3.04\u200ag/100\u200amL), plus the intervention lasted 28 rather than 21 days.Energy and protein content of HM samples were not analyzed in this study but estimated according to Tsang et al (22). Variability of protein, fat, and energy content of HM fed to preterm infants in the NICU is high (15,21). In addition, fat content may be reduced during processing of HM from expression to administration (44), which could be exacerbated with the use of continuous tube feeding (45). In our study, percentage of intake from mother's own milk, donor milk, and pasteurized HM was assessed. Pasteurized donor milk accounted for 51% of the fortified HM provided during the study, whereas 56% of mother's own milk was also pasteurized. Considering that protein content of donor HM is lower than that of mother's own milk (46) and that all the required processing steps (eg, collection, transfer, refrigeration, pasteurization, tube feeding) may significantly decrease fat and energy content (47), the characteristics of the HM used in the present study suggests that protein and energy content could be overestimated when based on a theoretical composition of preterm HM.In the present study, the mean increase in protein supplementation provided by nHMF compared to cHMF was 0.65\u200ag\u200a·\u200akg−1\u200a·\u200aday−1 or 7.4\u200ammol\u200a·\u200akg−1\u200a·\u200aday−1 of nitrogen, from which approximately 6.14\u200ammol\u200a·\u200akg−1\u200a·\u200aday−1 of nitrogen (83%) is absorbed (based on data from balance studies) (48). During the study, urea production increased significantly in the nHMF group leading to an increase in BUN of 1.7\u200ammol/L at D21 and in urea excretion of 2.3\u200ammol\u200a·\u200akg−1\u200a·\u200aday−1 (2.3\u200ammol/10\u200amg creatinine). These data suggest that the nitrogen balance was improved to ∼3.8\u200ammol nitrogen (52% of nitrogen intake) in preterm infants fed nHMF compared to control. This relatively limited protein utilization could result from reduced energy bioavailability of HM, and an increase in energy supply could improve protein utilization in preterm infants fed fortified HM. These data also suggest that specific nutritional recommendations should be formulated for infants fed fortified HM. Nevertheless, the increase in nitrogen retention (∼3.8\u200ammol\u200a·\u200akg−1\u200a·\u200aday−1) appears to be higher than the nitrogen content of the higher weight gain observed with the nHMF (12% of the 1.5\u200ag\u200a·\u200akg−1\u200a·\u200aday−1 corresponding to 2\u200ammol\u200a·\u200akg−1\u200a·\u200aday−1 of nitrogen), suggesting an increase in lean body mass accretion and a moderate reduction in fat mass gain as previously demonstrated in preterm infants fed protein-fortified HM (49).Indices of bone metabolism were satisfactory in both groups, with a significant decrease in serum alkaline phosphatase observed in both groups and 98% of the infants having normal serum phosphorus concentrations at D21. Adequate postnatal bone mineralization is difficult to obtain in preterm infants owing to the interruption of mineral transplacental transfer (50). Although elevated alkaline phosphatase activity may be associated with reduced bone mineralization when mineral intake is deficient (51), the decrease in enzyme levels observed in the presence of normal serum phosphorus values, as well as the low urinary calcium and moderate urinary phosphorus excretion observed in both groups in this study, suggest that intakes were adequate to promote bone mineralization and limit postnatal osteopenia. Mean serum creatinine concentration decreased significantly in both groups suggesting a similar maturation of renal function during this period. Urinary electrolyte concentrations were higher in nHMF versus cHMF at D21, likely in parallel with the higher electrolyte content of nHMF.A lack of HM composition data (allowing estimation of nutritional balance) is a limitation of our study, although standardized accurate techniques are still not available in the NICU. Additionally, the composition of the faster weight gain can only be estimated as lean body mass and/or bone mineralization were not determined. As a result, nutrient absorption and metabolism can only be estimated from serum and urinary metabolite concentrations. Lastly, the results need to be confirmed in a broader population of preterm infants commonly admitted to the NICU including SGA infants and partially breast-fed infants, as these infants were excluded by design. Strengths of this study include the size and multiple sites (11 pediatric hospitals in 4 European countries), which enhances external validity.In conclusion, these results indicate that the new HM fortifier, made with partially hydrolyzed whey protein and a higher protein:energy ratio is safe, well-tolerated, and improves weight gain of preterm infants compared to control fortifier. Providing some energy as fat and replacing extensively hydrolyzed with partially hydrolyzed protein in the new HM fortifier allows a reduction in osmolality <400\u200amOsm/kg immediately after fortification. Protein intakes from HM supplemented with the new fortifier are within the range of the most recent nutritional recommendations for preterm infants.AcknowledgmentsThe authors thank the families of the infants who participated in the study, as well as the research staff at each participating institution. The authors also thank Christelle Perdrieu and Samir Dahbane from the Clinical Development Unit at the Nestlé Research Center for assistance with trial management and Philippe Steenhout, Medical Director at Nestlé Nutrition, for input on study design and assistance with trial supervision.This study was sponsored by Nestlé Nutrition. J.J., L.A., and N.P.H. are employees of Nestlé SA. J.R., J.M.H., C.B., J.C.P., F.M., A.R., E.S., M.R., U.S., B.G., and J.S. received research funding from Nestlé Nutrition. J.R., J.C.P., and C.B. are consultants for Nestlé Nutrition. U.S. has been a speaker, consultant, and expert panel participant for Nestlé, Danone, and Bledina over the past 3 years. V.d.H. has no conflicts of interest to declare.www.clinicaltrials.gov NCT01771588This study was sponsored by Nestlé Nutrition.Portions of these data were presented in abstract form at the 1st Congress of joint European Neonatal Societies, Budapest, Hungary, 15–20 September 2015.REFERENCES1.GarciaCDuanRDBrevaut-MalatyV\nBioactive compounds in human milk and intestinal health and maturity in preterm newborn: an overview. Cell Mol Biol (Noisy-le-grand)\n2013; 59:108–131.253266482.CorpeleijnWEKouwenhovenSMPaapMC\nIntake of own mother's milk during the first days of life is associated with decreased morbidity and mortality in very low birth weight infants during the first 60 days of life. Neonatology\n2012; 102:276–281.229226753.PatelALJohnsonTJEngstromJL\nImpact of early human milk on sepsis and health-care costs in very low birth weight infants. J Perinatol\n2013; 33:514–519.233706064.ManzoniPStolfiIPedicinoR\nHuman milk feeding prevents retinopathy of prematurity (ROP) in preterm VLBW neonates. Early Hum Dev\n2013; 89\nsuppl 1:S64–S68.238093555.KooWTankSMartinS\nHuman milk and neurodevelopment in children with very low birth weight: a systematic review. Nutr J\n2014; 13:94.252313646.CarlsonSWojcikBBarkerA\nGuidelines for the use of human milk fortifier in the neonatal intensive care unit. University of Iowa Neonatology Handbook. 2011. Available at: http://www.uichildrens.org/iowa-neonatology-handbook/feeding/human-milk\nAccessed on January 22, 2017.7.AdamkinDHRadmacherPG\nFortification of human milk in very low birth weight infants (VLBW <1500\u200ag birth weight). Clin Perinatol\n2014; 41:405–421.248738408.MoroGEArslanogluSBertinoE\nXII. Human milk in feeding premature infants: consensus statement. J Pediatr Gastroenterol Nutr\n2015; 61\nsuppl 1:S16–S19.262959999.EinloftPRGarciaPCPivaJP\nSupplemented vs. unsupplemented human milk on bone mineralization in very low birth weight preterm infants: a randomized clinical trial. Osteoporos Int\n2015; 26:2265–2271.2597168610.GibertoniDCorvagliaLVandiniS\nPositive effect of human milk feeding during NICU hospitalization on 24 month neurodevelopment of very low birth weight infants: an Italian cohort study. PLoS ONE\n2015; 10:e0116552.2559063011.BrownJVEmbletonNDHardingJE\nMulti-nutrient fortification of human milk for preterm infants. Cochrane Database Syst Rev\n2016; 5:CD000343.12.SchanlerRJShulmanRJLauC\nFeeding strategies for premature infants: beneficial outcomes of feeding fortified human milk versus preterm formula. Pediatrics\n1999; 103\n(6 pt 1):1150–1157.1035392213.O’ConnorDLJacobsJHallR\nGrowth and development of premature infants fed predominantly human milk, predominantly premature infant formula, or a combination of human milk and premature formula. J Pediatr Gastroenterol Nutr\n2003; 37:437–446.1450821414.WeberALouiAJochumF\nBreast milk from mothers of very low birthweight infants: variability in fat and protein content. Acta Paediatr\n2001; 90:772–775.1151998015.CorvagliaLAcetiAPaolettiV\nStandard fortification of preterm human milk fails to meet recommended protein intake: bedside evaluation by near-infrared-reflectance-analysis. Early Hum Dev\n2010; 86:237–240.2044777916.ArslanogluSMoroGEZieglerEE\nPreterm infants fed fortified human milk receive less protein than they need. J Perinatol\n2009; 29:489–492.1944423717.ArslanogluSCorpeleijnWMoroG\nDonor human milk for preterm infants: current evidence and research directions. J Pediatr Gastroenterol Nutr\n2013; 57:535–542.2408437318.AgostoniCBuonocoreGCarnielliVP\nEnteral nutrient supply for preterm infants: commentary from the European Society for Paediatric Gastroenterology, Hepatology, and Nutrition Committee on Nutrition. J Pediatr Gastroenterol Nutr\n2010; 50:85–91.1988139019.KoletzkoBPoindexterBUauyR\nRecommended nutrient intake levels for stable, fully enterally fed very low birth weight infants. World Rev Nutr Diet\n2014; 110:297–299.2475163820.GidrewiczDAFentonTR\nA systematic review and meta-analysis of the nutrient content of preterm and term breast milk. BMC Pediatr\n2014; 14:216.2517443521.de HalleuxVRigoJ\nVariability in human milk composition: benefit of individualized fortification in very-low-birth-weight infants. Am J Clin Nutr\n2013; 98\nsuppl:529S–535S.2382472522.TsangRCUauyRKoletzkoB\nNutrition of the Preterm Infant, Scientific Basis and Practical Guidelines. Cincinnati: Digital Educational Publishing, Inc; 2005.23.KreisslAZwiauerVRepaA\nEffect of fortifiers and additional protein on the osmolarity of human milk: is it still safe for the premature infant?\nJ Pediatr Gastroenterol Nutr\n2013; 57:432–437.2385734024.BilleaudCSenterreJRigoJ\nOsmolality of the gastric and duodenal contents in low birth weight infants fed human milk or various formulae. Acta Paediatr Scand\n1982; 71:799–803.718044925.FentonTRKimJH\nA systematic review and meta-analysis to revise the Fenton growth chart for preterm infants. BMC Pediatr\n2013; 13:59.2360119026.NewmanDJPugiaMJLottJA\nUrinary protein and albumin excretion corrected by creatinine and specific gravity. Clin Chim Acta\n2000; 294:139–155.1072768027.Al-DahhanJStimmlerLChantlerC\nUrinary creatinine excretion in the newborn. Arch Dis Child\n1988; 63:398–402.336500928.ICH Expert Working Group. Guideline for good clinical practice E6(R1). 1996\nAvailable at: http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Efficacy/E6/E6_R1_Guideline.pdf\nAccessed on January 22, 2017.29.SpalingerJHSchmidtMBergerTM\nComparison of two human milk fortifiers: effects on growth and zinc status in premature infants. J Pediatr Gastroenterol Nutr\n2004; 39\nsuppl 1:1126.30.WangSKTsiatisAA\nApproximately optimal one-parameter boundaries for group sequential trials. Biometrics\n1987; 43:193–199.356730431.KnottnerusJASpigtMG\nWhen should an interim analysis be unblinded to the data monitoring committee?\nJ Clin Epidemiol\n2010; 63:350–352.1976221032.NicholsonJFPesceMA\nNelsonWEBehrmanREKliegmanRArvinAM\nLaboratory Testing and Reference Values (Table 670-2) in Infants and Children. Nelson Textbook of Pediatrics. Philadelphia: W.B. Saunders; 1996\n2031–2084.33.FentonTRNasserREliasziwM\nValidating the weight gain of preterm infants between the reference growth curve of the fetus and the term infant. BMC Pediatr\n2013; 13:92.2375880834.EhrenkranzRADusickAMVohrBR\nGrowth in the neonatal intensive care unit influences neurodevelopmental and growth outcomes of extremely low birth weight infants. Pediatrics\n2006; 117:1253–1261.1658532235.McCartyDBPeatJRMalcolmWF\nDolichocephaly in preterm infants: prevalence, risk factors, and early motor outcomes. Am J Perinatol\n2016; 34:372–378.2758893336.PorcelliPSchanlerRGreerF\nGrowth in human milk-fed very low birth weight infants receiving a new human milk fortifier. Ann Nutr Metab\n2000; 44:2–10.1083846037.ReisBBHallRTSchanlerRJ\nEnhanced growth of preterm infants fed a new powdered human milk fortifier: a randomized, controlled trial. Pediatrics\n2000; 106:581–588.1096910638.BersethCLVan AerdeJEGrossS\nGrowth, efficacy, and safety of feeding an iron-fortified human milk fortifier. Pediatrics\n2004; 114:e699–e706.1554561639.MillerJMakridesMGibsonRA\nEffect of increasing protein content of human milk fortifier on growth in preterm infants born at <31 wk gestation: a randomized controlled trial. Am J Clin Nutr\n2012; 95:648–655.2230193340.MoyaFSiskPMWalshKR\nA new liquid human milk fortifier and linear growth in preterm infants. Pediatrics\n2012; 130:e928–e935.2298787741.AlanSAtasayBCakirU\nAn intention to achieve better postnatal in-hospital-growth for preterm infants: adjustable protein fortification of human milk. Early Hum Dev\n2013; 89:1017–1023.2403503942.ThoeneMHansonCLydenE\nComparison of the effect of two human milk fortifiers on clinical outcomes in premature infants. Nutrients\n2014; 6:261–275.2439453843.LiuTTDangDLvXM\nHuman milk fortifier with high versus standard protein content for promoting growth of preterm infants: A meta-analysis. J Int Med Res\n2015; 43:279–289.2595615644.VieiraAASoaresFVPimentaHP\nAnalysis of the influence of pasteurization, freezing/thawing, and offer processes on human milk's macronutrient concentrations. Early Hum Dev\n2011; 87:577–580.2159268845.IgawaMMuraseMMizunoK\nIs fat content of human milk decreased by infusion?\nPediatr Int\n2014; 56:230–233.2484751446.WojcikKYRechtmanDJLeeML\nMacronutrient analysis of a nationwide sample of donor breast milk. J Am Diet Assoc\n2009; 109:137–140.1910333547.de HalleuxVPeiltainCSanterreT\nUse of donor milk in the neonatal intensive care unit. Semin Fetal Neonatal Med\n2017; 22:23–29.2764999548.PicaudJCPutetGRigoJ\nMetabolic and energy balance in small- and appropriate-for-gestational-age, very low-birth-weight infants. Acta Paediatr Suppl\n1994; 405:54–59.773479249.PutetGRigoJSalleB\nSupplementation of pooled human milk with casein hydrolysate: energy and nitrogen balance and weight gain composition in very low birth weight infants. Pediatr Res\n1987; 21:458–461.358808250.PieltainCde HalleuxVSenterreT\nPrematurity and bone health. World Rev Nutr Diet\n2013; 106:181–188.2342869951.RuskC\nRickets screening in the preterm infant. Neonatal Netw\n1998; 17:55–57.TABLE 1Calculated∗ nutrient composition of fortified preterm human milkPreterm HM\u2009+\u2009nHMFPreterm HM\u2009+\u2009cHMF4\u2009g fortifier alone4\u2009g fortifier per 100\u2009kcal milk4\u2009g fortifier per 100\u2009mL milk5\u2009g fortifier alone5\u2009g fortifier per 100\u2009kcal milk5\u2009g fortifier per 100\u2009mL milkRecommended intake range (per 100\u2009kcal)†NutrientEnergy, kcal17.410084.617.410084.5Protein, g1.423.63.041.03.102.623.2–4.1Protein sourcePartially hydrolyzed wheyExtensively hydrolyzed wheyFat, g0.725.004.230.024.163.524.4–6MCT, g0.500.590.50000DHA, mg6.319.316.3011.810.0(16.4–) 50–55Carbohydrate, g1.3010.178.603.3012.5310.6010.5–12Carbohydrate sourceMaltodextrinLactose and maltodextrinCalcium, mg7611910175118100109–182Phosphorus, mg44695845705955–127Magnesium, mg4.08.67.32.46.75.77.3–13.6Sodium, mg36.776.564.720.056.848.063–105Potassium, mg48.4116.498.442.0108.892.071–177Chloride, mg32.1106.690.117.088.775.095–161Iron, mg1.802.231.891.301.641.391.8–2.7Zinc, mg0.941.551.310.801.381.171.3–2.3Manganese, μg8.089.988.445.006.345.360.9–13.6Copper, mg0.050.110.090.040.090.080.09–0.21Iodine, μg16.936.630.915.034.329.09–50Selenium, μg3.77.26.11.54.63.94.5–9Vitamin A, IU1183175414835009468001217–3333Vitamin D, IU150187158100128108100–350Vitamin E, IU4.45.64.72.23.02.52.2–11.1Vitamin K, μg8.09.88.34.05.14.34–25Thiamin, mg0.150.190.160.050.070.060.13–0.27Riboflavin, mg0.200.270.230.100.150.130.18–0.36Vitamin B6, mg0.130.160.140.050.070.060.05–0.27Vitamin B12, μg0.200.260.220.100.140.120.09–0.73Niacin, mg1.502.021.710.801.191.010.9–5Folic acid, μg40.051.043.140.051.043.132–91Pantothenic acid, mg0.701.100.930.400.740.630.45–1.9Biotin, μg3.504.784.043.004.193.541.5–15Vitamin C, mg20.028.924.410.017.014.418–50Osmolality‡, mOsm/kg390441cHMF\u2009=\u2009control human milk fortifier; DHA\u2009=\u2009docosahexaenoic acid; HM\u2009=\u2009human milk; nHMF\u2009=\u2009new human milk fortifier; MCT\u2009=\u2009medium chain triglycerides.*Calculated based on preterm human milk composition from Tsang et al, 2005 (22).†Recommended nutrient intakes for fully enterally fed preterm very low birth weight infants (19).‡Measured immediately after fortification at room temperature (25°C).TABLE 2Demographic and baseline characteristics of infants and parentsnHMF (n\u2009=\u200976)cHMF (n\u2009=\u200974)Infant characteristicsSex\u2003Boys38 (50)35 (47)Delivery type\u2003Vaginal24 (32)20 (27)Twin18 (24)16 (22)Birth weight, g1147\u2009±\u20092581156\u2009±\u2009289Birth weight by birth weight category\u2003<1000\u2009g\u2003\u2003n (%)24 (32)26 (35)\u2003\u2003Birth weight, g850.5\u2009±\u2009118.9847.3\u2009±\u2009105.1\u2003≥1000\u2009g\u2003\u2003Birth weight, g1283.6\u2009±\u2009175.41323.9\u2009±\u2009206.2Birth length, cm37.1\u2009±\u20092.737.1\u2009±\u20093.1Birth head circumference, cm26.5\u2009±\u20092.726.7\u2009±\u20092.5Gestational age at birth, weeks28.8\u2009±\u20092.128.7\u2009±\u20091.8Postnatal age at study time points, days*\u2003FSI113 (11, 18)14 (10, 20)\u2003Day 116 (13, 20)17 (13, 23)\u2003Day 2136 (33, 40)37 (33, 43)\u2003Week 40 corrected age76 (66, 91)76 (67, 83)Apgar score\u20031 min5.8\u2009±\u20092.55.8\u2009±\u20092.3\u20035 min8.0\u2009±\u20091.87.7\u2009±\u20091.9Parent characteristicsSmoking status\u2003Mother smoker during pregnancy6 (9)18 (29)\u2003Father smoker3 (5)12 (21)\u2003Mother drank alcohol during pregnancy0 (0)4 (6)Mother's age, y31.1\u2009±\u20095.130.8\u2009±\u20095.5Mother's BMI before pregnancy, kg/m2*23.2 (20.6, 27.2)21.3 (19.7, 26.1)Mother's weight gain during pregnancy, kg11.2\u2009±\u20096.89.2\u2009±\u20095.2BMI\u2009=\u2009body mass index; cHMF\u2009=\u2009control human milk fortifier; FSI1\u2009=\u2009fortification strength increase day 1; nHMF\u2009=\u2009new human milk fortifier . Data are presented as n (%) for categorical variables and mean\u2009±\u2009SD for continuous variables except where noted.*Data are presented as median (Q1, Q3).TABLE 3Anthropometric gains from D1 to D21Treatment groupnnHMFncHMFP*Weight gain, g\u2009·\u2009kg−1\u2009·\u2009day−16418.3\u2009±\u20093.76716.8\u2009±\u20093.70.013†Length gain, cm/wk551.23\u2009±\u20090.62651.18\u2009±\u20090.490.842HC gain, cm/wk571.04\u2009±\u20090.32650.96\u2009±\u20090.260.125cHMF\u2009=\u2009control human milk fortifier; D1\u2009=\u2009study day 1 (first day of full-strength fortification); D21\u2009=\u2009study day 21; HC\u2009=\u2009head circumference; nHMF\u2009=\u2009new human milk fortifier. Data are presented as unadjusted mean\u2009±\u2009SD.*One-sided superiority P value based on analysis of covariance model adjusted for postmenstrual age and relevant anthropometric measure at D1, sex, and center.†Adjusted difference in weight gain (nHMF–cHMF): mean difference\u2009=\u20091.18\u2009g\u2009·\u2009kg−1\u2009·\u2009day−1; 95% CI\u2009=\u20090.14, 2.21.TABLE 4Body length and head circumference gains between study days 1 and 21, by infant sex and by birth weight categoryUnadjusted length gain, cm/wk*Unadjusted head circumference gain, cm/wk*nHMFcHMFnHMFcHMFnMean\u2009±\u2009SDnMean\u2009±\u2009SDP†nMean\u2009±\u2009SDnMean\u2009±\u2009SDP†Overall551.23\u2009±\u20090.62651.18\u2009±\u20090.490.842571.04\u2009±\u20090.32650.96\u2009±\u20090.260.126Boys271.40\u2009±\u20090.65281.18\u2009±\u20090.490.364281.12\u2009±\u20090.28280.99\u2009±\u20090.220.062Girls281.08\u2009±\u20090.56371.17\u2009±\u20090.500.510290.97\u2009±\u20090.35370.93\u2009±\u20090.290.598<1000\u2009g191.07\u2009±\u20090.52211.27\u2009±\u20090.520.563191.04\u2009±\u20090.34210.94\u2009±\u20090.280.223≥1000\u2009g361.32\u2009±\u20090.66441.13\u2009±\u20090.480.499381.05\u2009±\u20090.32440.96\u2009±\u20090.260.270cHMF\u2009=\u2009control human milk fortifier; nHMF\u2009=\u2009new human milk fortifier.*Data are presented as unadjusted mean\u2009±\u2009SD.†Superiority P value for gain differences adjusted for postmenstrual age and the relevant anthropometric measure at D1, sex, and center by analysis of covariance.TABLE 5Weight, length, and head circumference at selected study time pointsnHMFcHMFVariablenMeanSDnMeanSDWeight, g\u2003D1721346271741347270\u2003D21641884336671863328\u2003W40CA603076519632897416Length, cm\u2003D16738.72.57438.72.8\u2003D215841.82.46542.02.7\u2003W40CA6047.62.66247.32.5Head circumference, cm\u2003D16827.72.57327.61.9\u2003D215930.22.26630.32.0\u2003W40CA5935.31.46434.61.5cHMF\u2009=\u2009control human milk fortifier; D1\u2009=\u2009study day 1; D21\u2009=\u2009study day 21; nHMF\u2009=\u2009new human milk fortifier; SD\u2009=\u2009standard deviation; W40CA\u2009=\u2009week 40 corrected age.TABLE 6Markers of protein-energy status, electrolytes, and bone metabolic status at study days 1 and 21nHMFcHMFVariablenMedianIQRGeometric meannMedianIQRGeometric meanP*Serum creatinine, μmol/L\u2003D16944.036.2–48.041.57044.138.0–51.843.50.303\u2003D216328.023.5–32.026.76530.025.0–35.029.50.001BUN, mmol/L\u2003D1703.101.70–4.562.89712.501.65–4.672.730.585\u2003D21633.903.05–4.653.89642.151.50–2.632.15<0.001Serum prealbumin, mg/L\u2003D15110080–12096.8469080–10087.80.073\u2003D214611691.3–140113.84110090–12098.10.015Urinary urea†, mmol/10\u2009mg creatinine\u2003D1472.72.0–4.72.8532.51.9–3.32.50.302\u2003D21425.84.6–6.85.1402.82.0–3.32.7<0.001Serum calcium, mmol/L\u2003D1502.442.31–2.532.41542.472.38–2.562.440.445\u2003D21502.472.40–2.542.46482.432.34–2.532.430.019Serum phosphorus, mmol/L\u2003D1681.991.85–2.221.96711.941.76–2.251.940.816\u2003D21622.101.93–2.232.05642.121.93–2.262.080.681Alkaline phosphatase, U/L\u2003D167353.0298.5–459.5377.963333.0250.0–438.5343.80.208\u2003D2162320.5273.3–405.5337.562270.5233.0–354.3297.50.010Urinary calcium †, mmol/10\u2009mg creatinine\u2003D1600.110.07–0.190.12690.140.09–0.200.120.985\u2003D21550.140.09–0.230.15540.210.13–0.320.190.011Urinary phosphorus†, mmol/10\u2009mg creatinine\u2003D1590.410.12–0.660.22650.340.14–0.650.230.867\u2003D21520.680.44–1.100.53520.710.40–0.920.580.896Urinary calcium:phosphorus molar ratio\u2003D1590.390.15–0.900.50640.410.16–1.340.470.824\u2003D21530.220.12–0.480.28530.310.19–0.600.340.054Serum sodium, mmol/L\u2003D171138.0137.0–140.0138.672138.6136.6–140.0138.50.891\u2003D2165138.0136.4–140.0138.064138.0137.0–139.9138.30.449Serum potassium, mmol/L\u2003D1714.734.30–5.324.83724.774.40–5.104.780.685\u2003D21644.744.29–5.104.72644.514.14–4.884.540.091Serum chloride, mmol/L\u2003D171106.0104.0–109.0106.172105.0102.8–108.0105.20.148\u2003D2163105.0103.0–107.0104.662105.0104.0–107.0105.30.111BUN\u2009=\u2009blood urea nitrogen; cHMF\u2009=\u2009control human milk fortifier; D1\u2009=\u2009study day 1; D21\u2009=\u2009study day 21; IQR\u2009=\u2009interquartile range; nHMF\u2009=\u2009new human milk fortifier.*D1 geometric mean values were log-transformed and analyzed using t test; D21 geometric mean values were log-transformed and analyzed using analysis of covariance (adjusting for the relevant biochemical parameter at D1, sex, and center).†Corrected for urinary creatinine excretion of 10\u2009mg/kg body weight/day.TABLE 7Markers of kidney function, blood count, and urinary electrolyte status at study days 1 and 21nHMFcHMFVariablenMedianIQRGeometric meannMedianIQRGeometric meanP*Urinary creatinine, μmol/L\u2003D1631300.0785.5–1685.51224.7691105.0900.0–1500.01182.3\u2003D21571030.0660.0–1609.01000.355854.0618.0–1273.0900.80.447Serum hemoglobin, mmol/L\u2003D1682.081.84–2.292.14722.021.84–2.262.18\u2003D21631.711.56–1.911.83661.691.50–1.981.760.936Serum hematocrit, %\u2003D1680.400.35–0.430.39720.390.35–0.430.38\u2003D21630.320.29–0.380.33660.330.28–0.380.330.805Urinary sodium, mmol/L\u2003D16637.023.3–57.337.56932.019.4–54.031.2\u2003D215934.021.1–48.033.35623.014.3–36.424.00.037Urinary potassium, mmol/L\u2003D16625.913.6–37.023.66921.815.0–32.220.0\u2003D215930.016.9–45.027.65722.916.9–30.422.80.040Urinary chloride, mmol/L\u2003D16037.026.3–60.040.26733.020.5–55.034.2\u2003D215431.017.8–43.830.75526.018.0–39.527.80.558cHMF\u2009=\u2009control human milk fortifier; D1\u2009=\u2009study day 1; D21\u2009=\u2009study day 21; IQR\u2009=\u2009interquartile range; nHMF\u2009=\u2009new human milk fortifier .*D21 geometric mean values were log-transformed and analyzed using analysis of covariance (adjusting for the relevant biochemical measure at D1, sex, and center)."", 'title': 'Growth and Nutritional Biomarkers of Preterm Infants Fed a New Powdered Human Milk Fortifier: A Randomized Trial.', 'date': '2017-07-21'}}",0.0,Pediatrics & Neonatology
+18,"Is length gain higher, lower, or the same when comparing high protein concentration to low protein concentration?",uncertain effect,very low,no,"['26488118', '22301933', '22987877', '29772833', '28727654']",33215474,2020,"{'26488118': {'article_id': '26488118', 'content': ""J Pediatr Gastroenterol NutrJ. Pediatr. Gastroenterol. NutrJPGAJournal of Pediatric Gastroenterology and Nutrition0277-21161536-4801Lippincott Williams & Wilkins26488118464595610.1097/MPG.000000000000101000012Original Articles: NutritionGrowth and Tolerance of Preterm Infants Fed a New Extensively Hydrolyzed Liquid Human Milk FortifierKimJae H.∗ChanGary†SchanlerRichard‡Groh-WargoSharon§BloomBarry||DimmitReed¶WilliamsLarry#BaggsGeraldine#Barrett-ReisBridget#∗University of California, San Diego-Rady Children's Hospital of San Diego, San Diego†University of Utah, Salt Lake City‡Cohen Children's Medical Center of New York, New Hyde Park§Case Western Reserve University, MetroHealth Medical Center, Cleveland, OH||Wesley Medical Center, Wichita, KS¶University of Alabama, Birmingham#Abbott Nutrition, Columbus, OH.Address correspondence and reprint requests to Jae H. Kim, MD, PhD, University of California, San Diego, 200 W Arbor Dr, MPF 1140, San Diego, CA 92103 (e-mail: neojae@ucsd.edu).12201524112015616665671212201512102015Copyright 2015 by ESPGHAN and NASPGHAN. Unauthorized reproduction of this article is prohibited.2015This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License, where it is permissible to download and share the work, provided it is properly cited. The work cannot be changed in any way or used commercially. http://creativecommons.org/licenses/by-nc-nd/4.0ABSTRACTObjectives:This study was a comparison of growth and tolerance in premature infants fed either standard powdered human milk fortifier (HMF) or a newly formulated concentrated liquid that contained extensively hydrolyzed protein.Methods:This was an unblinded randomized controlled multicenter noninferiority study on preterm infants receiving human milk (HM) supplemented with 2 randomly assigned HMFs, either concentrated liquid HMF containing extensively hydrolyzed protein (LE-HMF) or a powdered intact protein HMF (PI-HMF) as the control. The study population consisted of preterm infants ≤33 weeks who were enterally fed HM. Infants were studied from the first day of HM fortification until day 29 or hospital discharge, whichever came first.Results:A total of 147 preterm infants were enrolled. Noninferiority was observed in weight gain reported in the intent-to-treat (ITT) analysis was 18.2 and 17.5 g · kg−1 · day−1 for the LE-HMF and PI-HMF groups, respectively. In an a priori defined subgroup of strict protocol followers (n\u200a=\u200a75), the infants fed LE-HMF achieved greater weight over time than those fed PI-HMF (P\u200a=\u200a0.036). The LE-HMF group achieved greater linear growth over time compared to the PI-HMF (P\u200a=\u200a0.029). The protein intake from fortified HM was significantly higher in the LE-HMF group compared with the PI-HMF group (3.9 vs 3.3 g · kg−1 · day−1, P\u200a<\u200a0.0001). Both fortifiers were well tolerated with no significant differences in overall morbidity.Conclusions:Both fortifiers showed excellent weight gain (grams per kilograms per day), tolerance, and low incidence of morbidity outcomes with the infants who were strict protocol followers fed LE-HMF having improved growth during the study. These data point to the safety and suitability of this new concentrated liquid HMF (LE-HMF) in preterm infants. Growth with this fortifier closely matches the recent recommendations for a weight gain of >18 g · kg−1 · day−1.Keywordsbreast-feedinggrowthhuman milk fortifierpreterm infantsOPEN-ACCESSTRUEWhat Is KnownPowdered infant milk products cannot be sterilized and is a source of bacterial infection.Very-low-birth-weight infants often require more protein than presently can be provided with conventional human milk fortifiers.A liquid fortifier with higher protein than conventional fortifiers is desirable to increase safety and improved growth.What Is NewA liquid human milk fortifier that is based on extensively hydrolyzed bovine casein with higher amounts of total protein than powder human milk fortifiers confers equal to improved growth to very-low-birth-weight infantsUse of this new liquid fortifier provides sterility without any increase in feeding intolerance or short-term adverse effects.Human milk (HM) is a source of essential nutrients and immunologic factors for the preterm infant, but alone it is not sufficient nutritionally to meet the high demands of the rapidly growing infant. Human milk fortifiers (HMFs) are nutritional supplements designed to increase total energy, protein, and micronutrient delivery to preterm infants. The primary benefits of HM fortification have been improved growth, bone mineralization, and protein status such as blood urea nitrogen (BUN) (1–5).Increasing survival and improving growth of the preterm infant to avoid extrauterine growth restriction have resulted in demands for protein that present powdered HMF may not achieve. Although some of these infants may compensate with higher volume intake, many are unable to consume a sufficient volume because of pulmonary or other clinical issues and therefore require further concentration of protein and energy. Higher intake of protein between 3 and 4 g · kg−1 · day−1 has been associated with improved growth without complications compared with a lower consumption of protein (<3 g · kg−1 · day−1) (6). Poor weight gain has been associated with a higher risk for retinopathy of prematurity and poor neurodevelopmental outcomes (7,8). It is common practice in the neonatal intensive care units (NICUs) to add protein modular (powder or liquid) to the feeding to better meet the protein needs of the smaller preterm infant. In fact, 42% of the respondents to a recent survey on nutritional practices in the NICU reported adding protein to HM (9).There has been a gradual transition to sterile liquid nutritionals in the neonatal environment during the last decade because of concerns about powder-based transmission of pathogens such as Cronobacteria sakasakii(10). The recent development of a liquid HM–based HMF and a partially hydrolyzed whey-acidified liquid HMF respond to these concerns (11,12). Unlike powder nutritionals, a liquid HMF may have the advantage of sterility and simpler liquid-liquid mixing with human milk (HM). One disadvantage of a liquid fortifier is volume displacement of HM.In this study, we evaluated a novel liquid HMF containing extensively hydrolyzed protein source to determine efficacy and safety in very-low-birth-weight preterm infants.METHODSStudy PopulationA total of 14 NICUs from across the United States participated in this study, including Tampa, Florida; Wichita, Kansas; Toledo, Ohio; Salt Lake City, Utah; Birmingham, Alabama; Cleveland, Ohio; Allentown, Pennsylvania; San Diego, California; Valhalla, New York; Manhasset, New York; Portland, Oregon; Cleveland, Ohio; South Bend, India; and Brooklyn, New York. The study population consisted of preterm infants born at ≤33 weeks’ gestational age with birth weights ranging from 700 to 1500 g who were enterally fed HM in the NICU. Infants identified as eligible for randomization and for whom consent was obtained were randomly assigned to one of the 2 study regimens. Sealed envelopes containing the subject treatment group assignment were prepared from randomization schedules that were computer-generated using a pseudorandom permuted blocks algorithm. A separate computer-generated randomization schedule was produced for twins to ensure that eligible twins were both assigned to the same product. The randomization was block stratified by birth weight (700–1000 g and 1000–1500\u200ag) and sex.Eligibility criteria included appropriate intrauterine growth and maternal intent to provide breast milk during the study. The use of donor HM was not permitted during the study period unless indicated by the clinical staff or PI but could have been used in the first week of life before study initiation. Infants were excluded for enteral feeds not started within 21 days of life, severe congenital anomalies, expectant transfer to another facility, 5-minute Apgar <5, severe intraventricular hemorrhage (grade 3 or 4), mechanical ventilation, major abdominal surgery, severe asphyxia, and necrotizing enterocolitis (NEC). Use of probiotics or postnatal corticosteroids was not permitted.Study DesignThis was an unblinded randomized controlled multicenter study conducted on preterm infants receiving HM supplemented with 2 randomly assigned HMFs, either a newly formulated concentrated liquid HMF containing extensively hydrolyzed protein (Abbott Nutrition, Columbus, OH; LE-HMF) or a conventional powdered intact protein HMF (Similac Human Milk Fortifier, PI-HMF, Abbott Nutrition) as control. For every 25 mL of HM, HMF was added as a 5-mL dose of LE-HMF or 1 single packet of PI-HMF. Study Day (SDAY) 1 was defined as the first day of HM fortification and occurred within 72 hours after the subject had reached an intake of at least 100 mL · kg−1 · day−1 of HM. The primary study period was from SDAY 1 until SDAY 29 or hospital discharge, whichever came first. This study was approved by institutional research ethics board as appropriate at each study sites. Table 1 shows the key study fortifier differences.Anthropometric indices (weight, length, and head circumference [HC]), tolerance, serum biochemistries, intake, and morbidity data were assessed. Anthropometric variables and tolerance outcomes were collected after SDAY 29 if the infant remained on study HMF.Weight, length, and HC of infants were measured according to standardized procedures from SDAY 1 to SDAY 29 or hospital discharge, whichever came first. Weight measures were taken daily using the hospital scales (incubator or bedside). Documentation of scale calibration was reviewed during routine visits. The other anthropometric measurements were performed weekly. Recumbent length was obtained with a fixed headboard and moveable footboard and HC using a nonstretchable tape.Feeding tolerance was assessed by variables such as stool characteristics (bloody, hard, black, and/or watery) and the incidence of feedings withheld because of abdominal distention, gastric residuals, and vomiting. Any nil per os periods were also collected.Enteral intake was collected from enrollment to SDAY 29. Intake of HM (including donor/banked HM) or other enteral feeding (including supplements such as protein modulars) were recorded. Although the LE-HMF contained the same amount of energy as the PI-HMF, it contained higher protein and a different source of protein. It also contained added lutein, docosahexaenoic acid, and arachidonic acid.Blood samples were drawn from each infant by venipuncture or, if necessary, by heelstick on SDAYs 1, 15, and 29. Serum electrolytes, bicarbonate, calcium, phosphorus, magnesium, alkaline phosphatase, BUN, and prealbumin were analyzed at the hospital site. Confirmed NEC (determined by using modified Bell staging criteria) and sepsis were recorded. The occurrence of these and other serious adverse events was documented throughout the study.Statistical AnalysisStudy data were analyzed on an intent-to-treat (ITT) basis including all enrolled infants who received study fortifier. Based on anticipated protocol deviations in this high-risk population, a subgroup analysis was prospectively planned to analyze data from infants who strictly adhered to the assigned HMF. The strict protocol followers (SPFs) were defined a priori as those infants who received <20% of total energy from sources other than the assigned study HMF; and <3 consecutive days on modular supplements (eg, protein supplements, another study HMF, nonstudy formula, or donor milk) for at least 2 weeks from SDAY 1 to SDAY 29.Sample size was calculated to test the hypothesis that LE-HMF was noninferior to PI-HMF using an equivalence limit of 1.6 g · kg−1 · day−1 in weight gain per day. With a noninferiority hypothesis and assuming that the expected difference in means is zero and the common standard deviation is 2.56 g · kg−1 · day−1, the total sample size required to have 80% power was 66 subjects who are SPF (33 per group). The power for this unbalanced sample size distribution is 83%. Assuming an attrition rate of approximately 46%, the target number for enrollment was 124 subjects (62 per group). A study designed for noninferiority does not preclude testing for superiority (13). Weight gain (grams per kilogram per day) for each subject was calculated by an exponential model that involved a regression line fit on loge (wt), where wt is weight (in grams) on each day (13). Weight gain (grams per kilogram per day) was analyzed using analysis of variance with factors for center and feeding (primary). Analyses were also made adjusting for sex, birth weight, and average fortified HM intake (milliliters per kilogram per day) diluted full strength during the study period. A 95% 1-sided confidence interval for the difference in means between groups was used for noninferiority evaluation.Length (centimeters per week) and HC gains (centimeters per week) were analyzed using the same models. Weight, length, and HC collected at 1-week intervals were analyzed with repeated measures analysis of covariance (ANCOVA) testing effects of center, feeding, sex, study day, interaction of feeding with sex, feeding with study day, and covariate birth weight. By time point analyses of weight, length, and HC using ANCOVA were made post-hoc using 1-sided tests consistent with a noninferiority design.Average daily volume enteral intake (milliliters per kilogram per day) was analyzed using analysis of variance. Complete blood cell counts with differential and serum blood biochemistries were analyzed using repeated measures ANCOVA with covariate SDAY 1 measure.Outcomes expressed as percent of infants (tolerance, morbidity, and respiratory variables) were analyzed using the Cochran-Mantel-Haenzsel test stratified by center. The frequencies of occurrence of adverse events by system organ class and preferred terms using MedDRA codes were tabulated and analyzed using Fisher exact test. Hypothesis testing for this study was done using 2-sided, 0.05 level tests. All analyses were made using SAS version 9.2 (SAS Institute, Cary, NC) on a computer.RESULTSStudy PopulationA total of 147 subjects were randomized into the study. Of the 147 subjects, 129 were included in the ITT group, that is, all randomized subjects who received study HMF. Of those subjects in the ITT group, 75% completed the study duration (45 PI-HMF, 52 LE-HMF). More than half the infants in the ITT group met the definition for the SPFs (Fig. 1). The number of days on the assigned study fortifier was 25 and 29 for the PI-HMF (n\u200a=\u200a63) and LE-HMF (n\u200a=\u200a66) groups, respectively. The median number of days on the assigned study fortifier for SPF was 29 days for both the PI-HMF and LE-HMF groups as some extended their use beyond the study period. Of note, some SPF subjects did not complete the study duration because they were discharged from the hospital.FIGURE 1Disposition of subjects.Demographic and Other Baseline CharacteristicsCharacteristics of the study patients are summarized in Table 2. There were no statistically significant differences among study subjects randomized to the PI-HMF or the LE-HMF group in gestational age, sex, race, mode of delivery and multiple birth status. There were, however, more Hispanic infants in the PI-HMF as compared to the LE-HMF group (28% vs 13%, P\u200a=\u200a0.041). In addition, there were no statistical differences between groups at birth or SDAY 1 for weight, length, and HC. Furthermore, there were no differences in clinical history and progression of enteral feeds. Infants in the 2 feeding groups who were SPF reflect comparable demographic and baseline characteristics patterns.GrowthThere were no statistical differences in the primary outcome of weight gain (grams per kilogram per day) during the study period regardless of whether the statistical analysis was performed on the ITT group or SPFs. Hence, noninferiority was achieved. Respective weight gains were 17.5 and 18.2 g · kg−1 · day−1 for PI-HMF and LE-HMF (Table 3). Likewise in the subgroup (SPF) analysis weight gains were 18.2 and 18.4 g · kg−1 · day−1 for PI-HMF and LE-HMF. There was, however, a main feeding effect that was the infants fed LE-HMF compared with infants fed PI-HMF had increased weight during the study among SPFs as depicted in Fig. 2A (P\u200a=\u200a0.036). When analyzing the data at separate time points the weight at SDAY 29 was significantly higher in LE-HMF group versus the PI-HMF group (P\u200a=\u200a0.024). Likewise, infants in the ITT group fed LE-HMF had higher weights at SDAYs 15, 22, and 29 than infants fed PI-HMF whether or not adjusted for differences in ethnicity. The SPF infants receiving LE-HMF reached 1800 g 7 days sooner than the infants fed PI-HMF (19 vs 26 days, respectively, P\u200a=\u200a0.049).FIGURE 2Evaluable analysis: A, weight (in grams); B, length (in centimeters); C, head circumference (in centimeters). A, Weight (in grams). Repeated measures analysis main effect, P\u200a=\u200a0.036; post-hoc per time point analysis: SDAY 29, P\u200a=\u200a0.024. B, Length (in centimeters). Repeated measures analysis main effect, P\u200a=\u200a0.029; post-hoc per time point analysis: SDAY 22, P\u200a=\u200a0.006, SDAY 29, P\u200a=\u200a0.037. C, Head circumference (in centimeters).The length and HC gains (centimeters per week) during the study period revealed no statistical differences between the groups and met growth targets (Table 3). The infants fed LE-HMF compared with infants fed PI-HMF had increased linear growth during the study among SPFs as depicted in Fig. 2B (P\u200a=\u200a0.029). When analyzing the data at separate time points adjusted for birth length, the length at SDAY 22 and SDAY 29 were significantly higher in LE-HMF group versus the PI-HMF group (P\u200a<\u200a0.05). HC was not different between the fortifier groups (Fig. 2C).Feeding Tolerance and Stool CharacteristicsIn both the ITT and SPF groups, both fortifiers were well tolerated with similar number and percentage of infants having feedings withheld because of abdominal distention, gastric residuals and/or vomiting. There was no difference in the percentage of infants who were nil per os between the groups (22.7 LE-HMF, 19 PI-HMF). The stool characteristics in both groups were similar with no differences in bloody stools, hard stools or black stools. Loose stools were commonly reported—56% in the PI-HMF group and 53% in the LE-HMF group—and were considered normal for infants who are receiving HM as their primary feeding.Enteral NutritionThe mean caloric and protein intakes are reported for both HMF groups. For the SPFs, the average percentage of calories from fortified HM was ∼96% in both the PI-HMF and LE-HMF groups. The mean intake of fortified HM was 116 and 114 kcal · kg−1 · day−1 in the PI-HMF and LE-HMF groups, respectively. The calculated protein intake from fortified HM was significantly higher in the LE-HMF group as compared to the PI-HMF group (3.9 vs 3.3\u200a g · kg−1 · day−1, P\u200a<\u200a0.0001). This difference was expected as LE-HMF contains more protein than PI-HMF. Energy intakes were not different between the groups.Blood ChemistriesThe blood chemistries reported in Table 4 include bicarbonate, BUN, prealbumin, calcium, phosphorus, magnesium, alkaline phosphatase, and electrolytes. In general, the blood biochemistries at SDAYs 1, 15, and 29 were within the normal reference ranges for preterm infants for both the ITT and SPF groups fed milk fortified with either fortifier (14,15). There were significant differences between groups in both the ITT and SPF analyses for BUN (P\u200a<\u200a0.001) and prealbumin (P\u200a<\u200a0.01), with both being higher in the LE-HMF group. Both groups were well within reference ranges for these parameters. Bicarbonate was significantly higher in the LE-HMF group only at SDAY1 in the ITT analysis.Safety and Morbidity DataIn the ITT group, fewer infants discontinued fortifier because of feeding intolerance in the LE-HMF group as compared to the PI-HMF group (2% vs 10%, P\u200a=\u200a0.048). There was a low incidence of confirmed NEC (1.5% in the LE-HMF group and 3.2% in the PI-HMF group) and confirmed sepsis (4.5% vs 3.2%, respectively)DISCUSSIONThe purpose of developing LE-HMF was to provide a concentrated liquid fortifier that would be superior to conventional powder HMF by virtue of sterility, higher protein concentration, and absence of intact cow's-milk protein. An extensively hydrolyzed protein source is included to promote feeding tolerance in preterm infants. The extensively hydrolyzed protein may be tolerated better for infants who are sensitive to the intact cow's-milk protein.The primary purpose of the present clinical trial was to assess whether the new HMF would promote targeted weight gain, with good tolerance and without association with specific comorbidities in a noninferiority comparison with a commercially available powder HMF that has demonstrated safety and efficacy in preterm infants (13).Weight gain and linear growth approaching intrauterine rates are important goals in the management of premature infants. The mean weight gain for both groups (PI-HMF and LE-HMF) exceeded the intrauterine growth rate of 15 g · kg−1 · day−1 and closely matched recent recommendations for a weight gain of >18 g · kg−1 · day−1(7). The mean HC gain for both groups also closely matched recent recommendations for a HC gain of >0.9 cm/wk (7). This result was not surprising given the excellent weight, length, and HC gains previously reported in infants fed PI-HMF powder (13).Ehrenkranz et al (7) have reported that as the rate of weight gain increased in hospitalized preterm infants, the incidence of cerebral palsy, neurodevelopmental impairment, and need for re-hospitalization decreased significantly. A weight gain rate of >18 g · kg−1 · day−1 and a HC growth rate of >0.9 cm/wk were associated with better neurodevelopmental and growth outcomes. Lower quartile growth was associated with the poorest neurodevelopmental outcomes.Weight and length differed between the groups. Although there were no significant differences in mean weight at birth or SDAY 1, infants receiving LE-HMF had ∼½ lb greater mean weight than the infants in the PI-HMF group at the end of the study period. Although the rate of linear growth was not statistically different, infants in the LE-HMF group had greater achieved linear growth during the study period. It is possible that the greater weight and length in the LE-HMF infants was because of the higher number of infants in this group that adhered to the assigned study feeding.New expert recommendations suggest that extremely-low-birth-weight infants (<1000 g birth weight) have higher protein requirements (3.5–4.5 g/100 kcal) (16). HMFs provide an important strategy to overcoming nutrient deficits for preterm and low-birth-weight infants. Differences in the level and ingredient sources of the macronutrients, especially the protein quantity, in PI-HMF versus LE-HMF may have contributed to the overall performance of the LE-HMF group. The higher protein intake in infants receiving LE-HMF (∼3.6 g/100 kcal) as compared to PI-HMF (∼3.0 g/100 kcal) was likely one of the reasons for the improved growth observed in these infants. Although infants in the LE-HMF group had higher protein intakes, energy intakes were not different between the groups.Preterm infants fed fortified HM have variable rates of growth at least partly because of differences in intake of calories, carbohydrates, electrolytes, calcium, phosphate, and protein. The acid-base status of the preterm infant also, however, affects growth. In preterm infants the kidney may not tolerate an acid load, leading to the development of metabolic acidosis. In a recent study, a liquid acidified HMF caused metabolic acidosis and poor growth in preterm infants in the NICU (17,18). In another study, Rochow et al (19) described a commercially available fortifier in Europe that had to be reformulated because of the development of metabolic acidosis from an imbalance of electrolytes. The authors recorded a mean weight gain of only 9.7 g · kg−1 · day−1 and decreased bone mineralization with metabolic acidosis. No infants in our study developed metabolic acidosis.The LE-HMF protein source may be beneficial for this population because it was extensively hydrolyzed casein formulation without any intact cow's-milk protein. It has been suggested that a combination of free amino acids and short chain peptides (di- and tri peptides) may allow more optimal nitrogen absorption (20,21). Intact bovine protein powder HMF has an excellent safety record; however, a recent study by Sullivan et al (11) suggested the possibility that even in the presence of a HM base diet, the addition of intact bovine protein powder HMF is associated with higher rates of total and surgical NEC. The mechanism for the higher NEC risk is not known yet. Although this study was not powdered for NEC there was no difference in the NEC or sepsis rates between the infants fed an intact bovine protein and the extensively hydrolyzed protein. Both groups had rates lower than previously reported (22–24).Intact bovine protein has higher associated long-term risk for allergy and atopy compared with HM-fed infants. Protein intolerance is seen in premature infants and in term infants (25). Because preterm infants have a similar risk for allergy and atopy compared with term infants and in the NICU have presented with symptoms suggestive of allergic colitis, avoiding intact bovine protein may be a desirable objective. For preterm infants fed HM the use of an extensively hydrolyzed protein-based HMF is an appropriate option.In general, blood chemistries were within normal reference ranges for preterm infants. The higher BUN and prealbumin seen in the LE-HMF group can be attributed to the higher protein content of LE-HMF. These higher values may be indicative of improved protein nutriture. It should be noted that although BUN is influenced by renal function and hydration state, all other influences being equal, it is proportional to protein intake and responds rapidly to changes in protein intake (4,5,26,27).Postnatal growth failure remains common in premature infants. Nearly 25 years ago Kashyap et al showed that even a small deficit in protein intake impairs both growth in lean body mass and linear growth (28). In recent years, Arslanoglu et al reported that addition of protein to preterm feedings of recovering VLBW infants resulted in significantly improved linear growth (4,5). This was accomplished by monitoring the BUN level so that when it was less than 9\u200amg/dL, increased protein was added to their feedings. It was observed in the present study that the mean BUN level fell <9 mg/dL by week 2 in infants receiving PI-HMF; however, in infants receiving LE-HMF it never fell <9 mg/dL during the entire study period. Our results, in part, agree with other investigators that an increased protein-to-calorie ratio in the feeds of preterm infants will improve linear growth (4,5,9,28). It is becoming increasingly evident that promoting catch-up growth in the NICU may have implications for long-term development and health (7,29).Our study did have several limitations. The study examined the combined effects of changing both protein content and type (hydrolyzed vs intact). Future studies may want to capture effects of changing one of these variables. A number of subjects in this study did not complete the protocol to SDAY 29. This partially diluted the effects seen in the ITT groups but still permitted demonstration of differential effects seen in the SPF subgroup. A larger study design may improve this in the future. Infants <700 g birth weight were excluded from this study and therefore the study findings cannot be readily extrapolated to this vulnerable group. It is expected however that this group would have higher protein demands than infants in this study and therefore would be as likely or more to have a favorable response to higher protein. Although no differences were seen between both groups for NEC and sepsis the study size was too small to discern true differences for these outcomes.CONCLUSIONSBoth fortifiers showed excellent tolerance and a low rate of morbidity outcomes, with the infants who were SPFs fed LE-HMF having improved growth. These data confirm the safety and suitability of this new concentrated liquid HMF for preterm infants.AcknowledgmentsThe authors thank the following individuals for their hard work and dedication: Coryn Commare, MS, RD; Christy Saulters, BS; Debra Lee-Butcher, BSN, RN; Holy Boyko, BSN, RN; Angela Worley; Carolyn Richardson; Sue Zhang, MS, MAS; Mustafa Vurma, PhD; Maggie Hroncich, BS; Aimee Diley; Kristen Fithian; Sue Nicholson, MS, RD; and Jennifer Teran, BS, RD. The authors also thank study investigators and their staff for their cooperation: Terri Ashmeade, MD; Anthony Killian, MD; Lance Parton, MD; Robert Schelonka, MD; Robert White, MD; Ivan Hand, MD, FAAP; Michelle Walsh, MD; Jeffrey Blumer, PhD, MD; Paula Delmore, RN; Carrie Rau, RN; Renee Bridge, RN; Lisa Lepis, RN; Judy Zaritt, RN; Claire Roane, RN, MSN; Julie Gualtier, RN; Diane Fierst, RN; Christina Gogal; Natalie Dweck; Debra Potak, RN; Barbara Wilkens, RN; Nakia Clay, BS; Mashelle Monhaut, NNP-BC; Rickey Taing, NPL; Susan Bergant, RN, CCRP; and Bonnie Rosolowski, RPT.www.clinicaltrials.gov registration number: NCT01373073.This study was funded by Abbott Nutrition.J.H.K., B.B., G.C., R.S. and S.G.-W. received research funds from the study sponsor, Abbott Nutrition, to conduct the study. J.H.K. is on the speakers’ bureaus for Abbott Nutrition, Mead Johnson Nutrition, Nestle Nutrition, Nutricia, and Medela. J.H.K. and R.S. are on the medical advisory board for Medela. J.H.K. owns shares in PediaSolutions and has provided medical expert testimony. B.B. received a grant from the Wichita Medical Research and Education Foundation. G.C. received a research grant from the University of Utah and has provided medical expert testimony. S.G.-W. is on the speakers’ bureau of Abbott Nutrition. B.B.-R., L.W., and G.B. are employees of Abbott Nutrition.The authors report no conflicts of interest.REFERENCES1.SchanlerRJ\nSuitability of human milk for the low-birthweight infant. Clin Perinatol\n1995; 22:207–222.77812532.SchanlerRJAbramsSA\nPostnatal attainment of intrauterine macromineral accretion rates in low birth weight infants fed fortified human milk. J Pediatr\n1995; 126:441–447.78692083.KuschelCAHardingJE\nMulticomponent fortified human milk for promoting growth in preterm infants. Cochrane Database Syst Rev\n2004; 1:CD000343.149739534.ArslanogluSBertinoECosciaA\nUpdate of adjustable fortification regimen for preterm infants: a new protocol. J Biol Regul Homeost Agents\n2012; 26\n(3 suppl):65���67.231585175.ArslanogluSMoroGEZieglerEE\nAdjustable fortification of human milk fed to preterm infants: does it make a difference?\nJ Perinatol\n2006; 26:614–621.168859896.PremjiSSFentonTRSauveRS\nHigher versus lower protein intake in formula-fed low birth weight infants. Cochrane Database Syst Rev\n2006; 1:CD003959.164374687.EhrenkranzRADusickAMVohrBR\nGrowth in the neonatal intensive care unit influences neurodevelopmental and growth outcomes of extremely low birth weight infants. Pediatrics\n2006; 117:1253–1261.165853228.VanderveenDKMartinCRMehendaleR\nEarly nutrition and weight gain in preterm newborns and the risk of retinopathy of prematurity. PLoS One\n2013; 8: e64325.9.WhitfieldJPunjabi-GuptaSHendriksonH\nImproved linear growth in VLBW infants at discharge: impact of increasing the protein/kcal ratio (PCR) of feeds. E-PAS Abstract\n2012; 4510:122.10.TaylorC\nHealth Professionals Letter on Enterobacter sakazakii Infections Associated With the Use of Powdered (Dry) Infant Formulas in Neonatal Intensive Care Units. Bethesda, MD: US Food and Drug Administration, Center for Food Safety and Applied Nutrition, Office of Nutritional Products, Labeling and Dietary Supplements; 2002.11.SullivanSSchanlerRJKimJH\nAn exclusively human milk-based diet is associated with a lower rate of necrotizing enterocolitis than a diet of human milk and bovine milk-based products. J Pediatr\n2010; 156:562–567.2003637812.MoyaFSiskPMWalshKR\nA new liquid human milk fortifier and linear growth in preterm infants. Pediatrics\n2012; 130:e928–e935.2298787713.Barrett-ReisBHallRTSchanlerRJ\nEnhanced growth of preterm infants fed a new powdered human milk fortifier: a randomized, controlled trial. Pediatrics\n2000; 106:581–588.1096910614.The Harriet Lane Handbook (The Johns Hopkins Hospital). 19th ed. New York: Elsevier Health Sciences; 2011: chap 27.15.RamelSEGeorgieffMK\nNutrition. In: Avery's Neonatology—Pathophysiology and Management of the Newborn. 7th ed. Philadelphia: Lippincott Williams & Wilkins; 2015.16.KoletzkoBPoindexterBUauyR\nRecommended nutrient intake levels for stable, fully enterally fed very low birth weight infants. World Rev Nutr Diet\n2014; 110:297–299.2475163817.ThoeneMHansonCLydenE\nComparison of the effect of two human milk fortifiers on clinical outcomes in premature infants. Nutrients\n2014; 6:261–275.2439453818.CibulskisCCArmbrechtES\nAssociation of metabolic acidosis with bovine milk-based human milk fortifiers. J Perinatol\n2015; 35:115–119.2510232119.RochowNJochumFRedlichA\nFortification of breast milk in VLBW infants: metabolic acidosis is linked to the composition of fortifiers and alters weight gain and bone mineralization. Clin Nutr\n2011; 30:99–105.2072762620.GrimbleGKKeohanePPHigginsBE\nEffect of peptide chain length on amino acid and nitrogen absorption from two lactalbumin hydrolysates in the normal human jejunum. Clin Sci (Lond)\n1986; 71:65–69.370907621.BozaJJMartinez-AugustinOBaroL\nProtein v. enzymic protein hydrolysates. Nitrogen utilization in starved rats. Br J Nutr\n1995; 73:65–71.785791622.PatoleS\nPrevention and treatment of necrotising enterocolitis in preterm neonates. Early Hum Dev\n2007; 83:635–642.1782600923.FanaroffAAStollBJWrightLL\nTrends in neonatal morbidity and mortality for very low birthweight infants. Am J Obstet Gynecol\n2007; 196:147e1-8.1730665924.StollBJHansenNIBellEF\nNeonatal outcomes of extremely preterm infants from the NICHD Neonatal Research Network. Pediatrics\n2010; 126:443–456.2073294525.D’NettoMAHersonVCHussainN\nAllergic gastroenteropathy in preterm infants. J Pediatr\n2000; 137:480–486.1103582526.ZieglerEE\nBreast-milk fortification. Acta Paediatr\n2001; 90:720–723.1151997227.PolbergerSKAxelssonIERaihaNC\nUrinary and serum urea as indicators of protein metabolism in very low birthweight infants fed varying human milk protein intakes. Acta Paediatr Scand\n1990; 79:737–742.223926628.KashyapSSchulzeKFForsythM\nGrowth, nutrient retention, and metabolic response in low birth weight infants fed varying intakes of protein and energy. J Pediatr\n1988; 113:713–721.313985629.HansonCSundermeierJDugickL\nImplementation, process, and outcomes of nutrition best practices for infants <1500\u200ag. Nutr Clin Pract\n2011; 26:614–624.2194764530.EhrenkranzRAYounesNLemonsJA\nLongitudinal growth of hospitalized very low birth weight infants. Pediatrics\n1999; 104\n(2 Pt 1):280–289.10429008TABLE 1Approximate nutrient composition of PI-HMF or LE-HMF added to HMNutrient PI-HMFLE-HMFEnergy, cal100100Fat, g5.25.1CHO, g10.410.1Protein, g33.6Source/type of proteinIntact whey protein concentrateExtensively hydrolyzed caseinDHA, mg1224Vitamin D, IU150150Calcium, mg175153Phosphorus, mg9886Osmolality, mOsm/kg water385450Lutein, μg*23Values per 100 calories mixed at a ratio of 1 pkt or 5 mL:25 mL HM (as fed). CHO\u2009=\u2009carbohydrate; DHA\u2009=\u2009docosahexaenoic acid; HM\u2009=\u2009human milk; LE-HMF \u2009=\u2009liquid HMF containing extensively hydrolyzed protein; PI-HMF\u2009=\u2009powdered intact protein HMF.*Lutein not added to product but available in varying amounts from HM.TABLE 2Neonatal and perinatal characteristics of preterm infantsTreatment group*PI-HMF (n\u2009=\u200963)LE-HMF (n\u2009=\u200966)Gestational age, wk28.7\u2009±\u20090.228.8\u2009±\u20090.2Birth weight, g1156\u2009±\u2009241193\u2009±\u200926Birth length, cm37.4\u2009±\u20090.337.7\u2009±\u20090.3Birth HC, cm26.1\u2009±\u20090.226.5\u2009±\u20090.2Male sex, n (%)35 (56)36 (55)Ethnicity: Hispanic, n (%)17 (28)8 (13)†Race, n (%)\u2003White42 (67)43 (65)\u2003Black13 (21)17 (26)\u2003Asian1 (2)1 (2)\u2003Other7 (11)3 (5)\u2003White/other0 (0)2 (3)C-section, n (%)38 (60)42 (64)Twin, n (%)16 (25)12 (18)Age at study day 1, d12.3\u2009±\u20090.712.8\u2009±\u20090.6Birth class, n (%)\u2003≤1000\u2009g16 (24)12 (19)\u2003>1000\u2009g66 (76)63 (81)LE-HMF\u2009=\u2009liquid HMF containing extensively hydrolyzed protein; PI-HMF\u2009=\u2009powdered intact protein HMF.*Mean\u2009±\u2009SEM.†P\u2009=\u20090.0407.TABLE 3Anthropometric gainsTreatment group*Targeted growth†,‡PI-HMF (n\u2009=\u200963)LE-HMF (n\u2009=\u200966)Weight gain, g kg−1 day−117.5\u2009±\u20090.618.2\u2009±\u20090.3>18Length gain, cm/wk1.2\u2009±\u20090.071.2\u2009±\u20090.06>0.9HC gain, cm/wk1.0\u2009±\u20090.041.0\u2009±\u20090.05>0.9LE-HMF\u2009=\u2009liquid HMF containing extensively hydrolyzed protein; PI-HMF\u2009=\u2009powdered intact protein HMF; HC\u2009=\u2009head circumference.*Intent-to-treat group, n\u2009=\u2009129.†Ehrenkranz et al (7).‡Ehrenkranz et al (30).TABLE 4Blood chemistry dataCharacteristicsReference rangesStudy dayTreatment group*PI-HMFLE-HMFBicarbonate, mEq/L†17–24123.27\u2009±\u20090.45 (59)25.05\u2009±\u20090.45 (62)1524.32\u2009±\u20090.50 (49)25.40\u2009±\u20090.39 (58)2925.04\u2009±\u20090.43 (40)25.54\u2009±\u20090.44 (50)BUN, mg/dL‡2.5–31.4111.47\u2009±\u20090.78 (56)11.89\u2009±\u20091.03 (61)158.30\u2009±\u20091.15 (50)11.72\u2009±\u20090.68 (58)295.81\u2009±\u20090.38 (40)9.31\u2009±\u20090.53 (49)Prealbumin, mg/dL§7.0–39.0110.05\u2009±\u20090.37 (58)9.69\u2009±\u20090.33 (54)1510.11\u2009±\u20090.37 (47)11.40\u2009±\u20090.41 (46)299.08\u2009±\u20090.35 (36)10.01\u2009±\u20090.35 (37)Calcium, mg/dL8.0–11.0110.10\u2009±\u20090.08 (56)9.93\u2009±\u20090.08 (60)159.93\u2009±\u20090.10 (50)9.95\u2009±\u20090.07 (57)299.89\u2009±\u20090.09 (40)9.82\u2009±\u20090.06 (49)Phosphorus, mg/dL4.2–8.716.41\u2009±\u20090.17 (54)6.20\u2009±\u20090.13 (58)156.71\u2009±\u20090.13 (46)6.50\u2009±\u20090.12 (56)296.66\u2009±\u20090.10 (40)6.46\u2009±\u20090.12 (47)Magnesium, mg/dL1.5–2.111.90\u2009±\u20090.03 (54)1.88\u2009±\u20090.02 (59)151.80\u2009±\u20090.03 (47)1.86\u2009±\u20090.03 (55)291.81\u2009±\u20090.02 (40)1.82\u2009±\u20090.03 (46)Alkaline phosphatase, U/L150–4001443.89\u2009±\u200924.50 (55)415.40\u2009±\u200915.78 (60)15366.13\u2009±\u200921.80 (48)332.68\u2009±\u200910.87 (57)29335.28\u2009±\u200921.84 (40)342.36\u2009±\u200913.10 (47)Sodium, mEq/L129–1431137.49\u2009±\u20090.49 (61)138.42\u2009±\u20090.34 (65)15137.46\u2009±\u20090.55 (52)137.56\u2009±\u20090.29 (59)29139.07\u2009±\u20090.41 (41)138.70\u2009±\u20090.40 (50)Potassium, mEq/L4.5–7.115.39\u2009±\u20090.11 (61)5.20\u2009±\u20090.09 (65)155.25\u2009±\u20090.09 (52)5.23\u2009±\u20090.09 (59)295.25\u2009±\u20090.10 (41)5.06\u2009±\u20090.07 (50)Chloride, mEq/L100–1171104.16\u2009±\u20090.60 (58)104.03\u2009±\u20090.55 (63)15104.10\u2009±\u20090.72 (49)103.88\u2009±\u20090.43 (57)29106.00\u2009±\u20090.57 (40)106.14\u2009±\u20090.37 (49)BUN\u2009=\u2009blood urea nitrogen; LE-HMF\u2009=\u2009liquid HMF containing extensively hydrolyzed protein; PI-HMF\u2009=\u2009powdered intact protein HMF; HC\u2009=\u2009head circumference.*Values are mean\u2009±\u2009SEM (n).†Bicarbonate (mEq/L): (SDAY 1) LE-HMF > PI-HMF, P\u2009=\u20090.0419, LSM\u2009±\u2009SE: LE-HMF\u2009=\u200924.71\u2009±\u20090.56, PI-HMF\u2009=\u200923.33\u2009±\u20090.62.‡BUN (mg/dL): Feeding main effect: LE-HMF > PI-HMF, P\u2009=\u20090.0013, LSM\u2009±\u2009SE: LE-HMF\u2009=\u200911.99\u2009±\u20090.73, PI-HMF\u2009=\u20098.99\u2009±\u20090.83.§Prealbumin (mg/dL): Feeding main effect: LE-HMF > PI-HMF, P\u2009=\u20090.0049, LSM\u2009±\u2009SE: LE-HMF\u2009=\u200910.61\u2009±\u20090.35, PI-HMF\u2009=\u20099.32\u2009±\u20090.38."", 'title': 'Growth and Tolerance of Preterm Infants Fed a New Extensively Hydrolyzed Liquid Human Milk Fortifier.', 'date': '2015-10-22'}, '22301933': {'article_id': '22301933', 'content': 'Preterm human milk-fed infants often experience suboptimal growth despite the use of human milk fortifier (HMF). The extra protein supplied in fortifiers may be inadequate to meet dietary protein requirements for preterm infants.\nWe assessed the effect of human milk fortified with a higher-protein HMF on growth in preterm infants.\nThis is a randomized controlled trial in 92 preterm infants born at <31 wk gestation who received maternal breast milk that was fortified with HMF containing 1.4 g protein/100 mL (higher-protein group) or 1.0 g protein/100 mL (current practice) until discharge or estimated due date, whichever came first. The HMFs used were isocaloric and differed only in the amount of protein or carbohydrate. Length, weight, and head-circumference gains were assessed over the study duration.\nLength gains did not differ between the higher- and standard-protein groups (mean difference: 0.06 cm/wk; 95% CI: -0.01, 0.12 cm/wk; P = 0.08). Infants in the higher-protein group achieved a greater weight at study end (mean difference: 220 g; 95% CI: 23, 419 g; P = 0.03). Secondary analyses showed a significant reduction in the proportion of infants who were less than the 10th percentile for length at the study end in the higher-protein group (risk difference: 0.186; 95% CI: 0.370, 0.003; P = 0.047).\nA higher protein intake results in less growth faltering in human milk-fed preterm infants. It is possible that a higher-protein fortifier than used in this study is needed. This trial was registered with the Australian New Zealand Clinical Trials Registry (http://www.anzctr.org.au/) as ACTRN12606000525583.', 'title': 'Effect of increasing protein content of human milk fortifier on growth in preterm infants born at <31 wk gestation: a randomized controlled trial.', 'date': '2012-02-04'}, '22987877': {'article_id': '22987877', 'content': 'To evaluate the growth, tolerance, and safety of a new ultraconcentrated liquid human milk fortifier (LHMF) designed to provide optimal nutrients for preterm infants receiving human breast milk in a safe, nonpowder formulation.\nPreterm infants with a body weight ≤ 1250 g fed expressed and/or donor breast milk were randomized to receive a control powder human milk fortifier (HMF) or a new LHMF for 28 days. When added to breast milk, the LHMF provided ∼20% more protein than the control HMF. Weight, length, head circumference, and serum prealbumin, albumin, blood urea nitrogen, electrolytes, and blood gases were measured. The occurrence of sepsis, necrotizing enterocolitis, and serious adverse events were monitored.\nThis multicenter, third party-blinded, randomized controlled, prospective study enrolled 150 infants. Achieved weight and linear growth rate were significantly higher in the LHMF versus control groups (P = .04 and 0.03, respectively). Among infants who adhered closely to the protocol, the LHMF had a significantly higher achieved weight, length, head circumference, and linear growth rate than the control HMF (P = .004, P = .003, P = .04, and P = .01, respectively). There were no differences in measures of feeding tolerance or days to achieve full feeding volumes. Prealbumin, albumin, and blood urea nitrogen were higher in the LHMF group versus the control group (all P < .05). There was no difference in the incidence of confirmed sepsis or necrotizing enterocolitis.\nUse of a new LHMF in preterm infants instead of powder HMF is safe. Benefits of LHMF include improvements in growth and avoidance of the use of powder products in the NICU.', 'title': 'A new liquid human milk fortifier and linear growth in preterm infants.', 'date': '2012-09-19'}, '29772833': {'article_id': '29772833', 'content': ""NutrientsNutrientsnutrientsNutrients2072-6643MDPI29772833598651310.3390/nu10050634nutrients-10-00634ArticleThe Effect of Increasing the Protein Content of Human Milk Fortifier to 1.8 g/100 mL on Growth in Preterm Infants: A Randomised Controlled TrialReidJessica1MakridesMaria12McPheeAndrew J.13StarkMichael J.34https://orcid.org/0000-0002-6474-0505MillerJacqueline15CollinsCarmel T.12*1Healthy Mothers, Babies and Children, South Australian Health and Medical Research Institute, Adelaide, SA 5006, Australia; jessica.reid@adelaide.edu.au (J.R.); maria.makrides@sahmri.com (M.M.); andrew.mcphee@sa.gov.au (A.J.M.); jacqueline.miller@sahmri.com (J.M.)2Adelaide Medical School, Discipline of Paediatrics, The University of Adelaide, Adelaide, SA 5006, Australia3Neonatal Medicine, Women’s and Children’s Hospital, Adelaide, SA 5006, Australia; michael.stark@adelaide.edu.au4The Robinson Research Institute, The University of Adelaide, Adelaide, SA 5006, Australia5Nutrition and Dietetics, Flinders University, Adelaide, SA 5006, Australia*Correspondence: carmel.collins@sahmri.com; Tel.: +61-8-8128-440917520185201810563426420181552018© 2018 by the authors.2018Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).The aim of this study was to assess the effect of feeding high protein human milk fortifier (HMF) on growth in preterm infants. In this single-centre randomised trial, 60 infants born 28–32 weeks’ gestation were randomised to receive a higher protein HMF providing 1.8 g protein (n = 31) or standard HMF providing 1 g protein per 100 mL expressed breast milk (EBM) (n = 29). The primary outcome was rate of weight gain. Baseline characteristics were similar between groups. There was no difference between high and standard HMF groups for weight gain (mean difference (MD) −14 g/week; 95% CI −32, 4; p = 0.12), length gain (MD −0.01 cm/week; 95% CI −0.06, 0.03; p = 0.45) or head circumference gain (MD 0.007 cm/week; 95% CI −0.05, 0.06; p = 0.79), despite achieving a 0.7 g/kg/day increase in protein intake in the high protein group. Infants in the high protein group had a higher proportion of lean body mass at trial entry; however, there was no group by time effect on lean mass gains over the study. Increasing HMF protein content to 1.8 g per 100 mL EBM does not improve growth in preterm infants born 28–32 weeks’ gestation.human milkgrowthlow birth weighthuman milk fortifier1. IntroductionIt is well established that fortified human milk improves growth rates in preterm infants [1,2,3]. However, the optimal amount of protein in the fortifier is yet to be determined, partly due to the variability in the protein content of human milk, both within mothers and over time. Too little protein results in a failure to meet protein requirements, estimated to be 4.0–4.5 g/kg/day for infants born <1000 g and 3.5–4.0 g/kg/day for those born 1000–1800 g [4]. Consequently, growth failure in the neonatal period is common in infants fed fortified human milk compared with infants fed preterm formula [5,6,7]. Conversely, too much protein may result in metabolic acidosis [8]. Individualized fortification, based on either the metabolic response of the infant [9,10,11], or the macronutrient content of mother’s milk [12] has been investigated and provides evidence in support of inadequate protein concentration of human milk fortifiers (HMFs) when used in a standardised approach. However, individualised approaches are time consuming and not easily translated to the clinical environment [13]. We previously investigated a fortifier providing 1.4 g compared with 1 g protein per 100 mL human milk in preterm infants <31 weeks’ gestation [14]. While we found no difference in the rate of weight and length gain between groups, there were fewer infants with length <10th percentile at discharge in the high protein group, suggesting a higher protein concentration fortifier may be needed to improve growth. We therefore aimed to determine the effect of further increasing the protein content of HMF to 1.8 g/100 mL compared with 1 g/100 mL, on growth in preterm infants born 28–32 weeks’ gestation.2. Materials and Methods2.1. Study DesignThe study was a single centre (Women’s and Children’s Hospital, North Adelaide, South Australia), parallel group randomised controlled trial conducted between February 2012 and May 2013.2.2. ParticipantsInfants born 28–32 completed weeks’ gestation whose mothers intended to provide breast milk were eligible to participate. Multiple births were eligible and were randomised individually. Infants with a major congenital or chromosomal abnormality likely to affect growth, or where protein therapy was contraindicated (e.g., major heart defects, cystic fibrosis, phenylketonuria, disorders of the urea cycle) were ineligible. Infants likely to transfer to remote locations and infants who had received standard practice HMF for more than four days were also excluded.2.3. Randomisation and BlindingInfants were randomised to one of two groups: the higher protein intervention group or the standard protein control group. An independent researcher created the randomisation schedule using a computer generated variable block design of 4 and 6. Stratification occurred for sex and gestational age 28–29 weeks and 30–32 weeks. Parents of eligible infants were approached by a neonatologist and followed-up for consent by a research nurse who was not involved in clinical care. Upon consent, infants were randomised by telephoning an independent researcher who held the randomisation schedule and assigned a unique study identification number. Participants, clinicians, outcome assessors and data analysts were blinded to randomisation group.2.4. InterventionsThe base HMF used for both trial groups was FM85 Human Milk Supplement (Nestlé Nutrition, Gland, Switzerland) which provides 1.0 g protein and 17.5 kcal when 5 g HMF is added to 100 mL expressed breast milk (EBM). The high protein fortifier was prepared by adding 0.9 g Protifar (Nutricia, Zoetermeer, The Netherlands), a bovine casein-based powder, to the FM 85. This resulted in an additional 0.8 g protein and 3.5 kcal per 100 mL EBM providing 1.8 g protein and 21 kcal when added to 100 mL of EBM. To ensure both fortifiers were isocaloric, thereby eliminating the effect of different energy intakes on growth, 0.9 g Polyjoule (Nutricia, Zoetermeer, The Netherlands), a glucose polymer, was added to the standard fortifier providing an additional 3.5 kcal but no extra protein, giving a total of 1.0 g protein and 21 kcal when added to 100 mL of EBM. The Polyjoule and Protifar supplements were packaged into identical 400-g containers each with a tamper proof seal (Pharmaceutical Packaging Professionals Pty Ltd., Thebarton, Australia). The containers were differentiated by four colour-coded labels to facilitate blinding, with each trial group separately color-coded into two groups. Infant nutrition attendants, under the direction of the Nutrition and Food Services Department, were trained in the preparation of the HMF. Trial fortifier was mixed at the rate of 5 g FM 85 plus either 0.9 g Protifar, or 0.9 g Polyjoule, for the high and standard protein groups respectively, with 4 mL of sterile water, to give a total volume of 8 mL for use with each 100 mL of EBM.2.5. Intervention AdministrationThe fortifier intervention and control fortifiers were delivered via the enteral tube, immediately prior to a feed (tube, bottle or breast). Trial HMFs were delivered at 8 mL HMF/100 mL EBM with the volume of HMF for each feed ordered daily by the medical or neonatal nurse practitioners. In cases where a mix of EBM and preterm formula was to be given, the trial HMF was only given if EBM was >50% of the total feed. When the infant received a direct breast feed, the timing of administration of the trial product (before, during or after the feed) was at the discretion of the primary care nurse in consultation with the mother. For each day, the trial HMFs were decanted into syringes and labelled with infant identification, volume of HMF and trial details. Syringes were stored refrigerated in the neonatal unit in each infant’s individually labelled container. Any syringes not administered in the 24-h period were recorded and discarded. Fluid balance records were audited daily for compliance with the trial protocol. Administration of trial HMF began as soon as practical after randomisation (within one to two days) and continued until study end, defined as the removal of the naso-gastric tube or estimated date of delivery, whichever came first.2.6. Nutritional IntakeMeasured protein and fat content of a weekly sample of unfortified EBM (MilkoScan Minor, Foss, Denmark) were used to represent the weekly composition of EBM [14]. The lactose concentration was assumed to be 6.8 g/100 mL. EBM was only sampled when the supply was surplus to the infant’s requirements. Missing values were substituted with the average macronutrient composition of all available samples (32 of the 45 mothers involved in the study were able to provide breast milk samples). Macronutrient intakes for the study fortifiers, EBM and formula were calculated from the volume ingested, the protein and fat concentration of EBM, and the manufacturer’s information on the study fortifiers and formula. The protein content of the preterm formula in use at the time of the study was 2.2 g/100 mL. Energy content was calculated by using the Atwater factors of 4, 4, and 9 kcal/g for protein, carbohydrate, and fat respectively.2.7. Outcome Assessments2.7.1. Primary outcomeThe primary outcome was rate of weight gain (g/week) from trial start (day of randomisation) to trial end. In addition to routine clinical measurements, a research nurse and J.R. weighed infants on randomisation, weekly and at study end; duplicate weight measurements were taken using electronic balance scales accurate to 5 g. Measurements were repeated if there was a discrepancy ≥10 g, with the average of the two closest measurements used.2.7.2. Secondary Efficacy and Safety OutcomesSecondary efficacy outcomes included length and head circumference gain (cm/week), infant weight at study end, small for gestational age (SGA) at study end and body composition (fat-free mass). Length measurements were taken weekly with the infant in the supine position and measured to the nearest 0.1 cm using a recumbent length board. Head circumference was measured weekly using a non-stretching tape placed around the largest occipito-frontal circumference. Duplicate measurements were done and repeated if there was a discrepancy ≥0.5 cm, with the average of the 2 closest measures taken. SGA was defined as below the 10th percentile for infants of the same sex and gestational age, as determined from Australian birth reference data [15]. Fat free (lean) mass was measured weekly by bioelectrical impedance spectroscopy (BIS) using the Imp™ SFB7 (ImpediMed Limited, Queensland, Australia) with the first measurement taken during the first week of the study.Secondary safety outcomes included feeding tolerance (days feeds interrupted and days to reach enteral intake ≥150 mL/kg/day). A protocol was developed for discontinuation of the trial fortifier based on uraemia (blood urea nitrogen (BUN) concentration >8.0 mmol/L) and/or a metabolic acidosis (base excess <−6 mmol/L) persisting for more than 48 hours. However, no infant met these criteria. Similarly, criteria were defined for the addition of protein to feeds if an infant had poor weight gain defined as <15 g/kg/day over the preceding 7-day period associated with a BUN of <2 mmol/L when feed volumes reached 170 to 180 mL/kg/day. In this case, Protifar could be added at the discretion of the attending neonatologist, in addition to the allocated intervention fortifier. Additional protein was ceased when weight gain of 15 g/kg/day and a BUN >2 mmol/L were achieved.2.7.3. Biochemical AnalysesWeekly blood samples were taken and BUN, plasma albumin, plasma creatinine, pH and base deficit measured. Blood spots were collected weekly on filter paper and amino acids measured using tandem mass spectrometry (SA Pathology, Neonatal Screening Centre, Adelaide, Australia).2.7.4. Sample Size and Statistical AnalysisA sample size of 60 (30 per group) would detect a difference in weight gain of 3.31 g per day between the high protein and standard protein groups (80% power, p = 0.05). Consultation with the neonatal medical team agreed that this was a clinically important difference on which clinical practice could be changed. Mean weight, length, head circumference and lean mass gains over the trial period, were calculated for each infant using a linear effects model with a random intercept and slope. Using the slope, a linear regression model was fitted for each infant. Clustering (multiple births) was accounted for by using a generalised estimating equation with an independent working correlation matrix. All analyses were intention-to-treat. All models were adjusted for sex and gestational age category (28–29 and 30–32 weeks’ gestation). A per protocol analysis was specified a priori for infants who consumed ≥70% of their prescribed trial fortifier.2.7.5. EthicsEthical approval was granted by the Women’s and Children’s Health Network Human Research Ethics Committee (REC2401/10/14). This trial was registered with the Australian New Zealand Clinical Trials Registry (http://www.anzctr.org.au/) as ACTRN12611001275954.3. Results3.1. Study PopulationSixty infants were enrolled in the trial with 31 infants randomised to the high protein group and 29 infants to the standard protein group (Figure 1). There were 31 infants born from multiple births (14 sets of twins, 1 set of triplets). In all multiple births, apart from two sets of twins, the infants were randomly allocated to different interventions. For the triplets, two were randomised to the high protein group and one to the standard protein group. Four infants, two from each group, were withdrawn from the study after randomisation but before the first dose of trial fortifier was administered after parents changed their minds about involvement. A further two infants (twins) in the high protein group did not have any available breast milk and withdrew before the commencement of fortifier. One set of twins and one singleton were withdrawn by the parents midway through the trial due to perceived feeding intolerance and another infant was withdrawn by the clinical team after developing necrotising enterocolitis. In all cases of withdrawal, parents consented to the ongoing collection of data and all were included in intention-to-treat analyses. Baseline infant and maternal demographic, clinical and nutritional characteristics at randomisation were comparable between groups except that there were more male infants in the high protein group, n = 16 (52%) than the standard protein group, n = 12 (41%), the mean ± SD birth weight was lower in the higher protein group (1483 ± 423 g versus 1551 ± 407 g in the high and standard groups, respectively) and there were more infants classified as SGA for weight in the high protein group, n = 5 (16%) than the standard protein group, n = 1 (3%) (Table 1).3.2. Nutritional ManagementForty infants received standard ward HMF, S-26 SMA HMF (Wyeth Nutrition) while waiting for consent, 18 in the high and 22 in the standard protein group (Table 1). The remaining twenty trial infants started immediately on their allocated trial intervention.Nutritional intake of the infants for the first 28 days of the study did not differ between the groups except that the high protein group received more protein (mean ± SD 4.2 ± 1.3 vs. 3.5 ± 0.93 g/kg/day in the high and standard protein groups respectively). The protein concentration of the EBM was not different between groups (mean ± SD 1.43 ± 0.27 and 1.45 ± 0.28 g protein/100 mL in the high and standard groups, respectively) and the difference in protein intake was due to more protein derived from the HMF (mean ± SD 1.9 ± 1.2 and 1.2 ± 0.6 g/kg/day, in the high and standard groups, respectively. Energy intakes and fluid volume were similar between the groups (energy: mean ± SD 124 ± 34 and 126 ± 27 kcal/kg/day and fluid: mean ± SD 154 ± 39 and 157 ± 32 mL/kg/day in the high and standard groups, respectively). The high protein group received 83% (±32) of their total enteral intake as EBM compared with the control group who received 90% (±23).3.3. Primary OutcomeThere was no difference in the rate of weight gain between groups (Table 2) (mean (95% CI) high protein 245 (230, 260) g/week and standard protein 258 (244, 272) g/week, adjusted mean difference −14 (−32, 4) p = 0.12). Results were similar when analysed per protocol (Table 2).3.4. Secondary Outcomes3.4.1. GrowthThere were no differences in rate of length or head circumference gain (Table 2). High protein HMF infants weighed less at study end but this was not statistically significant (Table 2) and is consistent with the difference in birth weight between the groups (Table 1). There were no differences in length or head circumference at study end between the groups (Table 2). There was no difference in SGA status for weight between high and standard protein HMF groups at the end of the study (n = 8, 25%, and n = 3, 10% SGA infants in the high and standard protein groups, respectively, adjusted Relative Risk (95% CI); 2.5 (0.8, 7.9), p = 0.11).Over the first four weeks of the trial, when >75% of participants were still in hospital, fat free (lean) mass was measured with the week one measurement taken a mean of 8 ± SD 2 days after randomisation. Fat free mass as a proportion of body weight (Figure 2) from weeks one to four was greater in high protein group infants than standard protein group infants (p = 0.03). However, there was no significant group by time interaction (p = 0.84). At week three alone, there was a significant increase for fat free mass as a proportion of body weight in the high protein group (p = 0.04).3.4.2. BiochemistryDue to the variable nature of blood chemistry data and length of hospital stay (to discharge), only the first three trial weeks could be accurately analysed using a linear mixed effects model.There was a significant group by time interaction for BUN levels (p < 0.001) with BUN levels significantly increased in the high protein group (Figure 3). This difference continued for the duration of the trial (p < 0.001). There were 12 occurrences in nine separate infants where BUN levels were measured over the pre-specified safety threshold of 8 mmol/L. Seven of these occurred during baseline blood tests taken at randomisation and were therefore not a result of the intervention. Six of these infants had BUN measurements in the normal range at their next weekly blood test. One infant had a BUN measurement >8 mmol/L at week one; the infant did not have another BUN measurement over 8 mmol/L for the rest of the trial. Two other infants, both in the high protein group, recorded BUN concentrations >8.0 mmol/L, peaking at 8.8 mmol/L, on five occasions, however the base excess remained above −6 mmol/L with no other abnormal biochemistry. There was one occurrence of an infant in the standard protein group requiring additional protein due to poor weight gain and BUN <2 mmol/L.There were no group by time interactions or group differences for albumin, creatinine, glucose, pH (results not shown). Phenylalanine (Phe) and tyrosine (Tyr), amino acids associated with increased protein intake, were both increased in the high protein group compared to the standard group at study week 3 (Phe median (IQR) μmol/L: 33 (28–42) vs. 25 (23–30), p <0.001 and Tyr median (IQR) μmol/L: 196 (151–267) vs. 128 (99–172) μmol/L, p <0.003 in the high and standard groups respectively.3.4.3. Clinical OutcomesHigh protein HMF infants were significantly more likely to have feeds interrupted (11 (35%) vs. 6 (21%), p = 0.01, in the high and standard protein groups, respectively) Table 3. There was no significant difference in the number of days spent on parenteral nutrition, days of intravenous lipid or the days taken to reach full enteral feeds. Likewise, there was no significant difference between the groups for any other clinical outcome (Table 3).4. DiscussionThe aim of this study was to assess the effect of a higher protein HMF on preterm infant growth. Our trial interventions resulted in the high protein group infants receiving 0.7 g/kg/day more protein than infants in the standard protein group, with mean protein intakes within recommended ranges for both groups. Despite this, there were no differences in growth between the two groups. The accumulation of fat free mass and fat mass, also did not differ between groups. While the higher protein group had a greater proportion of fat free mass from week one, the absence of a baseline measurement makes the interpretation of this difficult. It is unlikely that the intervention would have had an effect in the first week of the study, particularly as the change in fat free mass over time did not differ between groups. A significant difference between groups was noted at week three only and the implication of this is unclear. It is possible that this is a chance finding of no clinical significance.These results are confirmed by a recent study by Maas et al. [16] who compared 1 and 1.8 g protein concentration in powdered HMFs in a similar population to ours and found no difference in growth. Their trial interventions achieved a 0.6 g/kg/day median greater intake of protein, similar to our study, and protein intakes were within recommendations. Growth rates in both studies approximated foetal growth rates. A further two studies compared two different, newly formulated liquid HMFs with higher protein concentrations, with standard powdered HMFs. Moya et al. [17] compared Mead Johnson Nutrition products: a liquid fortifier with an Enfamil powdered fortifier, which when mixed with EBM provided 3.2 and 2.6 g protein/100 mL, respectively, equating to an additional 1.8 and 1.1 g protein. Kim et al. [18], in a non-inferiority trial, compared the Abbott Nutrition products of Similac HMF liquid, providing 3.6 g protein/100 kcal when mixed with EBM, with Similac HMF powder providing 3 g protein/100 kcal when mixed. These comparisons equate to an additional 1.6 and 1 g protein added to 100 mL EBM in the liquid and powder, respectively. The populations were similar between studies [17,18] except that Moya et al. [17] inclusion criteria (≤30 weeks’ gestation, birth weight ≤1250 g) resulted in a slightly less mature and smaller population than in both Kim et al. [18] study and this current study. Neither study [17,18] showed a difference in weight gain between groups, however, Moya et al. [17] found improved length gain with the higher protein. Both studies found infants in the high protein group were heavier at study end. Almost half the participants in Moya’s study were <1000 g at birth; hence their protein requirements of 4 to 4.5 g/kg would have been met by the high, but not the control, protein fortifier at volumes of 150 mL/kg. This may explain the effect seen on length gain. Two other studies have compared fortifiers containing 1 and 1.4 g protein added to 100 mL EBM with mixed results. Our previous trial [14] showed no effect of increased protein on growth, although did show a reduction in the number of infants SGA for length at discharge. However, Rigo et al. [19], in a non-inferiority trial, found improved weight gain of 2.3 g/day with the higher protein fortifier. The trial products in both these studies were similar, as were the population. It is possible that the smallest infants, with the highest protein needs, are the ones to benefit most from increased protein and that the larger sample size in Rigo (n = 153) compared to that in Miller (n = 92) elucidated the differences. Taken collectively, these results and ours suggest that protein concentrations in HMFs of 1.8 g provide no additional benefit in the population studied, but smaller infants are worthy of further investigation.The significantly elevated BUN levels seen at weeks 1, 2 and 3 were expected and have occurred in other high protein nutritional intervention studies [9,14,17]. Assuming adequate renal function, BUN is proportional to protein intake [20] and is often used as a crude marker of protein sufficiency. Low BUN levels suggest inadequate protein intake and high levels indicate possible excessive intake [9]. Blood phenylalanine and tyrosine concentrations were also significantly increased in the higher protein group, in week 3 only, and this is unlikely to be clinically significant. There were no differences in creatinine, albumin or other biochemical markers suggesting the intervention did not harm the infants.A strength of this study is the rigour with which dietary intake and growth were assessed. The protein and fat concentrations of EBM were measured, rather than assumed, resulting in accurate reporting of dietary intake and confirmation that, despite the variability of protein in EBM, we achieved a mean intake difference of 0.7 g/kg/day of protein between groups. Similarly, we measured both growth and body composition in an attempt to discern differences in weight gain arising from extra protein. This trial also has some limitations. Although all infants were included in the analyses, there were 10 who either did not receive, or ceased the intervention, which may have impacted results. In addition, the pragmatic nature of this trial may have influenced results as clinicians may have adjusted feed regimes if poor weight gain was identified. There was one instance of extra Protifar prescribed to an infant in the standard protein group and subtle increases in feed volume may also have occurred although volume of intake was not different between groups. This may have made it more difficult to detect differences between intervention groups. We used BIS to determine fat and fat free mass. BIS is the only cot-side technique available where infants requiring respiratory support can be assessed. While accuracy of BIS at the individual level is poor, BIS provides a useful means of determining differences in body composition between population means [21].Many of the recent trials discussed have already achieved mean growth rates approaching intra-uterine growth, with similar growth rates between groups. Findings from this current study are only generalisable to a similar population (infants born 28–32 week’s gestation). Therefore, to explicate the subtle effects of increasing protein on growth, future trials may need to focus on birth weight categories as they relate to protein requirements (i.e., <1000 g and 1000–1800 g). Due to the small proportion of infants born <1000 g, large multi-centre trials will be needed to tease out the effect.5. ConclusionsIncreasing the protein concentration of HMF from 1.0 to 1.8 g protein added per 100 mL EBM does not improve growth in preterm infants born 28–32 weeks’ gestation.AcknowledgmentsWe thank the families who participated in this study.Author ContributionsConceptualization, J.R., M.M., A.J.M. and C.T.C.; Formal analysis, J.R., M.M., A.J.M., M.J.S., J.M. and C.T.C.; Funding acquisition, J.R., M.M., A.J.M., M.J.S. and C.T.C.; Investigation, J.R., M.M., A.J.M., M.J.S. and C.T.C.; Supervision, M.M., A.J.M., M.J.S. and C.T.C.; Writing: original draft, J.R., J.M. and C.T.C.; Writing: review and editing, J.R., M.M., A.J.M., M.J.S., J.M. and C.T.C.FundingThis research was funded by a Women’s and Children’s Hospital Foundation Grant. Research Fellowships were provided by the National Health and Medical Research Council of Australia (M.M. Principal Research Fellow APP1061704) and the MS McLeod Research Fellowship, MS McLeod Research Fund, Women's and Children’s Hospital Research Foundation (C.T.C). The contents of the published material are solely the responsibility of the authors and do not reflect the views of the National Health and Medical Research Council of Australia.Conflicts of InterestOutside the submitted work, Maria Makrides serves on scientific advisory boards for Fonterra and Nestle. Honoraria are paid to her institution for continuing education of early career researchers. Maria Makrides also holds a Principal Research Fellowship from the NHMRC (APP1061704). Other authors declare no conflict of interest. Nestlé Nutrition donated half of the human milk fortifier used in the trial and Nutricia donated the Polyjoule and Protifar supplements. However, these sponsors had no role in the design of the study, in the collection, analyses or interpretation of data; in writing of the manuscript, and the decision to publish the results.References1.AdamkinD.H.RadmacherP.G.Fortification of human milk in very low birth weight infants (VLBW <1500 g birth weight)Clin. Perinatol.20144140542110.1016/j.clp.2014.02.010248738402.MoroG.E.ArslanogluS.BertinoE.CorvagliaL.MontirossoR.PicaudJ.C.PolbergerS.SchanlerR.J.SteelC.van GoudoeverJ.Human milk in feeding premature infants: Consensus statementJ. Pediatr. Gastroenterol. Nutr.201561Suppl. 1S16S1910.1097/01.mpg.0000471460.08792.4d262959993.BrownJ.V.E.EmbletonN.D.HardingJ.E.McGuireW.Multi-nutrient fortification of human milk for preterm infantsCochrane Database Syst. Rev.201610.1002/14651858.CD000343.pub3271558884.AgostoniC.BuonocoreG.CarnielliV.P.De CurtisM.DarmaunD.DecsiT.DomellofM.EmbletonN.D.FuschC.Genzel-BoroviczenyO.Enteral nutrient supply for preterm infants: Commentary from the European Society of Paediatric Gastroenterology, Hepatology and Nutrition committee on nutritionJ. Pediatr. Gastroenterol. Nutr.201050859110.1097/MPG.0b013e3181adaee0198813905.ColaizyT.T.CarlsonS.SaftlasA.F.MorrissF.H.Jr.Growth in vlbw infants fed predominantly fortified maternal and donor human milk diets: A retrospective cohort studyBMC Pediatr.20121212410.1186/1471-2431-12-124229005906.EmbletonN.E.PangN.CookeR.J.Postnatal malnutrition and growth retardation: An inevitable consequence of current recommendations in preterm infants?Pediatrics200110727027310.1542/peds.107.2.270111584577.MaasC.WiechersC.BernhardW.PoetsC.F.FranzA.R.Early feeding of fortified breast milk and in-hospital-growth in very premature infants: A retrospective cohort analysisBMC Pediatr.20131317810.1186/1471-2431-13-178241802398.CibulskisC.C.ArmbrechtE.S.Association of metabolic acidosis with bovine milk-based human milk fortifiersJ. Perinatol.20153511511910.1038/jp.2014.143251023219.ArslanogluS.MoroG.E.ZieglerE.E.Adjustable fortification of human milk fed to preterm infants: Does it make a difference?J. Perinatol.20062661462110.1038/sj.jp.72115711688598910.AlanS.AtasayB.CakirU.YildizD.KilicA.KahveciogluD.ErdeveO.ArsanS.An intention to achieve better postnatal in-hospital-growth for preterm infants: Adjustable protein fortification of human milkEarly Hum. Dev.2013891017102310.1016/j.earlhumdev.2013.08.0152403503911.BiasiniA.MarvulliL.NeriE.ChinaM.StellaM.MontiF.Growth and neurological outcome in ELBW preterms fed with human milk and extra-protein supplementation as routine practice: Do we need further evidence?J. Matern. Fetal Neonatal Med.201225Suppl. 4727410.3109/14767058.2012.7150322295802412.RochowN.FuschG.ChoiA.ChessellL.ElliottL.McDonaldK.KuiperE.PurchaM.TurnerS.ChanE.Target fortification of breast milk with fat, protein, and carbohydrates for preterm infantsJ. Pediatr.20131631001100710.1016/j.jpeds.2013.04.0522376949813.McLeodG.SherriffJ.HartmannP.E.NathanE.GeddesD.SimmerK.Comparing different methods of human breast milk fortification using measured v. Assumed macronutrient composition to target reference growth: A randomised controlled trialBr. J. Nutr.201611543143910.1017/S00071145150046142662789914.MillerJ.MakridesM.GibsonR.A.McPheeA.J.StanfordT.E.MorrisS.RyanP.CollinsC.T.Effect of increasing protein content of human milk fortifier on growth in preterm infants born at <31 wk gestation: A randomized controlled trialAm. J. Clin. Nutr.20129564865510.3945/ajcn.111.0263512230193315.BeebyP.J.BhutapT.TaylorL.K.New South Wales population-based birthweight percentile chartsJ. Paediatr. Child Health19963251251810.1111/j.1440-1754.1996.tb00965.x900778216.MaasC.MathesM.BleekerC.VekJ.BernhardW.WiechersC.PeterA.PoetsC.F.FranzA.R.Effect of increased enteral protein intake on growth in human milk–fed preterm infants: A randomized clinical trialJAMA Pediatr.2017171162210.1001/jamapediatrics.2016.26812789306417.MoyaF.SiskP.M.WalshK.R.BersethC.L.A new liquid human milk fortifier and linear growth in preterm infantsPediatrics2012130e928e93510.1542/peds.2011-31202298787718.KimJ.H.ChanG.SchanlerR.Groh-WargoS.BloomB.DimmitR.WilliamsL.BaggsG.Barrett-ReisB.Growth and tolerance of preterm infants fed a new extensively hydrolyzed liquid human milk fortifierJ. Pediatr. Gastroenterol. Nutr.20156166567110.1097/MPG.00000000000010102648811819.RigoJ.HascoetJ.M.BilleaudC.PicaudJ.C.MoscaF.RubioA.SalibaE.RadkeM.SimeoniU.GuilloisB.Growth and nutritional biomarkers of preterm infants fed a new powdered human milk fortifier: A randomized trialJ. Pediatr. Gastroenterol. Nutr.201765e83e9310.1097/MPG.00000000000016862872765420.PolbergerS.K.AxelssonI.E.RaihaN.C.Urinary and serum urea as indicators of protein metabolism in very low birthweight infants fed varying human milk protein intakesActa Paediatr. Scand.19907973774210.1111/j.1651-2227.1990.tb11548.x223926621.CollinsC.T.ReidJ.MakridesM.LingwoodB.E.McPheeA.J.MorrisS.A.GibsonR.A.WardL.C.Prediction of body water compartments in preterm infants by bioelectrical impedance spectroscopyEur. J. Clin. Nutr.201367Suppl. 1S47S5310.1038/ejcn.2012.16423299871Figure 1Participant flow through the trial. 1 from rural locations (n = 52), insufficient milk supply (n = 36), required interpreter (n = 6); congenital abnormality (n = 3); 2 did not want to take part (n = 25), did not want twins to be randomized individually (n = 8), parent not visiting (n = 1), immediately transferred to another centre (n = 1).Figure 2Fat free mass as a proportion of body weight for the first four weeks of the trial. Values are means, error bars are 95% CI. High protein n = 30, 30, 27, 26 and standard protein 29, 27, 26, 23 in weeks 1, 2, 3, 4 respectively. Adjusted for sex and gestational age, group interaction, p = 0.03, time interaction, p = 0.01. group × time interaction p = 0.84; * p = 0.04.Figure 3BUN from randomisation to week 3. Values are mean, error bars are 95% CI. High protein: n = 31, 28, 26, 25; Standard protein: n = 29, 26, 24, 24 for weeks baseline, 1, 2, 3. Adjusted for sex and GA, overall group effect <0.001, group * week interaction, p <0.001, * p = 0.04; ** p <0.001.nutrients-10-00634-t001_Table 1Table 1Baseline infant and maternal characteristics.CharacteristicHigh Protein (n = 31)Standard Protein (n = 29)\nInfant characteristics\n\n\nSingleton15 (48)16 (55)Twin15 (48)12 (41)Triplet2 (7)1 (3)Gestational age (week)30.5 ± 1.530.1 ± 1.428–29 weeks’ gestation10 (32)9 (31)30–32 weeks’ gestation21 (68)20 (69)Male infants16 (52)12 (41)Birth weight (g)1483 ± 4231551 ± 407SGA for weight at birth5 (16)1 (3)Birth length (cm)40.0 ± 3.340.2 ± 2.8Head circumference (cm)28.5 ± 328.5 ± 1.8Infants received standard ward HMF before randomisation18 (58)22 (76)Length of standard ward fortification before trial HMF start (day)1.3 ± 1.72.0 ± 1.5Time between birth and trial HMF start (day)8.9 ± 3.29.0 ± 2.5\nMaternal characteristics\n\n\nMaternal age (years)29.9 ± 6.331.7 ± 5.3Mother smoked during pregnancy5 (16.1)3 (10.3)Caucasian27 (96)23 (82)Primiparous19 (61.3)12 (41.4)Previous preterm birth4 (33.3)6 (35.3)Data are presented as n (%) or mean ± SD.nutrients-10-00634-t002_Table 2Table 2Anthropometric changes over the study.\nIntention to Treat AnalysesPer Protocol Analyses 1High Protein (n = 31)Standard Protein (n = 29)Adjusted Mean Difference 2\np\n2\nHigh Protein (n = 21)Standard Protein (n = 23)Adjusted Mean Difference 2\np\n2\nWeight gain (g/week)245 (230, 260)258 (244, 272)−14 (−32, 4)0.12245 (228, 262)262 (247, 277)−15 (−36, 5)0.14Length gain (cm/week)1.1 (1.1, 1.2)1.1 (1.1, 1.2)−0.01 (−0.06, 0.03)0.451.1 (1.1, 1.2)1.2 (1.1, 1.2)−0.01 (−0.06, 0.04)0.62Head circumference gain (cm/week)1.1 (1.0, 1.1)1.1 (1.0,1.1)0.007 (−0.05, 0.06)0.791.1 (1.1, 1.1)1.1 (1.1, 1.1)−0.004 (−0.06, 0.05)0.88Weight at study end (g) 32658 (2544, 2771)2757 (2632, 2883)−100 (−251, 50)0.192646 (2489, 2805)2815 (2675, 2955)−157 (−341, 28) 0.1Length at study end (cm)45.2 (44.5, 45.9)45.8 (45.0, 46.6)−0.5 (−1.3, 0.3)0.1945.2 (44.4, 46.0)46.3 (45.6, 47)−0.86 (−1.85, 0.12)0.09Head circumference at study end (cm)33.1 (32.5, 33.6)33.0 (32.4, 33.7)0.03 (−0.6, 0.7)0.9233.3 (32.7, 33.9)33.6 (33.0, 34.1)−0.16 (−0.90, 0.57)0.66Data are presented as mean, (95% CI); 1 For inclusion in ‘per protocol’ analysis, infants must have consumed 70% or more of their trial group HMF; 2 adjusted for sex and gestational age; 3 study end defined as removal of naso-gastric tube or term equivalent, whichever came first.nutrients-10-00634-t003_Table 3Table 3Feeding and clinical management.VariableHigh Protein (n = 31)Standard Protein (n = 29)\np\nInfant required enteral protein supplementation 101 (3.4)0.48Feeding interrupted 211 (35)6 (21)0.01Days receiving parenteral nutrition10 (7, 13)9 (7, 11)0.34Days of intravenous lipid4 (3, 7)4 (3, 6)0.72Days to full enteral feeds 38 (6, 10)8 (7, 10)0.72Confirmed necrotizing enterocolitis1 (3.2)0>0.99Oxygen at discharge2 (6.5)1 (3.4)0.15Late onset sepsis1 (3.2)0>0.99Data are reported as n (%) or mean (95% CI).1 One infant in the standard protein group was prescribed a protein supplement (Protifar) 2 Feeding interrupted was defined as one of more feeds not given in a day; 3 Full enteral feeds was defined as 150 mL/kg/day)."", 'title': 'The Effect of Increasing the Protein Content of Human Milk Fortifier to 1.8 g/100 mL on Growth in Preterm Infants: A Randomised Controlled Trial.', 'date': '2018-05-19'}, '28727654': {'article_id': '28727654', 'content': ""J Pediatr Gastroenterol NutrJ. Pediatr. Gastroenterol. NutrJPGAJournal of Pediatric Gastroenterology and Nutrition0277-21161536-4801Lippincott Williams & Wilkins287276545625962JPGN-16-82510.1097/MPG.000000000000168600025Original Articles: NutritionGrowth and Nutritional Biomarkers of Preterm Infants Fed a New Powdered Human Milk Fortifier: A Randomized TrialRigoJacques∗HascoëtJean-Michel†BilleaudClaude‡PicaudJean-Charles§MoscaFabio||RubioAmandine¶SalibaElie#RadkëMichaël∗∗SimeoniUmberto††GuilloisBernard‡‡de HalleuxVirginie∗JaegerJonathan§§AmeyeLaurent||||HaysNicholas P.¶¶SpalingerJohannes##∗Department of Neonatology, University of Liège, CHR Citadelle, Liège, Belgium†Maternité Régionale Universitaire A. Pinard, Nancy‡CIC Pédiatrique 1401 INSERM-CHU, Bordeaux§Service de Neonatologie, Hôpital de la Croix Rousse, Lyon, France||Neonatal Intensive Care Unit, Department of Clinical Science and Community Health, Fondazione IRCCS “Ca’ Granda” Ospedale Maggiore Policlinico, University of Milan, Milan, Italy¶Hôpital Couple Enfant, CHU de Grenoble, Grenoble#Hôpital Clocheville, CHU de Tours, Tours, France∗∗Klinikum Westbrandenburg GmbH, Potsdam, Germany††Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland‡‡Hôpital Clemenceau, CHU de Caen, Caen, France§§Nestlé Clinical Development Unit, Lausanne, Switzerland||||Nestlé Nutrition R&D, Vevey, Switzerland¶¶Nestlé Nutrition R&D, King of Prussia, PA##Children's Hospital of Lucerne, Lucerne, Switzerland.Address correspondence to Jacques Rigo, MD, PhD, Service Universitaire de Néonatologie, CHR de la Citadelle, Boulevard du Douzième de Ligne, 1 4000 Liège, Belgium (e-mail: J.Rigo@ulg.ac.be); Address reprint or protocol requests to: Nicholas P. Hays, PhD, 3000 Horizon Dr., Suite 100, King of Prussia, PA 19406 (e-mail: Nicholas.Hays@rd.nestle.com).1020172292017654e83e93231120162952017Copyright © The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the European Society for Pediatric Gastroenterology, Hepatology, and Nutrition and the North American Society for Pediatric Gastroenterology, Hepatology, and Nutrition2017This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc-nd/4.0ABSTRACTObjectives:The aim of this study was to assess growth and nutritional biomarkers of preterm infants fed human milk (HM) supplemented with a new powdered HM fortifier (nHMF) or a control HM fortifier (cHMF). The nHMF provides similar energy content, 16% more protein (partially hydrolyzed whey), and higher micronutrient levels than the cHMF, along with medium-chain triglycerides and docosahexaenoic acid.Methods:In this controlled, multicenter, double-blind study, a sample of preterm infants ≤32 weeks or ≤1500\u200ag were randomized to receive nHMF (n\u200a=\u200a77) or cHMF (n\u200a=\u200a76) for a minimum of 21 days. Weight gain was evaluated for noninferiority (margin\u200a=\u200a–1\u200ag/day) and superiority (margin\u200a=\u200a0\u200ag/day). Nutritional status and gut inflammation were assessed by blood, urine, and fecal biochemistries. Adverse events were monitored.Results:Adjusted mean weight gain (analysis of covariance) was 2.3\u200ag/day greater in nHMF versus cHMF; the lower limit of the 95% CI (0.4\u200ag/day) exceeded both noninferiority (P\u200a<\u200a0.001) and superiority margins (P\u200a=\u200a0.01). Weight gain rate (unadjusted) was 18.3 (nHMF) and 16.8\u200ag\u200a·\u200akg−1\u200a·\u200aday−1 (cHMF) between study days 1 and 21 (D1–D21). Length and head circumference (HC) gains between D1 and D21 were not different. Adjusted weight-for-age z score at D21 and HC-for-age z score at week 40 corrected age were greater in nHMF versus cHMF (P\u200a=\u200a0.013, P\u200a=\u200a0.003 respectively). nHMF had higher serum blood urea nitrogen, pre-albumin, alkaline phosphatase, and calcium (all within normal ranges; all P\u200a≤\u200a0.019) at D21 versus cHMF. Both HMFs were well tolerated with similar incidence of gastrointestinal adverse events.Conclusions:nHMF providing more protein and fat compared to a control fortifier is safe, well-tolerated, and improves the weight gain of preterm infants.Keywordsgrowthhuman milklow birth weightSTATUSONLINE-ONLYOPEN-ACCESSTRUEWhat Is KnownDue in part to variability in human milk composition, incidence of postnatal growth restriction is more frequently reported in very-low-birth-weight infants fed fortified human milk compared to those fed preterm formulas.The optimal composition of human milk fortifier and nutritional recommendations for preterm infants fed fortified human milk are still debated.What Is NewA new human milk fortifier containing partially hydrolyzed protein, fat, and carbohydrate provides a higher protein:energy ratio while achieving lower osmolality versus a current fortifier.In preterm infants, the new fortifier improves weight gain and reduces postnatal growth restriction compared to the current fortifier.Feeding of human milk (HM) rather than preterm formulas provides many benefits to preterm infants (eg, accelerated gut maturation (1); protection against infections (2), sepsis (3), necrotizing enterocolitis (2), and retinopathy of prematurity (4); possible protective effect on neurodevelopment (5)) that are mediated by protective biomolecules and trophic factors in HM. HM, however, provides inadequate protein and micronutrients to support the rapid growth and bone mineralization of preterm infants. These deficits are particularly acute in the smallest infants (birthweight <1500\u200ag) who have the highest protein and mineral needs (6). Fortification of mother's own milk or banked HM is therefore recommended for all preterm infants with birthweight <1800\u200ag to improve nutrient accretion and in-hospital growth (7,8).Feeding fortified HM helps support adequate growth and bone mineralization (9), and is associated with favorable neurodevelopmental outcomes (10), although evidence for improved outcomes other than in-hospital growth is limited (11). The nutritional content, however, of some currently available fortifiers may be inadequate for many preterm infants. Incidence of postnatal growth restriction is more frequently reported in very-low-birth-weight infants fed fortified HM compared to those fed preterm formulas (12,13). In addition, the nutritional profile of HM from mothers of premature infants varies greatly (14) and may differ from published reference compositional data, which may lead to less-than-recommended intakes of protein and energy (15,16). These nutritional inadequacies may worsen with use of donor HM, which is often from mothers of term infants >1-month postpartum (17).A new powdered HM fortifier has been developed with a higher protein:energy ratio (protein provided as partially hydrolyzed whey), non-protein energy from lipids and carbohydrate, and higher electrolyte and vitamin levels (enriching HM in line with ESPGHAN (18) and expert group (19) recommendations) versus a control fortifier. When mixed with HM containing 1.5\u200ag protein/100\u200amL (2–4 week milk) (20–22), it provides 3.6\u200ag protein/100 kcal (within the ESPGHAN-recommended ranges (18) for protein and energy intakes for a minimal intake volume of 140\u200amL/kg/day in very-low-birth-weight infants up to 1.8\u200akg body weight), with osmolality below the recommended threshold of 450\u200amOsm/kg (23,24).This study evaluated growth and nutritional biomarkers during a 21-day interval in clinically stable preterm infants receiving the new HM fortifier (nHMF) compared to infants fed a control fortifier (cHMF). The primary objective was to assess weight gain velocity (grams per day); evaluations of other growth parameters (including weight gain velocity in gram per kilograms per day) and intervals (eg, to 40 weeks corrected age [W40CA]), feeding tolerance, adverse events, time to full fortification/full enteral feeding, and markers of protein-energy, electrolytes, bone metabolic status, gut inflammation, and maturity of gastrointestinal (GI) function were also conducted as secondary outcomes. We hypothesized that weight gain of infants fed nHMF would be both noninferior (lower limit of 95% confidence interval [CI] of mean difference >–1\u200ag/day) and superior (lower limit of 95% CI of mean difference >0\u200ag/day) to that of infants fed cHMF.METHODSStudy design and participantsThis was a controlled, double-blind, randomized, parallel-group study conducted in neonatal intensive care units (NICUs) at 11 metropolitan hospitals in France, Belgium, Germany, Switzerland, and Italy. NICU size ranged from 25 to 45 beds. Clinically stable male and female preterm infants with gestational age ≤32 weeks or birthweight ≤1500\u200ag and born to mothers who had agreed to provide expressed or donor breastmilk for the entire 21-day study duration were enrolled in the study from April 2011 to March 2014. Infants were excluded if they had a history of or current systemic, metabolic, or chromosomic disease, any congenital anomalies of the GI tract, were small for gestational age (defined in this study as bodyweight ≤5th percentile (25)), or were receiving steroids or preterm formula during the study period. For multiple births, the first sibling was randomized and other siblings were allocated to the same group. The study was reviewed and approved by an institutional review board/independent Ethics Committee at each study site. Each subject's parent/legal representative provided written informed consent before participating in the study.Infants tolerating ≥100\u200amL\u200a·\u200akg−1\u200a·\u200aday−1 of HM for >24\u200ahours were randomized to receive either nHMF or cHMF for a minimum of 21 days; infants continued to receive their allocated study fortifier (or were transitioned to a routine/standard fortifier) until NICU discharge or medical decision to stop fortification, and fortification was stopped after discharge. The fortifiers were both cow's milk-based and provided similar energy supplementation (17\u200akcal/100\u200amL of HM). For every 100\u200amL of HM, nHMF provided 1.4\u200ag partially hydrolyzed whey protein, 0.7\u200ag lipids (primarily medium chain triglycerides and docosahexaenoic acid), 1.3\u200ag carbohydrate (maltodextrin), with a blend of micronutrients. cHMF (FM85 Human Milk Fortifier, Nestlé, Switzerland) provided 1.0\u200ag extensively hydrolyzed whey protein, no lipids, 3.3\u200ag carbohydrate (lactose and maltodextrin), with a blend of micronutrients. nHMF contained higher concentrations of some vitamins and electrolytes compared to cHMF, but both contained similar levels of minerals, including calcium (as calcium glycerophosphate and calcium phosphate) and phosphorus. Table 1 presents the estimated composition and osmolality of preterm HM (22) fortified with each fortifier. Fortifiers were fed beginning at half-strength (Fortification Strength Increase day 1; FSI1), then advanced per hospital practice, with full-strength fortification occurring once infants could maintain intakes of 150 to 180\u200amL\u200a·\u200akg−1\u200a·\u200aday−1 (ie, full enteral feeds; study day 1 [D1]). A study plan schematic is presented in Figure 1.FIGURE 1Study design. cHMF\u200a=\u200acontrol human milk fortifier; D1\u200a=\u200astudy day 1; D7\u200a=\u200astudy day 7; D10/11\u200a=\u200astudy day 10/11; D14\u200a=\u200astudy day 14; D21\u200a=\u200astudy day 21; DC\u200a=\u200adischarge (note that infants continued to receive their allocated study fortifier [or were transitioned to a routine/standard fortifier] until neonatal unit discharge or medical decision to stop fortification if length of stay was >21 days, and fortification was stopped after discharge) ; FSI1\u200a=\u200afortification strength increase day 1; HC\u200a=\u200ahead circumference; HM\u200a=\u200ahuman milk; nHMF\u200a=\u200anew human milk fortifier; W40CA\u200a=\u200aweek 40 corrected age.Study ProceduresGrowthInfant nude weight (to the nearest 1\u200ag) was measured daily by trained nursery personnel using a calibrated electronic scale (Baby Scale 717, Seca, Semur-en-Auxois, France). Recumbent length and head circumference (HC; both to the nearest 0.1\u200acm) were measured at FSI1, D1, and weekly thereafter. At least 2 trained examiners measured recumbent length using a length board (Mobile Measuring Board 417, Seca, Semur-en-Auxois, France) while maintaining proper body alignment and full body extension with feet flexed. HC was measured using a nonelastic measuring tape (Measuring Tape 212 or 218, Seca, Semur-en-Auxois, France) placed over the largest circumference of the skull (above the supraorbital ridges while covering the most prominent part of the frontal bulge anteriorly). The same calibrated equipment was used for anthropometric measures for each infant at all sites. Weight-for-age, length-for-age, and HC-for-age z scores were calculated using Fenton (25). Weight gain velocity (grams per kilograms per day) was calculated using the average of the start and end weights as the denominator.Markers of Protein-energy, Electrolyte, and Bone Metabolic StatusBlood and urine samples were collected at D1, D10/11, and D21 and analyzed for serum creatinine and prealbumin, blood urea nitrogen (BUN), urinary urea, hemoglobin, hematocrit, electrolyte status, and bone metabolic status. All blood and urine parameters were analyzed as part of routine clinical assessments at each NICU. Since 24-hour urine collections were not performed in this study owing to logistical infeasibility, urinary markers were corrected for 24-hour creatinine excretion (26) assuming a standard urinary excretion in preterm infants of 10\u200amg\u200a·\u200akg−1\u200a·\u200aday−1(27).Feeding Tolerance and Adverse EventsFeeding tolerance was evaluated by trained nursery staff who recorded daily milk intake (milliliters), stool pattern (defecation frequency and stool consistency [5\u200a=\u200ahard, 4\u200a=\u200aformed, 3\u200a=\u200asoft, 2\u200a=\u200aliquid, or 1\u200a=\u200awatery]), presence of abdominal distention, and incidence of spitting-up (defined as return of a small amount of swallowed food, usually a mouthful, and usually occurring during or shortly after feeding) and vomiting (defined as return of a larger amount of food with more complete emptying of the stomach, and usually occurring sometime after feeding). In addition, frequency, type, and attribution to fortifier intake of adverse events (AEs; including clinical and laboratory) were evaluated using physician-reported information recorded using standardized forms from enrollment to W40CA. AEs were categorized by the reporting investigator as “serious” in accordance with International Conference on Harmonization criteria (28) and as “related to the intervention” based on detailed, standardized criteria provided in the protocol.Statistical AnalysisSample size was based on a previous study (29), which investigated growth and zinc status in preterm infants fed fortified HM. In the present trial, a group-sequential design was chosen (Wang and Tsiatis) (30) with 1 interim analysis. To detect a noninferior weight gain in infants fed with nHMF versus cHMF from D1 to D21 (noninferiority margin –1\u200ag/day, expected weight gain difference 2\u200ag/day, standard deviation 4.73\u200ag/day, type I error 5%, power 80%) (29), 192 subjects (males and females combined) were needed. A computer-generated list of random numbers was used to allocate group assignments. Minimization algorithm with allocation ratio 1:1 and second best probability of 15% was used. Stratification factors were center, sex, and birthweight (100g intervals). Group coding was used with 2 nonspeaking codes per group; fortifier packaging was coded accordingly but otherwise identical in appearance. Infants were enrolled and assigned to their intervention by the study investigators or trained delegates. All study personnel (both site- and sponsor-based) and participants (infants’ families) were blind to group assignment. Noninferiority was demonstrated if the lower limit of the 2-sided 95% CI of the difference in weight gain from D1 to D21 was larger than the noninferiority margin. Superiority was evaluated if noninferiority was demonstrated. Weight gain was analyzed in the intent-to-treat (ITT) and per-protocol populations by analysis of covariance (ANCOVA) adjusting for D1 postmenstrual age and weight, sex, and center (random effect). Sensitivity analyses were conducted using ANCOVA models that adjusted for covariates that were determined post hoc to be significantly different between groups and which may have confounded the primary results (eg, mother smoking status). Secondary endpoints were analyzed in the ITT population only. For noninferiority and superiority tests, 1-sided P values are provided and should be compared to a reference value of 0.025. For other tests, 2-sided P values are provided and should be compared to a reference value of 0.05. 95% CIs provide estimates for feeding effects on all endpoints. Based on prespecified guidelines in the independent Data Monitoring Committee's (DMC) charter, a single interim analysis was conducted when 134 subjects had completed their D21 visit. The interim analysis was planned to occur when the first 100 infants completed at least 21 days of full fortification; however, the analysis was conducted using data from 134 infants owing to unforeseen delays in conducting the analysis (eg, performing statistical programming, data cleaning, and query resolution) while recruitment continued. The type 1 error rate was adjusted to account for the analysis being conducted at ∼70% enrollment rather than the planned 52%. The DMC consisted of independent experts (2 clinicians, 1 biostatistician) who reviewed growth, formula intake, and key biochemical data as well as AEs. The purpose of the interim analysis was to examine unblinded growth velocity results and determine whether the trial could be stopped early for success or futility, or whether the targeted sample size required adjustment (the interim statistical analysis plan was finalized before unblinding, and the analysis was unblinded only to the DMC to facilitate ethical decision-making) (31). On April 2, 2014, the DMC recommended to stop the trial, as noninferiority and superiority in regard to the primary outcome had been demonstrated. The sponsor was notified of this decision on April 3, 2014, and the final study population included infants enrolled through March 31, 2014.RESULTSA total of 274 infants were screened, with 153 enrolled and randomized to either nHMF (n\u200a=\u200a77) or cHMF (n\u200a=\u200a76) (Fig. 2). Demographic and baseline anthropometry data are summarized in Table 2. There was no evidence of imbalance between the 2 groups with respect to infant characteristics. A significantly lower percentage of mothers and fathers of infants in the nHMF group, however, smoked during pregnancy. Number of twins was similar in each group.FIGURE 2Flow of study participants. AE\u200a=\u200aadverse event; cHMF\u200a=\u200acontrol human milk fortifier; D21\u200a=\u200astudy day 21; ITT\u200a=\u200aintent-to-treat; NEC\u200a=\u200anecrotizing enterocolitis; nHMF\u200a=\u200anew human milk fortifier; NICU\u200a=\u200aneonatal intensive care unit; PP\u200a=\u200aper-protocol; SAE\u200a=\u200aserious adverse event. ∗Although screening procedures were standardized across sites, some variability in prescreening procedures did occur. Based on the typical clinical characteristics of infants who were admitted to each NICU during the study interval, the total number of infants who would have been theoretically considered eligible for the study was higher than the number shown here.The majority (84% and 87% by volume in nHMF and cHMF, respectively) of milk provided to infants was pasteurized. Donor milk was always pasteurized and accounted for 49% and 51% of the fortified HM volume provided in the nHMF and cHMF groups, respectively. There was no significant difference in mean volume of fortified milk intake between groups (152.7\u200a±\u200a13.0 and 152.6\u200a±\u200a17.2\u200amL\u200a·\u200akg−1\u200a·\u200aday−1 in nHMF and cHMF, respectively). Protein intake estimated using standard values for preterm HM composition per 100\u200amL (22) was significantly greater in nHMF compared to cHMF (4.48\u200a±\u200a0.38 vs 3.81\u200a±\u200a0.43\u200ag\u200a·\u200akg−1\u200a·\u200aday−1, respectively; P\u200a<\u200a0.001) because of higher protein content of the nHMF. Estimated energy intake was not significantly different between groups (125\u200akcal\u200a·\u200akg−1\u200a·\u200aday−1 in both groups). There was no significant difference in number of days between FSI1 and D1, but adjusted time between birth and D1 was significantly shorter in nHMF (16.8\u200a±\u200a5.4 vs 18.7\u200a±\u200a8.8 days; −8.5% [95% CI: −15.0%, −1.0%]).GrowthIn the ITT population, adjusted weight gain from D1 to D21 was 2.3\u200ag/day higher in nHMF, with the 95% CI ranging from 0.4 to 4.2\u200ag/day, demonstrating noninferiority (P\u200a<\u200a0.001) and superiority (P\u200a=\u200a0.01) of nHMF. Per-protocol results were similar. Weight gain from D1 to D21 remained significantly higher in nHMF when expressed in grams per kilogram per day (Table 3). Weight-for-age z scores (Fig. 3) remained stable from FSI1 to D21 in nHMF, but continued to decrease in cHMF (P\u200a=\u200a0.007 vs D1). At D21, weight-for-age z score was significantly higher in nHMF compared to cHMF (0.12 [95% CI: 0.03, 0.22]). Length and HC gains during the D1 to D21 period were not significantly different between groups (Table 3), with comparable results observed from analyses of unadjusted means (Table 4). Length-for-age z scores at D21 (Fig. 3) were significantly lower than D1 values in cHMF (P\u200a=\u200a0.041). Additionally, at W40CA, adjusted HC-for-age z scores were significantly higher in nHMF compared to cHMF (0.41 [95% CI: 0.14, 0.68]). Mean weight, length, and HC at D1, D21, and W40CA are summarized in Table 5.FIGURE 3Mean\u200a±\u200aSD weight-for-age (panel A), length-for-age (panel B), and head circumference-for-age (panel C) z scores for the overall ITT population. Circle symbols/solid line represents nHMF. Triangle symbols/dashed line represents cHMF. FSI1\u200a=\u200afortification strength increase day 1; ITT\u200a=\u200aintent-to-treat; SD\u200a=\u200astandard deviation; W40CA\u200a=\u200aweek 40 corrected age; z scores calculated using Fenton preterm growth chart (25). ∗P\u200a=\u200a0.013 vs cHMF (by analysis of covariance, adjusting for value at D1, sex, and center); †P\u200a=\u200a0.007 vs day 1 (by t test); ‡P\u200a=\u200a0.041 vs day 1 (by t test); ∗∗P\u200a=\u200a0.003 vs cHMF (by analysis of covariance, adjusting for value at D1, sex, and center).Protein-Energy StatusBUN decreased progressively in cHMF (P\u200a=\u200a0.004 for D21 vs D1), whereas it increased in nHMF (P\u200a<\u200a0.001 for D10/11 vs D1 [data not shown]) and remained stable up to D21 (Table 6). Prealbumin levels were similar at D1 and increased in both groups during the study (Table 6). The increase from D1 to D21, however, was only significant in nHMF (P\u200a=\u200a0.004). At D21, adjusted mean prealbumin in nHMF was significantly higher (+11.8% [95%CI: +2.3%, +22.2%]) than in cHMF. Urinary urea excretion (corrected for creatinine excretion) at D1 was similar in the 2 groups (Table 6). Urea excretion remained steady in cHMF but increased sharply in nHMF (P\u200a<\u200a0.001 for D10/11 vs D1 [data not shown]), after which it remained stable (to D21). At D21, urea excretion was significantly higher in nHMF versus cHMF (+108.7% [95% CI: +66.0%, +162.5%]).Bone Metabolic StatusSerum calcium concentrations were generally stable during the study (Table 6), with mean values for both groups within the normal range (32). Nevertheless, adjusted mean serum calcium concentration in nHMF was minimally but significantly higher than in cHMF at D21 (+1.9% [95% CI: +0.3%, +3.5%]). Serum phosphorus increased slightly in the 2 groups (Table 6). At D1, relative hypophosphatemia (<1.55\u200ammol/L) was observed in 13 infants in both groups; this was corrected in 11 infants by D10/11 and 12 infants by D21. At D1, serum alkaline phosphatase was not significantly different in nHMF versus cHMF (P\u200a=\u200a0.208). Thereafter, serum alkaline phosphatase decreased significantly in both groups (D21 vs D1: P\u200a=\u200a0.005 for nHMF, P\u200a<\u200a0.001 for cHMF), with mean values significantly higher in nHMF versus cHMF at D10/11 (+8.6% [95% CI: +1.0%, +16.8%]; data not shown) and D21 (+12.1% [95% CI: +2.8%, +22.3%]) (Table 6). Declines from baseline were significantly greater in cHMF versus nHMF at D10/11 (P\u200a<\u200a0.001; data not shown) and D21 (P\u200a=\u200a0.035). At D1, spot urinary excretions of calcium and phosphorus corrected for urinary creatinine excretion were similar in the 2 groups (Table 6). Calcium excretion tended to increase slowly during the study in both groups, with mean concentration significantly lower in nHMF compared to cHMF at D21 (P\u200a=\u200a0.011). Phosphorus excretion increased in both groups, resulting in a decreased median urinary calcium:phosphorus molar ratio in both groups (Table 6).ElectrolytesSerum electrolyte concentrations were stable during the study and similar in both groups (Table 6). Urinary sodium and potassium concentrations were significantly higher (sodium: +31.1% [95% CI: +1.7%, +68.9%], potassium: +22.5% [95% CI: +1.0%, +48.6%]) in nHMF compared to cHMF at D21 (Table 7).Stool Characteristics and Feeding ToleranceStool frequency from D1 to D21 was not significantly different in nHMF and cHMF (3.9\u200a±\u200a1.05 vs 3.6\u200a±\u200a0.93\u200astools/day; 0.29 [95% CI: −0.05, 0.63]). Stool consistency was slightly more “formed” in nHMF compared to cHMF during this interval (3.1\u200a±\u200a0.26 vs 3.0\u200a±\u200a0.27; 0.12 [95% CI: 0.02, 0.21]). Most infants (>90%) had stool consistency scores of “soft.” There were no significant differences between groups in frequencies of spitting-up, vomiting, or abdominal distention. There also were no group differences in incidence of AEs indicative of feeding intolerance (all P\u200a≥\u200a0.25).Adverse EventsThe overall incidence of AEs was significantly larger in nHMF (103 events in 56 infants, including 26 events categorized as GI disorders, 18 as infections or infestations, and 5 as metabolism and nutrition disorders) compared to cHMF (78 events in 41 infants, including 21 events categorized as GI disorders, 18 as infections or infestations, and 1 as metabolism and nutrition disorder; odds ratio: 2.26 [95% CI: 1.10, 4.47]). Other AEs that occurred more frequently in nHMF included several that were classified by study investigators as unlikely to be related to consumption of milk fortifiers (eg, cardiac disorders [16 events in nHMF vs 5 in cHMF], eye disorders [10 events in nHMF vs 3 in cHMF]). The number of AEs considered related to study product intake as determined by physician report was low (3 events in nHMF [2 events of hyponatremia, 1 of vomiting] and 0 events in cHMF). No significant difference was demonstrated in overall incidence of serious AEs between the 2 groups (7 events in 7 infants [including 2 events of necrotizing enterocolitis, 0 events of bronchopulmonary dysplasia, 0 events of sepsis, 0 events of retinopathy] in nHMF and 12 events in 11 subjects [including 4 events of necrotizing enterocolitis, 1 event of bronchopulmonary dysplasia, 0 events of sepsis, 0 events of retinopathy] in cHMF; odds ratio: 0.54 [95% CI: 0.17, 1.58]).DISCUSSIONThis study demonstrated that weight gain from D1 of full fortification until D21 in preterm infants fed HM fortified with a new fortifier designed to add 1.4\u200ag partially hydrolyzed protein and 0.7\u200ag fat to 100\u200amL of HM was significantly greater than weight gain in infants fed HM fortified with an isocaloric control fortifier designed to add 1.0\u200ag extensively hydrolyzed protein and no fat. The mean difference was 2.3\u200ag/day or 1.2\u200ag\u200a·\u200akg−1\u200a·\u200aday−1, consistent with our hypothesized difference of 2\u200ag/day, and which indicates the superiority of the new fortifier compared to the control with regard to weight gain. In addition, the weight gain benefit tended to persist until discharge, with a significantly higher adjusted weight gain difference in the nHMF group compared to cHMF from FSI1 to W40CA (2.01\u200ag/day; P\u200a=\u200a0.009). In the nHMF group, weight-for-age z scores were stable from FSI1 to D21 and average weight gain exceeded 18\u200ag\u200a·\u200akg−1\u200a·\u200aday−1, matching recommended rates of postnatal weight gain to mimic intrauterine growth (33,34). Consistent with the increased protein content of the new fortifier, the nHMF group had significantly higher serum prealbumin concentrations, suggesting an increase in nitrogen retention compared to cHMF. The lack of difference, however, in length gain during the study may be in part the result of the relatively limited period of protein supplementation (only 21 days) or because mean length gains in both groups were already quite high (ie, ≥1.1\u200acm/week), whereas the significantly higher HC-for-age z score at W40CA in the nHMF group may be because of the increased protein and lipid content of the new fortifier. In contrast, the absence of a significant difference at earlier timepoints could be attributable to the relatively high variability of HC gain (31% and 27% for nHMF and cHMF, respectively, from D1 to D21) induced by the natural dolichocephalic evolution of the skull that occurs in preterm infants (35). Feeding tolerance and stool patterns were similar in each group, and AEs related to feeding were low and not significantly different between groups, consistent with fortified HM osmolality values slightly lower in nHMF versus cHMF and below the recommended cutoff (23,24) in both groups.Although there was no evidence of imbalance between the 2 fortifier groups with respect to infant baseline characteristics, significant differences in maternal weight gain, smoking, and alcohol usage during pregnancy were observed. As these may be confounding factors in the analysis of weight gain, post hoc ANCOVAs including these parameters were performed. The post hoc results were essentially the same as the main results, indicating that differences in maternal baseline characteristics did not confound the results. Additionally, to determine the possible impact of including clustered data from twins in the analyses, a sensitivity analysis on weight gain (grams per day) from D1 to D21 accounting for the correlated multiple-birth data was performed. Again, these results were similar to those of the main analysis (weight gain 3.2\u200ag/day higher in nHMF [95% CI: 0.5, 5.9\u200ag/day]).Our results are consistent with those of previous studies (36–42). A recent meta-analysis of 5 studies (comprising 352 infants with birthweight ≤1750\u200ag and gestational age ≤34 weeks) compared growth of infants fed HM fortified with either lower-protein or higher-protein fortifier (43). Infants receiving higher-protein fortifier had significantly greater weight (mean difference 1.77\u200ag/kg/day), length (0.21\u200acm/week), and HC gains (0.19\u200acm/week) compared to those receiving lower-protein fortifier (43). Miller et al (39) used a higher-protein fortifier similar in protein content to the one used in the present study, and reported a higher bodyweight at study end among infants in the higher-protein HMF group (mean difference 220\u200ag), but no significant differences in length or HC. In contrast, Moya et al (40) observed a significantly higher achieved weight, length, and HC in the experimental group compared to controls, but their fortifier had a slightly higher protein content (3.2\u200ag/100\u200amL) versus the one used in the present study (3.04\u200ag/100\u200amL), plus the intervention lasted 28 rather than 21 days.Energy and protein content of HM samples were not analyzed in this study but estimated according to Tsang et al (22). Variability of protein, fat, and energy content of HM fed to preterm infants in the NICU is high (15,21). In addition, fat content may be reduced during processing of HM from expression to administration (44), which could be exacerbated with the use of continuous tube feeding (45). In our study, percentage of intake from mother's own milk, donor milk, and pasteurized HM was assessed. Pasteurized donor milk accounted for 51% of the fortified HM provided during the study, whereas 56% of mother's own milk was also pasteurized. Considering that protein content of donor HM is lower than that of mother's own milk (46) and that all the required processing steps (eg, collection, transfer, refrigeration, pasteurization, tube feeding) may significantly decrease fat and energy content (47), the characteristics of the HM used in the present study suggests that protein and energy content could be overestimated when based on a theoretical composition of preterm HM.In the present study, the mean increase in protein supplementation provided by nHMF compared to cHMF was 0.65\u200ag\u200a·\u200akg−1\u200a·\u200aday−1 or 7.4\u200ammol\u200a·\u200akg−1\u200a·\u200aday−1 of nitrogen, from which approximately 6.14\u200ammol\u200a·\u200akg−1\u200a·\u200aday−1 of nitrogen (83%) is absorbed (based on data from balance studies) (48). During the study, urea production increased significantly in the nHMF group leading to an increase in BUN of 1.7\u200ammol/L at D21 and in urea excretion of 2.3\u200ammol\u200a·\u200akg−1\u200a·\u200aday−1 (2.3\u200ammol/10\u200amg creatinine). These data suggest that the nitrogen balance was improved to ∼3.8\u200ammol nitrogen (52% of nitrogen intake) in preterm infants fed nHMF compared to control. This relatively limited protein utilization could result from reduced energy bioavailability of HM, and an increase in energy supply could improve protein utilization in preterm infants fed fortified HM. These data also suggest that specific nutritional recommendations should be formulated for infants fed fortified HM. Nevertheless, the increase in nitrogen retention (∼3.8\u200ammol\u200a·\u200akg−1\u200a·\u200aday−1) appears to be higher than the nitrogen content of the higher weight gain observed with the nHMF (12% of the 1.5\u200ag\u200a·\u200akg−1\u200a·\u200aday−1 corresponding to 2\u200ammol\u200a·\u200akg−1\u200a·\u200aday−1 of nitrogen), suggesting an increase in lean body mass accretion and a moderate reduction in fat mass gain as previously demonstrated in preterm infants fed protein-fortified HM (49).Indices of bone metabolism were satisfactory in both groups, with a significant decrease in serum alkaline phosphatase observed in both groups and 98% of the infants having normal serum phosphorus concentrations at D21. Adequate postnatal bone mineralization is difficult to obtain in preterm infants owing to the interruption of mineral transplacental transfer (50). Although elevated alkaline phosphatase activity may be associated with reduced bone mineralization when mineral intake is deficient (51), the decrease in enzyme levels observed in the presence of normal serum phosphorus values, as well as the low urinary calcium and moderate urinary phosphorus excretion observed in both groups in this study, suggest that intakes were adequate to promote bone mineralization and limit postnatal osteopenia. Mean serum creatinine concentration decreased significantly in both groups suggesting a similar maturation of renal function during this period. Urinary electrolyte concentrations were higher in nHMF versus cHMF at D21, likely in parallel with the higher electrolyte content of nHMF.A lack of HM composition data (allowing estimation of nutritional balance) is a limitation of our study, although standardized accurate techniques are still not available in the NICU. Additionally, the composition of the faster weight gain can only be estimated as lean body mass and/or bone mineralization were not determined. As a result, nutrient absorption and metabolism can only be estimated from serum and urinary metabolite concentrations. Lastly, the results need to be confirmed in a broader population of preterm infants commonly admitted to the NICU including SGA infants and partially breast-fed infants, as these infants were excluded by design. Strengths of this study include the size and multiple sites (11 pediatric hospitals in 4 European countries), which enhances external validity.In conclusion, these results indicate that the new HM fortifier, made with partially hydrolyzed whey protein and a higher protein:energy ratio is safe, well-tolerated, and improves weight gain of preterm infants compared to control fortifier. Providing some energy as fat and replacing extensively hydrolyzed with partially hydrolyzed protein in the new HM fortifier allows a reduction in osmolality <400\u200amOsm/kg immediately after fortification. Protein intakes from HM supplemented with the new fortifier are within the range of the most recent nutritional recommendations for preterm infants.AcknowledgmentsThe authors thank the families of the infants who participated in the study, as well as the research staff at each participating institution. The authors also thank Christelle Perdrieu and Samir Dahbane from the Clinical Development Unit at the Nestlé Research Center for assistance with trial management and Philippe Steenhout, Medical Director at Nestlé Nutrition, for input on study design and assistance with trial supervision.This study was sponsored by Nestlé Nutrition. J.J., L.A., and N.P.H. are employees of Nestlé SA. J.R., J.M.H., C.B., J.C.P., F.M., A.R., E.S., M.R., U.S., B.G., and J.S. received research funding from Nestlé Nutrition. J.R., J.C.P., and C.B. are consultants for Nestlé Nutrition. U.S. has been a speaker, consultant, and expert panel participant for Nestlé, Danone, and Bledina over the past 3 years. V.d.H. has no conflicts of interest to declare.www.clinicaltrials.gov NCT01771588This study was sponsored by Nestlé Nutrition.Portions of these data were presented in abstract form at the 1st Congress of joint European Neonatal Societies, Budapest, Hungary, 15–20 September 2015.REFERENCES1.GarciaCDuanRDBrevaut-MalatyV\nBioactive compounds in human milk and intestinal health and maturity in preterm newborn: an overview. Cell Mol Biol (Noisy-le-grand)\n2013; 59:108–131.253266482.CorpeleijnWEKouwenhovenSMPaapMC\nIntake of own mother's milk during the first days of life is associated with decreased morbidity and mortality in very low birth weight infants during the first 60 days of life. Neonatology\n2012; 102:276–281.229226753.PatelALJohnsonTJEngstromJL\nImpact of early human milk on sepsis and health-care costs in very low birth weight infants. J Perinatol\n2013; 33:514–519.233706064.ManzoniPStolfiIPedicinoR\nHuman milk feeding prevents retinopathy of prematurity (ROP) in preterm VLBW neonates. Early Hum Dev\n2013; 89\nsuppl 1:S64–S68.238093555.KooWTankSMartinS\nHuman milk and neurodevelopment in children with very low birth weight: a systematic review. Nutr J\n2014; 13:94.252313646.CarlsonSWojcikBBarkerA\nGuidelines for the use of human milk fortifier in the neonatal intensive care unit. University of Iowa Neonatology Handbook. 2011. Available at: http://www.uichildrens.org/iowa-neonatology-handbook/feeding/human-milk\nAccessed on January 22, 2017.7.AdamkinDHRadmacherPG\nFortification of human milk in very low birth weight infants (VLBW <1500\u200ag birth weight). Clin Perinatol\n2014; 41:405–421.248738408.MoroGEArslanogluSBertinoE\nXII. Human milk in feeding premature infants: consensus statement. J Pediatr Gastroenterol Nutr\n2015; 61\nsuppl 1:S16–S19.262959999.EinloftPRGarciaPCPivaJP\nSupplemented vs. unsupplemented human milk on bone mineralization in very low birth weight preterm infants: a randomized clinical trial. Osteoporos Int\n2015; 26:2265–2271.2597168610.GibertoniDCorvagliaLVandiniS\nPositive effect of human milk feeding during NICU hospitalization on 24 month neurodevelopment of very low birth weight infants: an Italian cohort study. PLoS ONE\n2015; 10:e0116552.2559063011.BrownJVEmbletonNDHardingJE\nMulti-nutrient fortification of human milk for preterm infants. Cochrane Database Syst Rev\n2016; 5:CD000343.12.SchanlerRJShulmanRJLauC\nFeeding strategies for premature infants: beneficial outcomes of feeding fortified human milk versus preterm formula. Pediatrics\n1999; 103\n(6 pt 1):1150–1157.1035392213.O’ConnorDLJacobsJHallR\nGrowth and development of premature infants fed predominantly human milk, predominantly premature infant formula, or a combination of human milk and premature formula. J Pediatr Gastroenterol Nutr\n2003; 37:437–446.1450821414.WeberALouiAJochumF\nBreast milk from mothers of very low birthweight infants: variability in fat and protein content. Acta Paediatr\n2001; 90:772–775.1151998015.CorvagliaLAcetiAPaolettiV\nStandard fortification of preterm human milk fails to meet recommended protein intake: bedside evaluation by near-infrared-reflectance-analysis. Early Hum Dev\n2010; 86:237–240.2044777916.ArslanogluSMoroGEZieglerEE\nPreterm infants fed fortified human milk receive less protein than they need. J Perinatol\n2009; 29:489–492.1944423717.ArslanogluSCorpeleijnWMoroG\nDonor human milk for preterm infants: current evidence and research directions. J Pediatr Gastroenterol Nutr\n2013; 57:535–542.2408437318.AgostoniCBuonocoreGCarnielliVP\nEnteral nutrient supply for preterm infants: commentary from the European Society for Paediatric Gastroenterology, Hepatology, and Nutrition Committee on Nutrition. J Pediatr Gastroenterol Nutr\n2010; 50:85–91.1988139019.KoletzkoBPoindexterBUauyR\nRecommended nutrient intake levels for stable, fully enterally fed very low birth weight infants. World Rev Nutr Diet\n2014; 110:297–299.2475163820.GidrewiczDAFentonTR\nA systematic review and meta-analysis of the nutrient content of preterm and term breast milk. BMC Pediatr\n2014; 14:216.2517443521.de HalleuxVRigoJ\nVariability in human milk composition: benefit of individualized fortification in very-low-birth-weight infants. Am J Clin Nutr\n2013; 98\nsuppl:529S–535S.2382472522.TsangRCUauyRKoletzkoB\nNutrition of the Preterm Infant, Scientific Basis and Practical Guidelines. Cincinnati: Digital Educational Publishing, Inc; 2005.23.KreisslAZwiauerVRepaA\nEffect of fortifiers and additional protein on the osmolarity of human milk: is it still safe for the premature infant?\nJ Pediatr Gastroenterol Nutr\n2013; 57:432–437.2385734024.BilleaudCSenterreJRigoJ\nOsmolality of the gastric and duodenal contents in low birth weight infants fed human milk or various formulae. Acta Paediatr Scand\n1982; 71:799–803.718044925.FentonTRKimJH\nA systematic review and meta-analysis to revise the Fenton growth chart for preterm infants. BMC Pediatr\n2013; 13:59.2360119026.NewmanDJPugiaMJLottJA\nUrinary protein and albumin excretion corrected by creatinine and specific gravity. Clin Chim Acta\n2000; 294:139–155.1072768027.Al-DahhanJStimmlerLChantlerC\nUrinary creatinine excretion in the newborn. Arch Dis Child\n1988; 63:398–402.336500928.ICH Expert Working Group. Guideline for good clinical practice E6(R1). 1996\nAvailable at: http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Efficacy/E6/E6_R1_Guideline.pdf\nAccessed on January 22, 2017.29.SpalingerJHSchmidtMBergerTM\nComparison of two human milk fortifiers: effects on growth and zinc status in premature infants. J Pediatr Gastroenterol Nutr\n2004; 39\nsuppl 1:1126.30.WangSKTsiatisAA\nApproximately optimal one-parameter boundaries for group sequential trials. Biometrics\n1987; 43:193–199.356730431.KnottnerusJASpigtMG\nWhen should an interim analysis be unblinded to the data monitoring committee?\nJ Clin Epidemiol\n2010; 63:350–352.1976221032.NicholsonJFPesceMA\nNelsonWEBehrmanREKliegmanRArvinAM\nLaboratory Testing and Reference Values (Table 670-2) in Infants and Children. Nelson Textbook of Pediatrics. Philadelphia: W.B. Saunders; 1996\n2031–2084.33.FentonTRNasserREliasziwM\nValidating the weight gain of preterm infants between the reference growth curve of the fetus and the term infant. BMC Pediatr\n2013; 13:92.2375880834.EhrenkranzRADusickAMVohrBR\nGrowth in the neonatal intensive care unit influences neurodevelopmental and growth outcomes of extremely low birth weight infants. Pediatrics\n2006; 117:1253–1261.1658532235.McCartyDBPeatJRMalcolmWF\nDolichocephaly in preterm infants: prevalence, risk factors, and early motor outcomes. Am J Perinatol\n2016; 34:372–378.2758893336.PorcelliPSchanlerRGreerF\nGrowth in human milk-fed very low birth weight infants receiving a new human milk fortifier. Ann Nutr Metab\n2000; 44:2–10.1083846037.ReisBBHallRTSchanlerRJ\nEnhanced growth of preterm infants fed a new powdered human milk fortifier: a randomized, controlled trial. Pediatrics\n2000; 106:581–588.1096910638.BersethCLVan AerdeJEGrossS\nGrowth, efficacy, and safety of feeding an iron-fortified human milk fortifier. Pediatrics\n2004; 114:e699–e706.1554561639.MillerJMakridesMGibsonRA\nEffect of increasing protein content of human milk fortifier on growth in preterm infants born at <31 wk gestation: a randomized controlled trial. Am J Clin Nutr\n2012; 95:648–655.2230193340.MoyaFSiskPMWalshKR\nA new liquid human milk fortifier and linear growth in preterm infants. Pediatrics\n2012; 130:e928–e935.2298787741.AlanSAtasayBCakirU\nAn intention to achieve better postnatal in-hospital-growth for preterm infants: adjustable protein fortification of human milk. Early Hum Dev\n2013; 89:1017–1023.2403503942.ThoeneMHansonCLydenE\nComparison of the effect of two human milk fortifiers on clinical outcomes in premature infants. Nutrients\n2014; 6:261–275.2439453843.LiuTTDangDLvXM\nHuman milk fortifier with high versus standard protein content for promoting growth of preterm infants: A meta-analysis. J Int Med Res\n2015; 43:279–289.2595615644.VieiraAASoaresFVPimentaHP\nAnalysis of the influence of pasteurization, freezing/thawing, and offer processes on human milk's macronutrient concentrations. Early Hum Dev\n2011; 87:577–580.2159268845.IgawaMMuraseMMizunoK\nIs fat content of human milk decreased by infusion?\nPediatr Int\n2014; 56:230–233.2484751446.WojcikKYRechtmanDJLeeML\nMacronutrient analysis of a nationwide sample of donor breast milk. J Am Diet Assoc\n2009; 109:137–140.1910333547.de HalleuxVPeiltainCSanterreT\nUse of donor milk in the neonatal intensive care unit. Semin Fetal Neonatal Med\n2017; 22:23–29.2764999548.PicaudJCPutetGRigoJ\nMetabolic and energy balance in small- and appropriate-for-gestational-age, very low-birth-weight infants. Acta Paediatr Suppl\n1994; 405:54–59.773479249.PutetGRigoJSalleB\nSupplementation of pooled human milk with casein hydrolysate: energy and nitrogen balance and weight gain composition in very low birth weight infants. Pediatr Res\n1987; 21:458–461.358808250.PieltainCde HalleuxVSenterreT\nPrematurity and bone health. World Rev Nutr Diet\n2013; 106:181–188.2342869951.RuskC\nRickets screening in the preterm infant. Neonatal Netw\n1998; 17:55–57.TABLE 1Calculated∗ nutrient composition of fortified preterm human milkPreterm HM\u2009+\u2009nHMFPreterm HM\u2009+\u2009cHMF4\u2009g fortifier alone4\u2009g fortifier per 100\u2009kcal milk4\u2009g fortifier per 100\u2009mL milk5\u2009g fortifier alone5\u2009g fortifier per 100\u2009kcal milk5\u2009g fortifier per 100\u2009mL milkRecommended intake range (per 100\u2009kcal)†NutrientEnergy, kcal17.410084.617.410084.5Protein, g1.423.63.041.03.102.623.2–4.1Protein sourcePartially hydrolyzed wheyExtensively hydrolyzed wheyFat, g0.725.004.230.024.163.524.4–6MCT, g0.500.590.50000DHA, mg6.319.316.3011.810.0(16.4–) 50–55Carbohydrate, g1.3010.178.603.3012.5310.6010.5–12Carbohydrate sourceMaltodextrinLactose and maltodextrinCalcium, mg7611910175118100109–182Phosphorus, mg44695845705955–127Magnesium, mg4.08.67.32.46.75.77.3–13.6Sodium, mg36.776.564.720.056.848.063–105Potassium, mg48.4116.498.442.0108.892.071–177Chloride, mg32.1106.690.117.088.775.095–161Iron, mg1.802.231.891.301.641.391.8–2.7Zinc, mg0.941.551.310.801.381.171.3–2.3Manganese, μg8.089.988.445.006.345.360.9–13.6Copper, mg0.050.110.090.040.090.080.09–0.21Iodine, μg16.936.630.915.034.329.09–50Selenium, μg3.77.26.11.54.63.94.5–9Vitamin A, IU1183175414835009468001217–3333Vitamin D, IU150187158100128108100–350Vitamin E, IU4.45.64.72.23.02.52.2–11.1Vitamin K, μg8.09.88.34.05.14.34–25Thiamin, mg0.150.190.160.050.070.060.13–0.27Riboflavin, mg0.200.270.230.100.150.130.18–0.36Vitamin B6, mg0.130.160.140.050.070.060.05–0.27Vitamin B12, μg0.200.260.220.100.140.120.09–0.73Niacin, mg1.502.021.710.801.191.010.9–5Folic acid, μg40.051.043.140.051.043.132–91Pantothenic acid, mg0.701.100.930.400.740.630.45–1.9Biotin, μg3.504.784.043.004.193.541.5–15Vitamin C, mg20.028.924.410.017.014.418–50Osmolality‡, mOsm/kg390441cHMF\u2009=\u2009control human milk fortifier; DHA\u2009=\u2009docosahexaenoic acid; HM\u2009=\u2009human milk; nHMF\u2009=\u2009new human milk fortifier; MCT\u2009=\u2009medium chain triglycerides.*Calculated based on preterm human milk composition from Tsang et al, 2005 (22).†Recommended nutrient intakes for fully enterally fed preterm very low birth weight infants (19).‡Measured immediately after fortification at room temperature (25°C).TABLE 2Demographic and baseline characteristics of infants and parentsnHMF (n\u2009=\u200976)cHMF (n\u2009=\u200974)Infant characteristicsSex\u2003Boys38 (50)35 (47)Delivery type\u2003Vaginal24 (32)20 (27)Twin18 (24)16 (22)Birth weight, g1147\u2009±\u20092581156\u2009±\u2009289Birth weight by birth weight category\u2003<1000\u2009g\u2003\u2003n (%)24 (32)26 (35)\u2003\u2003Birth weight, g850.5\u2009±\u2009118.9847.3\u2009±\u2009105.1\u2003≥1000\u2009g\u2003\u2003Birth weight, g1283.6\u2009±\u2009175.41323.9\u2009±\u2009206.2Birth length, cm37.1\u2009±\u20092.737.1\u2009±\u20093.1Birth head circumference, cm26.5\u2009±\u20092.726.7\u2009±\u20092.5Gestational age at birth, weeks28.8\u2009±\u20092.128.7\u2009±\u20091.8Postnatal age at study time points, days*\u2003FSI113 (11, 18)14 (10, 20)\u2003Day 116 (13, 20)17 (13, 23)\u2003Day 2136 (33, 40)37 (33, 43)\u2003Week 40 corrected age76 (66, 91)76 (67, 83)Apgar score\u20031 min5.8\u2009±\u20092.55.8\u2009±\u20092.3\u20035 min8.0\u2009±\u20091.87.7\u2009±\u20091.9Parent characteristicsSmoking status\u2003Mother smoker during pregnancy6 (9)18 (29)\u2003Father smoker3 (5)12 (21)\u2003Mother drank alcohol during pregnancy0 (0)4 (6)Mother's age, y31.1\u2009±\u20095.130.8\u2009±\u20095.5Mother's BMI before pregnancy, kg/m2*23.2 (20.6, 27.2)21.3 (19.7, 26.1)Mother's weight gain during pregnancy, kg11.2\u2009±\u20096.89.2\u2009±\u20095.2BMI\u2009=\u2009body mass index; cHMF\u2009=\u2009control human milk fortifier; FSI1\u2009=\u2009fortification strength increase day 1; nHMF\u2009=\u2009new human milk fortifier . Data are presented as n (%) for categorical variables and mean\u2009±\u2009SD for continuous variables except where noted.*Data are presented as median (Q1, Q3).TABLE 3Anthropometric gains from D1 to D21Treatment groupnnHMFncHMFP*Weight gain, g\u2009·\u2009kg−1\u2009·\u2009day−16418.3\u2009±\u20093.76716.8\u2009±\u20093.70.013†Length gain, cm/wk551.23\u2009±\u20090.62651.18\u2009±\u20090.490.842HC gain, cm/wk571.04\u2009±\u20090.32650.96\u2009±\u20090.260.125cHMF\u2009=\u2009control human milk fortifier; D1\u2009=\u2009study day 1 (first day of full-strength fortification); D21\u2009=\u2009study day 21; HC\u2009=\u2009head circumference; nHMF\u2009=\u2009new human milk fortifier. Data are presented as unadjusted mean\u2009±\u2009SD.*One-sided superiority P value based on analysis of covariance model adjusted for postmenstrual age and relevant anthropometric measure at D1, sex, and center.†Adjusted difference in weight gain (nHMF–cHMF): mean difference\u2009=\u20091.18\u2009g\u2009·\u2009kg−1\u2009·\u2009day−1; 95% CI\u2009=\u20090.14, 2.21.TABLE 4Body length and head circumference gains between study days 1 and 21, by infant sex and by birth weight categoryUnadjusted length gain, cm/wk*Unadjusted head circumference gain, cm/wk*nHMFcHMFnHMFcHMFnMean\u2009±\u2009SDnMean\u2009±\u2009SDP†nMean\u2009±\u2009SDnMean\u2009±\u2009SDP†Overall551.23\u2009±\u20090.62651.18\u2009±\u20090.490.842571.04\u2009±\u20090.32650.96\u2009±\u20090.260.126Boys271.40\u2009±\u20090.65281.18\u2009±\u20090.490.364281.12\u2009±\u20090.28280.99\u2009±\u20090.220.062Girls281.08\u2009±\u20090.56371.17\u2009±\u20090.500.510290.97\u2009±\u20090.35370.93\u2009±\u20090.290.598<1000\u2009g191.07\u2009±\u20090.52211.27\u2009±\u20090.520.563191.04\u2009±\u20090.34210.94\u2009±\u20090.280.223≥1000\u2009g361.32\u2009±\u20090.66441.13\u2009±\u20090.480.499381.05\u2009±\u20090.32440.96\u2009±\u20090.260.270cHMF\u2009=\u2009control human milk fortifier; nHMF\u2009=\u2009new human milk fortifier.*Data are presented as unadjusted mean\u2009±\u2009SD.†Superiority P value for gain differences adjusted for postmenstrual age and the relevant anthropometric measure at D1, sex, and center by analysis of covariance.TABLE 5Weight, length, and head circumference at selected study time pointsnHMFcHMFVariablenMeanSDnMeanSDWeight, g\u2003D1721346271741347270\u2003D21641884336671863328\u2003W40CA603076519632897416Length, cm\u2003D16738.72.57438.72.8\u2003D215841.82.46542.02.7\u2003W40CA6047.62.66247.32.5Head circumference, cm\u2003D16827.72.57327.61.9\u2003D215930.22.26630.32.0\u2003W40CA5935.31.46434.61.5cHMF\u2009=\u2009control human milk fortifier; D1\u2009=\u2009study day 1; D21\u2009=\u2009study day 21; nHMF\u2009=\u2009new human milk fortifier; SD\u2009=\u2009standard deviation; W40CA\u2009=\u2009week 40 corrected age.TABLE 6Markers of protein-energy status, electrolytes, and bone metabolic status at study days 1 and 21nHMFcHMFVariablenMedianIQRGeometric meannMedianIQRGeometric meanP*Serum creatinine, μmol/L\u2003D16944.036.2–48.041.57044.138.0–51.843.50.303\u2003D216328.023.5–32.026.76530.025.0–35.029.50.001BUN, mmol/L\u2003D1703.101.70–4.562.89712.501.65–4.672.730.585\u2003D21633.903.05–4.653.89642.151.50–2.632.15<0.001Serum prealbumin, mg/L\u2003D15110080–12096.8469080–10087.80.073\u2003D214611691.3–140113.84110090–12098.10.015Urinary urea†, mmol/10\u2009mg creatinine\u2003D1472.72.0–4.72.8532.51.9–3.32.50.302\u2003D21425.84.6–6.85.1402.82.0–3.32.7<0.001Serum calcium, mmol/L\u2003D1502.442.31–2.532.41542.472.38–2.562.440.445\u2003D21502.472.40–2.542.46482.432.34–2.532.430.019Serum phosphorus, mmol/L\u2003D1681.991.85–2.221.96711.941.76–2.251.940.816\u2003D21622.101.93–2.232.05642.121.93–2.262.080.681Alkaline phosphatase, U/L\u2003D167353.0298.5–459.5377.963333.0250.0–438.5343.80.208\u2003D2162320.5273.3–405.5337.562270.5233.0–354.3297.50.010Urinary calcium †, mmol/10\u2009mg creatinine\u2003D1600.110.07–0.190.12690.140.09–0.200.120.985\u2003D21550.140.09–0.230.15540.210.13–0.320.190.011Urinary phosphorus†, mmol/10\u2009mg creatinine\u2003D1590.410.12–0.660.22650.340.14–0.650.230.867\u2003D21520.680.44–1.100.53520.710.40–0.920.580.896Urinary calcium:phosphorus molar ratio\u2003D1590.390.15–0.900.50640.410.16–1.340.470.824\u2003D21530.220.12–0.480.28530.310.19–0.600.340.054Serum sodium, mmol/L\u2003D171138.0137.0–140.0138.672138.6136.6–140.0138.50.891\u2003D2165138.0136.4–140.0138.064138.0137.0–139.9138.30.449Serum potassium, mmol/L\u2003D1714.734.30–5.324.83724.774.40–5.104.780.685\u2003D21644.744.29–5.104.72644.514.14–4.884.540.091Serum chloride, mmol/L\u2003D171106.0104.0–109.0106.172105.0102.8–108.0105.20.148\u2003D2163105.0103.0–107.0104.662105.0104.0–107.0105.30.111BUN\u2009=\u2009blood urea nitrogen; cHMF\u2009=\u2009control human milk fortifier; D1\u2009=\u2009study day 1; D21\u2009=\u2009study day 21; IQR\u2009=\u2009interquartile range; nHMF\u2009=\u2009new human milk fortifier.*D1 geometric mean values were log-transformed and analyzed using t test; D21 geometric mean values were log-transformed and analyzed using analysis of covariance (adjusting for the relevant biochemical parameter at D1, sex, and center).†Corrected for urinary creatinine excretion of 10\u2009mg/kg body weight/day.TABLE 7Markers of kidney function, blood count, and urinary electrolyte status at study days 1 and 21nHMFcHMFVariablenMedianIQRGeometric meannMedianIQRGeometric meanP*Urinary creatinine, μmol/L\u2003D1631300.0785.5–1685.51224.7691105.0900.0–1500.01182.3\u2003D21571030.0660.0–1609.01000.355854.0618.0–1273.0900.80.447Serum hemoglobin, mmol/L\u2003D1682.081.84–2.292.14722.021.84–2.262.18\u2003D21631.711.56–1.911.83661.691.50–1.981.760.936Serum hematocrit, %\u2003D1680.400.35–0.430.39720.390.35–0.430.38\u2003D21630.320.29–0.380.33660.330.28–0.380.330.805Urinary sodium, mmol/L\u2003D16637.023.3–57.337.56932.019.4–54.031.2\u2003D215934.021.1–48.033.35623.014.3–36.424.00.037Urinary potassium, mmol/L\u2003D16625.913.6–37.023.66921.815.0–32.220.0\u2003D215930.016.9–45.027.65722.916.9–30.422.80.040Urinary chloride, mmol/L\u2003D16037.026.3–60.040.26733.020.5–55.034.2\u2003D215431.017.8–43.830.75526.018.0–39.527.80.558cHMF\u2009=\u2009control human milk fortifier; D1\u2009=\u2009study day 1; D21\u2009=\u2009study day 21; IQR\u2009=\u2009interquartile range; nHMF\u2009=\u2009new human milk fortifier .*D21 geometric mean values were log-transformed and analyzed using analysis of covariance (adjusting for the relevant biochemical measure at D1, sex, and center)."", 'title': 'Growth and Nutritional Biomarkers of Preterm Infants Fed a New Powdered Human Milk Fortifier: A Randomized Trial.', 'date': '2017-07-21'}}",0.0,Pediatrics & Neonatology
 19,"Is mortality higher, lower, or the same when comparing rapid ART to standard initiation?",lower,very low,yes,"['28742880', '27658873', '29509839', '27163694', '29136001', '29112963', '28542080']",31206168,2019,"{'28742880': {'article_id': '28742880', 'content': 'PLoS MedPLoS MedplosplosmedPLoS Medicine1549-12771549-1676Public Library of ScienceSan Francisco, CA USA28742880552652610.1371/journal.pmed.1002357PMEDICINE-D-17-00266Research ArticleBiology and Life SciencesImmunologyVaccination and ImmunizationAntiviral TherapyAntiretroviral TherapyMedicine and Health SciencesImmunologyVaccination and ImmunizationAntiviral TherapyAntiretroviral TherapyMedicine and Health SciencesPublic and Occupational HealthPreventive MedicineVaccination and ImmunizationAntiviral TherapyAntiretroviral TherapyBiology and Life SciencesMicrobiologyMedical MicrobiologyMicrobial PathogensViral PathogensImmunodeficiency VirusesHIVMedicine and Health SciencesPathology and Laboratory MedicinePathogensMicrobial PathogensViral PathogensImmunodeficiency VirusesHIVBiology and Life SciencesOrganismsVirusesViral PathogensImmunodeficiency VirusesHIVBiology and Life SciencesOrganismsVirusesImmunodeficiency VirusesHIVBiology and life sciencesOrganismsVirusesRNA virusesRetrovirusesLentivirusHIVBiology and Life SciencesMicrobiologyMedical MicrobiologyMicrobial PathogensViral PathogensRetrovirusesLentivirusHIVMedicine and Health SciencesPathology and Laboratory MedicinePathogensMicrobial PathogensViral PathogensRetrovirusesLentivirusHIVBiology and Life SciencesOrganismsVirusesViral PathogensRetrovirusesLentivirusHIVMedicine and health sciencesDiagnostic medicineHIV diagnosis and managementBiology and Life SciencesMicrobiologyMedical MicrobiologyMicrobial PathogensViral PathogensImmunodeficiency VirusesHIVHIV-1Medicine and Health SciencesPathology and Laboratory MedicinePathogensMicrobial PathogensViral PathogensImmunodeficiency VirusesHIVHIV-1Biology and Life SciencesOrganismsVirusesViral PathogensImmunodeficiency VirusesHIVHIV-1Biology and Life SciencesOrganismsVirusesImmunodeficiency VirusesHIVHIV-1Biology and life sciencesOrganismsVirusesRNA virusesRetrovirusesLentivirusHIVHIV-1Biology and Life SciencesMicrobiologyMedical MicrobiologyMicrobial PathogensViral PathogensRetrovirusesLentivirusHIVHIV-1Medicine and Health SciencesPathology and Laboratory MedicinePathogensMicrobial PathogensViral PathogensRetrovirusesLentivirusHIVHIV-1Biology and Life SciencesOrganismsVirusesViral PathogensRetrovirusesLentivirusHIVHIV-1Medicine and Health SciencesHealth CareHealth Care ProvidersMedical DoctorsPhysiciansPeople and PlacesPopulation GroupingsProfessionsMedical DoctorsPhysiciansPeople and placesGeographical locationsNorth AmericaCaribbeanHaitiBiology and Life SciencesMicrobiologyVirologyViral Transmission and InfectionViral LoadMedicine and health sciencesDiagnostic medicineHIV clinical manifestationsSame-day HIV testing with initiation of antiretroviral therapy versus standard care for persons living with HIV: A randomized unblinded trialSame-day HIV testing and antiretroviral therapy initiationhttp://orcid.org/0000-0001-7464-275XKoenigSerena P.ConceptualizationFunding acquisitionInvestigationMethodologySupervisionWriting – original draftWriting – review & editing12*DorvilNancyInvestigationMethodologyProject administrationSupervisionWriting – review & editing1DévieuxJessy G.ConceptualizationFunding acquisitionInvestigationMethodologySupervisionWriting – original draftWriting – review & editing3http://orcid.org/0000-0002-9689-5413Hedt-GauthierBethany L.ConceptualizationFormal analysisFunding acquisitionMethodologySoftwareSupervisionValidationVisualizationWriting – review & editing4RiviereCynthiaInvestigationMethodologyProject administrationSupervisionWriting – review & editing1FaustinMikerlyneInvestigationMethodologyProject administrationSupervisionWriting – review & editing1LavoileKerlyneInvestigationMethodologyProject administrationSupervisionWriting – review & editing1PerodinChristianFormal analysisInvestigationMethodologySoftwareValidationVisualizationWriting – review & editing1ApollonAlexandraConceptualizationInvestigationMethodologyProject administrationSupervisionWriting – review & editing1DuvergerLimatheInvestigationMethodologyProject administrationSupervisionWriting – review & editing1McNairyMargaret L.MethodologyWriting – review & editing56HennesseyKelly A.Formal analysisMethodologySoftwareValidationVisualizationWriting – review & editing1SouroutzidisAriadneFormal analysisMethodologySoftwareValidationVisualizationWriting – review & editing7CremieuxPierre-YvesFormal analysisMethodologySoftwareValidationVisualizationWriting – review & editing7SeverePatriceConceptualizationFunding acquisitionInvestigationMethodologyProject administrationSupervisionWriting – review & editing1PapeJean W.ConceptualizationFunding acquisitionInvestigationMethodologyProject administrationSupervisionWriting – review & editing151\nHaitian Study Group for Kaposi’s Sarcoma and Opportunistic Infections (GHESKIO), Port-au-Prince, Haiti2\nDivision of Global Health Equity, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America3\nAIDS Prevention Program, Florida International University, Miami, Florida, United States of America4\nDepartment of Global Health and Social Medicine, Harvard Medical School, Harvard University, Boston, Massachusetts, United States of America5\nCenter for Global Health, Department of Medicine, Weill Cornell Medical College, Cornell University, New York, New York, United States of America6\nDivision of General Internal Medicine, Department of Medicine, Weill Cornell Medical College, Cornell University, New York, New York, United States of America7\nAnalysis Group, Boston, Massachusetts, United States of AmericaGengElvin H.Academic EditorUniversity of California, San Francisco, UNITED STATESThe authors have declared that no competing interests exist.* E-mail: skoenig@bwh.harvard.edu257201772017147e100235724120171662017© 2017 Koenig et al2017Koenig et alThis is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.\nThe science of rapid start—From the when to the how of antiretroviral initiation\nBackgroundAttrition during the period from HIV testing to antiretroviral therapy (ART) initiation is high worldwide. We assessed whether same-day HIV testing and ART initiation improves retention and virologic suppression.Methods and findingsWe conducted an unblinded, randomized trial of standard ART initiation versus same-day HIV testing and ART initiation among eligible adults ≥18 years old with World Health Organization Stage 1 or 2 disease and CD4 count ≤500 cells/mm3. The study was conducted among outpatients at the Haitian Group for the Study of Kaposi’s Sarcoma and Opportunistic infections (GHESKIO) Clinic in Port-au-Prince, Haiti. Participants were randomly assigned (1:1) to standard ART initiation or same-day HIV testing and ART initiation. The standard group initiated ART 3 weeks after HIV testing, and the same-day group initiated ART on the day of testing. The primary study endpoint was retention in care 12 months after HIV testing with HIV-1 RNA <50 copies/ml. We assessed the impact of treatment arm with a modified intention-to-treat analysis, using multivariable logistic regression controlling for potential confounders. Between August 2013 and October 2015, 762 participants were enrolled; 59 participants transferred to other clinics during the study period, and were excluded as per protocol, leaving 356 in the standard and 347 in the same-day ART groups. In the standard ART group, 156 (44%) participants were retained in care with 12-month HIV-1 RNA <50 copies, and 184 (52%) had <1,000 copies/ml; 20 participants (6%) died. In the same-day ART group, 184 (53%) participants were retained with HIV-1 RNA <50 copies/ml, and 212 (61%) had <1,000 copies/ml; 10 (3%) participants died. The unadjusted risk ratio (RR) of being retained at 12 months with HIV-1 RNA <50 copies/ml was 1.21 (95% CI: 1.04, 1.38; p = 0.015) for the same-day ART group compared to the standard ART group, and the unadjusted RR for being retained with HIV-1 RNA <1,000 copies was 1.18 (95% CI: 1.04, 1.31; p = 0.012). The main limitation of this study is that it was conducted at a single urban clinic, and the generalizability to other settings is uncertain.ConclusionsSame-day HIV testing and ART initiation is feasible and beneficial in this setting, as it improves retention in care with virologic suppression among patients with early clinical HIV disease.Trial registrationThis study is registered with ClinicalTrials.gov number NCT01900080In a randomized unblinded trial in Port-au-Prince, Haiti, Serena Koenig and colleagues investigate whether initiating ART on the day of HIV diagnosis improved retention in care and viral suppression.Author summaryWhy was this study done?Multiple visits for counseling, laboratory testing, and other procedures to prepare patients for initiation of antiretroviral therapy (ART) are burdensome and contribute to the high rate of attrition during the period from HIV testing to ART initiation.The World Health Organization (WHO) recently changed their guidelines to recommend ART for all persons living with HIV, facilitating ART initiation.This study was conducted to determine if ART initiation on the day of HIV diagnosis could improve treatment initiation rates, retention in care, and HIV viral suppression for patients with asymptomatic or minimally symptomatic HIV disease.What did the researchers do and find?We randomly assigned patients who presented for HIV testing at a clinic in Port-au-Prince, Haiti to standard ART initiation or same-day HIV testing and ART initiation (356 in the standard and 347 in the same-day groups).The standard group had 3 weekly visits with a social worker and physician and then started ART 21 days after the date of HIV diagnosis; the same-day ART group initiated ART on the day of HIV diagnosis.All participants in the same-day ART group and 92% of participants in the standard group initiated ART.At 12 months after HIV testing, a higher proportion of participants in the same-day ART group were retained in care (80% versus 72%), and a higher proportion were retained in care with viral load <50 copies/ml (53% versus 44%) and viral load <1,000 copies/ml (61% versus 52%).What do these findings mean?This study demonstrates that it is feasible to initiate ART on the day of HIV diagnosis for patients with early HIV clinical disease and that same-day treatment leads to increased ART uptake, retention in care, and viral suppression.Though same-day ART initiation improves outcomes, retention in care and viral suppression remain suboptimal, so further interventions to maximize long-term outcomes will be essential.The study is limited by being conducted at 1 clinic in urban Haiti. Further study will be necessary to determine if this strategy will be effective in other settings.http://dx.doi.org/10.13039/100000060National Institute of Allergy and Infectious DiseasesR01AI104344http://orcid.org/0000-0001-7464-275XKoenigSerena P.This project was supported by the National Institute of Allergy and Infectious Diseases, grant number R01AI104344. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Data AvailabilityWe have included the anonymized dataset as a Supporting Information file (S1 Data).Data AvailabilityWe have included the anonymized dataset as a Supporting Information file (S1 Data).IntroductionThe Joint United Nations Programme on HIV/AIDS (UNAIDS) 90-90-90 targets state that 90% of HIV-infected persons know their status, 90% initiate antiretroviral therapy (ART), and 90% achieve virologic suppression by the year 2020 to curb the AIDS epidemic [1]. In 2015, the World Health Organization (WHO) updated their guidelines to recommend ART for all persons living with HIV based on evidence that earlier treatment improves outcomes and decreases transmission [2–4]. To achieve these goals, patients must be promptly linked to HIV services, initiated on ART, and retained in lifelong care [5].Attrition rates are particularly high during the period from HIV testing to ART initiation, with one-quarter to one-third of patients lost in the process of starting ART [6–9]. Even if many of these patients re-engage in care at a later date, they will return with more advanced disease. Though there are many factors that contribute to pretreatment attrition, the current standard of care in most settings, which requires multiple sequential visits for HIV testing and counseling, laboratory testing, and adherence counseling prior to ART initiation, creates barriers to treatment initiation. As of June 2016, WHO guidelines note inadequate evidence to support a recommendation of same-day HIV testing and ART initiation [2]. However, the availability of point-of-care tests, the fact that CD4 cell counts are no longer necessary prior to ART initiation, and the provision of same-day counseling can accelerate treatment initiation, potentially reducing attrition [10–12]. We conducted a randomized trial in Haiti to determine whether same-day HIV testing and ART initiation, as compared with standard ART initiation, improves retention in care with viral suppression.MethodsStudy design and settingWe conducted an unblinded, randomized controlled trial of standard ART initiation versus same-day HIV testing and ART initiation among HIV-infected adults at the Haitian Group for the Study of Kaposi’s Sarcoma and Opportunistic infections (GHESKIO) in Port-au-Prince, Haiti. Haiti is the poorest country in the Western Hemisphere, with adult HIV prevalence of 1.7% [13,14]. GHESKIO is a Haitian nongovernmental organization and the largest provider of HIV care in the Caribbean, treating up to 700 patients per day for HIV and/or tuberculosis (TB). All care is provided free of charge. The study was approved by the institutional review boards at Partners Healthcare, GHESKIO, Weill Cornell Medical College, and Florida International University. See supporting information files S1 Text for the study protocol and S2 Text for the CONSORT checklist.ParticipantsParticipants were recruited from the HIV voluntary counseling and testing center at GHESKIO from August 2013 to October 2015. They received HIV testing and posttest counseling; those with a positive HIV test were referred for same-day physician evaluation, CD4 count (FACS Count, Becton-Dickinson, Franklin Lakes, New Jersey), WHO staging, and chest radiograph. Patients were eligible for study inclusion if they were infected with HIV-1, ≥18 years of age, and had WHO Stage 1 or 2 disease and CD4 count ≤500 cells/mm3. Initially, enrollment was limited to patients with CD4 count ≤350 cells/mm3, but in February 2014, the cutoff was increased to ≤500 cells/mm3 in response to revised WHO and Haitian guidelines [15]. Patients were excluded if they were already aware of their HIV diagnosis, had received ART previously, were pregnant or breastfeeding, lived outside of the greater Port-au-Prince metropolitan area, planned to transfer care during the study period, or failed to demonstrate preparedness on an ART readiness survey, which was administered by a social worker prior to study enrollment. The survey includes a 5-point scale, with respondents ranking their preparedness from “not at all ready” to “completely ready” in response to 7 questions. Study inclusion required a response of “somewhat ready” or “completely ready” for all 7 questions (S3 Text) [16].Randomization and maskingAfter the patients had provided written informed consent, the study team performed a screening evaluation for study exclusion criteria, and eligible participants were enrolled and randomized on the day of HIV testing. Participants were randomly assigned with the use of a computer-generated random-number list to either standard ART or same-day ART initiation in a 1:1 ratio, with allocation concealment. The randomization sequence was generated by a computer in the GHESKIO data management unit by a data manager who had no other involvement in study procedures. Participants were enrolled in the study and assigned to groups by a study physician. Participants, site personnel, and study statisticians were not masked to group assignment.ProceduresAfter randomization, the standard group participants received ART initiation procedures that mirror national guidelines. Participants were referred to return on Day 7 for baseline laboratory tests (creatinine, alanine aminotransferase, aspartate aminotransferase, complete blood count, purified protein derivative [PPD]), physician evaluation, and counseling with a social worker. On Day 10, they received interpretation of PPD results, and on Days 14 and 21, they were seen by a physician and social worker for additional counseling, test results, and ongoing evaluations for opportunistic infections. Participants started ART on Day 21 and had an additional social worker and physician visit at Week 5 (Fig 1). The ART regimen was the same as that for nonstudy patients at GHESKIO. First-line therapy included a single combination tablet including tenofovir disoproxil fumarate, lamivudine, and efavirenz.10.1371/journal.pmed.1002357.g001Fig 1Study interventions for the standard ART and same-day ART groups.The same-day ART group had identical laboratory tests as the standard ART group, a 30-minute counseling session with a social worker, and physician evaluation, and then initiated the same ART regimen as the standard ART group. They returned on Day 3 for physician and social worker visits and receipt of baseline laboratory test results; those with creatinine clearance <50 mL/minute as calculated by the Cockcroft-Gault equation were switched from tenofovir to zidovudine or abacavir. They returned on Days 10 and 17 for additional physician and social worker visits and on Day 24 for a physician visit. The same number of scheduled physician visits and counseling sessions were provided to each group so that the only difference in care was in the schedule of visits during the first 5 weeks of the study and the timing of ART initiation.All care was delivered by GHESKIO clinic staff, and the same providers (physicians, nurses, social workers, pharmacists, and field workers) cared for both groups. A counseling manual was followed with an outline for the social workers to follow at each scheduled counseling visit; these were identical between groups, except for the timing of ART initiation, and each session took about 30 minutes. All counseling was provided for individual patients, rather than for groups. The counseling sessions were audiotaped and systematically evaluated for quality control purposes. If a participant in either group missed a study visit that included a scheduled social worker counseling session, the counseling was provided at the next visit.Participants in both groups had monthly physician visits throughout the follow-up period and received the same package of services provided to all HIV-infected patients at GHESKIO, including prophylactic treatment with trimethoprim-sulfamethoxazole and isoniazid. Field workers phoned patients who missed a visit and attempted a home visit for those not reachable by phone. Participants received a transportation subsidy of 100 Haitian gourdes (US$1.70) per visit.OutcomesThe primary endpoint was retention in care with HIV-1 RNA <50 copies/ml at 12 months after HIV testing. Retention was defined as attending the 12-month visit (1 clinic visit between 12 and 15 months after HIV testing). Lost to follow-up (LTFU) was defined as failure to attend the 12-month visit. Deaths were ascertained by review of medical records or report from family members. A National Institutes of Health Division of AIDS Expedited Adverse Event Form was filled out within 48 hours after the study team became aware of any death. Transfers were ascertained by confirmation that the participant was receiving care at a different site. Secondary outcomes include survival, ART initiation, retention in care with HIV-1 RNA <1,000 copies/ml at 12 months after HIV testing, adherence as measured by pharmacy refill records and self-report, and cost and cost-effectiveness of standard and same-day ART; the adherence and cost-effectiveness evaluations will be reported in separate manuscripts.Statistical analysisDemographic, clinical, and laboratory data from the electronic medical record and study forms were de-identified, entered into an Excel spreadsheet, and exported into Stata v14 software (StataCorp, 2011, College Station, Texas) for analysis. After study completion, all participants who were LTFU were recontacted to determine their vital status.The study was powered to detect a 10% absolute difference in the rate of retention with virologic suppression between the 2 groups at 12 months after enrollment (65% in the standard and 75% in the same-day ART group). At the α = 0.05 significance level, we estimated that we would need to enroll 349 participants per group (698 in total) to achieve 80% power to detect this difference. Because patients who transferred during the study period were excluded, we increased the total sample size to 762 participants. For all analyses, a modified intention-to-treat approach was used, in which all patients were analyzed according to their assignment group, excluding patients who transferred to another facility during the follow-up period, according to protocol.Baseline characteristics were summarized using simple frequencies and proportions and medians with interquartile ranges (IQRs) stratified by treatment arm. Among participants who died, baseline CD4 count was compared using the Wilcoxon rank-sum test. We compared the proportion of participants who were retained in care with HIV-1 RNA <50 copies/ml (primary endpoint), retained with HIV-1 RNA <1,000 copies/ml, retained regardless of HIV-1 RNA, initiated ART, and died (secondary endpoints) at 12 months after enrollment using a chi-square test. We conducted multivariable logistic regression including all covariates listed in Table 1 to control for any residual confounding. We present unadjusted and adjusted risk ratios (RR) with 95% confidence intervals. Because of the change in enrollment criteria mid-study, we conducted a sensitivity analysis that included only the participants who met the original enrollment criteria of CD4 count ≤350 cells/mm3. In response to a reviewer’s request, we also plotted retention in care, regardless of viral load, for both groups and compared the distributions with the log-rank test. The study is registered with ClinicalTrials.gov number NCT01900080.10.1371/journal.pmed.1002357.t001Table 1Baseline characteristics of study participants by group.CharacteristicStandard Group (n = 356)Same-Day ART Group (n = 347)Age (years)—Median (IQR)37 (30, 45)37 (29, 46)Female sex—no. (%)181 (51)166 (48)Education—no. (%)\xa0\xa0\xa0\xa0No school90 (25)93 (27)\xa0\xa0\xa0\xa0Primary school110 (31)111 (32)\xa0\xa0\xa0\xa0Secondary school or more156 (44)143 (41)Income—no. (%)\xa0\xa0\xa0\xa0No income92 (26)90 (26)\xa0\xa0\xa0\xa0>$0 to $1/day176 (49)159 (46)\xa0\xa0\xa0\xa0>$1 to $2/day67 (19)76 (22)\xa0\xa0\xa0\xa0>$2/day21 (6)22 (6)Marital status—no. (%)\xa0\xa0\xa0\xa0Single71 (20)71 (20)\xa0\xa0\xa0\xa0Currently married/living with partner222 (62)211 (61)\xa0\xa0\xa0\xa0Formerly married63 (18)65 (19)WHO Stage—no. (%)\xa0\xa0\xa0\xa0WHO Stage 1117 (33)101 (29)\xa0\xa0\xa0\xa0WHO Stage 2239 (67)246 (71)CD4 count (cells/mm3)—Median (IQR)247 (150, 349)249 (143, 336)Body mass index—Median (IQR)*21.6 (19.7, 23.9)20.9 (19.3, 23.5)* Body mass index differed significantly between the 2 groups (p = 0.025).ART, antiretroviral therapy; IQR, interquartile range, WHO, World Health Organization.ResultsA total of 821 patients were screened, and 762 were enrolled in the study and underwent randomization (Fig 2). After randomization, 59 participants (28 in the standard ART and 31 in same-day ART group) transferred to another clinic and were excluded from all analyses, as per protocol. The median age was 37 years old (IQR: 30–45 years), 347 (49%) were women, and the median CD4 count was 248 cells/mm3 (IQR: 148, 345).10.1371/journal.pmed.1002357.g002Fig 2Screening, randomization, and follow-up.Of the 356 participants in the standard group, 256 (72%) were retained in care, 20 (6%) died, and 80 (23%) were LTFU (Table 2). Among the 256 participants retained in the standard ART group, 156 (61% of retained and 44% overall) had HIV-1 RNA <50 copies/ml. Of the 347 participants in the same-day ART group, 277 (80%) were retained in care, 10 (3%) died, and 60 (17%) were LTFU. Among the 277 participants retained in the same-day ART group, 184 (66% of retained and 53% overall) had HIV-1 RNA <50 copies/ml. The unadjusted RR of being retained in care at 12 months and achieving HIV-1 RNA <50 copies/ml was 1.21 (95% CI: 1.04, 1.38; p = 0.015) for the same-day ART group compared to the standard group (Table 3); the adjusted RR for this comparison was 1.24 (95% CI: 1.06, 1.41; p = 0.008).10.1371/journal.pmed.1002357.t002Table 2Study outcomes by group.OutcomeStandard ART Group (n = 356)Same-Day ART Group (n = 347)Unadjusted Risk Difference (95% CI)p-valuePrimary OutcomeRetained in care at 12 months with VL <50 copies/ml156 (43.8%)184 (53.0%)9.2% (1.8%, 16.6%)0.015†Secondary OutcomesRetained in care at 12 months with VL <1,000 copies/ml184 (51.7%)212 (61.1%)9.4% (2.1%, 16.7%)0.012‡Retained in care at 12 months, regardless of VL results256 (71.9%)277 (79.8%)7.9% (1.6%, 14.2%)0.014††Died20 (5.6%)10 (2.9%)Lost to follow-up80 (22.5%)60 (17.3%)† p-value comparing the proportion of all patients who were retained in care with viral load <50 copies/ml between the 2 arms.‡ p-value comparing the proportion of all patients who were retained in care with viral load <1,000 copies/ml between the 2 arms.†† p-value comparing the proportion of all patients who were retained in care between the 2 arms.ART, antiretroviral therapy; VL, viral load.10.1371/journal.pmed.1002357.t003Table 3Unadjusted and adjusted risk ratios of study outcomes.UnadjustedAdjusted for All Baseline Co-variatesRR95% CIp-valueRR95% CIp-valueRetained in care with viral load <50 copies/mlStandard ART Group1.01.0Same-Day ART Group1.21(1.04, 1.38)0.0151.24(1.06, 1.41)0.008Retained in care with viral load <1,000 copies/mlStandard ART Group1.01.0Same-Day ART Group1.18(1.04, 1.31)0.0121.20(1.05, 1.33)0.008Mortality during study periodStandard ART Group1.01.0Same-Day ART Group0.51(0.24, 1.08)0.0730.43(0.19, 0.94)0.033ART, antiretroviral therapy; RR, risk ratio.In the standard ART group, 184 (72% of retained and 52% overall) participants who were retained in care had HIV-1 RNA <1,000 copies/ml. In the same-day ART group, 212 (77% of retained and 61% overall) participants who were retained in care had HIV-1 RNA <1,000 copies/ml. The unadjusted RR of being retained in care at 12 months and achieving HIV-1 RNA <1,000 copies/ml was 1.18 (95% CI: 1.04, 1.31; p = 0.012) for the same-day ART group compared to the standard ART group (Table 3); the adjusted RR for this comparison was 1.20 (95% CI: 1.05, 1.33; p = 0.008). In the sensitivity analysis that included only participants who met the original enrollment criteria (CD4 count ≤350 cells/mm3), the adjusted RR of being retained in care at 12 months and achieving HIV-1 RNA <50 copies/ml was 1.19 (95% CI: 0.99, 1.38; p = 0.060), and the adjusted RR of being retained in care at 12 months and achieving HIV-1 RNA < 1,000 copies/ml was 1.18 (95% CI: 1.01, 1.34; p = 0.035).Vital status at the end of the study was known for 328 (92%) participants in the standard ART group and 329 (95%) in the same-day ART group. The unadjusted RR for mortality was 0.51 (95% CI: 0.24, 1.08; p = 0.073) for the same-day group compared to the standard group; the adjusted RR for this comparison was 0.43 (95% CI: 0.19, 0.94; p = 0.033). In the sensitivity analysis that included only participants with CD4 count ≤350 cells/mm3, the adjusted RR for mortality was 0.41 (95% CI: 0.18, 0.93; p = 0.033). Among the participants who died, the median baseline CD4 count was 100 cells/mm3 (IQR: 45, 192) in the standard and 207 cells/mm3 (IQR: 112, 291) in the same-day ART group (p = 0.078). Eight of 20 (40%) deaths in the standard ART group occurred in participants who were LTFU prior to ART, 8 (40%) deaths occurred in those LTFU after starting ART, and 4 (20%) occurred while in care; the causes of death for those in care were stroke, trauma, and cancer in 3, and the fourth had pain and died after seeing a traditional healer. Three of the 10 (30%) deaths in the same-day ART group occurred in participants who were LTFU after starting ART; among the 7 (70%) participants who died while in care, 1 of each died of stroke, pneumonia, malaria, renal failure, and sudden death, and 2 died of gastroenteritis. No deaths for those in care were attributed to immune reconstitution syndrome or an opportunistic infection that was missed at ART initiation. In Fig 3, the Kaplan-Meier curve plots the retention in care, regardless of viral load, for both groups. The log-rank test comparing the curves between the standard and same-day ART group indicates a significant difference (p = 0.028).10.1371/journal.pmed.1002357.g003Fig 3Retention in care by study group.In the same-day ART group, 344 of 347 (99%) participants started ART on the day of HIV testing, and the remaining 3 patients started ART within the subsequent week. During the Day 3 follow-up visit, 13 patients (4%) in the same-day ART group had adjustments in their ART regimens (replacement of tenofovir with zidovudine or abacavir) because they had creatinine clearance <50 mL/minute on baseline testing. In the standard group, 281 (79%) participants initiated ART by Day 28, the end of the time window for the 3-week ART initiation visit. Thirty-six (10%) standard group participants initiated ART from Day 29 to Day 90, and 12 (3%) initiated ART after Day 90 due to late or missed visits. Twenty-seven (8%) standard group participants never started ART during the study period because they were LTFU or died prior to initiating treatment. Isoniazid prophylaxis was initiated for 337 (95%) participants in the standard group and 340 (98%) in the same-day group. Eight cases of TB were diagnosed during the first 3 months after ART initiation; 6 of these occurred in the standard group and 2 in the same-day ART group.DiscussionThe results of this randomized controlled trial show that among HIV-infected adults with early WHO Stage disease and CD4 count ≤500 cells/mm3, same-day HIV testing and ART initiation, as compared to standard care, improves retention in care with virologic suppression and, in the multivariable analysis, decreases mortality. These results are important given recent WHO 2016 guidelines stating the lack of evidence in support of same-day ART initiation.Our findings suggest that ART initiation as soon as possible after HIV testing may be beneficial for clinically stable patients. In resource-poor settings with fragile delivery systems, such as Haiti, the provision of immediate support by care providers at the time of HIV diagnosis can have both structural and individual impact. In addition to making treatment initiation logistically easier for patients, we believe that same-day counseling and ART initiation increase the sense of hope, optimism, and overall connectedness to the healthcare system for patients, which have been shown to be important for retention [17–20].Our findings are consistent with the results of the RapIT study, a randomized trial that included participants in South Africa with WHO Stage 3 or 4 disease or CD4 count ≤350 cells/mm3 [11]. Participants in the standard group in that study generally started ART at the sixth visit, and 72% of participants in the rapid group started ART on the day of study enrollment. Rapid ART initiation resulted in a 17% improvement in retention and 13% improvement in viral suppression. A stepped-wedge cluster-randomized trial in Uganda found an increase in ART initiation within 2 weeks after eligibility by implementing a multicomponent intervention to streamline ART initiation that included training healthcare workers, providing point-of-care CD4 count testing platforms, eliminating mandatory multiple preinitiation sessions, and giving feedback to facilities on their ART initiation rates [21]. A weighted proportion of 80% in the intervention group had started ART within 2 weeks after eligibility compared with 38% in the control group. A cohort study of same-day ART initiation in pregnant women in South Africa also found high rates of treatment initiation, with 91% initiating ART on the day of referral to the service [22]. In the intervention group of the Sustainable East Africa Research on Community Health (SEARCH) HIV test-and-treat study, a cluster-randomized controlled trial conducted in Kenya and Uganda, HIV-infected patients who were identified through community testing were referred to HIV care upon diagnosis and then offered immediate ART initiation; retention was high (89%) among patients newly linking to care [23].At ART initiation, it is critical that patients are ready to start lifelong therapy, that TB screening is conducted, and that renal function is evaluated to avoid the use of tenofovir in patients with renal insufficiency. In this study, ART readiness was remarkably high, with over 99% of patients screened for the study reporting they were ready to start lifelong ART. This is a particularly significant and timely finding for the provision of recommended universal ART because the majority of people living with HIV have early clinical disease, and there has been prior concern that healthier patients may be less willing to accept lifelong therapy [4]. Most patients with early clinical disease do not have TB symptoms (cough, fever, night sweats, or weight loss), so they do not require further work up to exclude TB, according to WHO guidelines [2]. With the exclusion of patients with a baseline chest x-ray that was suspicious for TB, we found that less than 1% of participants in the same-day ART group had TB that was missed at the time of ART initiation. We found that 4% of participants in the same-day ART group had creatinine clearance <50 mL/minute; ART regimens were adjusted on Day 3 for these patients.Both groups in our study received high-level care, with multiple counseling and physician visits in the first month, followed by monthly physician visits. At the time the study was started, this was the standard of care in Haiti. However, this standard has shifted over the past few years towards decreased frequency of visits and nonphysician providers [2,24–27]. We believe that same-day ART can be provided with fewer follow-up visits if proper counseling is provided during the early period after ART initiation. However, clinic-level procedures play a major role in the effectiveness of accelerated ART initiation strategies, as illustrated in Malawi, where among nearly 22,000 pregnant women who started ART for mother-to-child prevention, LTFU rates ranged from 0% to 58% between facilities and were highest among women who initiated ART on the day of HIV testing at large clinics [28].Though lower than anticipated, retention in both groups in our study was higher than reports of standard ART initiation from other resource-poor settings. Two studies from South Africa found that approximately one-third of patients remained in care from HIV testing through 12 months of ART, and systematic reviews of African studies have found high rates of pre-ART attrition [6,8,29,30]. In Haiti, data on pre-ART outcomes are limited, but 12-month retention after ART initiation is 73% nationwide [31]. We attribute the higher retention in our study in large part to faster ART initiation, even in the standard group, compared to many other HIV programs. We surmise that retention would have been lower in the standard group if there had been longer delays in ART initiation [5,11,30].The rates of retention with viral suppression in our study are lower than those reported from clinical trial cohorts, including at GHESKIO. In the GHESKIO Clinical Trials Unit, with a median monthly average of 483 subjects participating in NIH-funded clinical trials, retention is 97%. We attribute the lower retention and viral suppression rates in our study to 2 major reasons. First, nearly all patients meeting WHO stage and CD4 criteria were enrolled in the study on the day of HIV testing, including those with substantial barriers to retention in care and adherence. In contrast, over one-third of patients are generally lost to care prior to ART initiation or enrollment in clinical trials [6,8,29,30]. Second, the care that was provided in this study was similar to that received by nonstudy patients at GHESKIO, with the aim of producing findings that could be reproduced in other resource-poor settings. In order to achieve the UNAIDS 90-90-90 targets, it will be important to evaluate reasons for attrition and implement new strategies to improve retention in care. One approach that has been successful in a cohort of nonresearch patients at GHESKIO has been expedited follow-up care, with fewer visits of shorter duration for clinically stable patients [32]. Streamlined care has also been associated with high rates of retention in the SEARCH study, which is described above [23].Our study was conducted in a large urban clinic, which may limit the generalizability of our findings. In addition, though our study included patients with early clinical disease, the CD4 counts in our population were lower than would be expected with the provision of universal ART. It is possible that patients with higher CD4 counts may experience less benefit from same-day ART. It is also noteworthy that we conducted a chest x-ray prior to enrollment; if same-day ART is provided without a chest x-ray, it is possible that TB cases will be missed. Our study was not blinded. All participants in both groups received the same number of visits and the same retention plan, but we cannot exclude the possibility that awareness of study group impacted provider behavior.In conclusion, in a population of asymptomatic or minimally symptomatic HIV-infected patients, same-day HIV testing and ART initiation decreased mortality and improved the rate of retention in care with virologic suppression compared with standard ART initiation. Furthermore, human and material resources provided to each group were similar, so same-day ART is not expected to increase treatment costs. The new WHO recommendations to provide ART to all HIV-infected patients should facilitate same-day test and treat.Supporting informationS1 TextStudy protocol.(DOCX)Click here for additional data file.S2 TextCONSORT checklist.(DOC)Click here for additional data file.S3 TextHIV medication readiness scale.(PDF)Click here for additional data file.S1 DataAnonymized dataset.(XLSX)Click here for additional data file.Presented in part at the 21st International AIDS Conference, Durban, South Africa, July 18 to 22, 2016. We thank all of the patients who participated in this study and all of the GHESKIO staff who cared for them. We thank Drs. Paul Farmer, Daniel Fitzgerald, Martin Hirsch, Warren Johnson, Daniel Kuritzkes, and Paul Sax for expert advice on study design and Kaya Hedt and Anshul Saxena for manuscript formatting and preparation. We also thank Drs. Carlos del Rio, Kenneth Mayer, and Larry Moulton for serving on the data safety monitoring board and providing oversight of the study.AbbreviationsARTantiretroviral therapyGHESKIOHaitian Group for the Study of Kaposi’s Sarcoma and Opportunistic infectionsIQRinterquartile rangeLTFUlost to follow-upPPDpurified protein derivativeRRrisk ratioSEARCHSustainable East Africa Research on Community HealthUNAIDSThe Joint United Nations Programme on HIV/AIDSWHOWorld Health OrganizationReferences1UNAIDS Fast-Track, Ending the AIDS Epidemic by 2030. Accessed May 24, 2017 at: http://www.unaids.org/en/resources/campaigns/World-AIDS-Day-Report-2014.2Consolidated Guidelines on the Use of Antiretroviral Drugs for Treating and Preventing HIV Infection. Recommendations for a Public Health Approach. Second Edition, World Health Organization, 2016. Accessed May 24, 2017 at: http://www.who.int/hiv/pub/arv/arv-2016/en/.3The INSIGHT START Study Group, LundgrenJD, BabikerAG, GordinF, EmeryS, GrundB, et al\nInitiation of Antiretroviral Therapy in Early Asymptomatic HIV Infection. New Engl J Med. 2015;373(9):795–807. doi: 10.1056/NEJMoa1506816\n261928734The TEMPRANO ANRS 12136 Study Group. A Trial of Early Antiretrovirals and Isoniazid Preventive Therapy in Africa. New Engl J Med. 2015;373(9):808–22. doi: 10.1056/NEJMoa1507198\n261931265FoxMP, RosenS. Retention of Adult Patients on Antiretroviral Therapy in Low- and Middle-Income Countries: Systematic Review and Meta-analysis 2008–2013. J Acquir Immune Defic Syndr. 2015;69(1):98–108. doi: 10.1097/QAI.0000000000000553\n259424616ClouseK, PettiforAE, MaskewM, BassettJ, Van RieA, BehetsF, et al\nPatient retention from HIV diagnosis through one year on antiretroviral therapy at a primary health care clinic in Johannesburg, South Africa. J Acquir Immune Defic Syndr. 2013;62(2):e39–46. doi: 10.1097/QAI.0b013e318273ac48\n230114007ZachariahR, Tayler-SmithK, ManziM, MassaquoiM, MwagombaB, van GriensvenJ, et al\nRetention and attrition during the preparation phase and after start of antiretroviral treatment in Thyolo, Malawi, and Kibera, Kenya: implications for programmes?\nTrans Roy Soc Trop Med Hyg. 2011;105(8):421–30. doi: 10.1016/j.trstmh.2011.04.014\n217242198RosenS, FoxMP. Retention in HIV care between testing and treatment in sub-Saharan Africa: a systematic review. PLoS Med. 2011;8(7):e1001056\ndoi: 10.1371/journal.pmed.1001056\n218114039KoenigSP, BernardD, DevieuxJG, AtwoodS, McNairyML, SevereP, et al\nTrends in CD4 Count Testing, Retention in Pre-ART Care, and ART Initiation Rates over the First Decade of Expansion of HIV Services in Haiti. PLoS ONE. 2016;11(2):e0146903\ndoi: 10.1371/journal.pone.0146903\n2690179510SiednerMJ, LankowskiA, HabererJE, KembabaziA, EmenyonuN, TsaiAC, et al\nRethinking the ""pre"" in pre-therapy counseling: no benefit of additional visits prior to therapy on adherence or viremia in Ugandans initiating ARVs. PLoS ONE. 2012;7(6):e39894\ndoi: 10.1371/journal.pone.0039894\n2276192411RosenS, MaskewM, FoxMP, NyoniC, MongwenyanaC, MaleteG, et al\nInitiating Antiretroviral Therapy for HIV at a Patient\'s First Clinic Visit: The RapIT Randomized Controlled Trial. PLoS Med. 2016;13(5):e1002015\ndoi: 10.1371/journal.pmed.1002015\n2716369412JaniIV, SitoeNE, AlfaiER, ChongoPL, QuevedoJI, RochaBM, et al\nEffect of point-of-care CD4 cell count tests on retention of patients and rates of antiretroviral therapy initiation in primary health clinics: an observational cohort study. Lancet. 2011;378(9802):1572–9. doi: 10.1016/S0140-6736(11)61052-0\n2195165613UNAIDS—Haiti profile. Accessed May 24, 2017 at: http://www.unaids.org/en/regionscountries/countries/haiti.14International Human Development Indicators, Haiti Country Profile. United Nations Development Program. Accessed May 24, 2017 at: http://hdr.undp.org/en/countries/profiles/HTI.15Consolidated Guidelines on the Use of Antiretroviral Drugs for Treating and Preventing HIV Infection. Recommendations for a Public Health Approach. World Health Organization, 2013. Accessed May 24, 2017 at: http://www.who.int/hiv/pub/guidelines/arv2013/en/.16BalfourL, TascaGA, KowalJ, CoraceK, CooperCL, AngelJB, et al\nDevelopment and validation of the HIV Medication Readiness Scale. Assessment. 2007;14(4):408–16. doi: 10.1177/1073191107304295\n1798665817WareNC, WyattMA, GengEH, KaayaSF, AgbajiOO, MuyindikeWR, et al\nToward an understanding of disengagement from HIV treatment and care in sub-Saharan Africa: a qualitative study. PLoS Med. 2013;10(1):e1001369\ndoi: 10.1371/journal.pmed.1001369\n2334175318BernaysS, RhodesT, BarnettT. Hope: a new way to look at the HIV epidemic. AIDS. 2007;21\nSuppl 5:S5–11.19BarnettT, WestonM. Wealth, health, HIV and the economics of hope. AIDS. 2008;22\nSuppl 2:S27–34.20MasquillierC, WoutersE, MortelmansD, Booysen FleR. Families as catalysts for peer adherence support in enhancing hope for people living with HIV/AIDS in South Africa. J Int AIDS Soc. 2014;17:18802\ndoi: 10.7448/IAS.17.1.18802\n2470279721AmanyireG, SemitalaFC, NamusobyaJ, KaturamuR, KampiireL, WallentaJ, et al\nEffects of a multicomponent intervention to streamline initiation of antiretroviral therapy in Africa: a stepped-wedge cluster-randomised trial. Lancet HIV. 2016;3(11):e539–e48. doi: 10.1016/S2352-3018(16)30090-X\n2765887322MyerL, ZulligerR, BlackS, PienaarD, BekkerLG. Pilot programme for the rapid initiation of antiretroviral therapy in pregnancy in Cape Town, South Africa. AIDS Care. 2012;24(8):986–92. doi: 10.1080/09540121.2012.668173\n2251956123BrownLB, HavlirDV, AyiekoJ, MwangwaF, OwaraganiseA, KwarisiimaD, et al\nHigh levels of retention in care with streamlined care and universal test and treat in East Africa. AIDS. 2016;30(18):2855–64. doi: 10.1097/QAD.0000000000001250\n2760329024SanneI, OrrellC, FoxMP, ConradieF, IveP, ZeineckerJ, et al\nNurse versus doctor management of HIV-infected patients receiving antiretroviral therapy (CIPRA-SA): a randomised non-inferiority trial. Lancet. 2010;376(9734):33–40. doi: 10.1016/S0140-6736(10)60894-X\n2055792725LongL, BrennanA, FoxMP, NdibongoB, JaffrayI, SanneI, et al\nTreatment outcomes and cost-effectiveness of shifting management of stable ART patients to nurses in South Africa: an observational cohort. PLoS Med. 2011;8(7):e1001055\ndoi: 10.1371/journal.pmed.1001055\n2181140226HumphreysCP, WrightJ, WalleyJ, MamvuraCT, BaileyKA, NtshalintshaliSN, et al\nNurse led, primary care based antiretroviral treatment versus hospital care: a controlled prospective study in Swaziland. BMC Health Serv Res. 2010;10:229\ndoi: 10.1186/1472-6963-10-229\n2068795527FairallL, BachmannMO, LombardC, TimmermanV, UebelK, ZwarensteinM, et al\nTask shifting of antiretroviral treatment from doctors to primary-care nurses in South Africa (STRETCH): a pragmatic, parallel, cluster-randomised trial. Lancet. 2012;380(9845):889–98. doi: 10.1016/S0140-6736(12)60730-2\n2290195528TenthaniL, HaasAD, TweyaH, JahnA, van OosterhoutJJ, ChimbwandiraF, et al\nRetention in care under universal antiretroviral therapy for HIV-infected pregnant and breastfeeding women (\'Option B+\') in Malawi. AIDS. 2014;28(4):589–98. doi: 10.1097/QAD.0000000000000143\n2446899929FoxMP, ShearerK, MaskewM, Meyer-RathG, ClouseK, SanneI. Attrition through Multiple Stages of Pre-Treatment and ART HIV Care in South Africa. PLOS ONE. 2014;9(10):e110252\ndoi: 10.1371/journal.pone.0110252\n2533008730MugglinC, EstillJ, WandelerG, BenderN, EggerM, GsponerT, et al\nLoss to programme between HIV diagnosis and initiation of antiretroviral therapy in sub-Saharan Africa: systematic review and meta-analysis. Trop Med Int Health. 2012;17(12):1509–20. doi: 10.1111/j.1365-3156.2012.03089.x\n2299415131Bulletin de Surveillance, Epidemiologique VIH/SIDA, Programme National de Lutte contre les IST/VIH/SIDA, Juin, 2016.32Guiteau Moise C, Bellot C, Hennessey K, Rivera V, Severe P, Aubin D, et al. Retention of clinically stable ART patients in a rapid model of care in Haiti. Conference on Retroviruses and Opportunistic Infections (CROI), Boston, MA, USA, 2016.', 'title': 'Same-day HIV testing with initiation of antiretroviral therapy versus standard care for persons living with HIV: A randomized unblinded trial.', 'date': '2017-07-26'}, '27658873': {'article_id': '27658873', 'content': ""In Africa, up to 30% of HIV-infected patients who are clinically eligible for antiretroviral therapy (ART) do not start timely treatment. We assessed the effects of an intervention targeting prevalent health systems barriers to ART initiation on timing and completeness of treatment initiation.\nIn this stepped-wedge, non-blinded, cluster-randomised controlled trial, 20 clinics in southwestern Uganda were randomly assigned in groups of five clinics every 6 months to the intervention by a computerised random number generator. This procedure continued until all clinics had crossed over from control (standard of care) to the intervention, which consisted of opinion-leader-led training and coaching of front-line health workers, a point-of-care CD4 cell count testing platform, a revised counselling approach without mandatory multiple pre-initiation sessions, and feedback to the facilities on their ART initiation rates and how they compared with other facilities. Treatment-naive, HIV-infected adults (aged ≥18 years) who were clinically eligible for ART during the study period were included in the study population. The primary outcome was ART initiation 14 days after first clinical eligibility for ART. This study is registered with ClinicalTrials.gov, number NCT01810289.\nBetween April 11, 2013, and Feb 2, 2015, 12\u2008024 eligible patients visited one of the 20 participating clinics. Median CD4 count was 310 cells per μL (IQR 179-424). 3753 of 4747 patients (weighted proportion 80%) in the intervention group had started ART by 2 weeks after eligibility compared with 2585 of 7066 patients (38%) in the control group (risk difference 41·9%, 95% CI 40·1-43·8). Vital status was ascertained in a random sample of 208 patients in the intervention group and 199 patients in the control group. Four deaths (2%) occurred in the intervention group and five (3%) occurred in the control group.\nA multicomponent intervention targeting health-care worker behaviour increased the probability of ART initiation 14 days after eligibility. This intervention consists of widely accessible components and has been tested in a real-world setting, and is therefore well positioned for use at scale.\nNational Institute of Allergy and Infectious Diseases (NIAID) and the President's Emergency Fund for AIDS Relief (PEPFAR)."", 'title': 'Effects of a multicomponent intervention to streamline initiation of antiretroviral therapy in Africa: a stepped-wedge cluster-randomised trial.', 'date': '2016-10-30'}, '29509839': {'article_id': '29509839', 'content': 'Home-based HIV testing is a frequently used strategy to increase awareness of HIV status in sub-Saharan Africa. However, with referral to health facilities, less than half of those who test HIV positive link to care and initiate antiretroviral therapy (ART).\nTo determine whether offering same-day home-based ART to patients with HIV improves linkage to care and viral suppression in a rural, high-prevalence setting in sub-Saharan Africa.\nOpen-label, 2-group, randomized clinical trial (February 22, 2016-September 17, 2017), involving 6 health care facilities in northern Lesotho. During home-based HIV testing in 6655 households from 60 rural villages and 17 urban areas, 278 individuals aged 18 years or older who tested HIV positive and were ART naive from 268 households consented and enrolled. Individuals from the same household were randomized into the same group.\nParticipants were randomly assigned to be offered same-day home-based ART initiation (n\u2009=\u2009138) and subsequent follow-up intervals of 1.5, 3, 6, 9, and 12 months after treatment initiation at the health facility or to receive usual care (n\u2009=\u2009140) with referral to the nearest health facility for preparatory counseling followed by ART initiation and monthly follow-up visits thereafter.\nPrimary end points were rates of linkage to care within 3 months (presenting at the health facility within 90 days after the home visit) and viral suppression at 12 months, defined as a viral load of less than 100 copies/mL from 11 through 14 months after enrollment.\nAmong 278 randomized individuals (median age, 39 years [interquartile range, 28.0-52.0]; 180 women [65.7%]), 274 (98.6%) were included in the analysis (137 in the same-day group and 137 in the usual care group). In the same-day group, 134 (97.8%) indicated readiness to start ART that day and 2 (1.5%) within the next few days and were given a 1-month supply of ART. At 3 months, 68.6% (94) in same-day group vs 43.1% (59) in usual care group had linked to care (absolute difference, 25.6%; 95% CI, 13.8% to 36.3%; P\u2009<\u2009.001). At 12 months, 50.4% (69) in the same-day group vs 34.3% (47) in usual care group achieved viral suppression (absolute difference, 16.0%; 4.4%-27.2%; P\u2009=\u2009.007). Two deaths (1.5%) were reported in the same-day group, none in usual care group.\nAmong adults in rural Lesotho, a setting of high HIV prevalence, offering same-day home-based ART initiation to individuals who tested positive during home-based HIV testing, compared with usual care and standard clinic referral, significantly increased linkage to care at 3 months and HIV viral suppression at 12 months. These findings support the practice of offering same-day ART initiation during home-based HIV testing.\nclinicaltrials.gov Identifier: NCT02692027.', 'title': 'Effect of Offering Same-Day ART vs Usual Health Facility Referral During Home-Based HIV Testing on Linkage to Care and Viral Suppression Among Adults With HIV in Lesotho: The CASCADE Randomized Clinical Trial.', 'date': '2018-03-07'}, '27163694': {'article_id': '27163694', 'content': 'PLoS MedPLoS MedplosplosmedPLoS Medicine1549-12771549-1676Public Library of ScienceSan Francisco, CA USA27163694486268110.1371/journal.pmed.1002015PMEDICINE-D-15-03455Research ArticleBiology and Life SciencesImmunologyVaccination and ImmunizationAntiviral TherapyAntiretroviral TherapyMedicine and Health SciencesImmunologyVaccination and ImmunizationAntiviral TherapyAntiretroviral TherapyMedicine and Health SciencesPublic and Occupational HealthPreventive MedicineVaccination and ImmunizationAntiviral TherapyAntiretroviral TherapyBiology and Life SciencesMicrobiologyMedical MicrobiologyMicrobial PathogensViral PathogensImmunodeficiency VirusesHIVMedicine and Health SciencesPathology and Laboratory MedicinePathogensMicrobial PathogensViral PathogensImmunodeficiency VirusesHIVBiology and Life SciencesOrganismsVirusesViral PathogensImmunodeficiency VirusesHIVBiology and Life SciencesOrganismsVirusesImmunodeficiency VirusesHIVBiology and life sciencesOrganismsVirusesRNA virusesRetrovirusesLentivirusHIVBiology and Life SciencesMicrobiologyMedical MicrobiologyMicrobial PathogensViral PathogensRetrovirusesLentivirusHIVMedicine and Health SciencesPathology and Laboratory MedicinePathogensMicrobial PathogensViral PathogensRetrovirusesLentivirusHIVBiology and Life SciencesOrganismsVirusesViral PathogensRetrovirusesLentivirusHIVPeople and placesGeographical locationsAfricaSouth AfricaBiology and Life SciencesAnatomyBody FluidsBloodBlood CountsMedicine and Health SciencesAnatomyBody FluidsBloodBlood CountsBiology and Life SciencesPhysiologyBody FluidsBloodBlood CountsMedicine and Health SciencesPhysiologyBody FluidsBloodBlood CountsMedicine and Health SciencesHematologyBloodBlood CountsMedicine and Health SciencesHealth CareHealth Care ProvidersNursesPeople and PlacesPopulation GroupingsProfessionsNursesBiology and Life SciencesMicrobiologyVirologyViral Transmission and InfectionViral LoadMedicine and Health SciencesInfectious DiseasesBacterial DiseasesTuberculosisMedicine and Health SciencesTropical DiseasesTuberculosisMedicine and Health SciencesPharmaceuticsDrug TherapyInitiating Antiretroviral Therapy for HIV at a Patient’s First Clinic Visit: The RapIT Randomized Controlled TrialSingle-Visit ART InitiationRosenSydney\n1\n\n2\n*MaskewMhairi\n2\nFoxMatthew P.\n2\n\n3\nNyoniCynthia\n2\nMongwenyanaConstance\n2\nhttp://orcid.org/0000-0003-1473-880XMaleteGiven\n2\nSanneIan\n2\nhttp://orcid.org/0000-0001-5800-1960BokabaDorah\n4\nSaulsCeleste\n2\nhttp://orcid.org/0000-0002-1180-8764RohrJulia\n1\nLongLawrence\n2\n\n1\nDepartment of Global Health, Boston University School of Public Health, Boston, Massachusetts, United States of America\n\n2\nHealth Economics and Epidemiology Research Office, Department of Internal Medicine, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa\n\n3\nDepartment of Epidemiology, Boston University School of Public Health, Boston, Massachusetts, United States of America\n\n4\nHealth Department, City of Johannesburg, Johannesburg, South Africa\nBinagwahoAgnesAcademic Editor\nRwanda Ministry of Health, RWANDA\nThe authors have declared that no competing interests exist.Conceived and designed the experiments: SR LL MM IS MPF. Performed the experiments: CN CM DB CS JR. Analyzed the data: MM GM SR. Wrote the first draft of the manuscript: SR MM. Contributed to the writing of the manuscript: SR MM LL MPF. Enrolled patients: CN. Agree with the manuscript’s results and conclusions: SR MM LL MPF CN CM GM IS DB CS JR. All authors have read, and confirm that they meet, ICMJE criteria for authorship.* E-mail: sbrosen@bu.edu105201652016135e1002015171120152232016© 2016 Rosen et al2016Rosen et alThis is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.BackgroundHigh rates of patient attrition from care between HIV testing and antiretroviral therapy (ART) initiation have been documented in sub-Saharan Africa, contributing to persistently low CD4 cell counts at treatment initiation. One reason for this is that starting ART in many countries is a lengthy and burdensome process, imposing long waits and multiple clinic visits on patients. We estimated the effect on uptake of ART and viral suppression of an accelerated initiation algorithm that allowed treatment-eligible patients to be dispensed their first supply of antiretroviral medications on the day of their first HIV-related clinic visit.Methods and FindingsRapIT (Rapid Initiation of Treatment) was an unblinded randomized controlled trial of single-visit ART initiation in two public sector clinics in South Africa, a primary health clinic (PHC) and a hospital-based HIV clinic. Adult (≥18 y old), non-pregnant patients receiving a positive HIV test or first treatment-eligible CD4 count were randomized to standard or rapid initiation. Patients in the rapid-initiation arm of the study (“rapid arm”) received a point-of-care (POC) CD4 count if needed; those who were ART-eligible received a POC tuberculosis (TB) test if symptomatic, POC blood tests, physical exam, education, counseling, and antiretroviral (ARV) dispensing. Patients in the standard-initiation arm of the study (“standard arm”) followed standard clinic procedures (three to five additional clinic visits over 2–4 wk prior to ARV dispensing). Follow up was by record review only. The primary outcome was viral suppression, defined as initiated, retained in care, and suppressed (≤400 copies/ml) within 10 mo of study enrollment. Secondary outcomes included initiation of ART ≤90 d of study enrollment, retention in care, time to ART initiation, patient-level predictors of primary outcomes, prevalence of TB symptoms, and the feasibility and acceptability of the intervention. A survival analysis was conducted comparing attrition from care after ART initiation between the groups among those who initiated within 90 d. Three hundred and seventy-seven patients were enrolled in the study between May 8, 2013 and August 29, 2014 (median CD4 count 210 cells/mm3). In the rapid arm, 119/187 patients (64%) initiated treatment and were virally suppressed at 10 mo, compared to 96/190 (51%) in the standard arm (relative risk [RR] 1.26 [1.05–1.50]). In the rapid arm 182/187 (97%) initiated ART ≤90 d, compared to 136/190 (72%) in the standard arm (RR 1.36, 95% confidence interval [CI], 1.24–1.49). Among 318 patients who did initiate ART within 90 d, the hazard of attrition within the first 10 mo did not differ between the treatment arms (hazard ratio [HR] 1.06; 95% CI 0.61–1.84). The study was limited by the small number of sites and small sample size, and the generalizability of the results to other settings and to non-research conditions is uncertain.ConclusionsOffering single-visit ART initiation to adult patients in South Africa increased uptake of ART by 36% and viral suppression by 26%. This intervention should be considered for adoption in the public sector in Africa.Trial RegistrationClinicalTrials.gov NCT01710397, and South African National Clinical Trials Register DOH-27-0213-4177.In the RapIT randomized controlled trial, Sydney Rosen and colleagues investigate whether accelerated initiation of antiretroviral therapy can improve viral suppression for HIV patients in South Africa.Author SummaryWhy Was This Study Done?One of the most persistent operational challenges facing antiretroviral therapy (ART) programs for HIV/AIDS in sub-Saharan Africa is late presentation of patients for care and high rates of attrition from care between HIV testing and ART initiation.One reason for this is that starting ART in many countries is a lengthy and burdensome process, imposing long waits and multiple clinic visits on patients; in South Africa, the country with the world’s largest HIV treatment program, patients must typically make five or six clinic visits, starting with an HIV test, before they receive medications.There have not yet been any controlled evaluations of an integrated, rapid HIV treatment initiation algorithm that allows patients to initiate ART in a single clinic visit, so the RapIT trial was done to find out if “same-day initiation” of ART would increase the number of patients starting treatment and improve overall health outcomes, compared to current practices.What Did the Researchers Do and Find?We randomly assigned 377 adult patients at two public clinics in Johannesburg, South Africa, who had provided consent to participate in the study to one of two groups.Patients in the group assigned to receive rapid treatment initiation were offered the chance to start treatment on the same day as their first clinic visit, using rapid, point-of-care laboratory tests and an accelerated sequence of other steps, including a physical exam, education, and counseling.Patients in the group assigned to receive standard treatment initiation followed the standard schedule for treatment initiation used by the clinics, which usually required three to five additional clinic visits over a 2–4 wk period.After the study enrollment visit, patients were followed up by reviewing their regular clinic medical records, to determine how many did start treatment and how many were still in care and had good outcomes, as indicated by a suppressed viral load, 10 mo later.We found that 97% of patients in the rapid initiation group had started ART by 90 d after study enrollment—three-quarters of them on the same day—compared to 72% of patients in the standard initiation group.By 10 mo after study enrollment, 64% of patients in the rapid group had good outcomes compared to 51% in the standard group.Rapid initiation group patients spent roughly two and a half hours in the clinic to complete all the steps required before they got their medications.What Do These Findings Mean?The RapIT (Rapid Initiation of Treatment) trial showed that it is possible to initiate nearly all eligible patients on HIV therapy, and to do so in a much shorter time interval than previously required.By showing that offering the opportunity to start treatment on the spot, without delay, overcomes many barriers patients would otherwise face, this study demonstrates that same-day ART initiation is an effective strategy for improving health outcomes.More patients in the rapid initiation group dropped out of care after starting treatment than in the standard initiation group; although the rapid initiation group still had better health outcomes overall, adherence support after starting treatment remains essential.The findings of this study are limited because the study took place in only two clinics in one part of South Africa and was carried out by study staff, not by regular clinic staff.Based on this study’s results, consideration could be given to accelerating the process of ART initiation in many different settings and for different types of patients.http://dx.doi.org/10.13039/100000060National Institute of Allergy and Infectious Diseases1U01AI100015RosenSydneyFunding for this study was provided by the U.S. National Institutes of Health (National Institute of Allergy and Infectious Diseases) under the terms of grant 1U01AI100015 to Boston University. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Data AvailabilityData will be made publicly available in the Dryad repository (http://www.datadryad.org/) after the protocol has been closed (anticipated closure June 2018). Until then, data will remain under the supervision of the University of the Witwatersrand Human Research Ethics Committee (HREC). Requests should be sent to the HREC Research Administrator at: https://www.wits.ac.za/research/about-our-research/ethics-and-research-integrity/human-research-ethics-committee-medical/.Data AvailabilityData will be made publicly available in the Dryad repository (http://www.datadryad.org/) after the protocol has been closed (anticipated closure June 2018). Until then, data will remain under the supervision of the University of the Witwatersrand Human Research Ethics Committee (HREC). Requests should be sent to the HREC Research Administrator at: https://www.wits.ac.za/research/about-our-research/ethics-and-research-integrity/human-research-ethics-committee-medical/.IntroductionOne of the most persistent operational challenges facing antiretroviral therapy (ART) programs for HIV/AIDS in sub-Saharan Africa is late presentation of patients for care and high rates of attrition from care between HIV testing and ART initiation, with baseline median CD4 cell counts remaining well below 200 cells/mm3 in the region despite steadily rising eligibility thresholds [1]. Even among those who have been diagnosed and found to be treatment-eligible, loss to care before starting ART has consistently been estimated at a third to a quarter of patients [2,3]. While many of those who drop out of care prior to ART initiation will make their way back at a later time, they will almost certainly have lower CD4 counts and more symptoms of illness than when they first tested positive. Some will be very sick or die before treatment can be started, and those who do eventually start will have a poorer prognosis on treatment than if they had begun treatment earlier [4,5]. Offering ART to all who test positive regardless of CD4 count, as is now recommended by the World Health Organization [6], will make little difference if those who test positive fail to initiate treatment.There are likely many causes of loss to care before treatment initiation, but one reason observed is that starting ART in many countries is a lengthy and burdensome process, requiring long waits and multiple clinic visits [7,8]. In South Africa, the country with the world’s largest HIV treatment program [9], the process typically includes an HIV test (visit 1), determination of treatment eligibility (visit 2), adherence education and counseling and baseline blood tests (visits 3, 4, and 5), and physical examination and dispensing of antiretrovirals (ARVs) (visit 6). The proliferation of visits has three main causes. First, clinic receipt of printed test results from centralized laboratories typically takes several days, if not longer. Second, a belief remains that to ensure adherence, patients must participate in multiple preparatory educational and counseling sessions [2,10,11]. And third, clinics have had little motivation to accelerate the initiation process for patients who are not critically ill, as standard performance indicators do not include the proportion of eligible patients who actually initiate ART, nor the time required to do so.If patients are deterred from starting treatment by the complexity of the process, then one strategy for reducing loss of patients prior to ART initiation and encouraging earlier treatment initiation may be to shorten the time period, reduce the number of visits, and simplify the steps required before medications are dispensed. This strategy depends critically on two factors: a clinic’s willingness and ability to adjust its schedules and procedures to compress and accelerate the required steps, and the availability of rapid, point-of care (POC) laboratory assays that eliminate delays in receiving whatever lab results are required for initiation. There have not yet been any rigorous, controlled evaluations of an integrated, rapid HIV treatment initiation algorithm incorporating procedural changes and POC tests for adult, non-pregnant patients. We therefore conducted a randomized controlled trial of rapid ART initiation that allowed patients in public sector clinics in Johannesburg, South Africa to have treatment eligibility determined, all treatment preparation steps performed, and ARV medications dispensed on the day of their first HIV-related clinic visit.MethodsRapIT (Rapid Initiation of Treatment) was an unblinded, individually randomized, controlled trial of a service delivery intervention. It was approved by the Institutional Review Board of Boston University Medical Campus (H-31880) and the Human Research Ethics Committee (Medical) of the University of the Witwatersrand (M120843) and is registered with ClinicalTrials.gov, number NCT01710397.Study Sites, Infrastructure, and StaffingRapIT was conducted at two public sector outpatient clinics. Site 1 is a primary health clinic serving an urban informal settlement population on the edge of Johannesburg. Site 2 is a large, hospital-based HIV clinic serving an urban formal and informal population within Johannesburg. Both sites follow South African national treatment guidelines for ART initiation, ARV regimens, and monitoring [12]. During the period of study enrollment, May 8, 2013–August 29, 2014, the prevailing threshold for ART eligibility was a CD4 count ≤ 350 cells/mm3 or a WHO Stage 3/4 clinical condition. Requirements for care prior to initiating ART are not standardized in South Africa [13], but both sites generally required four to five clinic visits between HIV testing and dispensing the first month’s supply of ARVs.At each site, a small clinic room with security bars, running water, and basic furnishings was designated for study equipment and supplies, POC instruments, and files. As all the POC instruments were designed as desktop devices, no separate laboratory was needed. An outdoor booth for safe collection of sputum samples from tuberculosis (TB) suspects was constructed at Site 1 and made available for both study arms; existing facilities for this purpose were used at Site 2. Clinical procedures were performed by study nurses with the same level of clinical certification as existing primary health care nurses at the sites. Non-clinical procedures (consent, questionnaire, education, counseling, patient flow management) were implemented by study assistants with qualifications comparable to those of experienced lay counselors at the sites. All study staff received study and instrument-specific training. A small stipend (R1000/month, equivalent to US$86 at the exchange rate at the time of the study) was paid to clinic lay counselors at Site 1 and a messenger at Site 2 who assisted by referring potential study participants to the study assistant.Study PopulationThe study enrolled adult (≥18 y old), non-pregnant patients who presented to have an HIV test, provide a blood sample for a CD4 count if already known to be HIV-infected, or receive the results of the patient’s first treatment-eligible CD4 count. During pre-screening and screening, patients who had previously been found to be eligible for ART, were already on ART or reported receiving it in the past 12 mo, indicated that they intended to seek HIV care during the next 12 mo at a different clinic, were judged by clinic or study staff to be physically or emotionally unable to provide consent or participate in all study procedures, or did not meet other study inclusion criteria were excluded. Potential participants whose visit purpose was to have an HIV test were enrolled; those found post-enrollment not to be eligible for ART were subsequently withdrawn upon determination of ineligibility. Potential participants whose visit purpose was to receive a CD4 count result and were not eligible for treatment on the basis of that CD4 count were not enrolled.Participants were individually randomized 1:1 to either rapid treatment initiation or standard-of-care treatment initiation, using block randomization in blocks of 6. Sealed, opaque envelopes containing the allocations were prepared by the local principal investigator and numbered sequentially. The envelopes were kept in sequential, numbered order at the study sites. After obtaining written informed consent, the study assistant opened the next sequentially numbered envelope to reveal the allocation.Study Design and ProceduresProcedures for each study arm are illustrated in Fig 1. Standard-of-care treatment initiation followed existing procedures at the sites as closely as possible. Study staff interaction with participants was limited to screening for study eligibility, obtaining written informed consent, administering a questionnaire, and referring patients to clinic staff for either a blood draw for a CD4 count or a next visit appointment if the patient already had results of a CD4 count in hand. After referral, patients in the standard-initiation arm of the study were followed passively, through medical record review, and had no further interaction with the study. Standard-of-care procedures for ART initiation at both study sites included a CD4 count to determine eligibility, TB symptom screening followed by a TB test and TB treatment initiation if required, pre-initiation blood tests (hemoglobin, creatinine, and alanine aminotransferase (ALT)), group and individual counseling and education sessions, and a physical examination. All samples for laboratory tests were sent to centralized public sector laboratories, requiring patients to make separate clinic visits to provide biological samples and to receive results. Once ART eligibility was determined, initiation typically required three to four more clinic visits over a period of 2–4 wk. Patients who were very ill or found to have low CD4 counts could be “fast-tracked,” with the schedule shown in Fig 1 completed in as little as one week.10.1371/journal.pmed.1002015.g001Fig 1Standard initiation of treatment and rapid initiation procedures and visit schedule.For patients randomized to rapid initiation, all the same procedures were performed, but the use of a compressed and accelerated schedule and rapid laboratory instruments at point of care allowed them all to be completed in a single visit (Box 1). Patients offered rapid initiation typically completed each step in order, with little or no waiting time in between unless a TB test was required, which entailed a wait to process the sample. Patients who enrolled in the study too late in the day for all steps to be completed before the clinic closed were asked to return the next day to finish study procedures. Patients who were randomized to rapid initiation but did not have time to participate on the day of enrollment or wished to delay for other reasons were given up to 30 d to return and be initiated under rapid procedures. Those returning beyond 30 d were offered standard initiation by the clinic.Box 1. Rapid Initiation ProceduresCD4 countPatients who enrolled in the study and did not already have CD4 count results from a test performed within the previous 6 mo were given a rapid CD4 count using the Alere Pima CD4 Test (http://alerehiv.com/hiv-monitoring/alere-pima-cd4/) with venous blood draw. This test, previously evaluated in several studies in Africa [14–18], provides a CD4 count result from a capillary or venous blood sample in 20 min. Following the test, patients with a CD4 count ≤ 350 cells mm3 or evident physical symptoms or complaints that suggested a Stage 3 or 4 condition continued with study procedures. Those not eligible for ART were withdrawn from the study at this point and referred to the clinic for standard pre-ART monitoring.TB symptom screen and testWhile awaiting CD4 count results, a TB symptom screen was administered using South Africa’s four-question screening tool. All patients who reported symptoms were then asked to provide a sputum sample, which was immediately processed using the Cepheid Xpert MTB/RIF test (http://www.cepheid.com/us/cepheid-solutions/clinical-ivd-tests/critical-infectious-diseases/xpert-mtb-rif). This is the technology currently used for TB diagnosis in the public sector throughout South Africa, but it is located in centralized laboratories rather than at point of care [19]. It generates a TB diagnosis in 90 min [20]. Two sputum samples were run simultaneously to increase the reliability of results. Any patient who received a positive Xpert test was escorted to the clinic TB nurse to initiate TB treatment, which under national guidelines required a delay of at least 2 wk before ART could be initiated. Patients initiated on TB treatment were asked to return 2 wk later to complete rapid ART initiation on a second visit.Baseline testsOnce eligibility for ART was established, pre-initiation blood tests (hemoglobin, creatinine, and ALT) were run on a point-of-care Reflotron Plus instrument (Roche, http://www.roche-diagnostics.co.in/Products/Pages/ReflotronPlusDry.aspx)[14] using the same blood sample dawn for the CD4 count. This instrument takes approximately 2 min to complete each test. A standard clinic urine dipstick pregnancy test was also conducted for female patients of child-bearing age.Physical examA standard physical examination was conducted by the study nurse to identify any specific conditions or concerns prior to initiating ART. Initiation was delayed in patients found to have conditions that required referral to a hospital or consultation with the clinic’s doctor.Education sessionA condensed version of HIV/ART/adherence education was developed using the study clinics’ materials and provided to study participants. It was delivered in a one-on-one session by the study counselor in approximately 20 min.Counseling sessionAfter completing all tests, physical examination, and education session, each patient met individually with the study nurse, who reviewed results with the patient and provided an opportunity for the patient to ask any remaining questions and confirm that she or he was indeed ready for treatment initiation.Dispensing of ARVsThe study nurses, like other qualified nurses in South Africa, were authorized to write prescriptions for ARVs, which could then be filled directly by the nurse from study room stock (Site 1) or at the on-site clinic pharmacy (Site 2). Study patients at Site 2 were served at the pharmacy immediately, rather than being required to wait in pharmacy queues to fill prescriptions. Once the initial 4 wk supply of ARVs was dispensed, study interaction with rapid group patients ceased. Patients were asked to return to the clinic for monitoring and prescription refill by clinic staff in 1 mo, consistent with routine practice.After the enrollment visit, or completion of rapid initiation procedures for patients in the rapid-initiation arm of the study (“rapid arm”) who delayed initiation but returned to complete it within 30 d, the study team had no further contact with study patients. Patients who started ART in either arm received standard-of care treatment management from the clinic, which called for monitoring visits and medication refills at 1, 2, 3, 6, and 12 mo after initiation, with a routine viral load test at the 6 mo visit.Outcomes and DataThe primary, protocol-defined outcome for the study was viral suppression (≤400 copies/ml) within 10 mo of study enrollment, a time period selected to capture the 6 mo routine monitoring visit called for by national guidelines. Ten months was selected as the endpoint to allow patients to take up to 3 mo to initiate ART and to be up to 1 mo late for the 6 mo routine visit. Because the study sites occasionally omitted the 6 mo viral load and performed the test only at 12 mo, we considered a patient with a suppressed viral load test result any time from 3 to 12 mo after study enrollment to have achieved viral suppression. In this analysis, missing viral load test results were regarded as failures; only patients with recorded, suppressed viral load results were defined as virally suppressed. To account for the possibility that viral load results could be missing due to clinic oversight in not ordering the test, rather than patient default, and to investigate the possibility that rapid initiation merely shifts attrition from before to after treatment initiation, we also report the secondary outcome of retention in care at 10 mo after study enrollment, with retention defined as any HIV-related clinic visit in months 5–10 after study enrollment, regardless of viral load.Although viral suppression was the primary outcome assessed, the pathway by which the study aimed to increase suppression was reduction of attrition between HIV testing and treatment initiation. We therefore report initiation of treatment within 90 d of study enrollment as a secondary outcome, with initiation defined as being dispensed a first month’s supply of ARVs. We also report uptake of treatment within 180 d, as a CD4 count result is considered to be valid under South African guidelines for 6 mo—after that, a patient must have a new CD4 count to establish eligibility for ART. Finally, we report the distribution of time (d) to treatment initiation in each group.Other secondary outcomes evaluated in the study included the feasibility of the intervention, as indicated by the ability of both study sites to implement the accelerated algorithm; acceptability of the intervention, as measured by the proportion of patients offered rapid initiation who accepted it; patient-level predictors of the primary outcome; and, in the rapid arm, the prevalence of TB symptoms and confirmed TB disease and ART initiation among patients with TB.After the enrollment visit, all data collection for both groups was by passive medical record review. Both study sites routinely utilized an electronic medical record system called TherapyEdge-HIV, into which patient data were entered retrospectively by data clerks from paper files (Site 1) or by a combination of clinicians in real time and data clerks from paper files (Site 2)[21]. This record system improved the completeness of the follow-up dataset used in the study. In instances of incomplete follow-up data—for example, if the database reported a clinic visit 6 mo after ART initiation but contained no viral load test result—study staff searched the clinics’ paper files and registers and the online data portal of the National Health Laboratory Service to determine if any additional information existed but had not been recorded in the clinics’ databases. The study team had no further contact with study participants after the enrollment visit so as not to have any influence on retention in care, a study outcome.Data AnalysisWe designed the study to detect a 20% difference in viral suppression rates between the arms at 10 mo after study enrollment. With an α of 0.05, power of 90%, 1:1 randomization, and an uncorrected Fisher’s exact test, we estimated that we would need to enroll at least 124 HIV positive ART-eligible participants per group (248 total). We increased this to a maximum of 200 per group (400 total) to allow for stratification by site, sex, or age.Characteristics at study enrollment of all randomized participants who met ART initiation and study inclusion criteria were summarized using simple proportions and medians with interquartile ranges (IQR) stratified by treatment arm. For the remaining analyses, we excluded patients who were found after randomization not to be eligible for ART or not to meet study inclusion criteria. We compared the proportions of patients achieving each dichotomized study outcome and present crude risk ratios (RR) and risk differences (RD) with 95% confidence intervals (CI) stratified by group. Baseline predictors of outcomes that appeared imbalanced by treatment arm were also adjusted for using log-linear regression models to estimate adjusted risk ratios (aRR). We estimated time to treatment initiation in days using a cumulative incidence curve. To investigate whether attrition after initiation of ART differed between the study arms, we performed a survival analysis comparing attrition from care after ART initiation among those who initiated within 90 d between the groups. Person-time accrued from ART initiation date to the earliest of loss to follow up, transfer, or 10 mo of follow up, and hazard ratios of attrition from care were estimated with Cox proportional hazards models. A stratified analysis was performed to detect effect measure modification by site or patient-level factors. Finally, to confirm that no imbalance was created by excluding patients after randomization for reasons other than ineligibility for ART or evidence of a previous eligible CD4 count, we conducted sensitivity analysis incorporating the excluded patients and assigning each a negative outcome.ResultsBetween May 8, 2013, and August 29, 2014, 603 patients were screened for study eligibility and 463 provided written informed consent and were enrolled in the study (Fig 2). Of the 140 screened but excluded prior to randomization, 109 did not meet study eligibility criteria, including 43 who resided outside study clinic catchment areas or intended to seek further care elsewhere; 24 who were determined by the study assistant to be too ill for consent and study procedures; 16 who were not eligible on the basis of a prior CD4 count, were ineligible for ART, or were already on ART; 12 who were determined by the study assistant to be too emotionally upset to provide consent; 9 who did not speak any of the languages spoken by the study team; 3 who were found to be pregnant; and 2 who were excluded for other reasons. An additional 31 patients refused participation; of these, 18 were in a hurry and did not have time for study procedures, six did not wish to participate in the study, five stated that they would prefer standard care, and two were not willing to initiate therapy. Follow-up ended 10 mo after the last patient was enrolled (June 28, 2015).10.1371/journal.pmed.1002015.g002Fig 2Study enrollment and randomization.Characteristics of patients in each study arm at time of enrollment are reported in Table 1. There were no important differences between the study arms in the variables shown. Just over half the participants were female and the median age was 35 y. The median CD4 count was less than 200 cells/mm3. Age, sex, and CD4 count characteristics of the study sample were similar to those of the overall non-pregnant patient populations initiating ART at the study clinics in 2014.10.1371/journal.pmed.1002015.t001Table 1Baseline characteristics of study sample (n = 463).VariableStandard armRapid arm\nn (randomized participants)229234Enrollment site (n)\xa0\xa0\xa0\xa0Site 1 (primary health clinic)124126\xa0\xa0\xa0\xa0Site 2 (hospital-based HIV clinic)105108Age (median, IQR)35.8 (29.5–41.6)34.2 (29.0–40.1)Sex (% female)132 (58%)129 (55%)CD4 count (cells/mm3) (median, IQR)195 (103–322)224 (128–327)Purpose of clinic visit (%)\xa0\xa0\xa0\xa0Have HIV test (diagnosed today)100 (44%)90 (38%)\xa0\xa0\xa0\xa0Provide blood sample for CD4 count8 (4%)10 (4%)\xa0\xa0\xa0\xa0Receive first CD4 count results109 (47%)112 (48%)\xa0\xa0\xa0\xa0Other11 (5%)22 (10%)Reason for treatment eligibility (%)\xa0\xa0\xa0\xa0CD4 count below threshold182 (79%)183 (78%)\xa0\xa0\xa0\xa0Clinical condition Stage 3 or 43 (1%)4 (2%)\xa0\xa0\xa0\xa0Excluded (not eligible for treatment or study)44 (20%)47 (20%)Household composition\xa0\xa0\xa0\xa0Live alone (% yes)36 (16%)41 (18%)\xa0\xa0\xa0\xa0# other persons in house (median, IQR)2 (1–4)2 (1–3)Household type (%)\xa0\xa0\xa0\xa0Formal house or flat146 (63%)165 (71%)\xa0\xa0\xa0\xa0Informal dwelling or shack83 (37%)69 (29%)Travel time to clinic (minutes) (median, IQR)18 (9–24)15 (9–27)Employment status (%)\xa0\xa0\xa0\xa0Employed formally68 (30%)90 (38%)\xa0\xa0\xa0\xa0Work informally62 (27%)54 (23%)\xa0\xa0\xa0\xa0Unemployed, seeking work91 (40%)84 (36%)\xa0\xa0\xa0\xa0Unemployed, not seeking work8 (3%)6 (3%)Marital status (%)\xa0\xa0\xa0\xa0Married or long-term partner173 (76%)157 (67%)\xa0\xa0\xa0\xa0Single, no long-term partner41 (18%)57 (24%)\xa0\xa0\xa0\xa0Other (widowed, divorced)15 (6%)20 (9%)Reasons for excluding patients during the study screening process are reported in Fig 2. The 603 patients screened represent a subset of those pre-screened by clinic counselors and then referred to the study assistant for screening. While pre-screening data, which were collected by the counselors and not by study staff, are of uncertain quality, they do provide some indication of the proportion of all patients presenting at clinics who could be eligible for rapid initiation. At Site 1, for which the pre-screening data are more complete, a total of 2,636 patients presenting at the clinic’s HIV counseling and testing service were pre-screened. More than half of these were HIV-negative (1,468/2,636, 56%) or known to have CD4 counts above the eligibility threshold or already on ART (114/2,636, 4%). Of the remaining 1,054, 325 (31%) were referred for study screening. Another 293/1,054 (28%) were judged by the counselors not to meet study protocol eligibility criteria (age, residence location, language, not first CD4 count) but would likely have been eligible for the intervention if it were offered as routine care. A fifth (225/1,054, 21%) were regarded by the counselors as too sick for study participation (not necessarily for ART initiation) and were referred to a clinic doctor or nurse for immediate care; it is not clear if they would have been eligible for the intervention or not. The remainder (20%) included patients who refused study participation (36/1,054, 3%) or refused any further care (12/1,054, 1%), were deemed too upset or emotionally distressed to participate (25/1,054, 2%), were referred directly to the clinic’s HIV or TB nurse rather than the study assistant (75/1,054, 7%), or were in a hurry or had no reason stated (63/1,254, 6%).Among 463 patients screened and found eligible for study participation, 234 patients were randomized to rapid initiation and 229 to standard initiation (Fig 2). Upon completion of a CD4 count, which occurred after randomization for those who did not already have one in hand, 37 patients in each group were determined not to be eligible for ART under South African guidelines and were excluded from further data collection and from the analysis. An additional 12 patients were excluded after randomization, for reasons indicated in Fig 2. One hundred and ninety patients in the standard group and 187 in the rapid group (n = 377 total) were offered full study procedures and are included in the analysis below, with sensitivity analysis incorporating the six who were excluded after randomization for a reason other than ineligibility for ART or evidence of a prior eligible CD4 count.The protocol-defined primary outcome for the study was viral suppression within 10 mo of study enrollment. As presented in Table 2, viral suppression by 10 mo was 64% (119/187) in the rapid arm and 51% (96/190) in the standard arm, indicating a risk difference of 13% (3%–33%) and a crude relative risk of 1.26 (1.05–1.50).10.1371/journal.pmed.1002015.t002Table 2ART initiation, 10-mo retention in care, and 10-mo viral suppression.OutcomeStandard arm(%)n = 190Rapid arm(%)n = 187Crude risk difference(95% CI)Crude relative risk(95% CI)Initiated ≤ 90 d and suppressed by 10 mo (primary outcome)96 (51%)119 (64%)13% (3%–23%)1.26 (1.05–1.50)\xa0\xa0\xa0\xa0Of those\nnot\ninitiated ≤ 90 d and suppressed by 10 mo\n\n94 (49%)\n\n68 (36%)\n\n\xa0\xa0\xa0\xa0\xa0\xa0Not initiated\n\n54 (28%)\n\n5 (3%)\n\n\xa0\xa0\xa0\xa0\xa0\xa0Initiated but not suppressed\n\n40 (21%)\n\n63 (34%)\n\n\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0Of those initiated but not suppressed:\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0Retained, unsuppressed viral load test reported\n\n11 (6%)\n\n17 (9%)\n\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0Retained, no viral load test reported\n\n14 (7%)\n\n16 (9%)\n\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0Transferred to another clinic\n\n1 (1%)\n\n6 (3%)\n\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0Died\n\n3 (2%)\n\n0 (0%)\n\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0Lost to follow-up\n\n11 (6%)\n\n24 (13%)\nInitiated ≤ 90 d136 (72%)182 (97%)25% (19%–33%)1.36 (1.24–1.49)Initiated ≤ 90 d and retained at 10 mo (secondary outcome)121 (64%)151 (81%)17% (5%–23%)1.27 (1.12–1.44)\xa0\xa0\xa0\xa0Of those not initiated ≤ 90 d and retained at 10 mo:\n69 (36%)\n\n36 (19%)\n\xa0\xa0\xa0\xa0\xa0\xa0Initiated but not retained\n\n15 (8%)\n\n31 (17%)\n\xa0\xa0\xa0\xa0\xa0\xa0Not initiated\n\n54 (28%)\n\n5 (3%)\nBy 90 d after study enrollment, 97% (182/187) of participants in the rapid arm and 72% (136/190) of participants in the standard arm had initiated ART, equating to a risk difference of 25% (95% CI 19%–33%) and a crude relative risk of 1.36 (1.24–1.49) (Table 2). In adjusted analysis (S1 Table), neither age, sex, nor baseline CD4 count affected these values. By 180 d, one additional patient in the rapid arm and two in the standard arm had initiated, leaving four patients in the rapid arm and 52 in the standard arm who did not initiate within the period of validity of their CD4 count results. In the rapid arm, all four were referred to a clinic nurse or doctor for clinical confirmation of TB and did not return for ART initiation. In the standard arm, 73% (38/52) of the patients who did not initiate within 180 d made no further visits to the site after the visit in which they were enrolled in the study.\nFig 3 shows the cumulative incidence of treatment initiation in each study arm over the 180 d following enrollment. In the rapid arm, 72% (135/187) of patients started ART on the same day as study enrollment, an additional 7% (13/187) on the next day, and 96% (179/187) within 1 mo. In the standard arm, 58% of patients initiated within one month. The median (IQR) time to initiation in the standard arm for the subset who did initiate within 90 d (n = 136) was 17 (11–26) d. For rapid arm patients who did not initiate on the same day (n = 48), the reasons for delay were the need for clinical confirmation of TB or a Stage 3 or 4 condition or for TB treatment (25/48, 52%), insufficient time to complete all steps on the same day (6/48, 13%), patient preferences (5/48, 10%), lack of electricity in the clinic (2/48, 4%), and unknown reasons (10/48, 21%). Time to treatment initiation in the standard arm was shorter for patients who already had CD4 count results available upon study enrollment (median days 16, [IQR 11–22]) compared to those who enrolled in the study at the time of having an HIV test (22 [IQR 10–35]); the median for both types of patients in the rapid arm was 0 d (i.e., same-day initiation).10.1371/journal.pmed.1002015.g003Fig 3Time to ART initiation, by study arm.Cumulative incidence of ART initiation in each study arm, by number of days since study enrollment.All patients in the rapid arm had the opportunity to initiate treatment on the day of study enrollment (same-day initiation) unless one of the reasons for delay listed above pertained to them. To explore whether a delay in initiation was associated with different post-initiation outcomes, we compared patients who did initiate on the same day to those who delayed for any reason. There were no differences in either the primary outcome of viral suppression or the secondary outcome of retention in care between these two groups of patients (S3 Table). Because this analysis was limited to rapid arm patients, however, it is not a randomized comparison and should be interpreted with caution.Retention in care, defined as making a clinic visit between months 5 and 10 after study enrollment, was 81% (151/187) in the rapid arm and 64% (121/190) in the standard arm, for a risk difference of 17% (5%–23%) and a crude relative risk of 1.27 (1.12–1.44). Table 2 also indicates that 86% (31/36) of patients in the rapid arm who were not retained were lost from care after ART initiation, compared to just 22% (15/69) in the standard arm; the fall-off in the standard arm, in contrast, was mainly among those who never initiated (54/69, 78%). Although there was less loss to follow-up after initiation in the standard arm (15/190, 8% versus 31/187, 17%), this was more than offset by the higher pre-initiation loss in the standard arm (54/190, 28% versus 5/187, 3%), resulting in an overall increase in retention of 17%. Among the patients lost to care after initiation (15 in the standard arm and 31 in the rapid arm), a large majority of patients who initiated ART but were not retained in care either never came back after their initiation visit (40% of patients in the standard arm (6/15) and 45% in the rapid arm (14/31)) or came back just once (47% (7/15) and 35% (11/31), respectively), suggesting that most of these patients were never “established” on ART.To explore further the rate of loss to care, we estimated attrition from care within the first 10 mo after initiation among the subsample of 318 patients who did initiate ART within 90 d. In the standard arm, during 1,250 mo of total person-time, 22/136 (16%) dropped out of care after ART initiation, for an attrition rate of 1.8 per 100 person-months. In the rapid arm, during 1,626 mo of total person-time, 30/182 (16%) dropped out of care, for a rate of 1.8 per 100 person-months. The hazard of attrition within the first 10 mo after ART initiation among those who initiated within 90 d did not differ between the treatment arms (HR 1.06; 95% CI 0.61–1.84). We note that this result is subject to selection bias and confounding, however, due to the exclusion of those who did not start treatment within 90 d.In pooled analysis of both study arms, none of the variables presented in Table 1 predicted any of the outcomes reported above, with three exceptions (S2 Table). A slightly higher proportion of patients with baseline CD4 counts below 100 cells/mm3 initiated ART, but this difference did not persist through retention or viral suppression at 10 mo. As might be expected, patients who enrolled in the study at the time of receiving their CD4 count results (thus their second HIV-related clinic visit overall), rather than at the time of having an HIV test, were slightly more likely to achieve all three outcomes, though only for retention in care was this difference statistically significant. Finally, patients who reported being employed at the time of study enrollment, while no more likely to initiate ART, had significantly better retention in care and viral suppression than did those who reported being unemployed.In stratified analysis (S4 Table) we observed non-significant differences in effect sizes for the primary outcome (viral suppression at 10 mo) by sex, age group, and study site. A larger effect was seen among men aged <35 y (risk difference [95% CI] 34% [12%–55%]), while little effect was seen among men or women ≥35 (5% [-9%–19%]). The effect size was also greater at the primary health clinic (21% [8%–34%]), while little effect was seen at the hospital-based HIV clinic (2% [-12%–17%]). As noted, these differences were not statistically significant, and the study was not powered to detect differences among subgroups.In the rapid arm, for which TB diagnostic data were available, 29/187 patients (16%) presented with TB symptoms and were tested for TB using Xpert MTB/RIF. Four patients (17% of those with symptoms and 2% of all rapid arm patients) had a confirmed TB diagnosis. All four initiated ART within the 90-d outcome defined above, with a range of 11–54 d between study enrollment and ART initiation.The results of the sensitivity analysis incorporating the six patients who were excluded after randomization for reasons other than ART eligibility or prior CD4 count, and assigning each a negative outcome, did not differ substantively from the findings presented above, with a relative risk of viral suppression by 10 mo of 1.22 [1.02–1.46].Rapid initiation, using the procedures described above and as implemented by the study, appeared acceptable to patients at the time it was offered and feasible to implement at both study sites. We were not able to assess acceptability after patients received the intervention, as the study had no post-initiation interaction with those enrolled, and thus can surmise acceptability only on the basis of acceptance of the intervention. The study refusal rate was very low (31/603, 5%); nearly four out of five (148/187, 79%) patients offered the intervention accepted initiation on the same day or the next day, and rapid arm patients consistently expressed appreciation for the opportunity to start immediately.All steps in the rapid initiation process were completed on the same day as study enrollment for 72% (135/187) of those in the rapid arm, demonstrating the feasibility of the intervention, at least within the context of the study. From provision of informed consent (study enrollment) to dispensing of the first supply of ARV medications, rapid initiation took a median of 2.4 (IQR 2.1–2.8) hours for those who initiated on the same day as study enrollment. This interval was shorter for patients who already had CD4 count results in hand at study enrollment (median 2.25 hours). It was longer (median 4.5 hours) for those who required a TB test and did initiate ART on the same day, but 15/20 patients requiring TB tests did not initiate on the same day. The only obstacle encountered in implementing rapid procedures was fairly frequent power outages, a common occurrence in South Africa, at Site 1, which did not have a generator for backup power supply. Most rapid instrument tests could not be performed during power outages. The rapid test instruments otherwise performed well throughout the study, and no major delays or problems arose in the acceleration of clinic procedures.DiscussionIn this randomized controlled trial, we evaluated the effectiveness of an accelerated ART initiation algorithm that combined compressed and accelerated clinic procedures with point-of-care laboratory testing technologies that allowed eligible patients to initiate ART in a single clinic visit. This intervention increased the proportion of patients eligible for ART at study enrollment who initiated ART within 90 d by 25%, to 97% of all eligible patients and 100% of patients who were not delayed for TB treatment. By 10 mo after study enrollment, the intervention increased viral suppression among all treatment-eligible patients by 13% and retention in care by 17%. It was feasible and appeared acceptable at both study sites.The trial demonstrated that it is possible to initiate nearly all eligible patients on ART, and to do so in a much shorter time interval than previously required. The net benefit for overall viral suppression was clinically meaningful and may underestimate the true benefits of the intervention. Both the study sites were relatively well-managed, public sector clinics, resulting in a higher rate of ART initiation in the standard arm (72%) than is found elsewhere in the country, for example in rural KwaZulu Natal Province where the rate was 59% [2]. In addition, we observed a larger effect at Site 1, the primary health clinic, than at Site 2, the hospital-based HIV clinic. Primary health clinics, which have fewer resources than hospital-based clinics but treat 85% of HIV patients in South Africa, may struggle more with loss to follow-up before treatment initiation than do hospital-based clinics, creating a greater opportunity for a service delivery intervention like RapIT to be effective. The potential for reaching younger men, who have been among the least likely to access ART under standard care [22], is another important potential benefit of rapid initiation. Additional research is needed to explore further the non-significant differences in effect that we observed in our study.The patients who likely benefited most from RapIT were those who would not otherwise have initiated treatment at all, or who would have waited until they were sick enough to compromise their prognosis on treatment. In the standard arm, most patients who did not start treatment did not return to the study clinics for even one more visit, underscoring the importance of taking full advantage of the first visit to get as many patients started on treatment as possible. For those who would have initiated treatment, just not as soon, there is some evidence that even relatively short delays may be harmful. A recent modeling exercise using South African data estimated that compared to immediate initiation, a delay in initiating ART of 70 d would lead to a 34% increase in 12-mo mortality [22]. Delaying treatment initiation thus both deters some patients from starting at all and jeopardizes outcomes for those who do start.We hypothesize that the delays and multiple visits patients must endure before starting ART directly deter treatment initiation. Patients who cannot afford transport fare for multiple visits, have childcare obligations at home, or risk job or wage loss if they miss too many days of work may be directly deterred from returning. Others may simply grow impatient or lose their courage or motivation, particularly if they are asymptomatic when diagnosed. These patients are likely to drift away and only return when their CD4 counts are lower and symptoms have started, or to die before treatment can be started. Our results suggest that offering the opportunity to start treatment on the spot, without delay, overcomes these barriers, without risking poorer outcomes later on.Among patients who did initiate ART, post-initiation loss to care was higher in the rapid arm than the standard arm. This difference disappeared in the survival analysis, which controlled for number of months on ART but does not reflect the benefits of randomization. We speculate that some patients who did not want or were not ready for treatment chose to accept immediate initiation simply because it was offered or they wanted to participate in the study. For these patients, attrition from care was simply shifted from before ART initiation to after. While the intervention was successful in increasing the overall proportion of treatment-eligible patients with successful outcomes (viral suppression and/or retention in care), the rate of post-initiation attrition is a reminder that early retention in care and adherence support once patients start treatment remain high priorities for further research and intervention.Other studies have gauged the impact on treatment uptake of a single POC technology [23] or changes in service delivery [24], but we found only one prior report of a “single-visit initiation” intervention that was similar, to some degree, to RapIT. That study enrolled pregnant women initiating ART for prevention of mother-to-child transmission in South Africa and found very high uptake of ART among women offered rapid initiation, but it did not have a comparison arm to allow an effect to be estimated [25]. A study in Tanzania and Zambia compared the effect of community support on a two-visit algorithm and reported 99% uptake of ART in both study arms [26]. Taken together, these studies imply that accelerating ART initiation is effective in a wide range of settings.Nothing in the rapid initiation procedures used in this study differed fundamentally from existing clinic procedures. The intervention was delivered by study nurses and counselors with the same qualifications as existing clinic staff, though with study-specific training and supervision. The intervention imposed no major burdens on site management, though managerial acquiescence to the study and operational flexibility were needed to adjust the schedule and content of patient visits, staff responsibilities, and record keeping to allow for rapid initiation [27]. The main technical training required was in the use of the POC test instruments, which also required a secure location within the clinic, temperature control, and electricity.Although South Africa has better clinic infrastructure than do many other countries in the region, the RapIT intervention does not require anything that most urban and many rural clinics cannot provide. We speculate that the RapIT intervention would be feasible and potentially even more effective in other high HIV prevalence areas, where patients travel farther to reach clinics and results from centralized laboratories take even longer to return. As the new WHO guidelines are adopted, moreover, laboratory test results may not be required prior to ART initiation for patients who are asymptomatic, reducing the need for POC technology.The generalizability of our results is limited in several ways. The study was conducted in only two clinics in one province of one country. The trial intervention was delivered by study staff; it is uncertain if clinic staff delivering the same intervention will achieve the same outcomes (and whether their outcomes will be better or worse than those observed in the trial). As is typical in individually randomized trials of service delivery interventions, the possibility exists that quality of care in the standard arm was improved by the presence of the study, as clinic staff providing care for the standard arm may have been motivated by the study to make treatment initiation more efficient. If this occurred, the effect reported here would understate the true improvement in ART initiation that could be expected under routine implementation. As with many studies in which retention in care is an endpoint, we do not know the true outcomes of study patients who were not retained nor whether rapid arm patients who were not retained and who agreed to start treatment solely due to the presence of the study, and would otherwise not have done so, are at increased risk of developing ARV resistance. Finally, as reported above, rapid initiation under the study algorithm took 2–3 hours to complete, making same-day initiation impractical for patients who arrive late in the day (and for clinics with large numbers of such patients).We also do not know how clinic and patient characteristics will affect the net cost and cost-effectiveness of the intervention. Most of the changes introduced in the RapIT intervention entailed only adjustments in schedules and staff time, and we speculate that these will not result in a major net change to service delivery costs. The POC instruments used in the trial require an up-front investment, but it may be possible to initiate ART in a single visit without any POC instruments if there is no CD4 count threshold for initiation, patients with TB symptoms are identified and managed separately, and ARV regimen adjustments are routinely made at the first refill visit, rather than before initiation. Costs saved by patients, who must make just one clinic visit rather than four or five, should also be taken into account.The RapIT intervention as designed and implemented showed clinically meaningful improvements in ART uptake and viral suppression, providing “proof of principle” for a single-visit treatment initiation algorithm. Follow-on studies are needed to evaluate effectiveness and cost-effectiveness in routine practice in a variety of settings, and variations on the algorithm could also be considered. The RapIT trial has demonstrated that accelerating ART initiation can be effective and feasible in this setting and appeared acceptable to patients to whom it was offered; the next challenge will be adapting it to the range of settings and conditions found in clinics throughout Africa.Supporting InformationS1 TableStudy outcomes adjusted for baseline CD4 count, age, and sex.(DOCX)Click here for additional data file.S2 TableCrude patient-level predictors of treatment uptake, viral suppression, and retention in care.(DOCX)Click here for additional data file.S3 TableStudy outcomes stratified by immediate versus delayed initiation (rapid arm patients initiating ≤90 d only).(DOCX)Click here for additional data file.S4 TableAbsolute and relative effect measure modification of primary outcome (initiated ≤90 d and suppressed by 10 mo).(DOCX)Click here for additional data file.S1 TextResearch protocol.(PDF)Click here for additional data file.S2 TextCONSORT statement.(PDF)Click here for additional data file.AbbreviationsALTalanine aminotransferaseaRRadjusted risk ratioARTantiretroviral therapyARVantiretroviralIQRinterquartile rangeCIconfidence intervalHRhazard ratioPHCprimary health clinicPOCpoint-of-careRapITRapid Initiation of TreatmentRDrisk differenceRRrelative riskTBtuberculosisReferences1\nSiednerMJ, NgCK, Bassett IV, KatzIT, BangsbergDR, TsaiAC. Trends in CD4 count at presentation to care and treatment initiation in sub-Saharan Africa, 2002–2013: a meta-analysis. Clin Infect Dis. 2014; 60:1120–1127. 10.1093/cid/ciu1137\n255161892\nPlazyM, Dray-SpiraR, Orne-GliemannJ, DabisF, Newell M-L. Continuum in HIV care from entry to ART initiation in rural KwaZulu-Natal, South Africa. Trop Med Int Health. 2014; 19:680–689. 10.1111/tmi.12301\n246549903\nClouseK, PettiforAE, MaskewM, BassettJ, VanRie A, BehetsF, et al\nPatient retention from HIV diagnosis through one year on antiretroviral therapy at a primary health care clinic in Johannesburg, South Africa. J Acquir Immune Defic Syndr. 2013; 62: 39–46.4\nLahuertaM, UeF, HoffmanS, ElulB, KulkarniSG, WuY, et al\nThe problem of late ART initiation in Sub-Saharan Africa: a transient aspect of scale-up or a long-term phenomenon?\nJ Health Care Poor Underserved. 2013; 24: 359–383. 10.1353/hpu.2013.0014\n233777395\nINSIGHT START Study Group. Initiation of antiretroviral therapy in early asymptomatic HIV infection. N Engl J Med. 2015; 373: 795–807. 10.1056/NEJMoa1506816\n261928736\nWorld Health Organization. Guideline on when to start antiretroviral therapy and on pre-exposure prophylaxis for HIV\nGeneva: World Health Organization; 2015.7\nGovindasamyD, FordN, KranzerK. Risk factors, barriers and facilitators for linkage to ART care in sub-Saharan Africa: a systematic review. AIDS. 2012; 26: 2059–2067. 10.1097/QAD.0b013e3283578b9b\n227812278\nSiednerMJ, LankowskiA, HabererJE, KembabaziA, EmenyonuN, TsaiAC, et al\nRethinking the “‘pre’” in pre-therapy counseling: no benefit of additional visits prior to therapy on adherence or viremia in Ugandans initiating ARVs. PLoS ONE. 2012; 7: e39894\n10.1371/journal.pone.0039894\n227619249\nWorld Health Organization. Global update on the health sector response to HIV, 2014\nGeneva: World Health Organization; 2014.10\nIngleSM, MayM, UebelK, TimmermanV, KotzeE, BachmannM, et al\nOutcomes in patients waiting for antiretroviral treatment in the Free State Province, South Africa: prospective linkage study. AIDS. 2010; 24: 2717–2725. 10.1097/QAD.0b013e32833fb71f\n2093555411\nMyerL, ZulligerR, PienaarD. Diversity of patient preparation activities before initiation of antiretroviral therapy in Cape Town, South Africa. Trop Med Int Heal. 2012; 17: 972–977. 10.1111/j.1365-3156.2012.03033.x\n12\nNational Department of Health. The South African Antiretroviral Treatment Guideline 2013\nPretoria: National Department of Health; 2013.13\nScottV, ZweigenthalV, JenningsK. Between HIV diagnosis and initiation of antiretroviral therapy: assessing the effectiveness of care for people living with HIV in the public primary care service in Cape Town, South Africa. Trop Med Int Heal. 2011; 16:1384–1391. 10.1111/j.1365-3156.2011.02842.x\n14\nGousN, ScottL, PotgieterJ, NtabeniL, EnslinS, NewmanR, et al\nFeasibility of performing multiple point of care testing for HIV anti-retroviral treatment initiation and monitoring from multiple or single fingersticks. PLoS ONE. 2013; 8: e85265\n10.1371/journal.pone.0085265\n2437687315\nJani IV, SitoeNE, ChongoPL, AlfaiER, QuevedoJI, TobaiwaO, et al\nAccurate CD4 T-cell enumeration and antiretroviral drug toxicity monitoring in primary healthcare clinics using point-of-care testing. AIDS. 2011; 25:807–812. 10.1097/QAD.0b013e328344f424\n2137853516\nMnyaniCN, McIntyreJA, MyerL. The reliability of point-of-care CD4 testing in identifying HIV-infected pregnant women eligible for antiretroviral therapy. J Acquir Immune Defic Syndr. 2012; 60: 260–264. 10.1097/QAI.0b013e318256b651\n2248758917\nWadeD, DaneauG, AboudS, VercauterenGH, UrassaWSK, KestensL, et al\nWHO multicenter evaluation of FACSCount CD4 and Pima CD4 t-cell count systems\u202f: instrument performance and misclassification of HIV-infected patients. J Acquir Immune Defic Syndr. 2014; 66:98–107.18\nScottLE, CampbellJ, WestermanL, KestensL, VojnovL, KohastsuL, et al\nA meta-analysis of the performance of the Pima CD4 for point of care testing. BMC Med. 2015; 13:168\n10.1186/s12916-015-0396-2\n2620886719\nMeyer-RathG, SchnippelK, LongL, MacleodW, SanneI, StevensW, et al\nThe impact and cost of scaling up GeneXpert MTB/RIF in South Africa. PLoS ONE. 2012; 7:e36966\n10.1371/journal.pone.0036966. 10.1371/journal.pone.0036966\n2269356120\nUNITAID. Tuberculosis diagnostics technology and market landscape\nGeneva: UNITAID; 2013.21\nFoxMP, MaskewM, MacPhailA. Cohort profile: the Themba Lethu Clinical Cohort, Johannesburg, South Africa. International Journal of Epidemiology. 2013; 42:430–439. 10.1093/ije/dys029\n2243486022\nHoffmannCJ, LewisJJ, DowdyDW, FieldingKL, GrantAD, MartinsonN, et al\nMortality associated with delays between clinic entry and ART initiation in resource-limited settings: results of a transition-state model. J Acquir Immune Defic Syndr. 2013; 63:105–111. 10.1097/QAI.0b013e3182893fb4\n2339245723\nJani IV, SitoeNE, AlfaiER, ChongoPL, QuevedoJI, RochaBM, et al\nEffect of point-of-care CD4 cell count tests on retention of patients and rates of antiretroviral therapy initiation in primary health clinics: An observational cohort study. Lancet. 2011; 378:1572–1579. 10.1016/S0140-6736(11)61052-0\n2195165624\nBurtleD, WelfareW, EldenS, MamvuraC, VandelanotteJ, PetherickE, et al\nIntroduction and evaluation of a “pre-ART care” service in Swaziland: an operational research study. BMJ Open. 2012; 2:e000195\n10.1136/bmjopen-2011-000195\n25\nBlackS, ZulligerR, MyerL, MarcusR, JenekerS, HonsBA, et al\nSafety, feasibility and efficacy of a rapid ART initiation in pregnancy pilot programme in Cape Town, South Africa. S Afr Med J. 2013; 103:557–562. 10.7196/SAMJ.6565\n2388573926\nMfinangaS, ChandaD, KivuyoSL, GuinnessL, BottomleyC, SimmsV, et al\nCryptococcal meningitis screening and community-based early adherence support in people with advanced HIV infection starting antiretroviral therapy in Tanzania and Zambia: an open-label, randomised controlled trial. Lancet. 2015; 385:2173–2182. 10.1016/S0140-6736(15)60164-7\n2576569827\nClouseK, Page-ShippL, DanseyH, MoatlhodiB, ScottL, BassettJ, et al\nImplementation of Xpert MTB/RIF for routine point-of-care diagnosis of tuberculosis at the primary care level. S Afr Med J. 2012; 102:805–807. 10.7196/SAMJ.5851\n23034211', 'title': ""Initiating Antiretroviral Therapy for HIV at a Patient's First Clinic Visit: The RapIT Randomized Controlled Trial."", 'date': '2016-05-11'}, '29136001': {'article_id': '29136001', 'content': ""PLoS MedPLoS MedplosplosmedPLoS Medicine1549-12771549-1676Public Library of ScienceSan Francisco, CA USA29136001568543710.1371/journal.pmed.1002433PMEDICINE-D-17-02016Research ArticleMedicine and health sciencesDiagnostic medicineHIV diagnosis and managementBiology and Life SciencesMicrobiologyMedical MicrobiologyMicrobial PathogensViral PathogensImmunodeficiency VirusesHIVMedicine and Health SciencesPathology and Laboratory MedicinePathogensMicrobial PathogensViral PathogensImmunodeficiency VirusesHIVBiology and Life SciencesOrganismsVirusesViral PathogensImmunodeficiency VirusesHIVBiology and Life SciencesOrganismsVirusesImmunodeficiency VirusesHIVBiology and life sciencesOrganismsVirusesRNA virusesRetrovirusesLentivirusHIVBiology and Life SciencesMicrobiologyMedical MicrobiologyMicrobial PathogensViral PathogensRetrovirusesLentivirusHIVMedicine and Health SciencesPathology and Laboratory MedicinePathogensMicrobial PathogensViral PathogensRetrovirusesLentivirusHIVBiology and Life SciencesOrganismsVirusesViral PathogensRetrovirusesLentivirusHIVBiology and Life SciencesImmunologyVaccination and ImmunizationAntiviral TherapyAntiretroviral TherapyMedicine and Health SciencesImmunologyVaccination and ImmunizationAntiviral TherapyAntiretroviral TherapyMedicine and Health SciencesPublic and Occupational HealthPreventive MedicineVaccination and ImmunizationAntiviral TherapyAntiretroviral TherapyResearch and Analysis MethodsDatabase and Informatics MethodsHealth InformaticsElectronic Medical RecordsMedicine and health sciencesEpidemiologyHIV epidemiologyResearch and Analysis MethodsResearch DesignSurvey ResearchQuestionnairesMedicine and Health SciencesDiagnostic MedicineClinical Laboratory SciencesClinical LaboratoriesPeople and PlacesGeographical LocationsAfricaMozambiqueA combination intervention strategy to improve linkage to and retention in HIV care following diagnosis in Mozambique: A cluster-randomized studyA combination intervention strategy to improve HIV care in Mozambiquehttp://orcid.org/0000-0001-6101-3073ElulBatyaConceptualizationFunding acquisitionMethodologyProject administrationSupervisionWriting – original draftWriting – review & editing12*LambMatthew R.ConceptualizationData curationFormal analysisFunding acquisitionMethodologyWriting – original draftWriting – review & editing12http://orcid.org/0000-0002-9748-9273LahuertaMariaMethodologyProject administrationSupervisionWriting – original draftWriting – review & editing12AbacassamoFatimaInvestigationProject administrationWriting – review & editing3AhouaLaurenceConceptualizationFunding acquisitionInvestigationMethodologyProject administrationWriting – review & editing1http://orcid.org/0000-0001-7915-8553KujawskiStephanie A.Data curationFormal analysisWriting – review & editing2TomoMariaMethodologyProject administrationWriting – review & editing3JaniIleshMethodologyWriting – review & editing41\nICAP at Columbia University, Mailman School of Public Health, Columbia University, New York, New York, United States of America2\nDepartment of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York, United States of America3\nCenter for Collaboration in Health, Maputo, Mozambique4\nInstituto Nacional de Saúde, Maputo, MozambiqueLewinSharon R.Academic EditorUniversity of Melbourne, AUSTRALIAI have read the journal's policy and the authors of this manuscript have the following competing interests: FA and MT were employees of the Center for Collaboration in Health which was providing technical support to the study health facilities at the time of the study.* E-mail: be2124@columbia.edu141120171120171411e100243396201710102017© 2017 Elul et al2017Elul et alThis is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.BackgroundConcerning gaps in the HIV care continuum compromise individual and population health. We evaluated a combination intervention strategy (CIS) targeting prevalent barriers to timely linkage and sustained retention in HIV care in Mozambique.Methods and findingsIn this cluster-randomized trial, 10 primary health facilities in the city of Maputo and Inhambane Province were randomly assigned to provide the CIS or the standard of care (SOC). The CIS included point-of-care CD4 testing at the time of diagnosis, accelerated ART initiation, and short message service (SMS) health messages and appointment reminders. A pre–post intervention 2-sample design was nested within the CIS arm to assess the effectiveness of CIS+, an enhanced version of the CIS that additionally included conditional non-cash financial incentives for linkage and retention. The primary outcome was a combined outcome of linkage to care within 1 month and retention at 12 months after diagnosis. From April 22, 2013, to June 30, 2015, we enrolled 2,004 out of 5,327 adults ≥18 years of age diagnosed with HIV in the voluntary counseling and testing clinics of participating health facilities: 744 (37%) in the CIS group, 493 (25%) in the CIS+ group, and 767 (38%) in the SOC group. Fifty-seven percent of the CIS group achieved the primary outcome versus 35% in the SOC group (relative risk [RR]CIS vs SOC = 1.58, 95% CI 1.05–2.39). Eighty-nine percent of the CIS group linked to care on the day of diagnosis versus 16% of the SOC group (RRCIS vs SOC = 9.13, 95% CI 1.65–50.40). There was no significant benefit of adding financial incentives to the CIS in terms of the combined outcome (55% of the CIS+ group achieved the primary outcome, RRCIS+ vs CIS = 0.96, 95% CI 0.81–1.16). Key limitations include the use of existing medical records to assess outcomes, the inability to isolate the effect of each component of the CIS, non-concurrent enrollment of the CIS+ group, and exclusion of many patients newly diagnosed with HIV.ConclusionsThe CIS showed promise for making much needed gains in the HIV care continuum in our study, particularly in the critical first step of timely linkage to care following diagnosis.Trial registrationClinicalTrials.gov NCT01930084In a cluster-randomized trial done in Mozambique, Batya Elul and colleagues study a combined intervention for linkage to and retention of people with HIV in care.Author summaryWhy was this study done?In sub-Saharan Africa, HIV testing, care, and treatment programs have been widely scaled up over the past decade, but suboptimal outcomes across the HIV care continuum—particularly with regards to timely linkage to and sustained retention in care—compromise their effectiveness.Patients experience multiple barriers to linkage to and retention in HIV care including health system barriers, structural barriers, and behavioral barriers, yet prior studies have largely evaluated individual interventions targeting a single barrier to care.Our study was designed specifically to examine the effectiveness of a combination intervention strategy (CIS) composed of several scalable evidence-based interventions targeting the multiple and prevalent health system, structural and behavioral barriers that patients face across the HIV continuum.What did the researchers do and find?We randomly assigned 10 primary health facilities in the city of Maputo and Inhambane Province in Mozambique to provide the standard of care (SOC) or the CIS, which included point-of-care CD4 testing at the time of diagnosis, accelerated ART initiation, and short message service (SMS) health messages and appointment reminders. A pre–post intervention 2-sample design was nested within the intervention arm to assess the effectiveness of CIS+, an enhanced version of the CIS that additionally included conditional non-cash financial incentives for linkage and retention.We enrolled 2,004 adults diagnosed with HIV in the voluntary counseling and testing clinics of participating health facilities, and compared the proportion who achieved a combined outcome of linkage to HIV care within 1 month of diagnosis and retention in care at 12 months across the 3 study groups.We found an increased likelihood of achieving the combined outcome in the CIS group compared to the SOC group, driven primarily by very large increases in same-day linkage, but no difference between the CIS+ and CIS groups.What do these findings mean?The CIS may help improve outcomes across the HIV care continuum in high-burden settings, particularly in the critical first step of timely linkage to care following diagnosis.Further research is needed to understand whether financial incentives can be optimized in this setting, given their effectiveness in enhancing other health outcomes.http://dx.doi.org/10.13039/100000200United States Agency for International DevelopmentAID-OAA-A-12-00027http://orcid.org/0000-0001-6101-3073ElulBatyahttp://dx.doi.org/10.13039/100000060National Institute of Allergy and Infectious DiseasesT32 AI114398http://orcid.org/0000-0001-7915-8553KujawskiStephanieThis study was funded by the United States Agency for International Development (USAID), USAID Award Number: AID-OAA-A-12-00027 and the National Institute of Allergy & Infectious Diseases of the National Institutes of Health, T32 AI114398 (SAK). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Data AvailabilityAll relevant data are within the paper and its Supporting Information files.Data AvailabilityAll relevant data are within the paper and its Supporting Information files.IntroductionAlthough the extraordinary scale-up of HIV testing, care, and treatment programs in sub-Saharan Africa over the past decade has resulted in more than 19 million persons accessing antiretroviral therapy (ART) [1], the effectiveness of these programs has been significantly hindered by high levels of attrition across the HIV care continuum. Observational studies and systematic reviews have repeatedly reported disturbing gaps in care as patients move from HIV testing clinics to HIV care clinics (i.e., linkage to care) and that patient dropout among those enrolled in HIV care is far too common, both before and after ART initiation (i.e., retention in care) [2–7]. Indeed, available data suggest that less than 1/3 of individuals who are diagnosed with HIV are successfully linked to and remain engaged in HIV care 12 months later [4,8].Barriers to timely linkage to and sustained retention in HIV care have been well documented, and include health system barriers (e.g., multiple HIV clinic visits for counseling and clinical and laboratory assessments prior to ART initiation), structural barriers (e.g., transport costs and distances, work and childcare constraints), and behavioral barriers (e.g., forgetting appointments, lack of understanding of required care) [9–14]. Prior studies have overwhelmingly evaluated individual interventions targeting a single barrier at a single point in the HIV care continuum such as mobile phone short message service (SMS) messaging to augment linkage to care following diagnosis, or point-of-care CD4 testing to enhance retention among patients enrolled in HIV care [15,16]. However, it is increasingly recognized that multi-component approaches composed of several practical, evidence-based interventions that simultaneously target the multiple and recurrent barriers that patients face as they navigate across the HIV care continuum are needed to maximize individual and population health [17,18]. Further, implementation science research that evaluates proposed multi-component approaches in real-world settings is needed to assess not only effectiveness, but also implementation outcomes including reach, adoption, and sustainability [19]. To this end, we designed a combination intervention strategy (CIS) composed of several scalable evidence-based interventions targeting prevalent health system, structural, and behavioral barriers across the HIV care continuum, and determined its effect on a combined outcome of linkage to and retention in HIV care among adults newly diagnosed with HIV in Mozambique, while also collecting information on its implementation and potential for broader scale-up [20]. Data regarding intervention feasibility and patient acceptability have been published [21], and thus we present here the effectiveness results. Because the interventions included in the CIS are expected to be implemented at the facility level, as opposed to targeted at specific individuals, should they be scaled up, we evaluated effectiveness using a cluster design, which best mirrors this implementation approach.MethodsA detailed description of the study protocol has been published [22].Ethics statementEthical approval was provided by Mozambique’s National Committee for Bioethics for Health and Columbia University’s institutional review board (IRB) (protocol AAAL1354). Informed written consent was obtained from all participants.Study designBetween April 22, 2013, and June 30, 2016, we conducted a 2-arm cluster-randomized study (effectiveness–implementation hybrid design, Type 1) [20] in health facilities in Maputo and Inhambane Province in Mozambique in order to assess the effectiveness of the CIS. Additionally, a pre–post intervention 2-sample design was nested within the intervention arm to assess the additional effectiveness of an enhanced version of the CIS, referred to as CIS+. Consequently, the standard of care (SOC) arm enrolled 1 cohort of patients, while the intervention arm enrolled 2 sequential cohorts of patients (CIS and CIS+). CIS+ participants were enrolled after CIS enrollment was completed at each facility randomized to the intervention arm.Study settingThe city of Maputo, the nation’s capital, has an area of 300 km2 and an estimated population of 1,225,868 [23], with an HIV prevalence of 16.9% among those aged 15 to 59 years [24]. The Maputo City Health Network has a total of 37 health facilities, 32 of which offered comprehensive HIV care and treatment services at the time of study implementation [25]. In contrast, Inhambane is a rural province, with an estimated 1,475,318 people spread across 68,615 km2 [23]. HIV prevalence among adults aged 15 to 59 years is 14.1% [24]. The ratio of doctors to population (5.96/100,000) is one of the lowest in the country [26]. Of the 135 health facilities in the province, 76 offered HIV care and treatment services when our study was initiated [25]. Suboptimal health facility infrastructure, long distances to facilities, and weak referral systems in the province are all believed to compromise health service uptake [26].RandomizationPrimary health facilities providing HIV testing, care, and treatment services and operated by the Ministry of Health with technical support from the Center for Collaboration in Health, a local PEPFAR implementing partner, were the unit of randomization. We focused on primary health facilities, rather than larger provincial hospitals, to reflect the increasingly decentralized nature of HIV service delivery in Mozambique. Ten facilities in Maputo (N = 4) and Inhambane Province (N = 6) were selected from the 66 primary health facilities receiving technical support from the Center for Collaboration in Health in those regions. Participating facilities were purposely chosen because they had the highest volume of adults testing HIV positive and enrolling in HIV care in the year prior to study start and thus were expected to have sufficient participants for appropriate power. Facilities were matched into pairs by region (Maputo or Inhambane), level of urbanicity (urban versus rural), and average number of patients testing HIV positive in voluntary counseling and testing (VCT) in the year prior to study initiation (high versus low), resulting in 5 matched pairs. Matched pairs were randomized by one of the authors (MRL) using a computerized random number generator to either the CIS arm or the SOC arm using matched-pair randomization. Sequences were concealed until interventions were assigned. The study was non-blinded.Study populationParticipants were enrolled in the SOC group beginning on April 22, 2013, and in the CIS group beginning on April 25, 2013. The last patient was enrolled in the SOC group on November 20, 2014, and the last patient in the CIS group was enrolled on February 11, 2015. Enrollment in the CIS+ group began after each clinic randomized to the intervention arm completed CIS enrollment, and ran from June 16, 2014, through June 30, 2015. All participants were followed for 12 months, with the last patient completing follow-up on June 30, 2016.Broad inclusion criteria were used to reflect as accurately as possible the population of adults newly diagnosed with HIV in VCT clinics at the participating health facilities. We focused on individuals newly diagnosed in VCT clinics, as opposed to those diagnosed in antenatal clinics and tuberculosis clinics, because the latter groups of patients typically follow a modified clinic flow. All adults testing HIV positive in the VCT clinics within the participating health facilities were informed of the study by HIV testing counselors following diagnosis, and those who were interested were referred to study staff for further information, eligibility screening, and consent procedures. Patients were excluded if they were less than 18 years of age, were pregnant, planned to move from their community of residence in the next 12 months, had enrolled in HIV care or initiated ART in the past 6 months, did not understand Portuguese or Xitsua, or were incapable of providing informed consent. Study participants agreed to be referred to HIV care and treatment services at the same facility where they were diagnosed (referred to as the “diagnosing facility”); to complete a baseline, 1-month, and 12-month interview; to be traced at their homes if they could not be reached by phone for follow-up interviews; to provide contact information for a family member or friend who could provide information on their vital status if they could not be located for a follow-up interview; and, if they enrolled in HIV care and treatment services at the diagnosing facility, to have their clinical data abstracted from the facility’s existing electronic medical records.Study interventionsStandard of careParticipants at health facilities randomized to receive the SOC were managed as per prevailing Ministry of Health guidelines [27]. Individuals diagnosed with HIV received post-test counseling in the VCT clinic and were referred verbally to HIV services, typically in the diagnosing facility. Patients presenting to the facility receptionist to schedule a clinical consultation for HIV care were referred to the laboratory for CD4 cell count, chemistry, and hematology testing, and provided with an appointment 2–4 weeks later to allow sufficient time for the laboratory results to be received. ART eligibility was determined at that first clinical consultation based on CD4 cell count ≤ 350 cells/mm3 and/or WHO stage 3/4. Those found to be eligible for ART received at least 1 individual counseling session before initiating treatment. For ART-eligible patients, the time interval between enrollment in HIV care and ART initiation was estimated at 1–2 months at the time the study started. Participants initiating ART were requested to return every 2 weeks for the first month, at 2 months, at 6 months, and every 6 months thereafter. ART-ineligible patients were instructed to return at 6 months for repeat clinical evaluation and laboratory testing.Combination intervention strategyAt facilities randomized to the intervention arm, we introduced 4 evidence-based interventions that simplified the clinic flow and encouraged linkage to and retention in care. These interventions targeted several known health system, structural, and behavioral barriers across the HIV care continuum, and were adapted for the on-the-ground realities—including practice norms, physical space, and available staffing—at the health facilities. First, we introduced Pima (Inverness Medical Innovations) CD4 assay machines in the VCT clinics to enable HIV testing counselors to provide real-time, point-of-care CD4 test results immediately following diagnosis, and thus addressed a health system barrier by reducing the number of visits required for CD4 testing. We also hypothesized that receipt of additional information on one’s health at the time of diagnosis would advance patient understanding of the need for care, a documented behavioral barrier [10,28]. All patients regardless of CD4 count were provided with a paper-based referral to on-site HIV services that included their CD4 count, and were instructed to present for their first clinical consultation within 1 week. Second, to address additional health system barriers, patients with Pima CD4 cell count ≤ 350 cells/mm3 were provided with accelerated ART initiation, with the ultimate goal of decreasing the HIV morbidity and mortality that contributes to significant attrition among ART-eligible patients [4]. These individuals received an individual ART preparatory counseling session in the VCT clinic immediately following CD4 testing, on the day of diagnosis. Facility receptionists were instructed to expedite appointments for these patients when they presented to schedule their clinical consultations. Although the patients were directed to the laboratory to have their blood drawn for baseline laboratory tests required by national ART guidelines, clinicians were encouraged to initiate ART at the first clinical visit rather than await the results of the laboratory tests unless the patient presented with comorbid conditions. Patients who initiated ART received a 2-week supply and followed the visit schedule dictated by national guidelines, similar to the SOC procedures. Once baseline laboratory results were available, they were reviewed by clinic staff, and if abnormalities were noted, the participant was contacted to return to the clinic. Third, participants received health messages and appointment reminders via SMS messaging to address behavioral barriers associated with deferring care engagement and forgetting appointments. The messages were sent from the central study office to the participant’s phone or to a friend or relative’s phone per participant preference, and did not refer to HIV or a specific health facility or reveal any personal information. The health messages encouraged participants to care for their health, and were sent weekly for 1 month following diagnosis and then monthly (e.g., “Hi. Your health is the most important thing. Please remember to come to the health center for health services.”). Appointment reminders were sent only to participants who linked to care at the diagnosing facility, and were sent 3–7 days before each scheduled clinic visit (e.g., “Hi. Your health is the most important thing. We expect to see you at your upcoming appointment scheduled for the day ___.”). Participants were not asked to confirm receipt or reply to the messages. Finally, patients in the CIS+ cohort received the CIS interventions plus a series of non-cash financial incentives (FIs) in the form of prepaid cellular air-time cards to offset structural barriers associated with the direct and indirect costs of coming to the health facility to receive HIV care. Air-time cards rather than cash were selected as the incentive based on discussion with the Ministry of Health. Each card was valued at approximately US$5 and was provided conditionally upon the following achievements: linkage to care within 1 month of diagnosis, retention in care 6 months after diagnosis, and retention in care 12 months after diagnosis, for a total of approximately US$15. Participants who completed each achievement received the card when presenting for routine services. Participants without cellular phones could opt to give them to a family member, sell them for cash, or trade them for other goods. Both the point-of-care CD4 testing and accelerated ART initiation interventions were provided by health facility staff to all individuals diagnosed with HIV in the VCT clinic regardless of whether they were enrolled in the study, while the SMS messages and FIs were provided by study staff and only to study participants.Data collection and outcomesSite assessmentsData on the configuration of HIV services at the 10 participating study sites were collected at the beginning and at the end of the study using a standardized site assessment form. The purpose of the site assessments was to identify important similarities and differences between participating health facilities, as well as to better understand how services at the site could impact study implementation.Baseline interviewParticipants completed closed-ended questionnaires administered by trained research assistants at the time of study enrollment. The questionnaire took about 30 minutes to complete, and gathered information on sociodemographic characteristics, social and family support, mental health, alcohol use, HIV testing history, HIV knowledge and beliefs, and anticipated stigma and barriers to care. Anticipated stigma was assessed through 6 items adapted from the 12-item anticipated HIV stigma index developed by Earnshaw and Chaudoir [29]. Stigma scores were summed, then dichotomized into 2 groups: highest (>75th percentile) versus lower anticipated stigma. Mental health was assessed via a 7-question evaluation based on the Kessler 10-item scale for psychological distress [30]. Mental health scores were summed, then dichotomized into 2 groups: highest (<75th percentile) versus lower level of distress. Perceived availability of social support was assessed with 4 questions adapted from a 9-item scale by Wortman and colleagues [31]. Social support scores were summed, then dichotomized into 2 groups: higher (>50th percentile) versus lower social support. Questions assessing HIV-related knowledge and attitudes were based on those used by one of the authors in a previous study [32]. HIV knowledge scores were summed, then dichotomized into 2 groups: higher (>50th percentile) versus lower knowledge. Baseline interview data were double-entered into a study database, and a computer program identified discrepant double-entered results for correction against the paper-based forms.Patient tracing and follow-up interviewsOne and 12 months after enrollment, up until June 30, 2016, trained research assistants contacted participants by phone to ascertain their vital status and HIV care status, and to administer follow-up questionnaires. If the participant could not be contacted by phone after 3 attempts, research assistants visited the participant’s home up to 3 times. Participants who were located completed closed-ended interviews that gathered updated information on key domains from the baseline questionnaire, as well as self-reported information on linkage to (1- and 12-month questionnaires) and retention in HIV care (12-month questionnaire only), reasons for linkage/non-linkage (1- and 12-month questionnaires) and retention/non-retention (12-month questionnaire only), ART status, hospitalizations, and anticipated stigma. In cases where the participant could not be located, research assistants contacted a friend or family member as specified by the participant at study enrollment. Research assistants did not refer to HIV or the health facility during contact tracing but rather attempted to determine whether the participant was alive or dead. For those whose vital status could not be determined through contact tracing, research assistants searched existing electronic medical records at other primary health facilities supported by the Center for Collaboration in Health in the same district to assess whether patients had enrolled in HIV care at another facility, and reviewed death registers at the municipal and provincial levels to ascertain their vital status. Similar data entry and reconciliation procedures to those used for the baseline interview data were used for the tracing and follow-up data.Abstraction of clinical data for patients linking to HIV care at the diagnosing facilityAs part of routine clinical practice for HIV patients, clinicians documented patient information at every clinic visit on national HIV care forms, and trained data clerks entered those data into an Access-based electronic medical record. In its role as a PEPFAR implementing partner supporting the study sites, the Center for Collaboration in Health assessed the completeness and accuracy of these electronic data every 4 months and initiated targeted interventions to enhance data quality if there was greater than 15% disagreement on key data elements between the electronic and paper-based systems. During the study period, research assistants reviewed the electronic medical records to identify study participants who had linked to care at their diagnosing facility. For those located, we extracted the complete electronic medical record, capturing information on visit dates, vital status, transfer status, ART status, laboratory test results, and opportunistic infections.OutcomesThe primary outcome was a combined outcome of linkage to HIV care within 1 month of diagnosis plus retention in care 12 months after diagnosis measured at the individual level. We used a combined outcome to reflect the fact that improvements are needed across the HIV care continuum in order to maximize individual and population health. Linkage to care was defined by at least 1 clinical consultation for HIV that included assessment of the patient’s medical history and a physical exam. Retention in care was defined by a clinic visit in the 90 days prior to the end of the 12-month study follow-up period, with no documentation that the patient had transferred to another facility or had died. We assessed the combined outcome from the perspective of the diagnosing health facility using data from the electronic medical records maintained by the HIV clinics. All study participants were included in these analyses, including those who did not complete follow-up interviews. Participants whose electronic medical records were not located were considered not to have achieved the combined outcome for this analysis. As a secondary approach, we evaluated the combined outcome from the perspective of the Mozambican health program by supplementing data from the electronic medical records with patient reports of linkage to and retention in care at HIV clinics at different health facilities (obtained during follow-up interviews) and information obtained from electronic medical records at other health facilities. In these analyses, participants whose self-reported linkage and retention status suggested they were linked to and/or retained at a health facility other than their diagnosing clinic were considered to have achieved the respective linkage/retention outcomes. Participants who either did not complete follow-up interviews or did not self-report linkage to or retention at another clinic maintained their initial outcome designation. All study participants were included in these analyses.Secondary outcomes included linkage to care at several predefined time points, ART eligibility assessment (defined as receipt of WHO staging and/or CD4 cell count), ART initiation, disease progression (defined as a new WHO stage 3/4 condition or hospitalization noted in the electronic medical records or self-reported during follow-up interviews), retention in care 6 and 12 months after diagnosis regardless of the timing of linkage, and death.Statistical analysisThe trial was designed and powered to measure outcomes at the individual level, with outcomes assessed within each cluster (5 clusters per arm). In our initial power calculations, we anticipated that an average of 200 patients per clinic (in the CIS and SOC arms) would be eligible for enrollment based on historical data on the annual number of adults testing positive in the VCT clinics at the participating health facilities. With 5 facilities per study arm, an average of 200 patients per facility, an intraclass correlation coefficient (ICC) of 0.05, and an alpha of 0.05 and assuming that 35% of participants in the SOC arm would achieve the primary outcome, we estimated that the study would have 80% power to detect as statistically significant 55% of participants in the CIS group achieving the primary outcome, and greater than 80% power to detect as statistically significant 75% of participants in the CIS+ group achieving the primary outcome. Because enrollment proceeded slower than originally planned, at study midpoint we assessed the implications for power if each health facility enrolled an average of 150 participants rather than 200. Our calculations revealed minimal change in power with this reduction in the number of participants per health facility. Calculations were performed using PASS 8.0 software for 2 independent proportions in a cluster randomization study design and a 2-sided Farrington and Manning Likelihood Score Test [33]. Our power estimations and statistical analyses did not take into account the pair matching prior to randomization but rather followed recommendations from Diehr et al. [34] to break matches in statistical analyses of clustered studies when the number of pairs is between 3 and 9.An intent-to-treat analysis determined the relative risk (RR) of achieving study outcomes between the CIS and SOC groups, and between the CIS+ and CIS groups. For analyses of the primary outcome, we used random-intercept multilevel log-Poisson models to account for clustering within health facilities with an empirical variance adjustment for small numbers of sampling units described by Morel et al. [35]. We also assessed whether the primary outcome differed after adjustment for patient-level factors by constructing propensity scores that estimated the probability of inclusion in the CIS, CIS+, and SOC groups by age, sex, region, education, income, employment status, marital status, religion, prior year history of being away from home for more than 1 month, travel time to clinic, tuberculosis status, past hospitalizations, diagnosis history, and whether another family member was known to be living with HIV. The propensity score was included as a covariate in the multivariable log-Poisson models (adjusted analyses). In post hoc analyses, we further estimated the likelihood of key subgroups achieving the primary outcome using interaction contrast ratios. The subgroups assessed included subgroups based on baseline age, sex, region of health facility, employment status, marital status, whether the participant was away from home for more than 1 month in the year prior to study enrollment, travel time to clinic, whether a household member was known to be HIV positive, and dichotomous variables based on scales for self-reported anticipated stigma, HIV knowledge, mental health, and perceived social support as described earlier. For analyses of secondary outcomes, log-Poisson models were used for dichotomous outcomes, and t tests and 2-way median tests as appropriate for continuous outcomes, adjusting for clustering but not for patient-level differences.ResultsHealth facility characteristicsAs noted above, 10 primary health facilities participated in the study, 4 in Maputo and 6 in Inhambane. At study start, the 5 health facilities randomized to the intervention arm reported that they had experienced disruptions of 3 or more days in VCT services in the prior 12 months, while only 1 facility randomized to the SOC arm reported experiencing a similar disruption. By study end, no facilities—whether in the intervention or SOC arm—had experienced such disruptions. Throughout the study, only intervention sites conducted point-of-care CD4 testing using Pima machines in the VCT clinic. Two SOC sites reported that they had Pima machines available in their laboratories but only used them to monitor CD4 counts after patients had enrolled in HIV care. None of the SOC sites used SMS messaging for health messages or appointment reminders on a routine basis for all patients, but 2 sites sent SMS appointment reminders for patients participating in community ART groups [36]. Though the 2013 national HIV treatment guidelines stipulate that 1 ART preparatory counseling session is required for ART-eligible patients, all the facilities participating in the study typically conducted 2 to 3 sessions prior to ART initiation, with a slight reduction in the number of sessions observed between study start and end.Enrollment and participant characteristicsFig 1 shows the enrollment, exclusion, and flow of the patients by study group. During the study period, 5,327 adults ≥18 years of age were diagnosed with HIV in the VCT clinics at the 10 study facilities. A total of 265 of those individuals were not referred to the study staff for further information on the study because they informed the HIV testing counselor that they were not interested in the study, were already receiving HIV services, or were not willing to be referred to the diagnosing health facility. Among the 5,062 who were referred to the study staff for further information, 3,058 did not meet study eligibility criteria. The main reasons for exclusion were inability to provide informed consent due to distress following diagnosis (19%), inability to understand Portuguese or Xitsua (12%), and refusal to be referred to the diagnosing health facility for HIV services (10%).10.1371/journal.pmed.1002433.g001Fig 1Flow chart for study participation.CIS, combination intervention strategy; SOC, standard of care; VCT, voluntary counseling and testing.A total of 2,004 adults ≥18 years of age enrolled in the study at the 10 health facilities: 744 (37%) in the CIS group, 493 (25%) in the CIS+ group, and 767 (38%) in the SOC group. The majority of participants were female (64%), and the median age of participants was 34 years of age, with no meaningful differences observed by study group (Table 1). More than half of the participants (53%) were living with a partner at the time of diagnosis, and 65% of participants had a primary or lower level of education. Most participants (74%) were employed, and 43% had a monthly income of less than 1,500 meticais (approximately US$50). One-quarter (27%) reported that another household member was living with HIV. While no serious adverse events were reported during the study period, there was 1 unanticipated event of a female participant reporting intimate partner violence. The Mozambican National Committee for Bioethics for Health and the Columbia University IRB were informed of this event, and the participant asked to remain in the study but to conduct all study interviews at the facility (i.e., no follow-up phone calls).10.1371/journal.pmed.1002433.t001Table 1Participant characteristics at study enrollment in the 3 study groups (N = 2,004).CharacteristicTotalN = 2,004CISN = 744CIS+N = 493SOCN = 767p-ValueRegionMaputo1,077 (54%)396 (53%)275 (56%)406 (53%)0.58Inhambane927 (46%)348 (47%)218 (44%)361 (47%)Sex0.50Female1,292 (64%)490 (66%)319 (65%)483 (63%)Male712 (36%)254 (34%)174 (35%)284 (37%)Age (years)34.2 (9.6)34.9 (9.8)33.8 (9.9)33.8 (9.3)0.04518–24265 (13%)90 (12%)70 (14%)105 (14%)0.1225–391,233 (62%)440 (59%)301 (61%)492 (64%)40–49348 (17%)148 (2%)87 (18%)113 (15%)50+158 (8%)66 (9%)35 (7%)57 (7%)Marital status<0.001Married/partner and living together1,068 (53%)376 (51%)255 (52%)437 (57%)Married/partner, but not living together222 (11%)101 (14%)86 (17%)35 (5%)Single713 (36%)266 (36%)152 (31%)295 (38%)Missing/refused1 (0%)1 (0%)0 (0%)0 (0%)Education0.003None164 (8%)59 (8%)33 (7%)72 (9%)Primary1,149 (57%)442 (59%)256 (52%)451 (59%)Secondary471 (24%)164 (22%)130 (26%)177 (23%)Above secondary219 (11%)78 (1%)74 (15%)67 (9%)Missing/refused1 (0%)1 (0%)0 (0%)0 (9%)Employment0.46Employed1,473 (74%)537 (72%)361 (73%)575 (75%)Unemployed531 (26%)207 (28%)132 (27%)192 (25%)Monthly income<0.001≤1,500 meticais871 (43%)342 (46%)165 (33%)364 (47%)>1,500 meticais936 (47%)343 (46%)271 (55%)322 (42%)Missing/refused197 (1%)59 (8%)57 (12%)81 (11%)Another household member has HIV0.28Yes550 (27%)187 (25%)144 (29%)219 (29%)No913 (46%)361 (49%)219 (44%)333 (43%)Don’t know539 (27%)196 (26%)130 (26%)213 (28%)Missing/refused2 (0%)0 (0%)0 (0%)2 (0%)Data given as N (percent).CIS, combination intervention strategy; SOC, standard of care.Intervention effect on linkage to and retention in HIV care at the diagnosing facilityAs shown in Table 2, the CIS was associated with statistically significant improvements in the combined outcome of linkage to care within 1 month of diagnosis and retention in care 12 months following diagnosis when compared to the SOC. Analyses using data from electronic medical records to examine linkage to and retention at the diagnosing health facility showed that 57% of participants in the CIS group achieved the primary outcome versus 35% of those in the SOC group (RRCIS vs SOC = 1.58, 95% CI 1.05–2.39). Post hoc calculation of the ICC for the primary outcome according to the methods of Snijders and Bosker for binary outcome data [37] estimated an ICC of 0.066, similar to but slightly higher than the assumed ICC of 0.05 used in power and sample size estimation. These results were robust to adjustment for patient-level differences (adjusted RR [aRR]CIS vs SOC = 1.55, 95% CI 1.07–2.25). As shown in Fig 2, the greatest intervention effects were observed among young adults age 18–24 years (RRCIS vs SOC = 2.39, 95% CI 1.51–3.80, p-value for interaction between age and treatment arm = 0.07), those in Maputo (RRCIS vs SOC = 2.31, 95% CI 1.90–2.79, p-value for interaction between region and treatment arm < 0.0001), those who did not report that another household member was living with HIV (RRCIS vs SOC = 1.81: 95% CI 1.52–2.16, p-value for interaction between household member with HIV and treatment arm = 0.11), and those reporting high levels of anticipated stigma at enrollment (RRCIS vs SOC = 1.95, 95% CI 1.53–2.49, p-value for interaction between stigma and treatment arm = 0.10).10.1371/journal.pmed.1002433.g002Fig 2Relative risk of the CIS compared to the SOC on the primary outcome at the diagnosing health facility by patient characteristics.a Fifteen patients with missing information were excluded from this estimate. A description of the variables examined and categories used are provided in the Methods section.10.1371/journal.pmed.1002433.t002Table 2Linkage to and retention in HIV care: CIS versus SOC and CIS+ versus CIS.CategoryOutcomeCISN = 744CIS+N = 493SOCN = 767RR1 (95% CI), p-ValueaRR2 (95% CI), p-ValueNPercentNPercentNPercentCIS versus SOCCIS+ versus CISCIS versus SOCCIS+ versus CISPrimary outcomeAt diagnosing facilityLinked to care within 1 month of diagnosis and retained 12 months after diagnosis42557%27355%26835%1.58 (1.05–2.39)p = 0.030.96 (0.81–1.16)p = 0.661.55 (1.07–2.25)p = 0.040.94 (0.76–1.18)p = 0.52At any health facilityLinked to care within 1 month of diagnosis and retained 12 months after diagnosis54774%36073%36347%1.47 (1.08–2.01)p = 0.020.98 (0.85–1.15)p = 0.911.46 (1.05–2.04)p = 0.030.96 (0.83–1.11)p = 0.52Secondary outcomesLinkage at diagnosing facilitySame day as HIV test65989%45793%12016%9.13 (1.65–50.40)p = 0.021.04 (0.92–1.20)p = 0.38N/AWithin 1 week of HIV test67891%46194%34946%2.43 (0.70–8.41)p = 0.141.03 (0.91–1.16)p = 0.59N/AWithin 1 month of HIV test70394%46795%48263%1.48 (0.93–2.35)p = 0.091.00 (0.89–1.13)p = 0.96N/AWithin 12 months of HIV test71696%46795%59277%1.23 (1.03–1.48)p = 0.030.98 (0.87–1.11)p = 0,74N/ARetention at diagnosing facility6 months after diagnosis46262%32265%40553%1.18 (1.00–1.39)p = 0.061.05 (0.88–1.26)p = 0.48N/A12 months after diagnosis43558%27355%34144%1.32 (1.12–1.54)p = 0.0040.95 (0.79–1.13)p = 0.45N/A1RR accounts for clustering within sites using random-intercept log-Poisson regression with empirical standard error estimates.2aRR adjusts for patient-level differences using propensity scores.aRR, adjusted relative risk; CIS, combination intervention strategy; N/A, not applicable; RR, relative risk; SOC, standard of care.Eighty-nine percent of participants in the CIS group linked to the diagnosing facility on the same day as diagnosis compared to 16% (RRCIS vs SOC = 9.13, 95% CI 1.65–50.40) in the SOC group, 91% within 1 week compared to 46% (RRCIS vs SOC = 2.43, 95% CI 0.70–8.41), and 94% within 1 month compared to 63% (RRCIS vs SOC = 1.48, 95% CI 0.93–2.35). By 12 months, nearly all CIS participants (96%) had linked to care compared to 77% (RRCIS vs SOC = 1.23, 95% CI 1.03–1.48) of SOC participants. Among those linking to care, the median (interquartile range [IQR]) time from diagnosis to linkage was 0 days (0–0) in the CIS group and 3 days (1–26) in the SOC group (median test p < 0.001 for CIS versus SOC). The effect of the intervention on retention in care, regardless of the timing of linkage, was more modest but statistically significant (6-month retention: 62% CIS versus 53% SOC, RRCIS vs SOC = 1.18, 95% CI 1.00–1.39; 12-month retention: 58% CIS versus 44% SOC, RRCIS vs SOC = 1.32, 95% CI 1.12–1.54).In analyses restricted to the participants initiating ART, the median (IQR) time from diagnosis to ART initiation in the CIS and SOC groups was 32 (12–135), and 63 (33–230) days, respectively, while the median (IQR) time from enrollment in HIV care to ART initiation was 32 (11–127), and 50 (15–205) days, respectively. Median time from ART eligibility to ART initiation for the CIS, CIS+, and SOC groups was 21 (9–40), and 25 (11–56) days, respectively.There was no additional benefit of adding FIs to the CIS, with 55% (RRCIS+ vs CIS = 0.96, 95% CI 0.81–1.16; aRRCIS+ vs CIS = 0.94, 95% CI 0.76–1.18) of those in the CIS+ group achieving the primary outcome; 95% (RRCIS+ vs CIS = 1.00, 95% CI 0.83–1.13) linking to HIV care within 1 month of diagnosis, regardless of retention at 12 months; and 55% (RRCIS+ vs CIS = 0.95, 95% CI 0.79–1.13) being retained in care 12 months after diagnosis, regardless of the timing of linkage to care.Intervention effect on linkage to and retention in care at any health facilityAnalyses supplementing data from electronic medical records from participating facilities with data from patient interviews and other health facilities in the study regions to examine linkage to and retention at any health facility showed similar effects of the intervention package. A total of 74% (RRCIS vs SOC = 1.47, 95% CI 1.08–2.01) of participants in the CIS group and 47% in the SOC group were found to have linked to HIV care at any health facility within 1 month of diagnosis and were retained in HIV care 12 months after diagnosis (Table 2). Adjustment for patient-level differences did not result in any change in this finding (aRRCIS vs SOC = 1.46, 95% CI 1.05–2.04). Inclusion of FIs in the CIS also showed no additional benefit for linkage to and retention at any health facility, with 73% (RRCIS+ vs CIS = 0.98, 95% CI 0.85–1.15; aRRCIS+ vs CIS = 0.96, 95% CI 0.83–1.11) of those in the CIS+ group known to have linked to and been retained in HIV care at any health facility compared to the CIS group.Intervention effect on ART eligibility and initiation, disease progression, and deathData from electronic medical records at study sites indicated that compared to patients in the SOC group, patients in the CIS group were more likely to ever have their ART eligibility assessed (100% versus 76.9%, RRCIS vs SOC = 1.29, 95% CI 1.08–1.54), be identified as ART eligible (75% versus 60%, RRCIS vs SOC = 1.24, 95% CI 1.07–1.43), and initiate ART (65% versus 54%, RRCIS vs SOC = 1.20, 95% CI 1.00–1.43) (Table 3). Very few participants were diagnosed with a new WHO stage 3/4 event at the diagnosing facility or self-reported a hospitalization in the 12 months after HIV diagnosis. Those in the CIS group had a non-significantly but modestly decreased risk compared to those in the SOC group (1% versus 3%, RRCIS vs SOC = 0.38, 95% CI 0.07–2.03), while similar results were observed between the CIS and CIS+ groups (1% versus 1%, RRCIS+ vs CIS = 0.65, 95% CI 0.12–3.64). Neither the CIS nor the CIS+ interventions had a significant effect on mortality within 12 months of diagnosis, with 6%, 5%, and 7% of participants in the CIS, CIS+, and SOC groups, respectively, known to have died during study follow-up (RRCIS vs SOC = 0.87, 95% CI 0.40–1.91; RRCIS+ vs CIS = 0.88, 95% CI 0.45–1.74). The CIS also did not have a significant impact on mortality before (3%, RRCIS vs SOC = 0.78, 95% CI 0.46–1.32) or after ART initiation (3%, RRCIS vs SOC = 0.96, 95% CI 0.26–3.48); participants in the CIS+ group were less likely to die, though non-significantly so, before initiating ART compared to those in the CIS group (1% versus 3%, RRCIS+ vs CIS = 0.34, 95% CI 0.09–1.29).10.1371/journal.pmed.1002433.t003Table 3ART determination and initiation, disease progression, and death: CIS versus SOC and CIS+ versus CIS.\xa0OutcomeCIS(N = 744)CIS+(N = 493)SOC(N = 767)RR1 (95% CI), p-valueNPercentNPercentNPercentCIS versus SOC1CIS+ versus CIS1ART eligibility assessed744100%493100%59077%1.29 (1.08–1.54)p = 0.011.00 (0.89–1.12)p = 1.00Identified as ART eligible55775%37275%46460%1.24 (1.07–1.43)p = 0.011.01 (0.85–1.19)p = 0.91Initiated ART48465%33267%41654%1.20 (1.00–1.43)p = 0.051.03 (0.88–1.22)p = 0.59New WHO stage 3/4 or hospitalization71%31%233%0.38 (0.07–2.03)p = 0.220.65 (0.12–3.64)p = 0.53Death within 12 months466%275%547%0.87 (0.40–1.91)p = 0.690.88 (0.45–1.74)p = 0.63Death before ART initiation223%51%294%0.78 (0.46–1.32)p = 0.310.34 (0.09–1.29)p = 0.09Death after ART initiation243%224%253%0.96 (0.26–3.48)p = 0.941.38 (0.62–3.07)p = 0.331RR accounts for clustering within sites using random-intercept log-Poisson regression with empirical standard error estimates.ART, antiretroviral therapy; CIS, combination intervention strategy; RR, relative risk; SOC, standard of care.DiscussionWe conducted a cluster-randomized study in Mozambique to examine the effectiveness of a multi-component approach to increase linkage to and retention in HIV care—2 critical elements of the HIV care continuum—among adults newly diagnosed with HIV. The operational model of the CIS that we evaluated addresses known structural, biomedical, and behavioral barriers across the HIV care continuum and was composed of evidence-based, practical, and scalable interventions, including CD4 testing in VCT clinics with immediate turnaround of results, accelerated ART initiation for eligible individuals, and SMS health messages and appointment reminders. An enhanced version of the CIS additionally included FIs. In the spirit of implementation science, 2 of the interventions were implemented by existing health facility staff, rather than study staff, providing information on the real-world successes and challenges associated with the CIS that can be extrapolated to a range of settings with similar implementation contexts.Our study showed that participants receiving the CIS were 1.58 times more likely to link to HIV care at their diagnosing facility within 1 month of diagnosis and be retained in care at that same facility 12 months following diagnosis, representing not only a statistically significant but also a programmatically meaningful improvement. Particularly impressive gains were observed in timely linkage to care at the diagnosing facility: 89% of CIS participants linked to care on the day of diagnosis, representing a greater than 5-fold improvement compared to the SOC, and nearly universal linkage (96%) was achieved within 1 month of diagnosis. Notably, the intervention effect was greatest in subpopulations documented to have particularly poor outcomes across the HIV care continuum, including young adults [38,39] and those with high stigma perceptions [40–42]. The intervention also had beneficial effects on other important milestones in the HIV care continuum in the 12 months following diagnosis, including the likelihood of patients having their ART eligibility assessed and initiating ART. While the intervention significantly increased retention in HIV care at both 6 and 12 months following diagnosis, retention in the CIS group remained concerningly low and far short of what is needed to end the HIV epidemic in Mozambique and other high-burden countries.We found no additional gain in effectiveness from adding FIs to the CIS. Prior studies examining the effect of FIs in enhancing outcomes across the HIV care continuum among people living with HIV have shown inconsistent results. Studies from India, Uganda, and Democratic Republic of the Congo reported reductions in time to ART initiation and improvements in retention with the provision of incentives, while in the United States, randomized trials did not show any effect of FIs on linkage to care or viral load suppression [43–47]. While 89% of participants in the current study reported that the type of FI provided and the amount of the FIs (i.e., mobile phone air-time vouchers worth approximately US$5 at 3 points in time) were adequate, it is possible that the FIs were not sufficiently optimized to affect behaviors. Indeed, as reported elsewhere, patient reactions to the FIs were surprisingly tepid, with only 21% reporting it to be the “most useful” intervention for retention in care 12 months following diagnosis [21]. Additionally, fidelity to the FI component of the intervention package was imperfect, with, for example, 86% of participants eligible to receive the first incentive actually receiving it, which may have further limited the effect of this intervention [21]. However, given the benefits of FIs in other health sectors [48–50], further research is needed to understand whether and how they may be optimized to enhance outcomes across the HIV care continuum.This study has several important strengths. It is among the first studies to evaluate the impact of a multi-component approach on 2 important HIV care and treatment indicators: timely linkage to care following an HIV diagnosis and sustained retention in care. Improving performance for these 2 elements of the HIV care continuum is critical for realizing the individual and population benefits of HIV programming in sub-Saharan Africa. Further, while studies have examined the effectiveness of multi-component intervention packages that include FIs on HIV care outcomes [51,52], this study is the first to our knowledge to use a design that permits estimation of the additional benefit of including FIs as part of such a package.Our study also had limitations. First, in alignment with recent recommendations for implementation science studies [19], we used existing electronic medical records in the HIV clinics at the study sites to ascertain outcomes at the diagnosing facility, but such records may have limited data quality. However, data quality assessments were conducted regularly during the study period and ensured at least 85% concurrence between paper-based and electronic medical records on key data elements. Second, aside from the FI, we cannot unpack the effect of individual intervention components. Third, the relevance of point-of-care CD4 count testing may change as countries adopt “treatment for all” strategies, although our results suggest that providing people living with HIV with additional information on their health status immediately following diagnosis may be important in facilitating same-day linkage to care and likely same-day ART initiation. Fourth, the CIS+ cohort was enrolled once the target sample size had been reached in the CIS cohort, thus introducing the potential for secular trends to have biased the comparison of the CIS and CIS+ packages. However, because we found no difference in the primary outcome between the CIS+ and CIS groups, secular trends would have had to have operated in the direction of reducing overall linkage and retention for this bias to result in the failure to observe an additional benefit of FIs for linkage and retention. While this is plausible, we do not have any evidence that a substantial reduction in overall linkage and retention occurred over the relatively limited time frame of the study. Finally, while the study was implemented in 2 contrasting settings within Mozambique, study facilities were located primarily in urban and semi-urban areas within the city of Maputo and Inhambane Province, which may limit generalizability. Indeed, settings with lower education and cell phone coverage than those included in our study may experience greater challenges implementing the SMS health messages and appointment reminders. Similarly, while we set broad inclusion criteria, we did exclude people who did not understand Portuguese or Xitsua, were planning on leaving the community, or were not willing to receive services at the diagnosing facility, all factors that may have reduced generalizability. Finally, due to slower-than-expected enrollment, we enrolled fewer participants in the CIS+ group than intended, which decreased our power to detect statistically significant differences in study outcomes between the CIS+ and CIS groups. However, as the proportion achieving the combined outcome in the 2 groups was extremely similar (CIS 57% versus CIS+ 55%), it is unlikely that the inability to detect significant differences was primarily due to lack of power.ConclusionMulti-component intervention strategies have been proposed to address troubling gaps in the HIV care continuum [17,18]. To our knowledge, this is amongst the first studies to rigorously evaluate such an approach. The CIS we examined, comprising 3 evidence-based, practical, and scalable interventions, holds great promise as an approach to make much needed gains in the HIV care continuum in sub-Saharan Africa, particularly in the critical first step of timely linkage to care following diagnosis.Supporting informationS1 TextStudy protocol.(PDF)Click here for additional data file.S2 TextCONSORT checklist.(DOCX)Click here for additional data file.S1 DataData file.(CSV)Click here for additional data file.S2 DataData codebook.(XLSX)Click here for additional data file.We are grateful to the study participants, study staff, and participating health facilities for their contributions to this research. We also thank Antonia Mussa, Deborah Horowitz, Margaret McNairy, and Violante Viola for their inputs during study development and launch.AbbreviationsaRRadjusted relative riskARTantiretroviral therapyCIScombination intervention strategyFIfinancial incentiveICCintraclass correlation coefficientIQRinterquartile rangeIRBinstitutional review boardRRrelative riskSMSshort message serviceSOCstandard of careVCTvoluntary counseling and testingReferences1Joint United Nations Programme on HIV/AIDS. Fact sheet July 2017. Geneva: Joint United Nations Programme on HIV/AIDS; 2017 [cited 2017 Oct 18]. Available from: http://www.unaids.org/sites/default/files/media_asset/UNAIDS_FactSheet_en.pdf.2McGrathN, GlynnJR, SaulJ, KranzerK, JahnA, MwaunguluF, et al\nWhat happens to ART-eligible patients who do not start ART? Dropout between screening and ART initiation: a cohort study in Karonga, Malawi. BMC Public Health. 2010;10:601\ndoi: 10.1186/1471-2458-10-601\n209398723Hoffman S, Charalambous S, Churchyard G, Martinson N, Chaisson R. Delayed ART initiation and risk of death. 18th Conference on Retroviruses and Opportunistic Infections; 2011 Feb 27–Mar 2; Boston, MA, US.4RosenS, FoxMP. Retention in HIV care between testing and treatment in sub-Saharan Africa: a systematic review. PLoS Med. 2011;8(7):e1001056\ndoi: 10.1371/journal.pmed.1001056\n218114035KranzerK, GovindasamyD, FordN, JohnstonV, LawnSD. Quantifying and addressing losses along the continuum of care for people living with HIV infection in sub-Saharan Africa: a systematic review. J Int AIDS Soc. 2012;15(2):17383\ndoi: 10.7448/IAS.15.2.17383\n231997996MugglinC, EstillJ, WandelerG, BenderN, EggerM, GsponerT, et al\nLoss to programme between HIV diagnosis and initiation of antiretroviral therapy in sub-Saharan Africa: systematic review and meta-analysis. Trop Med Int Health. 2012;17(12):1509–20. doi: 10.1111/j.1365-3156.2012.03089.x\n229941517PlazyM, Orne-GliemannJ, DabisF, Dray-SpiraR. Retention in care prior to antiretroviral treatment eligibility in sub-Saharan Africa: a systematic review of the literature. BMJ Open. 2015;5(6):e006927\ndoi: 10.1136/bmjopen-2014-006927\n261091108FoxMP, RosenS. Patient retention in antiretroviral therapy programs up to three years on treatment in sub-Saharan Africa, 2007–2009: systematic review. Trop Med Int Health. 2010;15(Suppl 1):1–15.9GengEH, BangsbergDR, MusinguziN, EmenyonuN, BwanaMB, YiannoutsosCT, et al\nUnderstanding reasons for and outcomes of patients lost to follow-up in antiretroviral therapy programs in Africa through a sampling-based approach. J Acquir Immune Defic Syndr. 2010;53(3):405–11. doi: 10.1097/QAI.0b013e3181b843f0\n1974575310GovindasamyD, FordN, KranzerK. Risk factors, barriers and facilitators for linkage to antiretroviral therapy care: a systematic review. AIDS. 2012;26(16):2059–67. doi: 10.1097/QAD.0b013e3283578b9b\n2278122711Rabkin M, editor. High patient retention rates in a multinational HIV/AIDS treatment program: the Columbia University mother-to-child-plus experience. 17th Conference on Retroviruses and Opportunistic Infections; 2010 Feb 16–19; San Francisco, CA, US.12YuJK, ChenSC, WangKY, ChangCS, MakombeSD, SchoutenEJ, et al\nTrue outcomes for patients on antiretroviral therapy who are “lost to follow-up” in Malawi. Bull World Health Organ. 2007;85(7):550–4. doi: 10.2471/BLT.06.037739\n1776850413LankowskiAJ, SiednerMJ, BangsbergDR, TsaiAC. Impact of geographic and transportation-related barriers on HIV outcomes in sub-Saharan Africa: a systematic review. AIDS Behav. 2014;18(7):1199–223. doi: 10.1007/s10461-014-0729-8\n2456311514Ochieng-OokoV, OchiengD, SidleJE, HoldsworthM, Wools-KaloustianK, SiikaAM, et al\nInfluence of gender on loss to follow-up in a large HIV treatment programme in western Kenya. Bull World Health Organ. 2010;88(9):681–8. doi: 10.2471/BLT.09.064329\n2086507315SiednerMJ, SantorinoD, HabererJE, BangsbergDR. Know your audience: predictors of success for a patient-centered texting app to augment linkage to HIV care in rural Uganda. J Med Internet Res. 2015;17(3):e78\ndoi: 10.2196/jmir.3859\n2583126916JaniIV, SitoeNE, AlfaiER, ChongoPL, QuevedoJI, RochaBM, et al\nEffect of point-of-care CD4 cell count tests on retention of patients and rates of antiretroviral therapy initiation in primary health clinics: an observational cohort study. Lancet. 2011;378(9802):1572–9. doi: 10.1016/S0140-6736(11)61052-0\n2195165617DonnellD, BaetenJM, KiarieJ, ThomasKK, StevensW, CohenCR, et al\nHeterosexual HIV-1 transmission after initiation of antiretroviral therapy: a prospective cohort analysis. Lancet. 2010;375(9731):2092–8. doi: 10.1016/S0140-6736(10)60705-2\n2053737618AnglemyerA, RutherfordGW, HorvathT, BaggaleyRC, EggerM, SiegfriedN. Antiretroviral therapy for prevention of HIV transmission in HIV-discordant couples. Cochrane Database Syst Rev. 2013;2013(4):CD009153.19GengEH, PeirisD, KrukME. Implementation science: relevance in the real world without sacrificing rigor. PLoS Med. 2017;14(4):e1002288\ndoi: 10.1371/journal.pmed.1002288\n2844143520CurranGM, BauerM, MittmanB, PyneJM, StetlerC. Effectiveness-implementation hybrid designs: combining elements of clinical effectiveness and implementation research to enhance public health impact. Med Care. 2012;50(3):217–26. doi: 10.1097/MLR.0b013e3182408812\n2231056021SuttonR, LahuertaM, AbacassamoF, AhouaL, TomoM, LambMR, et al\nFeasibility and acceptability of health communication interventions within a combination intervention strategy for improving linkage and retention in HIV care in Mozambique. J Acquir Immune Defic Syndr. 2017;74(Suppl 1):S29–36.2793060922ElulB, LahuertaM, AbacassamoF, LambMR, AhouaL, McNairyML, et al\nA combination strategy for enhancing linkage to and retention in HIV care among adults newly diagnosed with HIV in Mozambique: study protocol for a site-randomized implementation science study. BMC Infect Dis. 2014;14:549\ndoi: 10.1186/s12879-014-0549-5\n2531199823Instituto Nacional de Estatística. Estatísticas e indicadores sociais 2013–2014. Maputo (Mozambique): Instituto Nacional de Estatística; 2015.24Instituto Nacional de Saúde, Instituto Nacional de Estatística, ICF. Moçambique: inquérito de indicadores de imunizaçao, malária e HIV/SIDA em Moçambique (IMASIDA) 2015—relatório de indicadores básicos de HIV. Rockville (Maryland): DHS Program; 2017 [cited 2017 Oct 18]. Available from: https://dhsprogram.com/pubs/pdf/PR85/PR85.pdf.25Centro de Colaboração em Saúde. Semi-annual report for the Center for Disease Control and Prevention. Maputo (Mozambique): Centro de Colaboração em Saúde; 2016.26Mozambique Ministry of Health. Annual report 2010, Inhambane Province. Maputo (Mozambique): Mozambique Ministry of Health; 2010.27Direcção Nacional de Assistência Médica. Guia de tratamento antiretroviral e infecções oportunistas no adulto, adolescente, grávida e criança. Maputo (Mozambique): Mozambique Ministry of Health; 2014.28LayerEH, KennedyCE, BeckhamSW, MbwamboJK, LikindikokiS, DavisWW, et al\nMulti-level factors affecting entry into and engagement in the HIV continuum of care in Iringa, Tanzania. PLoS ONE. 2014;9(8):e104961\ndoi: 10.1371/journal.pone.0104961\n2511966529EarnshawVA, ChaudoirSR. From conceptualizing to measuring HIV stigma: a review of HIV stigma mechanism measures. AIDS Behav. 2009;13(6):1160–77. doi: 10.1007/s10461-009-9593-3\n1963669930KesslerRC, AndrewsG, ColpeLJ, HiripiE, MroczekDK, NormandSL, et al\nShort screening scales to monitor population prevalences and trends in non-specific psychological distress. Psychol Med. 2002;32(6):959–76. 1221479531KesslerRC, PriceRH, WortmanCB. Social factors in psychopathology: stress, social support, and coping processes. Annu Rev Psychol. 1985;36:531–72. doi: 10.1146/annurev.ps.36.020185.002531\n388389332ElulB, BasingaP, Nuwagaba-BiribonwohaH, SaitoS, HorowitzD, NashD, et al\nHigh levels of adherence and viral suppression in a nationally representative sample of HIV-infected adults on antiretroviral therapy for 6, 12 and 18 months in Rwanda. PLoS ONE. 2013;8(1):e53586\ndoi: 10.1371/journal.pone.0053586\n2332646233DonnerA, KlarN. Design and analysis of cluster randomization trials in health research. London: Arnold; 2000.34DiehrP, MartinDC, KoepsellT, CheadleA. Breaking the matches in a paired t-test for community interventions when the number of pairs is small. Stat Med. 1995;14(13):1491–504. 748118735MorelJG, BokossaMC, NeerchalNK. Small sample correction for the variance of GEE estimators. Biom J. 2003;45(4):395–409.36JobartehK, ShiraishiRW, MalimaneI, Samo GudoP, DecrooT, AuldAF, et al\nCommunity ART support groups in Mozambique: the potential of patients as partners in care. PLoS ONE. 2016;11(12):e0166444\ndoi: 10.1371/journal.pone.0166444\n2790708437SnijdersTAB, BoskerRJ. Multilevel analysis: an introduction to basic and advanced mulitlevel modeling. Thousand Oaks (California): Sage; 1999.38LambMR, FayorseyR, Nuwagaba-BiribonwohaH, ViolaV, MutabaziV, AlwarT, et al\nHigh attrition before and after ART initiation among youth (15–24 years of age) enrolled in HIV care. AIDS. 2014;28(4):559–68. doi: 10.1097/QAD.0000000000000054\n2407666139NachegaJB, HislopM, NguyenH, DowdyDW, ChaissonRE, RegensbergL, et al\nAntiretroviral therapy adherence, virologic and immunologic outcomes in adolescents compared with adults in southern Africa. J Acquir Immune Defic Syndr. 2009;51(1):65–71. doi: 10.1097/QAI.0b013e318199072e\n1928278040MallS, MiddelkoopK, MarkD, WoodR, BekkerLG. Changing patterns in HIV/AIDS stigma and uptake of voluntary counselling and testing services: the results of two consecutive community surveys conducted in the Western Cape, South Africa. AIDS Care. 2013;25(2):194–201. doi: 10.1080/09540121.2012.689810\n2269460241MeibergAE, BosAE, OnyaHE, SchaalmaHP. Fear of stigmatization as barrier to voluntary HIV counselling and testing in South Africa. East Afr J Public Health. 2008;5(2):49–54. 1902441042KatzIT, RyuAE, OnuegbuAG, PsarosC, WeiserSD, BangsbergDR, et al\nImpact of HIV-related stigma on treatment adherence: systematic review and meta-synthesis. J Int AIDS Soc. 2013;16(3 Suppl 2):18640.2424225843SolomonSS, SrikrishnanAK, VasudevanCK, AnandS, KumarMS, BalakrishnanP, et al\nVoucher incentives improve linkage to and retention in care among HIV-infected drug users in Chennai, India. Clin Infect Dis. 2014;59(4):589–95. doi: 10.1093/cid/ciu324\n2480338144Emenyonu N, Thirumurthy N, Muyindike W, Mwebesa B, Ragland K, Geng E, et al., editors. Cash transfers to cover clinic transportation costs improve retention in care in an HIV treatment program in rural Uganda. 17th Conference on Retroviruses and Opportunistic Infections; 2010 Feb 16–19; San Francisco, CA, US.45El-Sadr WM, Branson BM, Beauchamp G, Hall HI, Torian LV, Zingman BS, et al. Effect of financial incentives on linkage to care and viral suppression: HPTN 065. Abstract number 29. Conference on Retroviruses and Opportunistic Infections; 2015 Feb 23–26; Seattle, Washington, US.46YotebiengM, ThirumurthyH, MoraccoKE, EdmondsA, TabalaM, KawendeB, et al\nConditional cash transfers to increase retention in PMTCT care, antiretroviral adherence, and postpartum virological suppression: a randomized controlled trial. J Acquir Immune Defic Syndr. 2016;72(Suppl 2):S124–9.2735549947MetschLR, FeasterDJ, GoodenL, MathesonT, StitzerM, DasM, et al\nEffect of patient navigation with or without financial incentives on viral suppression among hospitalized patients with HIV infection and substance use: a randomized clinical trial. JAMA. 2016;316(2):156–70. doi: 10.1001/jama.2016.8914\n2740418448FiszbeinA, SchadyN, FerreiraFHG, GroshM, KeleherN, OlintoP, et al\nConditional cash transfers: reducing present and future poverty. Washington (DC): World Bank; 2009.49RanganathanM, LagardeM. Promoting healthy behaviours and improving health outcomes in low and middle income countries: a review of the impact of conditional cash transfer programmes. Prev Med. 2012;55(Suppl):S95–105.2217804350RasellaD, AquinoR, SantosCA, Paes-SousaR, BarretoML. Effect of a conditional cash transfer programme on childhood mortality: a nationwide analysis of Brazilian municipalities. Lancet. 2013;382(9886):57–64. doi: 10.1016/S0140-6736(13)60715-1\n2368359951SiednerMJ, SantorinoD, LankowskiAJ, KanyesigyeM, BwanaMB, HabererJE, et al\nA combination SMS and transportation reimbursement intervention to improve HIV care following abnormal CD4 test results in rural Uganda: a prospective observational cohort study. BMC Med. 2015;13:160\ndoi: 10.1186/s12916-015-0397-1\n2614972252McNairy M, Lamb M, Gachuhi A, Nuwagaba-Biribonwoha H, Burke S, Mazibuko S, et al. Link4Health: a cluster randomized-controlled trial evaluating the effectiveness of a combination strategy for linkage to and retention in HIV care in Swaziland. International AIDS Conference; 2016 Jul 18–22; Durban, South Africa."", 'title': 'A combination intervention strategy to improve linkage to and retention in HIV care following diagnosis in Mozambique: A cluster-randomized study.', 'date': '2017-11-15'}, '29112963': {'article_id': '29112963', 'content': ""PLoS MedPLoS MedplosplosmedPLoS Medicine1549-12771549-1676Public Library of ScienceSan Francisco, CA USA29112963567537610.1371/journal.pmed.1002420PMEDICINE-D-17-02007Research ArticleBiology and Life SciencesMicrobiologyMedical MicrobiologyMicrobial PathogensViral PathogensImmunodeficiency VirusesHIVMedicine and Health SciencesPathology and Laboratory MedicinePathogensMicrobial PathogensViral PathogensImmunodeficiency VirusesHIVBiology and Life SciencesOrganismsVirusesViral PathogensImmunodeficiency VirusesHIVBiology and Life SciencesOrganismsVirusesImmunodeficiency VirusesHIVBiology and life sciencesOrganismsVirusesRNA virusesRetrovirusesLentivirusHIVBiology and Life SciencesMicrobiologyMedical MicrobiologyMicrobial PathogensViral PathogensRetrovirusesLentivirusHIVMedicine and Health SciencesPathology and Laboratory MedicinePathogensMicrobial PathogensViral PathogensRetrovirusesLentivirusHIVBiology and Life SciencesOrganismsVirusesViral PathogensRetrovirusesLentivirusHIVBiology and Life SciencesImmunologyVaccination and ImmunizationAntiviral TherapyAntiretroviral TherapyMedicine and Health SciencesImmunologyVaccination and ImmunizationAntiviral TherapyAntiretroviral TherapyMedicine and Health SciencesPublic and Occupational HealthPreventive MedicineVaccination and ImmunizationAntiviral TherapyAntiretroviral TherapySocial SciencesEconomicsFinancePeople and PlacesGeographical LocationsAfricaSwazilandMedicine and Health SciencesHealth CareHealth Education and AwarenessMedicine and health sciencesEpidemiologyHIV epidemiologyMedicine and Health SciencesHealth CarePatientsMedicine and health sciencesDiagnostic medicineHIV diagnosis and managementEffectiveness of a combination strategy for linkage and retention in adult HIV care in Swaziland: The Link4Health cluster randomized trialLink4Health: A combination intervention to improve HIV carehttp://orcid.org/0000-0001-7853-633XMcNairyMargaret L.ConceptualizationData curationFormal analysisFunding acquisitionInvestigationMethodologyProject administrationSupervisionValidationWriting – original draftWriting – review & editing12*LambMatthew R.ConceptualizationData curationFormal analysisInvestigationMethodologyWriting – original draftWriting – review & editing13GachuhiAverie B.Data curationProject administrationSupervisionWriting – original draftWriting – review & editing1Nuwagaba-BiribonwohaHarrietData curationProject administrationSupervisionWriting – original draftWriting – review & editing13BurkeSeanData curationInvestigationProject administrationSupervisionWriting – review & editing1MazibukoSikhatheleConceptualizationInvestigationProject administrationWriting – review & editing4http://orcid.org/0000-0003-1155-2735OkelloVelephiConceptualizationSupervisionWriting – review & editing4http://orcid.org/0000-0003-2028-4779EhrenkranzPeterConceptualizationSupervisionWriting – original draftWriting – review & editing5http://orcid.org/0000-0002-0180-1649SahaboRubenConceptualizationProject administrationSupervisionWriting – review & editing1http://orcid.org/0000-0003-3735-9781El-SadrWafaa M.ConceptualizationFunding acquisitionMethodologyProject administrationSupervisionValidationWriting – original draftWriting – review & editing131\nICAP at Columbia University, New York, New York, United States of America2\nDepartment of Medicine, Weill Cornell Medical College, New York, New York, United States of America3\nDepartment of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York, United States of America4\nMinistry of Health, Kingdom of Swaziland, Mbabane, Swaziland5\nBill and Melinda Gates Foundation, Seattle, Washington, United States of AmericaDeeksSteven G.Academic EditorSan Francisco General Hospital, UNITED STATESThe authors have declared that no competing interests exists.* E-mail: mm3780@columbia.edu71120171120171411e100242012620172992017© 2017 McNairy et al2017McNairy et alThis is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.BackgroundGaps in the HIV care continuum contribute to poor health outcomes and increase HIV transmission. A combination of interventions targeting multiple steps in the continuum is needed to achieve the full beneficial impact of HIV treatment.Methods and findingsLink4Health, a cluster-randomized controlled trial, evaluated the effectiveness of a combination intervention strategy (CIS) versus the standard of care (SOC) on the primary outcome of linkage to care within 1 month plus retention in care at 12 months after HIV-positive testing. Ten clusters of HIV clinics in Swaziland were randomized 1:1 to CIS versus SOC. The CIS included point-of-care CD4+ testing at the time of an HIV-positive test, accelerated antiretroviral therapy (ART) initiation for treatment-eligible participants, mobile phone appointment reminders, health educational packages, and noncash financial incentives. Secondary outcomes included each component of the primary outcome, mean time to linkage, assessment for ART eligibility, ART initiation and time to ART initiation, viral suppression defined as HIV-1 RNA < 1,000 copies/mL at 12 months after HIV testing among patients on ART ≥6 months, and loss to follow-up and death at 12 months after HIV testing. A total of 2,197 adults aged ≥18 years, newly tested HIV positive, were enrolled from 19 August 2013 to 21 November 2014 (1,096 CIS arm; 1,101 SOC arm) and followed for 12 months. The median participant age was 31 years (IQR 26–39), and 59% were women. In an intention-to-treat analysis, 64% (705/1,096) of participants at the CIS sites achieved the primary outcome versus 43% (477/1,101) at the SOC sites (adjusted relative risk [RR] 1.52, 95% CI 1.19–1.96, p = 0.002). Participants in the CIS arm versus the SOC arm had the following secondary outcomes: linkage to care regardless of retention at 12 months (RR 1.08, 95% CI 0.97–1.21, p = 0.13), mean time to linkage (2.5 days versus 7.5 days, p = 0.189), retention in care at 12 months regardless of time to linkage (RR 1.48, 95% CI 1.18–1.86, p = 0.002), assessment for ART eligibility (RR 1.20, 95% CI 1.07–1.34, p = 0.004), ART initiation (RR 1.16, 95% CI 0.96–1.40, p = 0.12), mean time to ART initiation from time of HIV testing (7 days versus 14 days, p < 0.001), viral suppression among those on ART for ≥6 months (RR 0.97, 95% CI 0.88–1.07, p = 0.55), loss to follow-up at 12 months after HIV testing (RR 0.56, 95% CI 0.40–0.79, p = 0.002), and death (N = 78) within 12 months of HIV testing (RR 0.80, 95% CI 0.46–1.35, p = 0.41). Limitations of this study include a small number of clusters and the inability to evaluate the incremental effectiveness of individual components of the combination strategy.ConclusionsA combination strategy inclusive of 5 evidence-based interventions aimed at multiple steps in the HIV care continuum was associated with significant increase in linkage to care plus 12-month retention. This strategy offers promise of enhanced outcomes for HIV-positive patients.Trial registrationClinicalTrials.gov NCT01904994.In a cluster-randomized trial done in Swaziland, Margaret McNairy and colleagues test a combined intervention for linking and retaining adults with HIV infection in care.Author summaryWhy was this study done?Linkage to care, retention in care, and achievement of viral load suppression on antiretroviral therapy (ART) among HIV-positive adults are necessary in order to achieve optimal health outcomes in terms of reduced morbidity and mortality and to decrease the risk of HIV transmission to others.Barriers to linkage to and retention in care are multifactorial and include both individual- and health system-level factors.We hypothesized that a multicomponent strategy using a combination of evidence-based interventions was needed to address the multiple gaps in linkage to and retention in care across the HIV care continuum.What did the researchers do and find?Ten clusters of affiliated HIV clinics in Swaziland were randomized to receive the standard of care (SOC; 1,101 participants) or a combination intervention strategy (CIS; 1,096 participants). The CIS included provision of participants with point-of-care CD4+ count testing at time of HIV testing, accelerated ART initiation among eligible patients, mobile phone appointment reminders, health educational packages, and noncash financial incentives.Participants were followed for 12 months from the time of testing HIV positive, and the primary study outcome was prompt linkage to care within 1 month of testing HIV-positive plus retention in care at 12 months after testing HIV positive. Secondary outcomes included additional steps in the HIV care continuum.We found that participants receiving care at HIV clinics randomized to the CIS study arm, as compared to the SOC study arm, were significantly more likely to achieve the primary outcome of prompt linkage to care plus 12-month retention (64% in the CIS arm versus 43% in the SOC arm, relative risk [RR] 1.52, 95% CI 1.19–1.96, p = 0.002).We also found that participants at CIS sites versus SOC sites had faster linkage to care, were more likely to be assessed for ART initiation, and had faster time to ART start. However, we did not find significant differences in viral suppression or mortality at 12-months after testing HIV positive.What do these findings mean?The Link4Health study showed that a CIS was 50% more effective than the SOC on prompt linkage to HIV care plus 12-month retention after HIV-testing and that the effect appeared to be primarily driven by enhanced retention in care.Limitations of this study include a small number of clusters and the inability to evaluate the contribution of each of the components of the strategy to the effect noted.The combination strategy used in this study could be easily adapted to other resource-limited settings and may be relevant to the challenges faced in engaging HIV-positive vulnerable and key populations.http://dx.doi.org/10.13039/100006492Division of Intramural Research, National Institute of Allergy and Infectious DiseasesRO1A1100059http://orcid.org/0000-0003-3735-9781El-SadrWafaa M.Gates FoundationOPP1145477http://orcid.org/0000-0001-7853-633XMcNairyMargaret L.This study was funded by the National Institutes of Health (NIH), NIH Award Number: RO1A1100059, and the Gates Foundation OPP1145477. No funding bodies had any role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Data AvailabilityData are from the Link4Health study, which may be contacted at icap-partnerships@columbia.edu. Deidentified data are uploaded as S1 Table.Data AvailabilityData are from the Link4Health study, which may be contacted at icap-partnerships@columbia.edu. Deidentified data are uploaded as S1 Table.IntroductionAchieving the desired impact of HIV treatment, as measured by individual health outcomes and reduced transmission to others, is contingent upon completing all steps in the HIV care continuum, from identifying all individuals who are living with HIV and linking those found to be HIV positive to HIV care to retaining them in lifelong care and on antiretroviral therapy (ART) [1]. Over the past decade, the scale-up of HIV programs has been substantial, with over 18 million persons having initiated ART by the end of 2015 in low- and middle-income countries and an associated substantial decrease in HIV-related morbidity and mortality, as well as evidence of a decrease in HIV incidence in many of the most severely affected countries [2]. However, in order to achieve epidemic control, further optimization of the HIV care continuum is needed so as to achieve the Joint United Nations Programme on HIV/AIDS (UNAIDS) 90/90/90 targets, which require that 90% of individuals with HIV are aware of their diagnosis, that 90% of those aware of their HIV infection are initiated on ART, and that 90% of those on treatment achieve and maintain viral suppression [3].Findings from HIV programs indicate that linkage to and retention in HIV care currently fall far short of the desired goals [4–6]. Linkage of HIV-positive individuals to HIV care varies from less than half of individuals linking to care within 6 months of an HIV-positive test to 72% who ever linked [5,7,8]. Once linked to care, less than half of HIV-positive patients are retained in care prior to initiation of ART, with only two-thirds of ART-eligible individuals initiating ART [5,7]. Lastly, only approximately three-quarters of patients initiated on ART have been noted to be retained in care at 12 months, with retention decreasing over the ensuing follow-up years [4].Barriers to linkage to and retention in care are multifactorial and include both individual- and health system-level factors such as stigma, fear of disclosure, distance and cost of travel to clinic, attitudes of providers, and cumbersome clinic procedures with long waiting times [9,10]. Previous studies that aimed to overcome such barriers have largely focused on the assessment of 1 intervention primarily targeting a single step in the HIV care continuum [11–14]. We postulated that in order to address the multiple gaps across the care continuum, a multi-component intervention strategy is needed, with each component targeting one or more steps in the HIV care continuum.The Link4Health study evaluated the effectiveness of a combination intervention strategy (CIS) utilizing 5 evidence-based interventions that address structural, behavioral, and biomedical barriers across the continuum of care, to improve linkage to and retention in care among newly identified HIV-positive adults in Swaziland.MethodsEthicsThe study was approved by the institutional review boards at Columbia University and the Swaziland Scientific and Ethics Committee.Study designA detailed description of the study methods was previously reported [15]. In brief, Link4Health was an implementation science study using a cluster site-randomized trial design. The study unit of randomization consisted of a public secondary-level HIV clinic paired with its largest affiliated public primary-level HIV clinic. Ten study units were selected from a total of 11 existing secondary-level HIV clinics in the country, based on clinic patient volume. Study units were pair matched, first by implementing partner (matching the 2 study units from implementing partner A) and then by location (urban [4] versus rural [4]) and clinic size, based on the estimated number of adults testing HIV positive in the 2 years prior to study implementation (<50 versus >50 per month for rural study units and <100 versus >100 per month for urban study units). Matched study units were randomized by a computerized random number generator to the CIS or standard of care (SOC) study arm. A cluster design was chosen to avoid disruption of service delivery, enable better fit within the routine workings at the clinical site, and allow the clinic staff to more easily implement the study. The study staff and clinic providers at each study unit were not blinded to the assigned arm for the site.Study setting and populationSwaziland is located in Southern Africa and has the world’s highest HIV prevalence, with HIV as the leading cause of death in the country [16]. The estimated adult (age 18–49 years) HIV prevalence is 31%, and the estimated incidence is 2.4% (95% CI 2.1–2.8) [17, 18]. The country has made impressive strides in responding to the epidemic, with nearly 70% of persons estimated to be living with HIV having initiated ART as of 2015 [19]. Nevertheless, historic rates of linkage to and retention in care at 12 months after ART initiation remain suboptimal [20]. Data available from Swaziland at the time of the initiation of the study showed that among 1,105 adults who tested HIV positive at community testing venues, only 37% linked to HIV care within 12 months of HIV testing [21]. Retention among adults at 36 months after ART start was 68% in 2011 per national estimates [22].Inclusion criteria were as follows: adults aged ≥18 years, newly tested HIV positive, and willing to receive HIV care at the study unit and consent to study procedures. Exclusion criteria were as follows: planning on leaving the community during the study, prior enrollment in HIV care or initiation of ART in the past 6 months, currently on ART, reports a current pregnancy, or not able to speak English or SiSwati.Study interventionsAll adults who tested HIV positive at participating sites were informed of the study by their providers. Interested individuals were referred to a study nurse who provided further information, confirmed eligibility, and, if eligible, obtained written consent. All consenting participants provided baseline information and thereafter were managed based on the study arm to which the clinic was randomly assigned.SOCParticipants at study units randomized to the SOC arm were managed according to country guidelines. These guidelines recommend that individuals identified as HIV positive receive post-test counseling and be referred to an HIV clinic using a national referral form [21]. Thereafter, upon presentation at their first HIV clinic visit, such individuals are to present their referral form, receive a clinical assessment, and have blood drawn for a CD4+ count test as well as hematology and chemistry tests and are instructed to return in 1–2 weeks for receipt of their results. Upon return, those eligible for ART according to then prevailing national guidelines (i.e., with a CD4+ count ≤ 350 cells/mm3) are to receive the first of 3 counseling sessions. Patients who are prescribed ART are instructed to return to the clinic every month for 6 months and then every 3 months, if they are stable on treatment. Patients who are ineligible for ART are instructed to return to clinic every 3 months for follow-up. Peer counselors are encouraged to call patients within 7 days of a missed clinic appointment. All patients are prescribed cotrimoxazole prophylaxis, and condoms, and health informational materials are to be made available in the clinics.CISParticipants at clinics randomized to the CIS arm received a multicomponent strategy of 5 evidence-based interventions, targeting structural, biomedical, and behavioral barriers, which are described briefly below (Table 1) [15]. All components of the combination strategy utilized in this study were selected based on evidence of their effectiveness, feasibility, and suitability to patients in diverse healthcare settings.10.1371/journal.pmed.1002420.t001Table 1Comparison of combination intervention strategy (CIS) to standard of care (SOC) procedures.InterventionStandard of care (SOC)Combination intervention strategy (CIS)Type of interventionStep targeted in HIV care continuumPoint-of-care CD4+ count testing• Point-of-care CD4 assays available in some primary care clinics and some secondary health centers/hospitals for patients enrolled in HIV care but not at the HIV testing site• CD4+ count (Cyflow and FACS Caliber) after linkage to HIV care in the clinic or lab• Turnaround time approximately 2 weeks• Point-of-care CD4 assays at the HIV testing site at the time of HIV testing• Turnaround time immediateStructural and biomedicalLinkage, ART eligibility assessment, and ART initiationAccelerated ART initiationART initiation per national guidelines for patients with CD4+ count ≤ 350 cells/mm3 or WHO Stage III/VI• Requires 3 counseling sessions and receipt of baseline lab tests• Initiation 2 weeks to 1 month from determination of ART eligibility• Accelerated ART initiation for patients with point-of-care CD4+ count ≤ 350 cells/mm3 within 1 week from testing• Two counseling sessions (1 at the time of HIV testing and the other at the first HIV clinic visit) and collection of blood for other baseline lab tests• Initiation of ART for eligible patients prior to return of results with use of a checklistStructural and biomedicalART initiation and retentionCellular phone visit reminders• Telephone call within 7 days of missed visit for ART patients only• SMS (or voice if illiterate) visit reminders 3 days prior to each scheduled visit• SMS (or voice if illiterate) reminder within 7 days after a missed visit for all patientsBehavioralLinkage and retentionHealth education packages• Cotrimoxazole was prescribed for all patients once enrolled in HIV care• Condoms available• A health education package was provided approximately every 3 months at visits. Packages included condoms, soap, cotrimoxazole, a pill box, and pictorial education about use of materials and HIVBiomedical and behavioralRetentionNoncash financial incentive• None• Noncash financial incentive (mobile airtime) were provided for those linked to care within 1 month of testing and completion of 6- and 12-month visitsStructuralLinkage and retentionAbbreviations: ART, antiretroviral therapy; SMS, short message service.The first intervention was provision of point-of-care (POC) CD4+ count testing performed immediately after an HIV-positive test, in the same physical location as the HIV testing site, with the aim of improving linkage to care, assessment for ART eligibility, and prompt ART initiation. Several studies have reported higher linkage and ART initiation rates with POC CD4+ count testing as compared to traditional CD4+ count testing [23–26].The second intervention of accelerated ART initiation for eligible patients (CD4+ count ≤ 350 cells/mm3 or WHO stage III/IV) involved 2 rather than 3 counseling sessions and recommended ART initiation within the first week after linkage to care. Delays in ART initiation among those eligible for treatment have been shown to be associated with increased morbidity and mortality [27]. Late ART initiation is also associated with a longer period of increased infectiousness due to ongoing viral replication [28]. In this study, initiation of ART promptly rather than waiting for the return of baseline safety laboratory test results was strongly encouraged, and a checklist was made available to the providers to assist in identifying those potential participants at risk for renal insufficiency who would require waiting for the serum creatinine results prior to ART initiation.The third intervention involved use of short-message-service (SMS) appointment reminders, sent from a central server, which aimed at improving linkage to and retention in care among participants. SMS reminders were sent 3 days prior to an appointment and after a missed appointment. The message did not contain any information relating to HIV status. SMS communications have been used in HIV care and other chronic disease management to improve health communication and patient adherence [29–37].The fourth intervention was a health education package that included health information and supplies such as soap, a toothbrush, and a pill box, which also aimed to improve both linkage to and retention in care. A package of different materials and information was given every 3 months. A similar intervention has been evaluated in Uganda and was associated with high rates of cotrimoxazole use, condom use, and HIV testing of family members [38].Lastly, financial incentives of modest amount were provided that served to reimburse participants for expenses or lost wages or transport costs for clinic attendance [39]. This intervention was selected because there has been great interest in the use of financial incentives as a structural intervention to achieve positive health behaviors including retention in care [39–45]. A noncash type of financial incentive was provided in the form of a prepaid mobile phone card valued at US$8 and was given to participants upon linkage to care within 1 month of HIV testing and at completion of 6 and 12 months in follow-up care.Data collection and study measuresAll participants completed a baseline questionnaire, at the time of study enrollment, which solicited information on sociodemographic characteristics, HIV disease history, barriers to care, travel time to clinic, depression, social and family support, and HIV-related knowledge. Follow-up questionnaires were conducted at 1 and 12 months after enrollment, at the participant’s home or a prespecified location in the community, to collect information on changes in sociodemographic characteristics, self-reported linkage to care and retention, preferences about the study interventions, and vital status, if the latter was not known. Clinical data including CD4+ count, WHO Stage, date of ART initiation, ART regimen, and clinic and pharmacy visit dates were abstracted from participants’ medical charts—the data source for the primary outcome. These data were collected between 3–6 months after the participant reached the end of the study follow-up period. If the participant’s medical record was missing, he/she was assumed to have not achieved the primary outcome. Death was ascertained via medical record reviews or at the time of the 1- or 12-month interview. Viral load measurement was done using dried blood samples (DBSs) (HIV-1 RNA Abbott m2000rt system) collected at the time of the 12-month questionnaire at the participant’s home or a prespecified location [46].Study outcomesThe primary outcome was a combined outcome of linkage to HIV care within 1 month of HIV testing plus retention in care at 12 months from HIV testing among participants at the individual level. Linkage to care was defined by participant attendance of at least 1 visit to an HIV clinic with completion of an intake assessment including medical history and physical exam. Retention in care at 12 months after HIV testing was defined as no documented death and a clinic visit at the study unit within 90 days prior to the end of the study follow-up period. Participants with a missing medical record at the time of medical record abstraction were considered nonretained.Secondary endpoints included evaluation of the effectiveness of the CIS compared to the SOC with regard to the following: each component of the primary outcome described above, time to linkage, ART eligibility, ART initiation, time to ART initiation, viral suppression defined as HIV-1 RNA < 1,000 copies/mL at 12 months among patients on ART for at least 6 months, and death and loss to follow-up at 12 months after HIV testing. Death and transfer status were ascertained from medical records and through the 12-month follow-up visit questionnaire. Linkage and retention at clinics other than the assigned study unit were assessed in a sensitivity analysis using self-reported linkage and retention data from the 1- and 12-month questionnaires.Statistical analysisThe study sample size was calculated by estimating that 35% of the participants in the SOC study arm would achieve the primary outcome (assuming that 50% link to HIV care within 1 month of testing and that 70% of those linking within 1 month are retained at 12 months after testing). We estimated that approximately 2,750 adults would be eligible for study enrollment based on historic HIV testing volume and the proportion of individuals testing HIV positive at the study units in the year prior to the study start. Assuming 80% of eligible individuals would consent to enrollment, we estimated an average enrollment of 220 participants per study unit or 2,200 in total (1,110 per study arm). With this sample size and 5 study units per study arm, we then estimated the minimum difference in the primary outcome we could detect with 80% power, 2-sided alpha of 0.05, assuming an interclass correlation coefficient (ICC) of 0.05. In a post hoc analysis, we estimated the ICC of the primary outcome using the method outlined by Snijders and Bosker for binary outcome data [47].An intent-to-treat analysis compared the relative risk (RR) of achieving the primary outcome between study arms, with each having 5 clusters per arm. Within study unit clustering was accounted for using random-intercept multilevel models. For dichotomous outcomes, log-Poisson models with robust standard error were used. For continuous outcomes, random-intercept linear regression models were used. Assessment of potential confounding despite cluster randomization was performed by constructing multivariable random-intercept regression models including covariates found statistically different between treatment arms at an alpha of 0.01. Additionally, we conducted a per-protocol analysis comparing the RR of achieving the primary outcome among participants who received the full package of the CIS for the duration of study participation. Sensitivity analysis assessed any changes to the intent-to-treat analysis after including self-reported linkage and retention obtained from follow-up surveys. In post hoc analyses, assessment of the RR for achieving the primary outcome by key subgroups was done using interaction contrast ratios.ResultsStudy populationOf the 10 study units included in this study, 6 were located in urban areas, and 4 in rural areas. At study units randomized to the CIS study arm, a total of 1,234 individuals were screened for eligibility, with 1,096 (89%) enrolled in the study from 19 August 2013 to 21 November 2014 (Fig 1). At study units assigned to the SOC study arm, a total of 1,316 were screened, with 1,101 (84%) enrolled. Study refusal differed by study arm, with 23 refusals (1.9%) in the CIS arm and 114 refusals (8.7%) in the SOC arm (p < 0.001). Reasons for ineligibility are shown in Fig 1, with 111 participants ineligible in the CIS arm compared to 101 in the SOC arm.10.1371/journal.pmed.1002420.g001Fig 1Flow diagram of study enrollment.ART, antiretroviral therapy; CIS, combination intervention strategy; SOC, standard of care; SU, study unit.Among 2,197 participants included in this analysis, 1,294 (59%) were female, and the median age was 31 years (IQR 26–39), with 445 (20%) of the participants being young adults aged 18–24 years (Table 2). Forty-five percent reported no education or only primary schooling; approximately half were unemployed. Median individual weekly income was US$9 (IQR US$0–US$37). Eighty-four percent reported living in the current residence for more than 1 year, with 16% reporting travel away from home for over a 1-month duration in the past year. The median travel time from residence to HIV clinic was 30 minutes (IQR 20–50). The majority (80%) were diagnosed with HIV through a voluntary counseling and testing site, with the remainder having received HIV testing through provider-initiated testing and counseling at clinics within the study units. Over half (54%) of the participants reported that this was their first HIV test, while 89% indicated that it was their first positive HIV test.10.1371/journal.pmed.1002420.t002Table 2Participant characteristics at HIV testing (N = 2,197).CharacteristicsCIS armSOC armTotal\xa0\xa0N%N%N%\xa01,096\xa01,101\xa02,197\xa0Female\xa065760%63758%1,29459%Age (years)Median (IQR)32 (26–40)30 (25–39)31 (26–39)18–2421019%23521%44520%25–3961256%60455%1,21655%40–4915814%16615%32415%>5011611%959%21110%Missing/refused\xa0\xa010%10%EducationNone/primary47844%51947%99745%Secondary or higher61756%58153%1,19855%Missing/refused10%10%20%Weekly incomeMedian (IQR)US$9 (US$0-US$37)US$14 (US$0-US$37)US$9 (US$0-US$37)Unemployed\xa062457%53148%1,15553%Married\xa040036%40837%80837%Number of living children020619%20719%41319%1 to 364559%68062%1,32560%>324322%21419%45721%Missing/refused20%00%20%Lives alone\xa011611%16015%27613%Away from home >1 month in past year\xa017916%17015%34916%Time at current residence1 year or less16415%19217%35616%Greater than 1 year93085%90682%1,83684%Missing/refused20%30%50%Travel time to clinicMedian (IQR) time minutes30 (20–45)30 (20–60)30 (20–50)<30 minutes69063%58453%1,27458%31–60 minutes33030%32329%65330%>60 minutes626%19117%25311%Missing/refused141%30%171%Currently on TB treatment\xa081%141%221%HIV testing siteVCT93785%82074%1,75780%PITC15915%28025%43920%Missing/refused00%10%10%First HIV test\xa064259%53949%1,18154%First positive HIV test\xa096788%97889%1,94589%Household member with HIV\xa042739%34832%77535%Alcohol consumption in the last 7 daysEvery day161%182%342%Some days23521%23421%46921%Never84577%84977%1,69477%Abbreviations: CIS, combination intervention strategy; PITC, provider-initiated testing and counselling; SOC, standard of care; TB, tuberculosis; VCT, voluntary HIV counselling and testing.Primary outcomeIn the intent-to-treat analysis, 705 (64%) participants at sites randomized to the CIS study arm and 477 (43%) participants at sites randomized to the SOC study arm achieved the primary outcome of linkage to HIV care within 1 month of HIV-positive testing plus retention in HIV care at 12 months after HIV testing, for an RR of 1.48 (95% CI 1.37–1.61, p < 0.001). Accounting for clustering within study units, the RR was 1.52 (95% CI 1.19–1.96, p = 0.002) (Fig 2, Table 3). Additionally, adjusting for covariates significant in the bivariate analyses listed in Table 2 did not appreciably change the results. A total of 64 (6%) of participants in the CIS arm and 144 (13%) of participants in the SOC arm did not have a medical record and were classified as “not linked” to HIV care.10.1371/journal.pmed.1002420.g002Fig 2Proportion of participants who achieved the primary outcome of linkage to HIV care within 1 month of HIV testing plus retention in HIV care at 12 months after HIV testing by study arm (combination intervention strategy [CIS] and standard of care [SOC]).10.1371/journal.pmed.1002420.t003Table 3Primary and secondary outcomes for the combination intervention strategy (CIS) and standard of care (SOC) study arms.CIS group (N = 1,096)SOC group (N = 1,101)Relative risk (RR)N%N%RR95% CIp-ValuePrimary outcomeIntention to treat70564%47743%1.48(1.37–1.61)<0.001Intention to treat accounting for clustering170564%47743%1.52(1.19–1.96)0.002Intention to treat accounting for clustering and differences in covariates1,370564%47743%1.50(1.12–1.99)0.009Per protocol1,267269%44743%1.68(1.32–2.15)<0.001Sensitivity analysis1,476169%55751%1.41(1.13–1.74)0.004Secondary outcomesLinkageLinked to care (ever)1103294%95787%1.08(0.97–1.21)0.13Mean (SD) time from HIV testing to linkage2.5 days (19.5)7.5 days (46.6)0.189ART eligibilityAssessed for ART eligibility11,096100%92084%1.20(1.07–1.34)0.004Became ART eligible183376%72165%1.18(1.01–1.37)0.038Mean (SD) time from HIV testing to ART eligibility assessment50 (0)6.3 (35.5)<0.001ART initiation*Initiated ART (ever)1*71065%63558%1.16(0.96–1.40)0.12Median (IQR) time from testing HIV positive to ART initiation among ART eligible, days67.0 (3.0–21.0)14.0 (7.0–31.0)<0.001Retention regardless of time to linkage and ART statusRetained 12 months after HIV testing172066%49845%1.48(1.18–1.86)0.002Viral suppressionViral suppression (HIV-1 RNA < 1,000 copies/ml) among participants on ART for ≥6 months (N = 477 CIS and N = 451 SOC)1,741988%40690%0.97(0.88–1.07)0.55Deaths within 12 months of HIV testingTotal deaths1353%434%0.80(0.46–1.35)0.41Death before ART initiation1101%232%0.44(0.19–1.01)0.05Death after ART initiation1252%202%1.18(0.57–2.47)0.63Transfers within 12 months of HIV testingTotal transfers1232%262%0.88(0.44–1.77)0.71Transfers before ART initiation171%192%0.37(0.16–0.85)0.02Transfers after ART initiation1161%71%2.10(0.72–6.18)0.16Lost to follow-up within 12 months of HIV testingTotal lost to follow-up131829%53449%0.56(0.40–0.79)0.002Lost to follow-up before ART initiation124022%35732%0.60(0.40–0.89)0.014Lost to follow-up after ART initiation1787%17716%0.51(0.31–0.85)0.0131 Accounting for within-study unit clustering using random intercept log-Poisson regression models with robust standard error.2 The per-protocol analysis compared all patients in the SOC arm to those in the CIS arm self-reporting receipt of all interventions: point-of-care (POC) CD4+ count, accelerated antiretroviral therapy (ART) initiation (if eligible), health education package, short message service (SMS), and financial incentives. A total of 937 of the 1,096 patients in the CIS arm were included. Patients were excluded for the following: missing PIMA (2), ART counseling session #1 (24), ART counseling session #2 (14), first health education package (7), second health education package (12), third health education package (4), fourth health education package (2), financial incentive for linkage to care (86), second financial incentive (8), or third financial incentive (4).3 Additionally adjusting for covariates significantly different between groups at an alpha of 0.1: employment status, number of children, whether the participant lives alone, HIV testing location, family member with HIV, travel time to clinic, and whether this was the participant’s first HIV test.4 The sensitivity analysis considers participants linked to HIV care or retained in HIV care if they are recorded as linked and retained in their medical records or if they self-reported linkage or retention in the 1- and/or 12-month study questionnaire.5 All participants in the SOC arm were assessed for ART eligibility at the time of testing HIV positive. Of the SOC participants, 920/1,101 (84%) were assessed at enrollment into HIV care or clinical follow-up.6 Time to ART initiation measured from date of HIV-positive test to ART initiation among those becoming ART eligible. The p-values are Wilcoxon tests of differences between medians.7 The proportion of viral load suppression (<1,000 copies/ml) among participants who were on ART for ≥6 months with available viral loads is reported in the table. Among all participants who were on ART for ≥6 months, 85% (419/493) in the CIS arm and 89% (406/458) in the SOC arm had viral suppression.* In the CIS arm, 85% of those ART eligible initiated ART. In the SOC arm, 88% of those eligible initiated ART.The RR in the per-protocol analysis accounting for clustering for achieving the primary outcome was 1.68 (95% CI 1.32–2.15, p = 0.003) (Table 3). The RR in the sensitivity analysis, accounting for clustering, which included participants who self-reported linkage and retention in the 1- and 12-month surveys at a clinic other than 1 with their assigned study unit, was 1.41 (95% CI 1.13–1.74, p = 0.004), respectively (Table 3). Using this approach, we calculated an ICC of 0.086, similar to but slightly higher than the assumed ICC used in power and sample size estimation.The CIS strategy was delivered according to the study protocol to 937 (85%) of the 1,096 participants enrolled in study units assigned to the CIS. Reasons for not receiving all of the CIS strategy intervention components included missing POC CD4+ count testing (<1% CIS participants), missing an ART counseling session per accelerated ART procedures (3%), missing receipt of 1 healthcare bag (2%), and missing receipt of 1 financial incentive (9%). There was heterogeneity in the primary outcome across the 5 pairs of matched study units. The proportion of participants who achieved the primary outcome in study units randomized to the CIS ranged from 49% to 82%, while this ranged from 22% to 57% in the study units randomized to SOC.Secondary outcomesA similar proportion of participants linked to care anytime during the study follow-up period in both study arms: 1,032 (94%) in the CIS arm as compared to 957 (87%) in the SOC arm (RR 1.08, 95% CI 0.97–1.21, p = 0.13), with no significant differences in linkage within the same day or 1 month after testing (Table 3). The mean time to linkage to care was shorter in the CIS arm versus the SOC study arm but was not statistically different (2.5 compared to 7.5 days, p = 0.189). However, among those who ever linked to care (1,032 in the CIS study arm and 957 in the SOC study arm), significantly fewer patients (13%) in CIS sites versus SOC sites (18%) did not return for subsequent visits after the first clinic visit (p = 0.008).Assessment for ART eligibility through either a CD4+ count or WHO staging was done for all participants in the CIS arm as compared to 84% of participants in the SOC arm (RR 1.20, 95% CI 1.07–1.34, p = 0.004). The mean time to ART eligibility assessment was 0 days in the CIS study arm compared to 6.3 days in the SOC arm (p < 0.001). The median CD4+ count among 1,096 participants in the CIS arm who had POC CD4+ count testing done at the time of HIV testing was 311 cells/mm3 (IQR 159–443). Among the 907 (82%) participants in the SOC arm who linked to HIV care and had a CD4+ count done, the median CD4 count was 285 cells/mm3 (155–444) (p = 0.07).A total of 710 participants (85% of ART-eligible participants) in the CIS arm as compared to 635 (88% among ART-eligible participants) in the SOC arm initiated ART within the study follow-up period (RR 1.16, 95% CI 0.96–1.40, p = 0.12) (Table 3). The median time from HIV testing to ART initiation among eligible patients was 7.0 days (IQR 3.0–21.0) as compared to 14.0 days (IQR 7.0–13.0) in the CIS and SOC study arms, respectively (p < 0.001).Retention in care, regardless of time to linkage or ART status, at 12 months was significantly greater in participants in the CIS as compared to the SOC study arm, with an RR of 1.48 (95% CI 1.18–1.86, p = 0.002). Loss to follow-up during pre-ART care (RR = 0.60, 95% CI 0.40–0.89, p = 0.014) and after ART initiation (RR = 0.51, 95% CI 0.31–0.85, p = 0.013) was significantly lower in the CIS arm as compared to the SOC arm.For participants on ART for at least 6 months during follow-up regardless of retention status, viral load data were available for 97% (N = 477/493) of participants in the CIS arm and 98% (N = 451/458) in the SOC arm. Viral suppression among participants on ART ≥6 months with available viral loads was similar by study arm at 88% in CIS and 90% in SOC (RR 0.97, 95% CI 0.88–1.07, p = 0.55).There were 78 deaths (3.6% of the study population) that occurred during follow-up, and this did not differ by study arm (35 deaths [3%] in the CIS study arm versus 43 deaths [4%] in the SOC arm, with an RR of 0.80, 95% CI 0.46–1.35, p = 0.40) (Table 3). However, there was nonsignificantly lower mortality among participants prior to ART initiation in the CIS arm (10 deaths) compared to the SOC arm (23 deaths), with an RR of 0.44 (95% CI 0.19–1.01, p = 0.05). Fig 3 compares the CIS study arm versus the SOC study arm across the HIV care continuum from linkage to care within 1 month of testing through retention in care at 12 months after testing HIV positive.10.1371/journal.pmed.1002420.g003Fig 3HIV care continuum comparing the combination intervention strategy (CIS) study arm versus the standard of care (SOC) study arm.In post hoc analyses, we examined achievement of the primary outcome between study arms by key subgroups. The effect of the CIS, as compared to the SOC, was consistent across all prespecified subgroups, including by age, sex, income, employment, marital status, travel away from home in the past year, travel time to clinic, past HIV testing history, household members with HIV, and type of clinic (Fig 4, S1 Table).10.1371/journal.pmed.1002420.g004Fig 4Primary outcome by subgroups of participants.USD, US dollars; yrs, years.DiscussionIn this cluster-randomized study, a novel combination strategy, inclusive of 5 evidence-based interventions, was 50% more effective than the SOC in enhancing linkage to care plus retention in care among HIV-positive individuals. The robustness of this outcome is supported by the consistent findings in the per-protocol analysis, in sensitivity analyses, and across subgroups of participants. In addition, the combination strategy was associated with improvements across multiple steps of the care continuum, with an increased proportion of participants who were assessed for ART eligibility, decreased time to ART eligibility assessment, decreased time to ART initiation, increased retention at 12 months after HIV testing regardless of time to linkage and ART status, and decreased mortality among participants prior to ART initiation. However, high rates of viral suppression were similar among ART patients by study arm.In our study, the effect noted on the primary outcome appeared to be largely driven by enhanced retention rather than by the linkage-to-care component. This finding may be due to the high proportion of participants in both study arms who linked to care within 1 month of HIV testing in both arms of the study (87% in the SOC arm and 92% in the CIS arm), and thus, our sample size was insufficient to show a difference between the arms. The high proportion of participant linkage was likely influenced by a national campaign to improve linkage that was implemented during the study period [21].The combination strategy significantly reduced loss to follow-up among participants regardless of whether they were in pre-ART care or on treatment. Loss to follow-up, in both study arms, was higher among pre-ART participants, as compared to participants who had initiated ART. This is consistent with findings from other studies, including those from a large study of 390,603 HIV-positive adults in Kenya, Mozambique, Rwanda, and Tanzania, in which 34.8% of all patients who had not initiated ART were lost from care at 12 months, compared to 5.8% among patients on ART [6]. While the pre-ART care phase should largely be minimized with the release of the recent WHO guidelines that recommend offering ART to all HIV-positive individuals irrespective of CD4+ count or WHO disease stage, evidence suggests that retention in care and on ART remains a challenge even in the context of “treatment for all” [48]. For example, while adoption of Option B+, which entails initiation of ART for all HIV-positive pregnant women, has been associated with an increase in the number of pregnant women on ART, loss to follow-up has remained a challenge. Among 21,939 HIV-infected pregnant women who started ART as per Option B+ in Malawi, 17% were lost to follow-up at 6 months after treatment start, with a 5-fold higher loss to follow-up compared to those who initiated ART at a more advanced stage of HIV disease [49]. Thus, the findings from our study remain relevant even though the study was conducted at a time when a CD4+ count threshold was recommended for ART initiation.In this study, viral suppression was high among all participants on ART for a minimum of 6 months, irrespective of study arm. This confirms the potency of the first-line regimen, consisting of tenofovir, lamivudine, and efavirenz or nevirapine, and suggests that participants were highly adherent to their medications. These findings build upon those from the Population-based HIV Impact Assessment Project surveys that were conducted recently in Malawi, Zambia, Zimbabwe, and Swaziland, which included nationally representative samples of individuals in which 87% of HIV-positive adults who reported being on ART were virally suppressed (HIV-1 RNA < 1,000 copies/ml) [50,51]. Findings from this project survey in Swaziland showed that among adults who were aware of their HIV-positive status and who indicated being on ART, 92% had a suppressed viral load [52]. The finding of similarly high proportions of viral suppression among participants in both arms of our study suggests that the sample size was insufficient to detect a difference. In addition, it is important to note that the interventions used in this study were not designed with a focus specifically on enhancing medication adherence and viral suppression. Design of future combination strategies may prioritize the use of interventions that focus specifically on enhancing adherence to ART, such as the use of financial incentives to improve viral suppression [53].Every effort was made to ascertain accurate loss to follow-up and mortality outcomes in our study. It should be noted that reporting of accurate loss to follow-up and mortality outcomes by HIV programs has been a controversial topic. This is due to the fact that when tracing was done for individuals reported as lost to follow-up by HIV programs, a substantial proportion were found to have either died or transferred care to another health facility [54]. We feel confident that it is unlikely that such misclassification occurred in our study as home tracing was conducted for all study participants to ascertain their outcomes at the end of the 12-month follow-up period. While the study was not powered to detect a difference between the study arms in terms of mortality, the combination strategy appeared to have a meaningful—albeit not statistically significant—effect, with as much as 50% lower mortality noted among pre-ART patients. This may be due to better retention in care among participants in the intervention arm. Poor retention in care has been demonstrated to be associated with increased mortality, likely due to missed clinic visits that deprive patients of clinical and laboratory assessments for diagnosis of early complications and delay prompt initiation of ART [55].We observed substantial heterogeneity in the primary outcome across clinics in both the CIS and SOC study arms. This may reflect clinic-level differences such as clinic size and location. For example, the CIS study unit with the lowest achievement of the study’s primary outcome was the largest clinic in urban Swaziland. It is possible that individuals who test HIV positive at such a large clinic may seek ongoing care at clinics closer to their homes. Other reasons could be differences in patient-level factors, such as sex, age, and immunological status, which warrant further analyses.To date, most intervention studies to address gaps in the HIV care continuum have focused on 1 step in the continuum, largely that of ART initiation. The Rapid Initiation of Treatment trial showed that single-visit ART initiation that included POC CD4+ count testing was associated with significantly higher ART initiation (97%) compared to the standard of care (72%) [56]. The START-ART trial was a stepped-wedge cluster-randomized trial of 20 clinics in Uganda that evaluated an intervention aimed at improving ART initiation among eligible patients; this intervention was associated with a higher proportion of patients initiating ART (80%) within 14 days after determination of ART eligibility compared to 38% in the control group [13]. Finally, the Same Day ART Initiation Study in Haiti, which evaluated the effect of same-day ART initiation on the day of HIV diagnosis among asymptomatic HIV-positive adults with CD4+ count ≤ 500 cells/uL and WHO stage I or II disease, noted that a higher proportion (53%) of participants randomized to same-day ART initiation were retained in care at 12 months with viral suppression compared to those in the standard of care arm (44%) [14].Our study had several strengths, including the use of a pragmatic approach consistent with implementation science design. Specifically, it utilized broad eligibility criteria, was conducted within established health facilities, tested feasible interventions that were delivered primarily by available staff rather than research staff, and assessed the primary outcome largely through routinely available data. In addition, the study included the majority of clinics in Swaziland and involved cluster-randomized design rather than randomization of individual participants, which allowed for ease of implementation and avoided disruption of services within the clinics. The study also uniquely assessed the effect of the delivery of multiple interventions packaged in 1 strategy aimed at multiple steps in the HIV care continuum. Thus, implementation of the study strategy has the potential to achieve not only prompt ART initiation but also better retention in care and on ART, consequently enhancing individual and society benefits from the “treat all” approach.The primary limitations of this study included a limited number of clusters, although it was inclusive of all the available clusters in the country. At the time of study initiation, there were only 11 secondary health facilities offering HIV services in Swaziland, and we selected 10 of these for participation in this study. Consequently, it is possible that the cluster randomization did not evenly distribute all determinants of linkage and retention other than the study interventions between treatment arms. While it is encouraging that analyses adjusting for individual-level differences between treatment arms did not appreciably change the results, we cannot definitively rule out residual confounding as a potential explanation of the findings. In addition, the design focused on evaluation of a package of interventions as 1 strategy and, thus, it did not allow for evaluation of the effectiveness of individual components of the combination approach. Another limitation was use of self-reported linkage and retention at other clinics to ascertain undocumented transfers to other clinics outside of the study unit.ConclusionsThe Link4Health study demonstrated that a combination strategy of evidence-based interventions, aimed at gaps in various steps of the HIV care continuum, was highly effective in enhancing linkage of HIV-positive individuals to care plus increasing their retention in care and on ART. The study also showed that once participants initiated ART, viral load suppression was high irrespective of the study arm. Cost effectiveness and qualitative analyses are ongoing in order to inform decision makers considering adoption of this strategy. Our findings offer an effective strategy that can advance the quality of HIV programs in Swaziland and that can be adapted to other similar contexts.Supporting informationS1 TextConsolidated Standards of Reporting Trials (CONSORT) statement.(DOCX)Click here for additional data file.S1 DataLink4Health deidentified dataset.(XLSX)Click here for additional data file.S1 TablePrimary outcome by prespecified participant subgroup.(DOCX)Click here for additional data file.AbbreviationsARTantiretroviral therapyCIScombination intervention strategyCONSORTConsolidated Standards of Reporting TrialsDBSdried blood sampleICCinterclass correlation coefficientPITCprovider-initiated testing and counsellingPOCpoint of careRRrelative riskSMSshort message serviceSOCstandard of careSUstudy unitTBtuberculosisUNAIDSJoint United Nations Programme on HIV/AIDSVCTvoluntary HIV counselling and testingReferences1McNairyML, El-SadrWM. The HIV care continuum: no partial credit given. AIDS. 2012;26(14):1735–8. doi: 10.1097/QAD.0b013e328355d67b .226148882UNAIDS. UNAIDS Website. Accessed May 24, 2016 at: http://www.unaids.org/en/resources/presscentre/pressreleaseandstatementarchive/2015/july/20150714_PR_MDG6report.3UNAIDS. 90-90-90 An ambitious treatment target to help end the AIDS epidemic. Accessed May 1, 2016 at: http://www.unaids.org/sites/default/files/media_asset/90-90-90_en_0.pdf.4FoxMP, RosenS. Retention of Adult Patients on Antiretroviral Therapy in Low- and Middle-Income Countries: Systematic Review and Meta-analysis 2008–2013. J Acquir Immune Defic Syndr. 2015;69(1):98–108. doi: 10.1097/QAI.0000000000000553 ; PubMed Central PMCID: PMC4422218.259424615MugglinC, EstillJ, WandelerG, BenderN, EggerM, GsponerT, et al\nLoss to programme between HIV diagnosis and initiation of antiretroviral therapy in sub-Saharan Africa: systematic review and meta-analysis. Trop Med Int Health. 2012;17(12):1509–20. Epub 2012/09/22. doi: 10.1111/j.1365-3156.2012.03089.x ; PubMed Central PMCID: PMC3895621.229941516McNairyML, LambMR, AbramsEJ, ElulB, SahaboR, HawkenMP, et al\nUse of a Comprehensive HIV Care Cascade for Evaluating HIV Program Performance: Findings From 4 Sub-Saharan African Countries. J Acquir Immune Defic Syndr. 2015;70(2):e44–51. doi: 10.1097/QAI.0000000000000745 .263754667FoxMP, ShearerK, MaskewM, Meyer-RathG, ClouseK, SanneI. Attrition through Multiple Stages of Pre-Treatment and ART HIV Care in South Africa. PLoS ONE. 2014;9(10):e110252\ndoi: 10.1371/journal.pone.0110252 ; PubMed Central PMCID: PMC4203772.253300878IwujiCC, Orne-GliemannJ, LarmarangeJ, OkesolaN, TanserF, ThiebautR, et al\nUptake of Home-Based HIV Testing, Linkage to Care, and Community Attitudes about ART in Rural KwaZulu-Natal, South Africa: Descriptive Results from the First Phase of the ANRS 12249 TasP Cluster-Randomised Trial. PLoS Med. 2016;13(8):e1002107\ndoi: 10.1371/journal.pmed.1002107 ; PubMed Central PMCID: PMC4978506.275046379GovindasamyD, FordN, KranzerK. Risk factors, barriers and facilitators for linkage to antiretroviral therapy care: a systematic review. AIDS. 2012;26(16):2059–67. Epub 2012/07/12. doi: 10.1097/QAD.0b013e3283578b9b .2278122710HallBJ, SouKL, BeanlandR, LackyM, TsoLS, MaQ, et al\nBarriers and Facilitators to Interventions Improving Retention in HIV Care: A Qualitative Evidence Meta-Synthesis. AIDS Behav. 2016;21(6):1755–67. doi: 10.1007/s10461-016-1537-0 .2758208811FoxMP, RosenS, GeldsetzerP, BarnighausenT, NegussieE, BeanlandR. Interventions to improve the rate or timing of initiation of antiretroviral therapy for HIV in sub-Saharan Africa: meta-analyses of effectiveness. J Int AIDS Soc. 2016;19(1):20888 10.7448/IAS.19.1.20888. 27507249; PubMed Central PMCID: PMC4978859. doi: 10.7448/IAS.19.1.20888\n2750724912GovindasamyD, MeghijJ, Kebede NegussiE, BaggaleyRC, FordN, KranzerK. Interventions to improve or facilitate linkage to or retention in pre-ART (HIV) care and initiation of ART in low- and middle-income settings—a systematic review. J Int AIDS Soc. 2014;17:19032\ndoi: 10.7448/IAS.17.1.19032 ; PubMed Central PMCID: PMC4122816.2509583113AmanyireG, SemitalaFC, NamusobyaJ, KaturamuR, KampiireL, WallentaJ, et al\nEffects of a multicomponent intervention to streamline initiation of antiretroviral therapy in Africa: a stepped-wedge cluster-randomised trial. Lancet HIV. 2016;3(11):e539–e48. doi: 10.1016/S2352-3018(16)30090-X .2765887314KoenigSP, DorvilN, DevieuxJG, Hedt-GauthierBL, RiviereC, FaustinM, et al\nSame-day HIV testing with initiation of antiretroviral therapy versus standard care for persons living with HIV: A randomized unblinded trial. PLoS Med. 2017;14(7):e1002357\ndoi: 10.1371/journal.pmed.1002357 ; PubMed Central PMCID: PMC5526526.2874288015McNairyML, GachuhiAB, LambMR, Nuwagaba-BiribonwohaH, BurkeS, EhrenkranzP, et al\nThe Link4Health study to evaluate the effectiveness of a combination intervention strategy for linkage to and retention in HIV care in Swaziland: protocol for a cluster randomized trial. Implementation science: IS. 2015;10:101\ndoi: 10.1186/s13012-015-0291-4 ; PubMed Central PMCID: PMC4506770.2618915416Institute for Health Metrics and Evaluation. IHME Health Data Swaziland. Accessed May 30, 2017 at: http://www.healthdata.org/swaziland.17JustmanJ, ReedJB, BicegoG, DonnellD, LiK, BockN, et al\nSwaziland HIV Incidence Measurement Survey (SHIMS): a prospective national cohort study. Lancet HIV. 2016;4(2):e83–e92. doi: 10.1016/S2352-3018(16)30190-4 .2786399818CDC In Swaziland. Swaziland Health Factsheet. Accessed November 15 2013 at: https://www.cdc.gov/globalhealth/countries/swaziland/pdf/swaziland_factsheet.pdf.19The World Band. Swaziland HIV Data. Accessed December 20 2016 at: http://data.worldbank.org/indicator/SH.HIV.ARTC.ZS.20AuldAF, KamiruH, AzihC, BaughmanAL, Nuwagaba-BiribonwohaH, EhrenkranzP, et al\nImplementation and Operational Research: Evaluation of Swaziland's Hub-and-Spoke Model for Decentralizing Access to Antiretroviral Therapy Services. J Acquir Immune Defic Syndr. 2015;69(1):e1–e12. doi: 10.1097/QAI.0000000000000547 .2594246521MacKellarDA, WilliamsD, StorerN, OkelloV, AzihC, DrummondJ, et al\nEnrollment in HIV Care Two Years after HIV Diagnosis in the Kingdom of Swaziland: An Evaluation of a National Program of New Linkage Procedures. PLoS ONE. 2016;11(2):e0150086\ndoi: 10.1371/journal.pone.0150086 ; PubMed Central PMCID: PMC4766101.2691084722The Kingdom of Swaziland Ministry of Health. Annual HIV Programs Report 2015. Mbabane, Swaziland: Swaziland Ministry of Health, 2015.23LarsonBA, BrennaA, McNamaraL, LongL, RosenS, SanneI, et al\nLost Opportunities to complete CD4+ lymphocyte testing among patients who tested positive for HIV in South Africa. Bull World Health Organ. 2010;88(9):675–80. doi: 10.2471/BLT.09.068981 .2086507224JaniIV, SitoeNE, AlfaiER, ChongPL, QuevedoJI, RochaBM, et al\nEffect of point-of-care CD4 cell count tests on retention of patients and rates of antiretroviral therapy initiation in primary health clinics: an observational cohort study. Lancet. 2011;378(9802):1572–9. doi: 10.1016/S0140-6736(11)61052-0 .2195165625FaalM, NaidooN, GlenncrossDK, VenterWD, OsihR. Providing immediate CD4 count results at HIV testing improves ART initiation. J Acquir Immune Defic Syndr. 2011;58(3):e54–9. doi: 10.1097/QAI.0b013e3182303921 .2185735626LarsonBA, SchnippelK, NdibongoB, XuluT, BrennanA, LongL, et al\nRapid point-of-care CD4 testing at mobile HIV testing sites to increase linkage to care: an evaluation of a pilot program in South Africa. J Acquir Immune Defic Syndr. 2012;61(2):e13–7. doi: 10.1097/QAI.0b013e31825eec60 ; PubMed Central PMCID: PMC3458178.2265965027LahuertaM, UeF, HoffmanS, ElulB, KulkarniSG, WuY, et al\nThe problem of late ART initiation in Sub-Saharan Africa: a transient aspect of scale-up or a long-term phenomenon?\nJ Health Care Poor Underserved. 2013;24(1):359–83. doi: 10.1353/hpu.2013.0014 ; PubMed Central PMCID: PMC3655523.2337773928CohenMS, ChenYQ, McCauleyM, GambleT, HosseinipourMC, KumarasamyN, et al\nPrevention of HIV-1 infection with early antiretroviral therapy. N Engl J Med. 2011;365(6):493–505. Epub 2011/07/20. doi: 10.1056/NEJMoa1105243 ; PubMed Central PMCID: PMC3200068.2176710329ChangL, KagaayiJ, NakigoziG, PackerAH, SerwaddaD, QuinnTC, et al\nResponding to the human resource crisis: peer health workers, mobile phones, and HIV care in Rakai, Uganda. AIDS Patient Care STDS. 2008;22(3):173–4. doi: 10.1089/apc.2007.0234 PubMed Central PMCID 2674572 1829075030DownerSR, MearJG, Da CostaAC, SethuramanK. SMS text messaging improves outpatient attendance. Aust Health Rev. 8\n2006;30(3):389–96. .1687909831FjeldsoeBS, MarshallAL, MillerYD. Behavior change interventions delivered by mobile telephone short-message service. Am J Prev Med. 2\n2009;36(2):165–73. doi: 10.1016/j.amepre.2008.09.040 .1913590732HabererJE, KiwanukaJ, NanseraD, WilsonIB, BangsbergDR. Challenges in using mobile phones for collection of antiretroviral therapy adherence data in a resource-limited setting. AIDS Behav. Dec\n2010;14(6):1294–301. doi: 10.1007/s10461-010-9720-1 .2053260533LesterRT, RitvoP, MillsEJ, KaririA, KaranjaS, ChungMH, et al\nEffects of a mobile phone short message service on antiretroviral treatment adherence in Kenya (WelTel Kenya1): a randomised trial. Lancet. 11\n27\n2010;376(9755):1838–45. doi: 10.1016/S0140-6736(10)61997-6 .2107107434LiewSM, TongSF, LeeVK, NgCJ, LeongKC, TengCL. Text messaging reminders to reduce non-attendance in chronic disease follow-up: a clinical trial. Br J Gen Pract. 12\n2009;59(569):916–20. doi: 10.3399/bjgp09X472250 .1971254435Mukund BahadurKC, MurrayPJ. Cell phone short messaging service (SMS) for HIV/AIDS in South Africa: a literature review. Stud Health Technol Inform. 2010;160(Pt 1):530–5. .2084174336Pop-ElechesC, ThirumurthyH, HabyarimanaJP, ZivinJG, GoldsteinMP, de WalqueD, et al\nMobile phone technologies improve adherence to antiretroviral treatment in a resource-limited setting: a randomized controlled trial of text message reminders. AIDS. Mar 27\n2011;25(6):825–34. doi: 10.1097/QAD.0b013e32834380c1 .2125263237ShetA, de CostaA. India calling: harnessing the promise of mobile phones for HIV healthcare. Trop Med Int Health. 2011;16(2):214–6. doi: 10.1111/j.1365-3156.2010.02678.x .2137121438ColindresP, MerminJ, EzatiE, KambabaziS, BuyungoP, SekabembeL, et al\nUtilization of a basic care and prevention package by HIV-infected persons in Uganda. AIDS Care. 2008;20(2):139–45. Epub 2007/09/27. doi: 10.1080/09540120701506804 .1789619639GiuffridaA, TorgersonDJ. Should we pay the patient? Review of financial incentives to enhance patient compliance. BMJ. 1997;315(7110):703–7. Epub 1997/10/07. ; PubMed Central PMCID: PMC2127496.931475440VolppKG, JohnLK, TroxelAB, NortonL, FassbenderJ, LoewensteinG. Financial incentive-based approaches for weight loss: a randomized trial. JAMA. 2008;300(22):2631–7. Epub 2008/12/11. doi: 10.1001/jama.2008.804 .1906638341VolppKG, LoewensteinG, TroxelAB, DoshiJ, PriceM, LaskinM, et al\nA test of financial incentives to improve warfarin adherence. BMC Health Serv Res. 2008;8:272 Epub 2008/12/24. doi: 10.1186/1472-6963-8-272 ; PubMed Central PMCID: PMC2635367.1910278442VolppKG, TroxelAB, PaulyMV, GlickHA, PuigA, AschDA, et al\nA randomized, controlled trial of financial incentives for smoking cessation. N Engl J Med. 2009;360(7):699–709. Epub 2009/02/14. doi: 10.1056/NEJMsa0806819 .1921368343CharnessG, GneezyU. Incentives to exercise. Econometrica. 2009;77(3):909–31.44MarcusAC, KaplanCP, CraneLA, BerekJS, BernsteinG, GunningJE, et al\nReducing loss-to-follow-up among women with abnormal Pap smears. Results from a randomized trial testing an intensive follow-up protocol and economic incentives. Med Care. 1998;36(3):397–410. Epub 1998/04/01. .952096345MalotteCK, RhodesF, MaisKE. Tuberculosis screening and compliance with return for skin test reading among active drug users. Am J Public Health. 1998;88(5):792–6. Epub 1998/05/20. ; PubMed Central PMCID: PMC1508952.958574746WHO. Technical and Operational Considerations for Implementing HIV Viral Load Testing. Geneva: WHO, 2014.47SnijdersTA, BoskerRJ. Multilevel analysis: An introduction to basic and advanced mulitlevel modeling. Thousand Oaks, California: Sage; 1999.48WHO. Guideline on when to start antiretroviral therapy and on pre-exposure prophylaxis for HIV. Geneva: WHO, 2015 9\n2015.49TenthaniL, HaasAD, TweyaH, JahnA, van OosterhoutJJ, ChimbwandiraF, et al\nRetention in care under universal antiretroviral therapy for HIV-infected pregnant and breastfeeding women ('Option B+') in Malawi. AIDS. 2014;28(4):589–98. doi: 10.1097/QAD.0000000000000143 ; PubMed Central PMCID: PMC4009400.2446899950Columbia University. The Population HIV Impact Assessment (PHIA) Project. Accessed May 31 2017 at: www.phia.icap.columbia.edu.51Justman J. Real Progress in the HIV Epidemic: PHIA Findings from Zimbabwe, Malawi, and Zambia. Oral Abstract. Conference of Retroviruses and Opportunistic Infections February 13–15, 2017; Seattle, WA2017.52Nkambule R, Nuwagaba-Biribownwoha H, Mnisi Z, Ao T, Duong Y, Patel H, et al. Substantial progress in confronting the HIV epidemic in Swaziland: first evidence of national impact. Abstract 204LB. International AIDS Society 2017; July 24, 2017; Paris, France 2017.53El-SadrWM, DonnellD, BeauchampG, HallHI, TorianLV, ZingmanB, et al\nFinancial Incentives for Linkage to Care and Viral Suppression Among HIV-Positive Patients: A Randomized Clinical Trial (HPTN 065). JAMA. 2017;177(8):1083–92. doi: 10.1001/jamainternmed.2017.2158 .2862870254GengEH, GliddenDV, BwanaMB, MusinguziN, EmenyonuN, MuyindikeW, et al\nRetention in care and connection to care among HIV-infected patients on antiretroviral therapy in Africa: estimation via a sampling-based approach. PLoS ONE. 2011;6(7):e21797\ndoi: 10.1371/journal.pone.0021797 ; PubMed Central PMCID: PMC3144217.2181826555GiordanoTP, GiffordAL, WhiteACJr., Suarez-AlmazorME, RabeneckL, HartmanC, et al\nRetention in care: a challenge to survival with HIV infection. Clin Infect Dis. 2007;44(11):1493–9. Epub 2007/05/08. doi: 10.1086/516778 .1747994856RosenS, MaskewM, FoxMP, NyoniC, MongwenyanaC, MaleteG, et al\nInitiating Antiretroviral Therapy for HIV at a Patient's First Clinic Visit: The RapIT Randomized Controlled Trial. PLoS Med. 2016;13(5):e1002015\ndoi: 10.1371/journal.pmed.1002015 .27163694"", 'title': 'Effectiveness of a combination strategy for linkage and retention in adult HIV care in Swaziland: The Link4Health cluster randomized trial.', 'date': '2017-11-08'}, '28542080': {'article_id': '28542080', 'content': 'Lack of accessible laboratory infrastructure limits HIV antiretroviral therapy (ART) initiation, monitoring, and retention in many resource-limited settings. Point-of-care testing (POCT) is advocated as a mechanism to overcome these limitations. We executed a pragmatic, prospective, randomized, controlled trial comparing the impact of POCT vs. standard of care (SOC) on treatment initiation and retention in care.\nSelected POC technologies were embedded at 3 primary health clinics in South Africa. Confirmed HIV-positive participants were randomized to either SOC or POC: SOC participants were venesected and specimens referred to the laboratory with patient follow-up as per algorithm (∼3 visits); POC participants had phlebotomy and POCT immediately on-site using Pima CD4 to assess ART eligibility followed by hematology, chemistry, and tuberculosis screening with the goal of receiving same-day adherence counseling and treatment initiation. Participant outcomes measured at recruitment 6 and 12 months after initiation.\nFour hundred thirty-two of 717 treatment eligible participants enrolled between May 2012 and September 2013: 198 (56.7%) SOC; 234 (63.6%) POC. Mean age was 37.4 years; 60.5% were female. Significantly more participants were initiated using POC [adjusted prevalence ratio (aPR) 0.83; 95% confidence interval (CI): 0.74 to 0.93; P < 0.0001], the median time to initiation was 1 day for POC and 26.5 days for SOC. The proportion of patients in care and on ART was similar for both arms at 6 months (47 vs. 50%) (aPR 0.96; 95% CI: 0.79 to 1.16) and 12 months (32 vs. 32%) (aPR 1.05; 95% CI: 0.80 to 1.38), with similar mortality rates. Loss to follow-up at 12 months was higher for POC (36% vs. 51%) (aPR 0.82; 95% CI: 0.65 to 1.04).\nAdoption of POCT accelerated ART initiation but once on treatment, there was unexpectedly higher loss to follow-up on POC and no improvement in outcomes at 12 months over SOC.', 'title': 'Multidisciplinary Point-of-Care Testing in South African Primary Health Care Clinics Accelerates HIV ART Initiation but Does Not Alter Retention in Care.', 'date': '2017-05-26'}}",0.142857143,"Public Health, Epidemiology & Health Systems"
 20,"Is retention in care at 12 months higher, lower, or the same when comparing rapid ART to standard initiation?",higher,low,yes,"['28742880', '29509839', '27163694', '29136001', '29112963', '28542080']",31206168,2019,"{'28742880': {'article_id': '28742880', 'content': 'PLoS MedPLoS MedplosplosmedPLoS Medicine1549-12771549-1676Public Library of ScienceSan Francisco, CA USA28742880552652610.1371/journal.pmed.1002357PMEDICINE-D-17-00266Research ArticleBiology and Life SciencesImmunologyVaccination and ImmunizationAntiviral TherapyAntiretroviral TherapyMedicine and Health SciencesImmunologyVaccination and ImmunizationAntiviral TherapyAntiretroviral TherapyMedicine and Health SciencesPublic and Occupational HealthPreventive MedicineVaccination and ImmunizationAntiviral TherapyAntiretroviral TherapyBiology and Life SciencesMicrobiologyMedical MicrobiologyMicrobial PathogensViral PathogensImmunodeficiency VirusesHIVMedicine and Health SciencesPathology and Laboratory MedicinePathogensMicrobial PathogensViral PathogensImmunodeficiency VirusesHIVBiology and Life SciencesOrganismsVirusesViral PathogensImmunodeficiency VirusesHIVBiology and Life SciencesOrganismsVirusesImmunodeficiency VirusesHIVBiology and life sciencesOrganismsVirusesRNA virusesRetrovirusesLentivirusHIVBiology and Life SciencesMicrobiologyMedical MicrobiologyMicrobial PathogensViral PathogensRetrovirusesLentivirusHIVMedicine and Health SciencesPathology and Laboratory MedicinePathogensMicrobial PathogensViral PathogensRetrovirusesLentivirusHIVBiology and Life SciencesOrganismsVirusesViral PathogensRetrovirusesLentivirusHIVMedicine and health sciencesDiagnostic medicineHIV diagnosis and managementBiology and Life SciencesMicrobiologyMedical MicrobiologyMicrobial PathogensViral PathogensImmunodeficiency VirusesHIVHIV-1Medicine and Health SciencesPathology and Laboratory MedicinePathogensMicrobial PathogensViral PathogensImmunodeficiency VirusesHIVHIV-1Biology and Life SciencesOrganismsVirusesViral PathogensImmunodeficiency VirusesHIVHIV-1Biology and Life SciencesOrganismsVirusesImmunodeficiency VirusesHIVHIV-1Biology and life sciencesOrganismsVirusesRNA virusesRetrovirusesLentivirusHIVHIV-1Biology and Life SciencesMicrobiologyMedical MicrobiologyMicrobial PathogensViral PathogensRetrovirusesLentivirusHIVHIV-1Medicine and Health SciencesPathology and Laboratory MedicinePathogensMicrobial PathogensViral PathogensRetrovirusesLentivirusHIVHIV-1Biology and Life SciencesOrganismsVirusesViral PathogensRetrovirusesLentivirusHIVHIV-1Medicine and Health SciencesHealth CareHealth Care ProvidersMedical DoctorsPhysiciansPeople and PlacesPopulation GroupingsProfessionsMedical DoctorsPhysiciansPeople and placesGeographical locationsNorth AmericaCaribbeanHaitiBiology and Life SciencesMicrobiologyVirologyViral Transmission and InfectionViral LoadMedicine and health sciencesDiagnostic medicineHIV clinical manifestationsSame-day HIV testing with initiation of antiretroviral therapy versus standard care for persons living with HIV: A randomized unblinded trialSame-day HIV testing and antiretroviral therapy initiationhttp://orcid.org/0000-0001-7464-275XKoenigSerena P.ConceptualizationFunding acquisitionInvestigationMethodologySupervisionWriting – original draftWriting – review & editing12*DorvilNancyInvestigationMethodologyProject administrationSupervisionWriting – review & editing1DévieuxJessy G.ConceptualizationFunding acquisitionInvestigationMethodologySupervisionWriting – original draftWriting – review & editing3http://orcid.org/0000-0002-9689-5413Hedt-GauthierBethany L.ConceptualizationFormal analysisFunding acquisitionMethodologySoftwareSupervisionValidationVisualizationWriting – review & editing4RiviereCynthiaInvestigationMethodologyProject administrationSupervisionWriting – review & editing1FaustinMikerlyneInvestigationMethodologyProject administrationSupervisionWriting – review & editing1LavoileKerlyneInvestigationMethodologyProject administrationSupervisionWriting – review & editing1PerodinChristianFormal analysisInvestigationMethodologySoftwareValidationVisualizationWriting – review & editing1ApollonAlexandraConceptualizationInvestigationMethodologyProject administrationSupervisionWriting – review & editing1DuvergerLimatheInvestigationMethodologyProject administrationSupervisionWriting – review & editing1McNairyMargaret L.MethodologyWriting – review & editing56HennesseyKelly A.Formal analysisMethodologySoftwareValidationVisualizationWriting – review & editing1SouroutzidisAriadneFormal analysisMethodologySoftwareValidationVisualizationWriting – review & editing7CremieuxPierre-YvesFormal analysisMethodologySoftwareValidationVisualizationWriting – review & editing7SeverePatriceConceptualizationFunding acquisitionInvestigationMethodologyProject administrationSupervisionWriting – review & editing1PapeJean W.ConceptualizationFunding acquisitionInvestigationMethodologyProject administrationSupervisionWriting – review & editing151\nHaitian Study Group for Kaposi’s Sarcoma and Opportunistic Infections (GHESKIO), Port-au-Prince, Haiti2\nDivision of Global Health Equity, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America3\nAIDS Prevention Program, Florida International University, Miami, Florida, United States of America4\nDepartment of Global Health and Social Medicine, Harvard Medical School, Harvard University, Boston, Massachusetts, United States of America5\nCenter for Global Health, Department of Medicine, Weill Cornell Medical College, Cornell University, New York, New York, United States of America6\nDivision of General Internal Medicine, Department of Medicine, Weill Cornell Medical College, Cornell University, New York, New York, United States of America7\nAnalysis Group, Boston, Massachusetts, United States of AmericaGengElvin H.Academic EditorUniversity of California, San Francisco, UNITED STATESThe authors have declared that no competing interests exist.* E-mail: skoenig@bwh.harvard.edu257201772017147e100235724120171662017© 2017 Koenig et al2017Koenig et alThis is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.\nThe science of rapid start—From the when to the how of antiretroviral initiation\nBackgroundAttrition during the period from HIV testing to antiretroviral therapy (ART) initiation is high worldwide. We assessed whether same-day HIV testing and ART initiation improves retention and virologic suppression.Methods and findingsWe conducted an unblinded, randomized trial of standard ART initiation versus same-day HIV testing and ART initiation among eligible adults ≥18 years old with World Health Organization Stage 1 or 2 disease and CD4 count ≤500 cells/mm3. The study was conducted among outpatients at the Haitian Group for the Study of Kaposi’s Sarcoma and Opportunistic infections (GHESKIO) Clinic in Port-au-Prince, Haiti. Participants were randomly assigned (1:1) to standard ART initiation or same-day HIV testing and ART initiation. The standard group initiated ART 3 weeks after HIV testing, and the same-day group initiated ART on the day of testing. The primary study endpoint was retention in care 12 months after HIV testing with HIV-1 RNA <50 copies/ml. We assessed the impact of treatment arm with a modified intention-to-treat analysis, using multivariable logistic regression controlling for potential confounders. Between August 2013 and October 2015, 762 participants were enrolled; 59 participants transferred to other clinics during the study period, and were excluded as per protocol, leaving 356 in the standard and 347 in the same-day ART groups. In the standard ART group, 156 (44%) participants were retained in care with 12-month HIV-1 RNA <50 copies, and 184 (52%) had <1,000 copies/ml; 20 participants (6%) died. In the same-day ART group, 184 (53%) participants were retained with HIV-1 RNA <50 copies/ml, and 212 (61%) had <1,000 copies/ml; 10 (3%) participants died. The unadjusted risk ratio (RR) of being retained at 12 months with HIV-1 RNA <50 copies/ml was 1.21 (95% CI: 1.04, 1.38; p = 0.015) for the same-day ART group compared to the standard ART group, and the unadjusted RR for being retained with HIV-1 RNA <1,000 copies was 1.18 (95% CI: 1.04, 1.31; p = 0.012). The main limitation of this study is that it was conducted at a single urban clinic, and the generalizability to other settings is uncertain.ConclusionsSame-day HIV testing and ART initiation is feasible and beneficial in this setting, as it improves retention in care with virologic suppression among patients with early clinical HIV disease.Trial registrationThis study is registered with ClinicalTrials.gov number NCT01900080In a randomized unblinded trial in Port-au-Prince, Haiti, Serena Koenig and colleagues investigate whether initiating ART on the day of HIV diagnosis improved retention in care and viral suppression.Author summaryWhy was this study done?Multiple visits for counseling, laboratory testing, and other procedures to prepare patients for initiation of antiretroviral therapy (ART) are burdensome and contribute to the high rate of attrition during the period from HIV testing to ART initiation.The World Health Organization (WHO) recently changed their guidelines to recommend ART for all persons living with HIV, facilitating ART initiation.This study was conducted to determine if ART initiation on the day of HIV diagnosis could improve treatment initiation rates, retention in care, and HIV viral suppression for patients with asymptomatic or minimally symptomatic HIV disease.What did the researchers do and find?We randomly assigned patients who presented for HIV testing at a clinic in Port-au-Prince, Haiti to standard ART initiation or same-day HIV testing and ART initiation (356 in the standard and 347 in the same-day groups).The standard group had 3 weekly visits with a social worker and physician and then started ART 21 days after the date of HIV diagnosis; the same-day ART group initiated ART on the day of HIV diagnosis.All participants in the same-day ART group and 92% of participants in the standard group initiated ART.At 12 months after HIV testing, a higher proportion of participants in the same-day ART group were retained in care (80% versus 72%), and a higher proportion were retained in care with viral load <50 copies/ml (53% versus 44%) and viral load <1,000 copies/ml (61% versus 52%).What do these findings mean?This study demonstrates that it is feasible to initiate ART on the day of HIV diagnosis for patients with early HIV clinical disease and that same-day treatment leads to increased ART uptake, retention in care, and viral suppression.Though same-day ART initiation improves outcomes, retention in care and viral suppression remain suboptimal, so further interventions to maximize long-term outcomes will be essential.The study is limited by being conducted at 1 clinic in urban Haiti. Further study will be necessary to determine if this strategy will be effective in other settings.http://dx.doi.org/10.13039/100000060National Institute of Allergy and Infectious DiseasesR01AI104344http://orcid.org/0000-0001-7464-275XKoenigSerena P.This project was supported by the National Institute of Allergy and Infectious Diseases, grant number R01AI104344. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Data AvailabilityWe have included the anonymized dataset as a Supporting Information file (S1 Data).Data AvailabilityWe have included the anonymized dataset as a Supporting Information file (S1 Data).IntroductionThe Joint United Nations Programme on HIV/AIDS (UNAIDS) 90-90-90 targets state that 90% of HIV-infected persons know their status, 90% initiate antiretroviral therapy (ART), and 90% achieve virologic suppression by the year 2020 to curb the AIDS epidemic [1]. In 2015, the World Health Organization (WHO) updated their guidelines to recommend ART for all persons living with HIV based on evidence that earlier treatment improves outcomes and decreases transmission [2–4]. To achieve these goals, patients must be promptly linked to HIV services, initiated on ART, and retained in lifelong care [5].Attrition rates are particularly high during the period from HIV testing to ART initiation, with one-quarter to one-third of patients lost in the process of starting ART [6–9]. Even if many of these patients re-engage in care at a later date, they will return with more advanced disease. Though there are many factors that contribute to pretreatment attrition, the current standard of care in most settings, which requires multiple sequential visits for HIV testing and counseling, laboratory testing, and adherence counseling prior to ART initiation, creates barriers to treatment initiation. As of June 2016, WHO guidelines note inadequate evidence to support a recommendation of same-day HIV testing and ART initiation [2]. However, the availability of point-of-care tests, the fact that CD4 cell counts are no longer necessary prior to ART initiation, and the provision of same-day counseling can accelerate treatment initiation, potentially reducing attrition [10–12]. We conducted a randomized trial in Haiti to determine whether same-day HIV testing and ART initiation, as compared with standard ART initiation, improves retention in care with viral suppression.MethodsStudy design and settingWe conducted an unblinded, randomized controlled trial of standard ART initiation versus same-day HIV testing and ART initiation among HIV-infected adults at the Haitian Group for the Study of Kaposi’s Sarcoma and Opportunistic infections (GHESKIO) in Port-au-Prince, Haiti. Haiti is the poorest country in the Western Hemisphere, with adult HIV prevalence of 1.7% [13,14]. GHESKIO is a Haitian nongovernmental organization and the largest provider of HIV care in the Caribbean, treating up to 700 patients per day for HIV and/or tuberculosis (TB). All care is provided free of charge. The study was approved by the institutional review boards at Partners Healthcare, GHESKIO, Weill Cornell Medical College, and Florida International University. See supporting information files S1 Text for the study protocol and S2 Text for the CONSORT checklist.ParticipantsParticipants were recruited from the HIV voluntary counseling and testing center at GHESKIO from August 2013 to October 2015. They received HIV testing and posttest counseling; those with a positive HIV test were referred for same-day physician evaluation, CD4 count (FACS Count, Becton-Dickinson, Franklin Lakes, New Jersey), WHO staging, and chest radiograph. Patients were eligible for study inclusion if they were infected with HIV-1, ≥18 years of age, and had WHO Stage 1 or 2 disease and CD4 count ≤500 cells/mm3. Initially, enrollment was limited to patients with CD4 count ≤350 cells/mm3, but in February 2014, the cutoff was increased to ≤500 cells/mm3 in response to revised WHO and Haitian guidelines [15]. Patients were excluded if they were already aware of their HIV diagnosis, had received ART previously, were pregnant or breastfeeding, lived outside of the greater Port-au-Prince metropolitan area, planned to transfer care during the study period, or failed to demonstrate preparedness on an ART readiness survey, which was administered by a social worker prior to study enrollment. The survey includes a 5-point scale, with respondents ranking their preparedness from “not at all ready” to “completely ready” in response to 7 questions. Study inclusion required a response of “somewhat ready” or “completely ready” for all 7 questions (S3 Text) [16].Randomization and maskingAfter the patients had provided written informed consent, the study team performed a screening evaluation for study exclusion criteria, and eligible participants were enrolled and randomized on the day of HIV testing. Participants were randomly assigned with the use of a computer-generated random-number list to either standard ART or same-day ART initiation in a 1:1 ratio, with allocation concealment. The randomization sequence was generated by a computer in the GHESKIO data management unit by a data manager who had no other involvement in study procedures. Participants were enrolled in the study and assigned to groups by a study physician. Participants, site personnel, and study statisticians were not masked to group assignment.ProceduresAfter randomization, the standard group participants received ART initiation procedures that mirror national guidelines. Participants were referred to return on Day 7 for baseline laboratory tests (creatinine, alanine aminotransferase, aspartate aminotransferase, complete blood count, purified protein derivative [PPD]), physician evaluation, and counseling with a social worker. On Day 10, they received interpretation of PPD results, and on Days 14 and 21, they were seen by a physician and social worker for additional counseling, test results, and ongoing evaluations for opportunistic infections. Participants started ART on Day 21 and had an additional social worker and physician visit at Week 5 (Fig 1). The ART regimen was the same as that for nonstudy patients at GHESKIO. First-line therapy included a single combination tablet including tenofovir disoproxil fumarate, lamivudine, and efavirenz.10.1371/journal.pmed.1002357.g001Fig 1Study interventions for the standard ART and same-day ART groups.The same-day ART group had identical laboratory tests as the standard ART group, a 30-minute counseling session with a social worker, and physician evaluation, and then initiated the same ART regimen as the standard ART group. They returned on Day 3 for physician and social worker visits and receipt of baseline laboratory test results; those with creatinine clearance <50 mL/minute as calculated by the Cockcroft-Gault equation were switched from tenofovir to zidovudine or abacavir. They returned on Days 10 and 17 for additional physician and social worker visits and on Day 24 for a physician visit. The same number of scheduled physician visits and counseling sessions were provided to each group so that the only difference in care was in the schedule of visits during the first 5 weeks of the study and the timing of ART initiation.All care was delivered by GHESKIO clinic staff, and the same providers (physicians, nurses, social workers, pharmacists, and field workers) cared for both groups. A counseling manual was followed with an outline for the social workers to follow at each scheduled counseling visit; these were identical between groups, except for the timing of ART initiation, and each session took about 30 minutes. All counseling was provided for individual patients, rather than for groups. The counseling sessions were audiotaped and systematically evaluated for quality control purposes. If a participant in either group missed a study visit that included a scheduled social worker counseling session, the counseling was provided at the next visit.Participants in both groups had monthly physician visits throughout the follow-up period and received the same package of services provided to all HIV-infected patients at GHESKIO, including prophylactic treatment with trimethoprim-sulfamethoxazole and isoniazid. Field workers phoned patients who missed a visit and attempted a home visit for those not reachable by phone. Participants received a transportation subsidy of 100 Haitian gourdes (US$1.70) per visit.OutcomesThe primary endpoint was retention in care with HIV-1 RNA <50 copies/ml at 12 months after HIV testing. Retention was defined as attending the 12-month visit (1 clinic visit between 12 and 15 months after HIV testing). Lost to follow-up (LTFU) was defined as failure to attend the 12-month visit. Deaths were ascertained by review of medical records or report from family members. A National Institutes of Health Division of AIDS Expedited Adverse Event Form was filled out within 48 hours after the study team became aware of any death. Transfers were ascertained by confirmation that the participant was receiving care at a different site. Secondary outcomes include survival, ART initiation, retention in care with HIV-1 RNA <1,000 copies/ml at 12 months after HIV testing, adherence as measured by pharmacy refill records and self-report, and cost and cost-effectiveness of standard and same-day ART; the adherence and cost-effectiveness evaluations will be reported in separate manuscripts.Statistical analysisDemographic, clinical, and laboratory data from the electronic medical record and study forms were de-identified, entered into an Excel spreadsheet, and exported into Stata v14 software (StataCorp, 2011, College Station, Texas) for analysis. After study completion, all participants who were LTFU were recontacted to determine their vital status.The study was powered to detect a 10% absolute difference in the rate of retention with virologic suppression between the 2 groups at 12 months after enrollment (65% in the standard and 75% in the same-day ART group). At the α = 0.05 significance level, we estimated that we would need to enroll 349 participants per group (698 in total) to achieve 80% power to detect this difference. Because patients who transferred during the study period were excluded, we increased the total sample size to 762 participants. For all analyses, a modified intention-to-treat approach was used, in which all patients were analyzed according to their assignment group, excluding patients who transferred to another facility during the follow-up period, according to protocol.Baseline characteristics were summarized using simple frequencies and proportions and medians with interquartile ranges (IQRs) stratified by treatment arm. Among participants who died, baseline CD4 count was compared using the Wilcoxon rank-sum test. We compared the proportion of participants who were retained in care with HIV-1 RNA <50 copies/ml (primary endpoint), retained with HIV-1 RNA <1,000 copies/ml, retained regardless of HIV-1 RNA, initiated ART, and died (secondary endpoints) at 12 months after enrollment using a chi-square test. We conducted multivariable logistic regression including all covariates listed in Table 1 to control for any residual confounding. We present unadjusted and adjusted risk ratios (RR) with 95% confidence intervals. Because of the change in enrollment criteria mid-study, we conducted a sensitivity analysis that included only the participants who met the original enrollment criteria of CD4 count ≤350 cells/mm3. In response to a reviewer’s request, we also plotted retention in care, regardless of viral load, for both groups and compared the distributions with the log-rank test. The study is registered with ClinicalTrials.gov number NCT01900080.10.1371/journal.pmed.1002357.t001Table 1Baseline characteristics of study participants by group.CharacteristicStandard Group (n = 356)Same-Day ART Group (n = 347)Age (years)—Median (IQR)37 (30, 45)37 (29, 46)Female sex—no. (%)181 (51)166 (48)Education—no. (%)\xa0\xa0\xa0\xa0No school90 (25)93 (27)\xa0\xa0\xa0\xa0Primary school110 (31)111 (32)\xa0\xa0\xa0\xa0Secondary school or more156 (44)143 (41)Income—no. (%)\xa0\xa0\xa0\xa0No income92 (26)90 (26)\xa0\xa0\xa0\xa0>$0 to $1/day176 (49)159 (46)\xa0\xa0\xa0\xa0>$1 to $2/day67 (19)76 (22)\xa0\xa0\xa0\xa0>$2/day21 (6)22 (6)Marital status—no. (%)\xa0\xa0\xa0\xa0Single71 (20)71 (20)\xa0\xa0\xa0\xa0Currently married/living with partner222 (62)211 (61)\xa0\xa0\xa0\xa0Formerly married63 (18)65 (19)WHO Stage—no. (%)\xa0\xa0\xa0\xa0WHO Stage 1117 (33)101 (29)\xa0\xa0\xa0\xa0WHO Stage 2239 (67)246 (71)CD4 count (cells/mm3)—Median (IQR)247 (150, 349)249 (143, 336)Body mass index—Median (IQR)*21.6 (19.7, 23.9)20.9 (19.3, 23.5)* Body mass index differed significantly between the 2 groups (p = 0.025).ART, antiretroviral therapy; IQR, interquartile range, WHO, World Health Organization.ResultsA total of 821 patients were screened, and 762 were enrolled in the study and underwent randomization (Fig 2). After randomization, 59 participants (28 in the standard ART and 31 in same-day ART group) transferred to another clinic and were excluded from all analyses, as per protocol. The median age was 37 years old (IQR: 30–45 years), 347 (49%) were women, and the median CD4 count was 248 cells/mm3 (IQR: 148, 345).10.1371/journal.pmed.1002357.g002Fig 2Screening, randomization, and follow-up.Of the 356 participants in the standard group, 256 (72%) were retained in care, 20 (6%) died, and 80 (23%) were LTFU (Table 2). Among the 256 participants retained in the standard ART group, 156 (61% of retained and 44% overall) had HIV-1 RNA <50 copies/ml. Of the 347 participants in the same-day ART group, 277 (80%) were retained in care, 10 (3%) died, and 60 (17%) were LTFU. Among the 277 participants retained in the same-day ART group, 184 (66% of retained and 53% overall) had HIV-1 RNA <50 copies/ml. The unadjusted RR of being retained in care at 12 months and achieving HIV-1 RNA <50 copies/ml was 1.21 (95% CI: 1.04, 1.38; p = 0.015) for the same-day ART group compared to the standard group (Table 3); the adjusted RR for this comparison was 1.24 (95% CI: 1.06, 1.41; p = 0.008).10.1371/journal.pmed.1002357.t002Table 2Study outcomes by group.OutcomeStandard ART Group (n = 356)Same-Day ART Group (n = 347)Unadjusted Risk Difference (95% CI)p-valuePrimary OutcomeRetained in care at 12 months with VL <50 copies/ml156 (43.8%)184 (53.0%)9.2% (1.8%, 16.6%)0.015†Secondary OutcomesRetained in care at 12 months with VL <1,000 copies/ml184 (51.7%)212 (61.1%)9.4% (2.1%, 16.7%)0.012‡Retained in care at 12 months, regardless of VL results256 (71.9%)277 (79.8%)7.9% (1.6%, 14.2%)0.014††Died20 (5.6%)10 (2.9%)Lost to follow-up80 (22.5%)60 (17.3%)† p-value comparing the proportion of all patients who were retained in care with viral load <50 copies/ml between the 2 arms.‡ p-value comparing the proportion of all patients who were retained in care with viral load <1,000 copies/ml between the 2 arms.†† p-value comparing the proportion of all patients who were retained in care between the 2 arms.ART, antiretroviral therapy; VL, viral load.10.1371/journal.pmed.1002357.t003Table 3Unadjusted and adjusted risk ratios of study outcomes.UnadjustedAdjusted for All Baseline Co-variatesRR95% CIp-valueRR95% CIp-valueRetained in care with viral load <50 copies/mlStandard ART Group1.01.0Same-Day ART Group1.21(1.04, 1.38)0.0151.24(1.06, 1.41)0.008Retained in care with viral load <1,000 copies/mlStandard ART Group1.01.0Same-Day ART Group1.18(1.04, 1.31)0.0121.20(1.05, 1.33)0.008Mortality during study periodStandard ART Group1.01.0Same-Day ART Group0.51(0.24, 1.08)0.0730.43(0.19, 0.94)0.033ART, antiretroviral therapy; RR, risk ratio.In the standard ART group, 184 (72% of retained and 52% overall) participants who were retained in care had HIV-1 RNA <1,000 copies/ml. In the same-day ART group, 212 (77% of retained and 61% overall) participants who were retained in care had HIV-1 RNA <1,000 copies/ml. The unadjusted RR of being retained in care at 12 months and achieving HIV-1 RNA <1,000 copies/ml was 1.18 (95% CI: 1.04, 1.31; p = 0.012) for the same-day ART group compared to the standard ART group (Table 3); the adjusted RR for this comparison was 1.20 (95% CI: 1.05, 1.33; p = 0.008). In the sensitivity analysis that included only participants who met the original enrollment criteria (CD4 count ≤350 cells/mm3), the adjusted RR of being retained in care at 12 months and achieving HIV-1 RNA <50 copies/ml was 1.19 (95% CI: 0.99, 1.38; p = 0.060), and the adjusted RR of being retained in care at 12 months and achieving HIV-1 RNA < 1,000 copies/ml was 1.18 (95% CI: 1.01, 1.34; p = 0.035).Vital status at the end of the study was known for 328 (92%) participants in the standard ART group and 329 (95%) in the same-day ART group. The unadjusted RR for mortality was 0.51 (95% CI: 0.24, 1.08; p = 0.073) for the same-day group compared to the standard group; the adjusted RR for this comparison was 0.43 (95% CI: 0.19, 0.94; p = 0.033). In the sensitivity analysis that included only participants with CD4 count ≤350 cells/mm3, the adjusted RR for mortality was 0.41 (95% CI: 0.18, 0.93; p = 0.033). Among the participants who died, the median baseline CD4 count was 100 cells/mm3 (IQR: 45, 192) in the standard and 207 cells/mm3 (IQR: 112, 291) in the same-day ART group (p = 0.078). Eight of 20 (40%) deaths in the standard ART group occurred in participants who were LTFU prior to ART, 8 (40%) deaths occurred in those LTFU after starting ART, and 4 (20%) occurred while in care; the causes of death for those in care were stroke, trauma, and cancer in 3, and the fourth had pain and died after seeing a traditional healer. Three of the 10 (30%) deaths in the same-day ART group occurred in participants who were LTFU after starting ART; among the 7 (70%) participants who died while in care, 1 of each died of stroke, pneumonia, malaria, renal failure, and sudden death, and 2 died of gastroenteritis. No deaths for those in care were attributed to immune reconstitution syndrome or an opportunistic infection that was missed at ART initiation. In Fig 3, the Kaplan-Meier curve plots the retention in care, regardless of viral load, for both groups. The log-rank test comparing the curves between the standard and same-day ART group indicates a significant difference (p = 0.028).10.1371/journal.pmed.1002357.g003Fig 3Retention in care by study group.In the same-day ART group, 344 of 347 (99%) participants started ART on the day of HIV testing, and the remaining 3 patients started ART within the subsequent week. During the Day 3 follow-up visit, 13 patients (4%) in the same-day ART group had adjustments in their ART regimens (replacement of tenofovir with zidovudine or abacavir) because they had creatinine clearance <50 mL/minute on baseline testing. In the standard group, 281 (79%) participants initiated ART by Day 28, the end of the time window for the 3-week ART initiation visit. Thirty-six (10%) standard group participants initiated ART from Day 29 to Day 90, and 12 (3%) initiated ART after Day 90 due to late or missed visits. Twenty-seven (8%) standard group participants never started ART during the study period because they were LTFU or died prior to initiating treatment. Isoniazid prophylaxis was initiated for 337 (95%) participants in the standard group and 340 (98%) in the same-day group. Eight cases of TB were diagnosed during the first 3 months after ART initiation; 6 of these occurred in the standard group and 2 in the same-day ART group.DiscussionThe results of this randomized controlled trial show that among HIV-infected adults with early WHO Stage disease and CD4 count ≤500 cells/mm3, same-day HIV testing and ART initiation, as compared to standard care, improves retention in care with virologic suppression and, in the multivariable analysis, decreases mortality. These results are important given recent WHO 2016 guidelines stating the lack of evidence in support of same-day ART initiation.Our findings suggest that ART initiation as soon as possible after HIV testing may be beneficial for clinically stable patients. In resource-poor settings with fragile delivery systems, such as Haiti, the provision of immediate support by care providers at the time of HIV diagnosis can have both structural and individual impact. In addition to making treatment initiation logistically easier for patients, we believe that same-day counseling and ART initiation increase the sense of hope, optimism, and overall connectedness to the healthcare system for patients, which have been shown to be important for retention [17–20].Our findings are consistent with the results of the RapIT study, a randomized trial that included participants in South Africa with WHO Stage 3 or 4 disease or CD4 count ≤350 cells/mm3 [11]. Participants in the standard group in that study generally started ART at the sixth visit, and 72% of participants in the rapid group started ART on the day of study enrollment. Rapid ART initiation resulted in a 17% improvement in retention and 13% improvement in viral suppression. A stepped-wedge cluster-randomized trial in Uganda found an increase in ART initiation within 2 weeks after eligibility by implementing a multicomponent intervention to streamline ART initiation that included training healthcare workers, providing point-of-care CD4 count testing platforms, eliminating mandatory multiple preinitiation sessions, and giving feedback to facilities on their ART initiation rates [21]. A weighted proportion of 80% in the intervention group had started ART within 2 weeks after eligibility compared with 38% in the control group. A cohort study of same-day ART initiation in pregnant women in South Africa also found high rates of treatment initiation, with 91% initiating ART on the day of referral to the service [22]. In the intervention group of the Sustainable East Africa Research on Community Health (SEARCH) HIV test-and-treat study, a cluster-randomized controlled trial conducted in Kenya and Uganda, HIV-infected patients who were identified through community testing were referred to HIV care upon diagnosis and then offered immediate ART initiation; retention was high (89%) among patients newly linking to care [23].At ART initiation, it is critical that patients are ready to start lifelong therapy, that TB screening is conducted, and that renal function is evaluated to avoid the use of tenofovir in patients with renal insufficiency. In this study, ART readiness was remarkably high, with over 99% of patients screened for the study reporting they were ready to start lifelong ART. This is a particularly significant and timely finding for the provision of recommended universal ART because the majority of people living with HIV have early clinical disease, and there has been prior concern that healthier patients may be less willing to accept lifelong therapy [4]. Most patients with early clinical disease do not have TB symptoms (cough, fever, night sweats, or weight loss), so they do not require further work up to exclude TB, according to WHO guidelines [2]. With the exclusion of patients with a baseline chest x-ray that was suspicious for TB, we found that less than 1% of participants in the same-day ART group had TB that was missed at the time of ART initiation. We found that 4% of participants in the same-day ART group had creatinine clearance <50 mL/minute; ART regimens were adjusted on Day 3 for these patients.Both groups in our study received high-level care, with multiple counseling and physician visits in the first month, followed by monthly physician visits. At the time the study was started, this was the standard of care in Haiti. However, this standard has shifted over the past few years towards decreased frequency of visits and nonphysician providers [2,24–27]. We believe that same-day ART can be provided with fewer follow-up visits if proper counseling is provided during the early period after ART initiation. However, clinic-level procedures play a major role in the effectiveness of accelerated ART initiation strategies, as illustrated in Malawi, where among nearly 22,000 pregnant women who started ART for mother-to-child prevention, LTFU rates ranged from 0% to 58% between facilities and were highest among women who initiated ART on the day of HIV testing at large clinics [28].Though lower than anticipated, retention in both groups in our study was higher than reports of standard ART initiation from other resource-poor settings. Two studies from South Africa found that approximately one-third of patients remained in care from HIV testing through 12 months of ART, and systematic reviews of African studies have found high rates of pre-ART attrition [6,8,29,30]. In Haiti, data on pre-ART outcomes are limited, but 12-month retention after ART initiation is 73% nationwide [31]. We attribute the higher retention in our study in large part to faster ART initiation, even in the standard group, compared to many other HIV programs. We surmise that retention would have been lower in the standard group if there had been longer delays in ART initiation [5,11,30].The rates of retention with viral suppression in our study are lower than those reported from clinical trial cohorts, including at GHESKIO. In the GHESKIO Clinical Trials Unit, with a median monthly average of 483 subjects participating in NIH-funded clinical trials, retention is 97%. We attribute the lower retention and viral suppression rates in our study to 2 major reasons. First, nearly all patients meeting WHO stage and CD4 criteria were enrolled in the study on the day of HIV testing, including those with substantial barriers to retention in care and adherence. In contrast, over one-third of patients are generally lost to care prior to ART initiation or enrollment in clinical trials [6,8,29,30]. Second, the care that was provided in this study was similar to that received by nonstudy patients at GHESKIO, with the aim of producing findings that could be reproduced in other resource-poor settings. In order to achieve the UNAIDS 90-90-90 targets, it will be important to evaluate reasons for attrition and implement new strategies to improve retention in care. One approach that has been successful in a cohort of nonresearch patients at GHESKIO has been expedited follow-up care, with fewer visits of shorter duration for clinically stable patients [32]. Streamlined care has also been associated with high rates of retention in the SEARCH study, which is described above [23].Our study was conducted in a large urban clinic, which may limit the generalizability of our findings. In addition, though our study included patients with early clinical disease, the CD4 counts in our population were lower than would be expected with the provision of universal ART. It is possible that patients with higher CD4 counts may experience less benefit from same-day ART. It is also noteworthy that we conducted a chest x-ray prior to enrollment; if same-day ART is provided without a chest x-ray, it is possible that TB cases will be missed. Our study was not blinded. All participants in both groups received the same number of visits and the same retention plan, but we cannot exclude the possibility that awareness of study group impacted provider behavior.In conclusion, in a population of asymptomatic or minimally symptomatic HIV-infected patients, same-day HIV testing and ART initiation decreased mortality and improved the rate of retention in care with virologic suppression compared with standard ART initiation. Furthermore, human and material resources provided to each group were similar, so same-day ART is not expected to increase treatment costs. The new WHO recommendations to provide ART to all HIV-infected patients should facilitate same-day test and treat.Supporting informationS1 TextStudy protocol.(DOCX)Click here for additional data file.S2 TextCONSORT checklist.(DOC)Click here for additional data file.S3 TextHIV medication readiness scale.(PDF)Click here for additional data file.S1 DataAnonymized dataset.(XLSX)Click here for additional data file.Presented in part at the 21st International AIDS Conference, Durban, South Africa, July 18 to 22, 2016. We thank all of the patients who participated in this study and all of the GHESKIO staff who cared for them. We thank Drs. Paul Farmer, Daniel Fitzgerald, Martin Hirsch, Warren Johnson, Daniel Kuritzkes, and Paul Sax for expert advice on study design and Kaya Hedt and Anshul Saxena for manuscript formatting and preparation. We also thank Drs. Carlos del Rio, Kenneth Mayer, and Larry Moulton for serving on the data safety monitoring board and providing oversight of the study.AbbreviationsARTantiretroviral therapyGHESKIOHaitian Group for the Study of Kaposi’s Sarcoma and Opportunistic infectionsIQRinterquartile rangeLTFUlost to follow-upPPDpurified protein derivativeRRrisk ratioSEARCHSustainable East Africa Research on Community HealthUNAIDSThe Joint United Nations Programme on HIV/AIDSWHOWorld Health OrganizationReferences1UNAIDS Fast-Track, Ending the AIDS Epidemic by 2030. Accessed May 24, 2017 at: http://www.unaids.org/en/resources/campaigns/World-AIDS-Day-Report-2014.2Consolidated Guidelines on the Use of Antiretroviral Drugs for Treating and Preventing HIV Infection. Recommendations for a Public Health Approach. Second Edition, World Health Organization, 2016. Accessed May 24, 2017 at: http://www.who.int/hiv/pub/arv/arv-2016/en/.3The INSIGHT START Study Group, LundgrenJD, BabikerAG, GordinF, EmeryS, GrundB, et al\nInitiation of Antiretroviral Therapy in Early Asymptomatic HIV Infection. New Engl J Med. 2015;373(9):795–807. doi: 10.1056/NEJMoa1506816\n261928734The TEMPRANO ANRS 12136 Study Group. A Trial of Early Antiretrovirals and Isoniazid Preventive Therapy in Africa. New Engl J Med. 2015;373(9):808–22. doi: 10.1056/NEJMoa1507198\n261931265FoxMP, RosenS. Retention of Adult Patients on Antiretroviral Therapy in Low- and Middle-Income Countries: Systematic Review and Meta-analysis 2008–2013. J Acquir Immune Defic Syndr. 2015;69(1):98–108. doi: 10.1097/QAI.0000000000000553\n259424616ClouseK, PettiforAE, MaskewM, BassettJ, Van RieA, BehetsF, et al\nPatient retention from HIV diagnosis through one year on antiretroviral therapy at a primary health care clinic in Johannesburg, South Africa. J Acquir Immune Defic Syndr. 2013;62(2):e39–46. doi: 10.1097/QAI.0b013e318273ac48\n230114007ZachariahR, Tayler-SmithK, ManziM, MassaquoiM, MwagombaB, van GriensvenJ, et al\nRetention and attrition during the preparation phase and after start of antiretroviral treatment in Thyolo, Malawi, and Kibera, Kenya: implications for programmes?\nTrans Roy Soc Trop Med Hyg. 2011;105(8):421–30. doi: 10.1016/j.trstmh.2011.04.014\n217242198RosenS, FoxMP. Retention in HIV care between testing and treatment in sub-Saharan Africa: a systematic review. PLoS Med. 2011;8(7):e1001056\ndoi: 10.1371/journal.pmed.1001056\n218114039KoenigSP, BernardD, DevieuxJG, AtwoodS, McNairyML, SevereP, et al\nTrends in CD4 Count Testing, Retention in Pre-ART Care, and ART Initiation Rates over the First Decade of Expansion of HIV Services in Haiti. PLoS ONE. 2016;11(2):e0146903\ndoi: 10.1371/journal.pone.0146903\n2690179510SiednerMJ, LankowskiA, HabererJE, KembabaziA, EmenyonuN, TsaiAC, et al\nRethinking the ""pre"" in pre-therapy counseling: no benefit of additional visits prior to therapy on adherence or viremia in Ugandans initiating ARVs. PLoS ONE. 2012;7(6):e39894\ndoi: 10.1371/journal.pone.0039894\n2276192411RosenS, MaskewM, FoxMP, NyoniC, MongwenyanaC, MaleteG, et al\nInitiating Antiretroviral Therapy for HIV at a Patient\'s First Clinic Visit: The RapIT Randomized Controlled Trial. PLoS Med. 2016;13(5):e1002015\ndoi: 10.1371/journal.pmed.1002015\n2716369412JaniIV, SitoeNE, AlfaiER, ChongoPL, QuevedoJI, RochaBM, et al\nEffect of point-of-care CD4 cell count tests on retention of patients and rates of antiretroviral therapy initiation in primary health clinics: an observational cohort study. Lancet. 2011;378(9802):1572–9. doi: 10.1016/S0140-6736(11)61052-0\n2195165613UNAIDS—Haiti profile. Accessed May 24, 2017 at: http://www.unaids.org/en/regionscountries/countries/haiti.14International Human Development Indicators, Haiti Country Profile. United Nations Development Program. Accessed May 24, 2017 at: http://hdr.undp.org/en/countries/profiles/HTI.15Consolidated Guidelines on the Use of Antiretroviral Drugs for Treating and Preventing HIV Infection. Recommendations for a Public Health Approach. World Health Organization, 2013. Accessed May 24, 2017 at: http://www.who.int/hiv/pub/guidelines/arv2013/en/.16BalfourL, TascaGA, KowalJ, CoraceK, CooperCL, AngelJB, et al\nDevelopment and validation of the HIV Medication Readiness Scale. Assessment. 2007;14(4):408–16. doi: 10.1177/1073191107304295\n1798665817WareNC, WyattMA, GengEH, KaayaSF, AgbajiOO, MuyindikeWR, et al\nToward an understanding of disengagement from HIV treatment and care in sub-Saharan Africa: a qualitative study. PLoS Med. 2013;10(1):e1001369\ndoi: 10.1371/journal.pmed.1001369\n2334175318BernaysS, RhodesT, BarnettT. Hope: a new way to look at the HIV epidemic. AIDS. 2007;21\nSuppl 5:S5–11.19BarnettT, WestonM. Wealth, health, HIV and the economics of hope. AIDS. 2008;22\nSuppl 2:S27–34.20MasquillierC, WoutersE, MortelmansD, Booysen FleR. Families as catalysts for peer adherence support in enhancing hope for people living with HIV/AIDS in South Africa. J Int AIDS Soc. 2014;17:18802\ndoi: 10.7448/IAS.17.1.18802\n2470279721AmanyireG, SemitalaFC, NamusobyaJ, KaturamuR, KampiireL, WallentaJ, et al\nEffects of a multicomponent intervention to streamline initiation of antiretroviral therapy in Africa: a stepped-wedge cluster-randomised trial. Lancet HIV. 2016;3(11):e539–e48. doi: 10.1016/S2352-3018(16)30090-X\n2765887322MyerL, ZulligerR, BlackS, PienaarD, BekkerLG. Pilot programme for the rapid initiation of antiretroviral therapy in pregnancy in Cape Town, South Africa. AIDS Care. 2012;24(8):986–92. doi: 10.1080/09540121.2012.668173\n2251956123BrownLB, HavlirDV, AyiekoJ, MwangwaF, OwaraganiseA, KwarisiimaD, et al\nHigh levels of retention in care with streamlined care and universal test and treat in East Africa. AIDS. 2016;30(18):2855–64. doi: 10.1097/QAD.0000000000001250\n2760329024SanneI, OrrellC, FoxMP, ConradieF, IveP, ZeineckerJ, et al\nNurse versus doctor management of HIV-infected patients receiving antiretroviral therapy (CIPRA-SA): a randomised non-inferiority trial. Lancet. 2010;376(9734):33–40. doi: 10.1016/S0140-6736(10)60894-X\n2055792725LongL, BrennanA, FoxMP, NdibongoB, JaffrayI, SanneI, et al\nTreatment outcomes and cost-effectiveness of shifting management of stable ART patients to nurses in South Africa: an observational cohort. PLoS Med. 2011;8(7):e1001055\ndoi: 10.1371/journal.pmed.1001055\n2181140226HumphreysCP, WrightJ, WalleyJ, MamvuraCT, BaileyKA, NtshalintshaliSN, et al\nNurse led, primary care based antiretroviral treatment versus hospital care: a controlled prospective study in Swaziland. BMC Health Serv Res. 2010;10:229\ndoi: 10.1186/1472-6963-10-229\n2068795527FairallL, BachmannMO, LombardC, TimmermanV, UebelK, ZwarensteinM, et al\nTask shifting of antiretroviral treatment from doctors to primary-care nurses in South Africa (STRETCH): a pragmatic, parallel, cluster-randomised trial. Lancet. 2012;380(9845):889–98. doi: 10.1016/S0140-6736(12)60730-2\n2290195528TenthaniL, HaasAD, TweyaH, JahnA, van OosterhoutJJ, ChimbwandiraF, et al\nRetention in care under universal antiretroviral therapy for HIV-infected pregnant and breastfeeding women (\'Option B+\') in Malawi. AIDS. 2014;28(4):589–98. doi: 10.1097/QAD.0000000000000143\n2446899929FoxMP, ShearerK, MaskewM, Meyer-RathG, ClouseK, SanneI. Attrition through Multiple Stages of Pre-Treatment and ART HIV Care in South Africa. PLOS ONE. 2014;9(10):e110252\ndoi: 10.1371/journal.pone.0110252\n2533008730MugglinC, EstillJ, WandelerG, BenderN, EggerM, GsponerT, et al\nLoss to programme between HIV diagnosis and initiation of antiretroviral therapy in sub-Saharan Africa: systematic review and meta-analysis. Trop Med Int Health. 2012;17(12):1509–20. doi: 10.1111/j.1365-3156.2012.03089.x\n2299415131Bulletin de Surveillance, Epidemiologique VIH/SIDA, Programme National de Lutte contre les IST/VIH/SIDA, Juin, 2016.32Guiteau Moise C, Bellot C, Hennessey K, Rivera V, Severe P, Aubin D, et al. Retention of clinically stable ART patients in a rapid model of care in Haiti. Conference on Retroviruses and Opportunistic Infections (CROI), Boston, MA, USA, 2016.', 'title': 'Same-day HIV testing with initiation of antiretroviral therapy versus standard care for persons living with HIV: A randomized unblinded trial.', 'date': '2017-07-26'}, '29509839': {'article_id': '29509839', 'content': 'Home-based HIV testing is a frequently used strategy to increase awareness of HIV status in sub-Saharan Africa. However, with referral to health facilities, less than half of those who test HIV positive link to care and initiate antiretroviral therapy (ART).\nTo determine whether offering same-day home-based ART to patients with HIV improves linkage to care and viral suppression in a rural, high-prevalence setting in sub-Saharan Africa.\nOpen-label, 2-group, randomized clinical trial (February 22, 2016-September 17, 2017), involving 6 health care facilities in northern Lesotho. During home-based HIV testing in 6655 households from 60 rural villages and 17 urban areas, 278 individuals aged 18 years or older who tested HIV positive and were ART naive from 268 households consented and enrolled. Individuals from the same household were randomized into the same group.\nParticipants were randomly assigned to be offered same-day home-based ART initiation (n\u2009=\u2009138) and subsequent follow-up intervals of 1.5, 3, 6, 9, and 12 months after treatment initiation at the health facility or to receive usual care (n\u2009=\u2009140) with referral to the nearest health facility for preparatory counseling followed by ART initiation and monthly follow-up visits thereafter.\nPrimary end points were rates of linkage to care within 3 months (presenting at the health facility within 90 days after the home visit) and viral suppression at 12 months, defined as a viral load of less than 100 copies/mL from 11 through 14 months after enrollment.\nAmong 278 randomized individuals (median age, 39 years [interquartile range, 28.0-52.0]; 180 women [65.7%]), 274 (98.6%) were included in the analysis (137 in the same-day group and 137 in the usual care group). In the same-day group, 134 (97.8%) indicated readiness to start ART that day and 2 (1.5%) within the next few days and were given a 1-month supply of ART. At 3 months, 68.6% (94) in same-day group vs 43.1% (59) in usual care group had linked to care (absolute difference, 25.6%; 95% CI, 13.8% to 36.3%; P\u2009<\u2009.001). At 12 months, 50.4% (69) in the same-day group vs 34.3% (47) in usual care group achieved viral suppression (absolute difference, 16.0%; 4.4%-27.2%; P\u2009=\u2009.007). Two deaths (1.5%) were reported in the same-day group, none in usual care group.\nAmong adults in rural Lesotho, a setting of high HIV prevalence, offering same-day home-based ART initiation to individuals who tested positive during home-based HIV testing, compared with usual care and standard clinic referral, significantly increased linkage to care at 3 months and HIV viral suppression at 12 months. These findings support the practice of offering same-day ART initiation during home-based HIV testing.\nclinicaltrials.gov Identifier: NCT02692027.', 'title': 'Effect of Offering Same-Day ART vs Usual Health Facility Referral During Home-Based HIV Testing on Linkage to Care and Viral Suppression Among Adults With HIV in Lesotho: The CASCADE Randomized Clinical Trial.', 'date': '2018-03-07'}, '27163694': {'article_id': '27163694', 'content': 'PLoS MedPLoS MedplosplosmedPLoS Medicine1549-12771549-1676Public Library of ScienceSan Francisco, CA USA27163694486268110.1371/journal.pmed.1002015PMEDICINE-D-15-03455Research ArticleBiology and Life SciencesImmunologyVaccination and ImmunizationAntiviral TherapyAntiretroviral TherapyMedicine and Health SciencesImmunologyVaccination and ImmunizationAntiviral TherapyAntiretroviral TherapyMedicine and Health SciencesPublic and Occupational HealthPreventive MedicineVaccination and ImmunizationAntiviral TherapyAntiretroviral TherapyBiology and Life SciencesMicrobiologyMedical MicrobiologyMicrobial PathogensViral PathogensImmunodeficiency VirusesHIVMedicine and Health SciencesPathology and Laboratory MedicinePathogensMicrobial PathogensViral PathogensImmunodeficiency VirusesHIVBiology and Life SciencesOrganismsVirusesViral PathogensImmunodeficiency VirusesHIVBiology and Life SciencesOrganismsVirusesImmunodeficiency VirusesHIVBiology and life sciencesOrganismsVirusesRNA virusesRetrovirusesLentivirusHIVBiology and Life SciencesMicrobiologyMedical MicrobiologyMicrobial PathogensViral PathogensRetrovirusesLentivirusHIVMedicine and Health SciencesPathology and Laboratory MedicinePathogensMicrobial PathogensViral PathogensRetrovirusesLentivirusHIVBiology and Life SciencesOrganismsVirusesViral PathogensRetrovirusesLentivirusHIVPeople and placesGeographical locationsAfricaSouth AfricaBiology and Life SciencesAnatomyBody FluidsBloodBlood CountsMedicine and Health SciencesAnatomyBody FluidsBloodBlood CountsBiology and Life SciencesPhysiologyBody FluidsBloodBlood CountsMedicine and Health SciencesPhysiologyBody FluidsBloodBlood CountsMedicine and Health SciencesHematologyBloodBlood CountsMedicine and Health SciencesHealth CareHealth Care ProvidersNursesPeople and PlacesPopulation GroupingsProfessionsNursesBiology and Life SciencesMicrobiologyVirologyViral Transmission and InfectionViral LoadMedicine and Health SciencesInfectious DiseasesBacterial DiseasesTuberculosisMedicine and Health SciencesTropical DiseasesTuberculosisMedicine and Health SciencesPharmaceuticsDrug TherapyInitiating Antiretroviral Therapy for HIV at a Patient’s First Clinic Visit: The RapIT Randomized Controlled TrialSingle-Visit ART InitiationRosenSydney\n1\n\n2\n*MaskewMhairi\n2\nFoxMatthew P.\n2\n\n3\nNyoniCynthia\n2\nMongwenyanaConstance\n2\nhttp://orcid.org/0000-0003-1473-880XMaleteGiven\n2\nSanneIan\n2\nhttp://orcid.org/0000-0001-5800-1960BokabaDorah\n4\nSaulsCeleste\n2\nhttp://orcid.org/0000-0002-1180-8764RohrJulia\n1\nLongLawrence\n2\n\n1\nDepartment of Global Health, Boston University School of Public Health, Boston, Massachusetts, United States of America\n\n2\nHealth Economics and Epidemiology Research Office, Department of Internal Medicine, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa\n\n3\nDepartment of Epidemiology, Boston University School of Public Health, Boston, Massachusetts, United States of America\n\n4\nHealth Department, City of Johannesburg, Johannesburg, South Africa\nBinagwahoAgnesAcademic Editor\nRwanda Ministry of Health, RWANDA\nThe authors have declared that no competing interests exist.Conceived and designed the experiments: SR LL MM IS MPF. Performed the experiments: CN CM DB CS JR. Analyzed the data: MM GM SR. Wrote the first draft of the manuscript: SR MM. Contributed to the writing of the manuscript: SR MM LL MPF. Enrolled patients: CN. Agree with the manuscript’s results and conclusions: SR MM LL MPF CN CM GM IS DB CS JR. All authors have read, and confirm that they meet, ICMJE criteria for authorship.* E-mail: sbrosen@bu.edu105201652016135e1002015171120152232016© 2016 Rosen et al2016Rosen et alThis is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.BackgroundHigh rates of patient attrition from care between HIV testing and antiretroviral therapy (ART) initiation have been documented in sub-Saharan Africa, contributing to persistently low CD4 cell counts at treatment initiation. One reason for this is that starting ART in many countries is a lengthy and burdensome process, imposing long waits and multiple clinic visits on patients. We estimated the effect on uptake of ART and viral suppression of an accelerated initiation algorithm that allowed treatment-eligible patients to be dispensed their first supply of antiretroviral medications on the day of their first HIV-related clinic visit.Methods and FindingsRapIT (Rapid Initiation of Treatment) was an unblinded randomized controlled trial of single-visit ART initiation in two public sector clinics in South Africa, a primary health clinic (PHC) and a hospital-based HIV clinic. Adult (≥18 y old), non-pregnant patients receiving a positive HIV test or first treatment-eligible CD4 count were randomized to standard or rapid initiation. Patients in the rapid-initiation arm of the study (“rapid arm”) received a point-of-care (POC) CD4 count if needed; those who were ART-eligible received a POC tuberculosis (TB) test if symptomatic, POC blood tests, physical exam, education, counseling, and antiretroviral (ARV) dispensing. Patients in the standard-initiation arm of the study (“standard arm”) followed standard clinic procedures (three to five additional clinic visits over 2–4 wk prior to ARV dispensing). Follow up was by record review only. The primary outcome was viral suppression, defined as initiated, retained in care, and suppressed (≤400 copies/ml) within 10 mo of study enrollment. Secondary outcomes included initiation of ART ≤90 d of study enrollment, retention in care, time to ART initiation, patient-level predictors of primary outcomes, prevalence of TB symptoms, and the feasibility and acceptability of the intervention. A survival analysis was conducted comparing attrition from care after ART initiation between the groups among those who initiated within 90 d. Three hundred and seventy-seven patients were enrolled in the study between May 8, 2013 and August 29, 2014 (median CD4 count 210 cells/mm3). In the rapid arm, 119/187 patients (64%) initiated treatment and were virally suppressed at 10 mo, compared to 96/190 (51%) in the standard arm (relative risk [RR] 1.26 [1.05–1.50]). In the rapid arm 182/187 (97%) initiated ART ≤90 d, compared to 136/190 (72%) in the standard arm (RR 1.36, 95% confidence interval [CI], 1.24–1.49). Among 318 patients who did initiate ART within 90 d, the hazard of attrition within the first 10 mo did not differ between the treatment arms (hazard ratio [HR] 1.06; 95% CI 0.61–1.84). The study was limited by the small number of sites and small sample size, and the generalizability of the results to other settings and to non-research conditions is uncertain.ConclusionsOffering single-visit ART initiation to adult patients in South Africa increased uptake of ART by 36% and viral suppression by 26%. This intervention should be considered for adoption in the public sector in Africa.Trial RegistrationClinicalTrials.gov NCT01710397, and South African National Clinical Trials Register DOH-27-0213-4177.In the RapIT randomized controlled trial, Sydney Rosen and colleagues investigate whether accelerated initiation of antiretroviral therapy can improve viral suppression for HIV patients in South Africa.Author SummaryWhy Was This Study Done?One of the most persistent operational challenges facing antiretroviral therapy (ART) programs for HIV/AIDS in sub-Saharan Africa is late presentation of patients for care and high rates of attrition from care between HIV testing and ART initiation.One reason for this is that starting ART in many countries is a lengthy and burdensome process, imposing long waits and multiple clinic visits on patients; in South Africa, the country with the world’s largest HIV treatment program, patients must typically make five or six clinic visits, starting with an HIV test, before they receive medications.There have not yet been any controlled evaluations of an integrated, rapid HIV treatment initiation algorithm that allows patients to initiate ART in a single clinic visit, so the RapIT trial was done to find out if “same-day initiation” of ART would increase the number of patients starting treatment and improve overall health outcomes, compared to current practices.What Did the Researchers Do and Find?We randomly assigned 377 adult patients at two public clinics in Johannesburg, South Africa, who had provided consent to participate in the study to one of two groups.Patients in the group assigned to receive rapid treatment initiation were offered the chance to start treatment on the same day as their first clinic visit, using rapid, point-of-care laboratory tests and an accelerated sequence of other steps, including a physical exam, education, and counseling.Patients in the group assigned to receive standard treatment initiation followed the standard schedule for treatment initiation used by the clinics, which usually required three to five additional clinic visits over a 2–4 wk period.After the study enrollment visit, patients were followed up by reviewing their regular clinic medical records, to determine how many did start treatment and how many were still in care and had good outcomes, as indicated by a suppressed viral load, 10 mo later.We found that 97% of patients in the rapid initiation group had started ART by 90 d after study enrollment—three-quarters of them on the same day—compared to 72% of patients in the standard initiation group.By 10 mo after study enrollment, 64% of patients in the rapid group had good outcomes compared to 51% in the standard group.Rapid initiation group patients spent roughly two and a half hours in the clinic to complete all the steps required before they got their medications.What Do These Findings Mean?The RapIT (Rapid Initiation of Treatment) trial showed that it is possible to initiate nearly all eligible patients on HIV therapy, and to do so in a much shorter time interval than previously required.By showing that offering the opportunity to start treatment on the spot, without delay, overcomes many barriers patients would otherwise face, this study demonstrates that same-day ART initiation is an effective strategy for improving health outcomes.More patients in the rapid initiation group dropped out of care after starting treatment than in the standard initiation group; although the rapid initiation group still had better health outcomes overall, adherence support after starting treatment remains essential.The findings of this study are limited because the study took place in only two clinics in one part of South Africa and was carried out by study staff, not by regular clinic staff.Based on this study’s results, consideration could be given to accelerating the process of ART initiation in many different settings and for different types of patients.http://dx.doi.org/10.13039/100000060National Institute of Allergy and Infectious Diseases1U01AI100015RosenSydneyFunding for this study was provided by the U.S. National Institutes of Health (National Institute of Allergy and Infectious Diseases) under the terms of grant 1U01AI100015 to Boston University. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Data AvailabilityData will be made publicly available in the Dryad repository (http://www.datadryad.org/) after the protocol has been closed (anticipated closure June 2018). Until then, data will remain under the supervision of the University of the Witwatersrand Human Research Ethics Committee (HREC). Requests should be sent to the HREC Research Administrator at: https://www.wits.ac.za/research/about-our-research/ethics-and-research-integrity/human-research-ethics-committee-medical/.Data AvailabilityData will be made publicly available in the Dryad repository (http://www.datadryad.org/) after the protocol has been closed (anticipated closure June 2018). Until then, data will remain under the supervision of the University of the Witwatersrand Human Research Ethics Committee (HREC). Requests should be sent to the HREC Research Administrator at: https://www.wits.ac.za/research/about-our-research/ethics-and-research-integrity/human-research-ethics-committee-medical/.IntroductionOne of the most persistent operational challenges facing antiretroviral therapy (ART) programs for HIV/AIDS in sub-Saharan Africa is late presentation of patients for care and high rates of attrition from care between HIV testing and ART initiation, with baseline median CD4 cell counts remaining well below 200 cells/mm3 in the region despite steadily rising eligibility thresholds [1]. Even among those who have been diagnosed and found to be treatment-eligible, loss to care before starting ART has consistently been estimated at a third to a quarter of patients [2,3]. While many of those who drop out of care prior to ART initiation will make their way back at a later time, they will almost certainly have lower CD4 counts and more symptoms of illness than when they first tested positive. Some will be very sick or die before treatment can be started, and those who do eventually start will have a poorer prognosis on treatment than if they had begun treatment earlier [4,5]. Offering ART to all who test positive regardless of CD4 count, as is now recommended by the World Health Organization [6], will make little difference if those who test positive fail to initiate treatment.There are likely many causes of loss to care before treatment initiation, but one reason observed is that starting ART in many countries is a lengthy and burdensome process, requiring long waits and multiple clinic visits [7,8]. In South Africa, the country with the world’s largest HIV treatment program [9], the process typically includes an HIV test (visit 1), determination of treatment eligibility (visit 2), adherence education and counseling and baseline blood tests (visits 3, 4, and 5), and physical examination and dispensing of antiretrovirals (ARVs) (visit 6). The proliferation of visits has three main causes. First, clinic receipt of printed test results from centralized laboratories typically takes several days, if not longer. Second, a belief remains that to ensure adherence, patients must participate in multiple preparatory educational and counseling sessions [2,10,11]. And third, clinics have had little motivation to accelerate the initiation process for patients who are not critically ill, as standard performance indicators do not include the proportion of eligible patients who actually initiate ART, nor the time required to do so.If patients are deterred from starting treatment by the complexity of the process, then one strategy for reducing loss of patients prior to ART initiation and encouraging earlier treatment initiation may be to shorten the time period, reduce the number of visits, and simplify the steps required before medications are dispensed. This strategy depends critically on two factors: a clinic’s willingness and ability to adjust its schedules and procedures to compress and accelerate the required steps, and the availability of rapid, point-of care (POC) laboratory assays that eliminate delays in receiving whatever lab results are required for initiation. There have not yet been any rigorous, controlled evaluations of an integrated, rapid HIV treatment initiation algorithm incorporating procedural changes and POC tests for adult, non-pregnant patients. We therefore conducted a randomized controlled trial of rapid ART initiation that allowed patients in public sector clinics in Johannesburg, South Africa to have treatment eligibility determined, all treatment preparation steps performed, and ARV medications dispensed on the day of their first HIV-related clinic visit.MethodsRapIT (Rapid Initiation of Treatment) was an unblinded, individually randomized, controlled trial of a service delivery intervention. It was approved by the Institutional Review Board of Boston University Medical Campus (H-31880) and the Human Research Ethics Committee (Medical) of the University of the Witwatersrand (M120843) and is registered with ClinicalTrials.gov, number NCT01710397.Study Sites, Infrastructure, and StaffingRapIT was conducted at two public sector outpatient clinics. Site 1 is a primary health clinic serving an urban informal settlement population on the edge of Johannesburg. Site 2 is a large, hospital-based HIV clinic serving an urban formal and informal population within Johannesburg. Both sites follow South African national treatment guidelines for ART initiation, ARV regimens, and monitoring [12]. During the period of study enrollment, May 8, 2013–August 29, 2014, the prevailing threshold for ART eligibility was a CD4 count ≤ 350 cells/mm3 or a WHO Stage 3/4 clinical condition. Requirements for care prior to initiating ART are not standardized in South Africa [13], but both sites generally required four to five clinic visits between HIV testing and dispensing the first month’s supply of ARVs.At each site, a small clinic room with security bars, running water, and basic furnishings was designated for study equipment and supplies, POC instruments, and files. As all the POC instruments were designed as desktop devices, no separate laboratory was needed. An outdoor booth for safe collection of sputum samples from tuberculosis (TB) suspects was constructed at Site 1 and made available for both study arms; existing facilities for this purpose were used at Site 2. Clinical procedures were performed by study nurses with the same level of clinical certification as existing primary health care nurses at the sites. Non-clinical procedures (consent, questionnaire, education, counseling, patient flow management) were implemented by study assistants with qualifications comparable to those of experienced lay counselors at the sites. All study staff received study and instrument-specific training. A small stipend (R1000/month, equivalent to US$86 at the exchange rate at the time of the study) was paid to clinic lay counselors at Site 1 and a messenger at Site 2 who assisted by referring potential study participants to the study assistant.Study PopulationThe study enrolled adult (≥18 y old), non-pregnant patients who presented to have an HIV test, provide a blood sample for a CD4 count if already known to be HIV-infected, or receive the results of the patient’s first treatment-eligible CD4 count. During pre-screening and screening, patients who had previously been found to be eligible for ART, were already on ART or reported receiving it in the past 12 mo, indicated that they intended to seek HIV care during the next 12 mo at a different clinic, were judged by clinic or study staff to be physically or emotionally unable to provide consent or participate in all study procedures, or did not meet other study inclusion criteria were excluded. Potential participants whose visit purpose was to have an HIV test were enrolled; those found post-enrollment not to be eligible for ART were subsequently withdrawn upon determination of ineligibility. Potential participants whose visit purpose was to receive a CD4 count result and were not eligible for treatment on the basis of that CD4 count were not enrolled.Participants were individually randomized 1:1 to either rapid treatment initiation or standard-of-care treatment initiation, using block randomization in blocks of 6. Sealed, opaque envelopes containing the allocations were prepared by the local principal investigator and numbered sequentially. The envelopes were kept in sequential, numbered order at the study sites. After obtaining written informed consent, the study assistant opened the next sequentially numbered envelope to reveal the allocation.Study Design and ProceduresProcedures for each study arm are illustrated in Fig 1. Standard-of-care treatment initiation followed existing procedures at the sites as closely as possible. Study staff interaction with participants was limited to screening for study eligibility, obtaining written informed consent, administering a questionnaire, and referring patients to clinic staff for either a blood draw for a CD4 count or a next visit appointment if the patient already had results of a CD4 count in hand. After referral, patients in the standard-initiation arm of the study were followed passively, through medical record review, and had no further interaction with the study. Standard-of-care procedures for ART initiation at both study sites included a CD4 count to determine eligibility, TB symptom screening followed by a TB test and TB treatment initiation if required, pre-initiation blood tests (hemoglobin, creatinine, and alanine aminotransferase (ALT)), group and individual counseling and education sessions, and a physical examination. All samples for laboratory tests were sent to centralized public sector laboratories, requiring patients to make separate clinic visits to provide biological samples and to receive results. Once ART eligibility was determined, initiation typically required three to four more clinic visits over a period of 2–4 wk. Patients who were very ill or found to have low CD4 counts could be “fast-tracked,” with the schedule shown in Fig 1 completed in as little as one week.10.1371/journal.pmed.1002015.g001Fig 1Standard initiation of treatment and rapid initiation procedures and visit schedule.For patients randomized to rapid initiation, all the same procedures were performed, but the use of a compressed and accelerated schedule and rapid laboratory instruments at point of care allowed them all to be completed in a single visit (Box 1). Patients offered rapid initiation typically completed each step in order, with little or no waiting time in between unless a TB test was required, which entailed a wait to process the sample. Patients who enrolled in the study too late in the day for all steps to be completed before the clinic closed were asked to return the next day to finish study procedures. Patients who were randomized to rapid initiation but did not have time to participate on the day of enrollment or wished to delay for other reasons were given up to 30 d to return and be initiated under rapid procedures. Those returning beyond 30 d were offered standard initiation by the clinic.Box 1. Rapid Initiation ProceduresCD4 countPatients who enrolled in the study and did not already have CD4 count results from a test performed within the previous 6 mo were given a rapid CD4 count using the Alere Pima CD4 Test (http://alerehiv.com/hiv-monitoring/alere-pima-cd4/) with venous blood draw. This test, previously evaluated in several studies in Africa [14–18], provides a CD4 count result from a capillary or venous blood sample in 20 min. Following the test, patients with a CD4 count ≤ 350 cells mm3 or evident physical symptoms or complaints that suggested a Stage 3 or 4 condition continued with study procedures. Those not eligible for ART were withdrawn from the study at this point and referred to the clinic for standard pre-ART monitoring.TB symptom screen and testWhile awaiting CD4 count results, a TB symptom screen was administered using South Africa’s four-question screening tool. All patients who reported symptoms were then asked to provide a sputum sample, which was immediately processed using the Cepheid Xpert MTB/RIF test (http://www.cepheid.com/us/cepheid-solutions/clinical-ivd-tests/critical-infectious-diseases/xpert-mtb-rif). This is the technology currently used for TB diagnosis in the public sector throughout South Africa, but it is located in centralized laboratories rather than at point of care [19]. It generates a TB diagnosis in 90 min [20]. Two sputum samples were run simultaneously to increase the reliability of results. Any patient who received a positive Xpert test was escorted to the clinic TB nurse to initiate TB treatment, which under national guidelines required a delay of at least 2 wk before ART could be initiated. Patients initiated on TB treatment were asked to return 2 wk later to complete rapid ART initiation on a second visit.Baseline testsOnce eligibility for ART was established, pre-initiation blood tests (hemoglobin, creatinine, and ALT) were run on a point-of-care Reflotron Plus instrument (Roche, http://www.roche-diagnostics.co.in/Products/Pages/ReflotronPlusDry.aspx)[14] using the same blood sample dawn for the CD4 count. This instrument takes approximately 2 min to complete each test. A standard clinic urine dipstick pregnancy test was also conducted for female patients of child-bearing age.Physical examA standard physical examination was conducted by the study nurse to identify any specific conditions or concerns prior to initiating ART. Initiation was delayed in patients found to have conditions that required referral to a hospital or consultation with the clinic’s doctor.Education sessionA condensed version of HIV/ART/adherence education was developed using the study clinics’ materials and provided to study participants. It was delivered in a one-on-one session by the study counselor in approximately 20 min.Counseling sessionAfter completing all tests, physical examination, and education session, each patient met individually with the study nurse, who reviewed results with the patient and provided an opportunity for the patient to ask any remaining questions and confirm that she or he was indeed ready for treatment initiation.Dispensing of ARVsThe study nurses, like other qualified nurses in South Africa, were authorized to write prescriptions for ARVs, which could then be filled directly by the nurse from study room stock (Site 1) or at the on-site clinic pharmacy (Site 2). Study patients at Site 2 were served at the pharmacy immediately, rather than being required to wait in pharmacy queues to fill prescriptions. Once the initial 4 wk supply of ARVs was dispensed, study interaction with rapid group patients ceased. Patients were asked to return to the clinic for monitoring and prescription refill by clinic staff in 1 mo, consistent with routine practice.After the enrollment visit, or completion of rapid initiation procedures for patients in the rapid-initiation arm of the study (“rapid arm”) who delayed initiation but returned to complete it within 30 d, the study team had no further contact with study patients. Patients who started ART in either arm received standard-of care treatment management from the clinic, which called for monitoring visits and medication refills at 1, 2, 3, 6, and 12 mo after initiation, with a routine viral load test at the 6 mo visit.Outcomes and DataThe primary, protocol-defined outcome for the study was viral suppression (≤400 copies/ml) within 10 mo of study enrollment, a time period selected to capture the 6 mo routine monitoring visit called for by national guidelines. Ten months was selected as the endpoint to allow patients to take up to 3 mo to initiate ART and to be up to 1 mo late for the 6 mo routine visit. Because the study sites occasionally omitted the 6 mo viral load and performed the test only at 12 mo, we considered a patient with a suppressed viral load test result any time from 3 to 12 mo after study enrollment to have achieved viral suppression. In this analysis, missing viral load test results were regarded as failures; only patients with recorded, suppressed viral load results were defined as virally suppressed. To account for the possibility that viral load results could be missing due to clinic oversight in not ordering the test, rather than patient default, and to investigate the possibility that rapid initiation merely shifts attrition from before to after treatment initiation, we also report the secondary outcome of retention in care at 10 mo after study enrollment, with retention defined as any HIV-related clinic visit in months 5–10 after study enrollment, regardless of viral load.Although viral suppression was the primary outcome assessed, the pathway by which the study aimed to increase suppression was reduction of attrition between HIV testing and treatment initiation. We therefore report initiation of treatment within 90 d of study enrollment as a secondary outcome, with initiation defined as being dispensed a first month’s supply of ARVs. We also report uptake of treatment within 180 d, as a CD4 count result is considered to be valid under South African guidelines for 6 mo—after that, a patient must have a new CD4 count to establish eligibility for ART. Finally, we report the distribution of time (d) to treatment initiation in each group.Other secondary outcomes evaluated in the study included the feasibility of the intervention, as indicated by the ability of both study sites to implement the accelerated algorithm; acceptability of the intervention, as measured by the proportion of patients offered rapid initiation who accepted it; patient-level predictors of the primary outcome; and, in the rapid arm, the prevalence of TB symptoms and confirmed TB disease and ART initiation among patients with TB.After the enrollment visit, all data collection for both groups was by passive medical record review. Both study sites routinely utilized an electronic medical record system called TherapyEdge-HIV, into which patient data were entered retrospectively by data clerks from paper files (Site 1) or by a combination of clinicians in real time and data clerks from paper files (Site 2)[21]. This record system improved the completeness of the follow-up dataset used in the study. In instances of incomplete follow-up data—for example, if the database reported a clinic visit 6 mo after ART initiation but contained no viral load test result—study staff searched the clinics’ paper files and registers and the online data portal of the National Health Laboratory Service to determine if any additional information existed but had not been recorded in the clinics’ databases. The study team had no further contact with study participants after the enrollment visit so as not to have any influence on retention in care, a study outcome.Data AnalysisWe designed the study to detect a 20% difference in viral suppression rates between the arms at 10 mo after study enrollment. With an α of 0.05, power of 90%, 1:1 randomization, and an uncorrected Fisher’s exact test, we estimated that we would need to enroll at least 124 HIV positive ART-eligible participants per group (248 total). We increased this to a maximum of 200 per group (400 total) to allow for stratification by site, sex, or age.Characteristics at study enrollment of all randomized participants who met ART initiation and study inclusion criteria were summarized using simple proportions and medians with interquartile ranges (IQR) stratified by treatment arm. For the remaining analyses, we excluded patients who were found after randomization not to be eligible for ART or not to meet study inclusion criteria. We compared the proportions of patients achieving each dichotomized study outcome and present crude risk ratios (RR) and risk differences (RD) with 95% confidence intervals (CI) stratified by group. Baseline predictors of outcomes that appeared imbalanced by treatment arm were also adjusted for using log-linear regression models to estimate adjusted risk ratios (aRR). We estimated time to treatment initiation in days using a cumulative incidence curve. To investigate whether attrition after initiation of ART differed between the study arms, we performed a survival analysis comparing attrition from care after ART initiation among those who initiated within 90 d between the groups. Person-time accrued from ART initiation date to the earliest of loss to follow up, transfer, or 10 mo of follow up, and hazard ratios of attrition from care were estimated with Cox proportional hazards models. A stratified analysis was performed to detect effect measure modification by site or patient-level factors. Finally, to confirm that no imbalance was created by excluding patients after randomization for reasons other than ineligibility for ART or evidence of a previous eligible CD4 count, we conducted sensitivity analysis incorporating the excluded patients and assigning each a negative outcome.ResultsBetween May 8, 2013, and August 29, 2014, 603 patients were screened for study eligibility and 463 provided written informed consent and were enrolled in the study (Fig 2). Of the 140 screened but excluded prior to randomization, 109 did not meet study eligibility criteria, including 43 who resided outside study clinic catchment areas or intended to seek further care elsewhere; 24 who were determined by the study assistant to be too ill for consent and study procedures; 16 who were not eligible on the basis of a prior CD4 count, were ineligible for ART, or were already on ART; 12 who were determined by the study assistant to be too emotionally upset to provide consent; 9 who did not speak any of the languages spoken by the study team; 3 who were found to be pregnant; and 2 who were excluded for other reasons. An additional 31 patients refused participation; of these, 18 were in a hurry and did not have time for study procedures, six did not wish to participate in the study, five stated that they would prefer standard care, and two were not willing to initiate therapy. Follow-up ended 10 mo after the last patient was enrolled (June 28, 2015).10.1371/journal.pmed.1002015.g002Fig 2Study enrollment and randomization.Characteristics of patients in each study arm at time of enrollment are reported in Table 1. There were no important differences between the study arms in the variables shown. Just over half the participants were female and the median age was 35 y. The median CD4 count was less than 200 cells/mm3. Age, sex, and CD4 count characteristics of the study sample were similar to those of the overall non-pregnant patient populations initiating ART at the study clinics in 2014.10.1371/journal.pmed.1002015.t001Table 1Baseline characteristics of study sample (n = 463).VariableStandard armRapid arm\nn (randomized participants)229234Enrollment site (n)\xa0\xa0\xa0\xa0Site 1 (primary health clinic)124126\xa0\xa0\xa0\xa0Site 2 (hospital-based HIV clinic)105108Age (median, IQR)35.8 (29.5–41.6)34.2 (29.0–40.1)Sex (% female)132 (58%)129 (55%)CD4 count (cells/mm3) (median, IQR)195 (103–322)224 (128–327)Purpose of clinic visit (%)\xa0\xa0\xa0\xa0Have HIV test (diagnosed today)100 (44%)90 (38%)\xa0\xa0\xa0\xa0Provide blood sample for CD4 count8 (4%)10 (4%)\xa0\xa0\xa0\xa0Receive first CD4 count results109 (47%)112 (48%)\xa0\xa0\xa0\xa0Other11 (5%)22 (10%)Reason for treatment eligibility (%)\xa0\xa0\xa0\xa0CD4 count below threshold182 (79%)183 (78%)\xa0\xa0\xa0\xa0Clinical condition Stage 3 or 43 (1%)4 (2%)\xa0\xa0\xa0\xa0Excluded (not eligible for treatment or study)44 (20%)47 (20%)Household composition\xa0\xa0\xa0\xa0Live alone (% yes)36 (16%)41 (18%)\xa0\xa0\xa0\xa0# other persons in house (median, IQR)2 (1–4)2 (1–3)Household type (%)\xa0\xa0\xa0\xa0Formal house or flat146 (63%)165 (71%)\xa0\xa0\xa0\xa0Informal dwelling or shack83 (37%)69 (29%)Travel time to clinic (minutes) (median, IQR)18 (9–24)15 (9–27)Employment status (%)\xa0\xa0\xa0\xa0Employed formally68 (30%)90 (38%)\xa0\xa0\xa0\xa0Work informally62 (27%)54 (23%)\xa0\xa0\xa0\xa0Unemployed, seeking work91 (40%)84 (36%)\xa0\xa0\xa0\xa0Unemployed, not seeking work8 (3%)6 (3%)Marital status (%)\xa0\xa0\xa0\xa0Married or long-term partner173 (76%)157 (67%)\xa0\xa0\xa0\xa0Single, no long-term partner41 (18%)57 (24%)\xa0\xa0\xa0\xa0Other (widowed, divorced)15 (6%)20 (9%)Reasons for excluding patients during the study screening process are reported in Fig 2. The 603 patients screened represent a subset of those pre-screened by clinic counselors and then referred to the study assistant for screening. While pre-screening data, which were collected by the counselors and not by study staff, are of uncertain quality, they do provide some indication of the proportion of all patients presenting at clinics who could be eligible for rapid initiation. At Site 1, for which the pre-screening data are more complete, a total of 2,636 patients presenting at the clinic’s HIV counseling and testing service were pre-screened. More than half of these were HIV-negative (1,468/2,636, 56%) or known to have CD4 counts above the eligibility threshold or already on ART (114/2,636, 4%). Of the remaining 1,054, 325 (31%) were referred for study screening. Another 293/1,054 (28%) were judged by the counselors not to meet study protocol eligibility criteria (age, residence location, language, not first CD4 count) but would likely have been eligible for the intervention if it were offered as routine care. A fifth (225/1,054, 21%) were regarded by the counselors as too sick for study participation (not necessarily for ART initiation) and were referred to a clinic doctor or nurse for immediate care; it is not clear if they would have been eligible for the intervention or not. The remainder (20%) included patients who refused study participation (36/1,054, 3%) or refused any further care (12/1,054, 1%), were deemed too upset or emotionally distressed to participate (25/1,054, 2%), were referred directly to the clinic’s HIV or TB nurse rather than the study assistant (75/1,054, 7%), or were in a hurry or had no reason stated (63/1,254, 6%).Among 463 patients screened and found eligible for study participation, 234 patients were randomized to rapid initiation and 229 to standard initiation (Fig 2). Upon completion of a CD4 count, which occurred after randomization for those who did not already have one in hand, 37 patients in each group were determined not to be eligible for ART under South African guidelines and were excluded from further data collection and from the analysis. An additional 12 patients were excluded after randomization, for reasons indicated in Fig 2. One hundred and ninety patients in the standard group and 187 in the rapid group (n = 377 total) were offered full study procedures and are included in the analysis below, with sensitivity analysis incorporating the six who were excluded after randomization for a reason other than ineligibility for ART or evidence of a prior eligible CD4 count.The protocol-defined primary outcome for the study was viral suppression within 10 mo of study enrollment. As presented in Table 2, viral suppression by 10 mo was 64% (119/187) in the rapid arm and 51% (96/190) in the standard arm, indicating a risk difference of 13% (3%–33%) and a crude relative risk of 1.26 (1.05–1.50).10.1371/journal.pmed.1002015.t002Table 2ART initiation, 10-mo retention in care, and 10-mo viral suppression.OutcomeStandard arm(%)n = 190Rapid arm(%)n = 187Crude risk difference(95% CI)Crude relative risk(95% CI)Initiated ≤ 90 d and suppressed by 10 mo (primary outcome)96 (51%)119 (64%)13% (3%–23%)1.26 (1.05–1.50)\xa0\xa0\xa0\xa0Of those\nnot\ninitiated ≤ 90 d and suppressed by 10 mo\n\n94 (49%)\n\n68 (36%)\n\n\xa0\xa0\xa0\xa0\xa0\xa0Not initiated\n\n54 (28%)\n\n5 (3%)\n\n\xa0\xa0\xa0\xa0\xa0\xa0Initiated but not suppressed\n\n40 (21%)\n\n63 (34%)\n\n\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0Of those initiated but not suppressed:\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0Retained, unsuppressed viral load test reported\n\n11 (6%)\n\n17 (9%)\n\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0Retained, no viral load test reported\n\n14 (7%)\n\n16 (9%)\n\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0Transferred to another clinic\n\n1 (1%)\n\n6 (3%)\n\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0Died\n\n3 (2%)\n\n0 (0%)\n\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0Lost to follow-up\n\n11 (6%)\n\n24 (13%)\nInitiated ≤ 90 d136 (72%)182 (97%)25% (19%–33%)1.36 (1.24–1.49)Initiated ≤ 90 d and retained at 10 mo (secondary outcome)121 (64%)151 (81%)17% (5%–23%)1.27 (1.12–1.44)\xa0\xa0\xa0\xa0Of those not initiated ≤ 90 d and retained at 10 mo:\n69 (36%)\n\n36 (19%)\n\xa0\xa0\xa0\xa0\xa0\xa0Initiated but not retained\n\n15 (8%)\n\n31 (17%)\n\xa0\xa0\xa0\xa0\xa0\xa0Not initiated\n\n54 (28%)\n\n5 (3%)\nBy 90 d after study enrollment, 97% (182/187) of participants in the rapid arm and 72% (136/190) of participants in the standard arm had initiated ART, equating to a risk difference of 25% (95% CI 19%–33%) and a crude relative risk of 1.36 (1.24–1.49) (Table 2). In adjusted analysis (S1 Table), neither age, sex, nor baseline CD4 count affected these values. By 180 d, one additional patient in the rapid arm and two in the standard arm had initiated, leaving four patients in the rapid arm and 52 in the standard arm who did not initiate within the period of validity of their CD4 count results. In the rapid arm, all four were referred to a clinic nurse or doctor for clinical confirmation of TB and did not return for ART initiation. In the standard arm, 73% (38/52) of the patients who did not initiate within 180 d made no further visits to the site after the visit in which they were enrolled in the study.\nFig 3 shows the cumulative incidence of treatment initiation in each study arm over the 180 d following enrollment. In the rapid arm, 72% (135/187) of patients started ART on the same day as study enrollment, an additional 7% (13/187) on the next day, and 96% (179/187) within 1 mo. In the standard arm, 58% of patients initiated within one month. The median (IQR) time to initiation in the standard arm for the subset who did initiate within 90 d (n = 136) was 17 (11–26) d. For rapid arm patients who did not initiate on the same day (n = 48), the reasons for delay were the need for clinical confirmation of TB or a Stage 3 or 4 condition or for TB treatment (25/48, 52%), insufficient time to complete all steps on the same day (6/48, 13%), patient preferences (5/48, 10%), lack of electricity in the clinic (2/48, 4%), and unknown reasons (10/48, 21%). Time to treatment initiation in the standard arm was shorter for patients who already had CD4 count results available upon study enrollment (median days 16, [IQR 11–22]) compared to those who enrolled in the study at the time of having an HIV test (22 [IQR 10–35]); the median for both types of patients in the rapid arm was 0 d (i.e., same-day initiation).10.1371/journal.pmed.1002015.g003Fig 3Time to ART initiation, by study arm.Cumulative incidence of ART initiation in each study arm, by number of days since study enrollment.All patients in the rapid arm had the opportunity to initiate treatment on the day of study enrollment (same-day initiation) unless one of the reasons for delay listed above pertained to them. To explore whether a delay in initiation was associated with different post-initiation outcomes, we compared patients who did initiate on the same day to those who delayed for any reason. There were no differences in either the primary outcome of viral suppression or the secondary outcome of retention in care between these two groups of patients (S3 Table). Because this analysis was limited to rapid arm patients, however, it is not a randomized comparison and should be interpreted with caution.Retention in care, defined as making a clinic visit between months 5 and 10 after study enrollment, was 81% (151/187) in the rapid arm and 64% (121/190) in the standard arm, for a risk difference of 17% (5%–23%) and a crude relative risk of 1.27 (1.12–1.44). Table 2 also indicates that 86% (31/36) of patients in the rapid arm who were not retained were lost from care after ART initiation, compared to just 22% (15/69) in the standard arm; the fall-off in the standard arm, in contrast, was mainly among those who never initiated (54/69, 78%). Although there was less loss to follow-up after initiation in the standard arm (15/190, 8% versus 31/187, 17%), this was more than offset by the higher pre-initiation loss in the standard arm (54/190, 28% versus 5/187, 3%), resulting in an overall increase in retention of 17%. Among the patients lost to care after initiation (15 in the standard arm and 31 in the rapid arm), a large majority of patients who initiated ART but were not retained in care either never came back after their initiation visit (40% of patients in the standard arm (6/15) and 45% in the rapid arm (14/31)) or came back just once (47% (7/15) and 35% (11/31), respectively), suggesting that most of these patients were never “established” on ART.To explore further the rate of loss to care, we estimated attrition from care within the first 10 mo after initiation among the subsample of 318 patients who did initiate ART within 90 d. In the standard arm, during 1,250 mo of total person-time, 22/136 (16%) dropped out of care after ART initiation, for an attrition rate of 1.8 per 100 person-months. In the rapid arm, during 1,626 mo of total person-time, 30/182 (16%) dropped out of care, for a rate of 1.8 per 100 person-months. The hazard of attrition within the first 10 mo after ART initiation among those who initiated within 90 d did not differ between the treatment arms (HR 1.06; 95% CI 0.61–1.84). We note that this result is subject to selection bias and confounding, however, due to the exclusion of those who did not start treatment within 90 d.In pooled analysis of both study arms, none of the variables presented in Table 1 predicted any of the outcomes reported above, with three exceptions (S2 Table). A slightly higher proportion of patients with baseline CD4 counts below 100 cells/mm3 initiated ART, but this difference did not persist through retention or viral suppression at 10 mo. As might be expected, patients who enrolled in the study at the time of receiving their CD4 count results (thus their second HIV-related clinic visit overall), rather than at the time of having an HIV test, were slightly more likely to achieve all three outcomes, though only for retention in care was this difference statistically significant. Finally, patients who reported being employed at the time of study enrollment, while no more likely to initiate ART, had significantly better retention in care and viral suppression than did those who reported being unemployed.In stratified analysis (S4 Table) we observed non-significant differences in effect sizes for the primary outcome (viral suppression at 10 mo) by sex, age group, and study site. A larger effect was seen among men aged <35 y (risk difference [95% CI] 34% [12%–55%]), while little effect was seen among men or women ≥35 (5% [-9%–19%]). The effect size was also greater at the primary health clinic (21% [8%–34%]), while little effect was seen at the hospital-based HIV clinic (2% [-12%–17%]). As noted, these differences were not statistically significant, and the study was not powered to detect differences among subgroups.In the rapid arm, for which TB diagnostic data were available, 29/187 patients (16%) presented with TB symptoms and were tested for TB using Xpert MTB/RIF. Four patients (17% of those with symptoms and 2% of all rapid arm patients) had a confirmed TB diagnosis. All four initiated ART within the 90-d outcome defined above, with a range of 11–54 d between study enrollment and ART initiation.The results of the sensitivity analysis incorporating the six patients who were excluded after randomization for reasons other than ART eligibility or prior CD4 count, and assigning each a negative outcome, did not differ substantively from the findings presented above, with a relative risk of viral suppression by 10 mo of 1.22 [1.02–1.46].Rapid initiation, using the procedures described above and as implemented by the study, appeared acceptable to patients at the time it was offered and feasible to implement at both study sites. We were not able to assess acceptability after patients received the intervention, as the study had no post-initiation interaction with those enrolled, and thus can surmise acceptability only on the basis of acceptance of the intervention. The study refusal rate was very low (31/603, 5%); nearly four out of five (148/187, 79%) patients offered the intervention accepted initiation on the same day or the next day, and rapid arm patients consistently expressed appreciation for the opportunity to start immediately.All steps in the rapid initiation process were completed on the same day as study enrollment for 72% (135/187) of those in the rapid arm, demonstrating the feasibility of the intervention, at least within the context of the study. From provision of informed consent (study enrollment) to dispensing of the first supply of ARV medications, rapid initiation took a median of 2.4 (IQR 2.1–2.8) hours for those who initiated on the same day as study enrollment. This interval was shorter for patients who already had CD4 count results in hand at study enrollment (median 2.25 hours). It was longer (median 4.5 hours) for those who required a TB test and did initiate ART on the same day, but 15/20 patients requiring TB tests did not initiate on the same day. The only obstacle encountered in implementing rapid procedures was fairly frequent power outages, a common occurrence in South Africa, at Site 1, which did not have a generator for backup power supply. Most rapid instrument tests could not be performed during power outages. The rapid test instruments otherwise performed well throughout the study, and no major delays or problems arose in the acceleration of clinic procedures.DiscussionIn this randomized controlled trial, we evaluated the effectiveness of an accelerated ART initiation algorithm that combined compressed and accelerated clinic procedures with point-of-care laboratory testing technologies that allowed eligible patients to initiate ART in a single clinic visit. This intervention increased the proportion of patients eligible for ART at study enrollment who initiated ART within 90 d by 25%, to 97% of all eligible patients and 100% of patients who were not delayed for TB treatment. By 10 mo after study enrollment, the intervention increased viral suppression among all treatment-eligible patients by 13% and retention in care by 17%. It was feasible and appeared acceptable at both study sites.The trial demonstrated that it is possible to initiate nearly all eligible patients on ART, and to do so in a much shorter time interval than previously required. The net benefit for overall viral suppression was clinically meaningful and may underestimate the true benefits of the intervention. Both the study sites were relatively well-managed, public sector clinics, resulting in a higher rate of ART initiation in the standard arm (72%) than is found elsewhere in the country, for example in rural KwaZulu Natal Province where the rate was 59% [2]. In addition, we observed a larger effect at Site 1, the primary health clinic, than at Site 2, the hospital-based HIV clinic. Primary health clinics, which have fewer resources than hospital-based clinics but treat 85% of HIV patients in South Africa, may struggle more with loss to follow-up before treatment initiation than do hospital-based clinics, creating a greater opportunity for a service delivery intervention like RapIT to be effective. The potential for reaching younger men, who have been among the least likely to access ART under standard care [22], is another important potential benefit of rapid initiation. Additional research is needed to explore further the non-significant differences in effect that we observed in our study.The patients who likely benefited most from RapIT were those who would not otherwise have initiated treatment at all, or who would have waited until they were sick enough to compromise their prognosis on treatment. In the standard arm, most patients who did not start treatment did not return to the study clinics for even one more visit, underscoring the importance of taking full advantage of the first visit to get as many patients started on treatment as possible. For those who would have initiated treatment, just not as soon, there is some evidence that even relatively short delays may be harmful. A recent modeling exercise using South African data estimated that compared to immediate initiation, a delay in initiating ART of 70 d would lead to a 34% increase in 12-mo mortality [22]. Delaying treatment initiation thus both deters some patients from starting at all and jeopardizes outcomes for those who do start.We hypothesize that the delays and multiple visits patients must endure before starting ART directly deter treatment initiation. Patients who cannot afford transport fare for multiple visits, have childcare obligations at home, or risk job or wage loss if they miss too many days of work may be directly deterred from returning. Others may simply grow impatient or lose their courage or motivation, particularly if they are asymptomatic when diagnosed. These patients are likely to drift away and only return when their CD4 counts are lower and symptoms have started, or to die before treatment can be started. Our results suggest that offering the opportunity to start treatment on the spot, without delay, overcomes these barriers, without risking poorer outcomes later on.Among patients who did initiate ART, post-initiation loss to care was higher in the rapid arm than the standard arm. This difference disappeared in the survival analysis, which controlled for number of months on ART but does not reflect the benefits of randomization. We speculate that some patients who did not want or were not ready for treatment chose to accept immediate initiation simply because it was offered or they wanted to participate in the study. For these patients, attrition from care was simply shifted from before ART initiation to after. While the intervention was successful in increasing the overall proportion of treatment-eligible patients with successful outcomes (viral suppression and/or retention in care), the rate of post-initiation attrition is a reminder that early retention in care and adherence support once patients start treatment remain high priorities for further research and intervention.Other studies have gauged the impact on treatment uptake of a single POC technology [23] or changes in service delivery [24], but we found only one prior report of a “single-visit initiation” intervention that was similar, to some degree, to RapIT. That study enrolled pregnant women initiating ART for prevention of mother-to-child transmission in South Africa and found very high uptake of ART among women offered rapid initiation, but it did not have a comparison arm to allow an effect to be estimated [25]. A study in Tanzania and Zambia compared the effect of community support on a two-visit algorithm and reported 99% uptake of ART in both study arms [26]. Taken together, these studies imply that accelerating ART initiation is effective in a wide range of settings.Nothing in the rapid initiation procedures used in this study differed fundamentally from existing clinic procedures. The intervention was delivered by study nurses and counselors with the same qualifications as existing clinic staff, though with study-specific training and supervision. The intervention imposed no major burdens on site management, though managerial acquiescence to the study and operational flexibility were needed to adjust the schedule and content of patient visits, staff responsibilities, and record keeping to allow for rapid initiation [27]. The main technical training required was in the use of the POC test instruments, which also required a secure location within the clinic, temperature control, and electricity.Although South Africa has better clinic infrastructure than do many other countries in the region, the RapIT intervention does not require anything that most urban and many rural clinics cannot provide. We speculate that the RapIT intervention would be feasible and potentially even more effective in other high HIV prevalence areas, where patients travel farther to reach clinics and results from centralized laboratories take even longer to return. As the new WHO guidelines are adopted, moreover, laboratory test results may not be required prior to ART initiation for patients who are asymptomatic, reducing the need for POC technology.The generalizability of our results is limited in several ways. The study was conducted in only two clinics in one province of one country. The trial intervention was delivered by study staff; it is uncertain if clinic staff delivering the same intervention will achieve the same outcomes (and whether their outcomes will be better or worse than those observed in the trial). As is typical in individually randomized trials of service delivery interventions, the possibility exists that quality of care in the standard arm was improved by the presence of the study, as clinic staff providing care for the standard arm may have been motivated by the study to make treatment initiation more efficient. If this occurred, the effect reported here would understate the true improvement in ART initiation that could be expected under routine implementation. As with many studies in which retention in care is an endpoint, we do not know the true outcomes of study patients who were not retained nor whether rapid arm patients who were not retained and who agreed to start treatment solely due to the presence of the study, and would otherwise not have done so, are at increased risk of developing ARV resistance. Finally, as reported above, rapid initiation under the study algorithm took 2–3 hours to complete, making same-day initiation impractical for patients who arrive late in the day (and for clinics with large numbers of such patients).We also do not know how clinic and patient characteristics will affect the net cost and cost-effectiveness of the intervention. Most of the changes introduced in the RapIT intervention entailed only adjustments in schedules and staff time, and we speculate that these will not result in a major net change to service delivery costs. The POC instruments used in the trial require an up-front investment, but it may be possible to initiate ART in a single visit without any POC instruments if there is no CD4 count threshold for initiation, patients with TB symptoms are identified and managed separately, and ARV regimen adjustments are routinely made at the first refill visit, rather than before initiation. Costs saved by patients, who must make just one clinic visit rather than four or five, should also be taken into account.The RapIT intervention as designed and implemented showed clinically meaningful improvements in ART uptake and viral suppression, providing “proof of principle” for a single-visit treatment initiation algorithm. Follow-on studies are needed to evaluate effectiveness and cost-effectiveness in routine practice in a variety of settings, and variations on the algorithm could also be considered. The RapIT trial has demonstrated that accelerating ART initiation can be effective and feasible in this setting and appeared acceptable to patients to whom it was offered; the next challenge will be adapting it to the range of settings and conditions found in clinics throughout Africa.Supporting InformationS1 TableStudy outcomes adjusted for baseline CD4 count, age, and sex.(DOCX)Click here for additional data file.S2 TableCrude patient-level predictors of treatment uptake, viral suppression, and retention in care.(DOCX)Click here for additional data file.S3 TableStudy outcomes stratified by immediate versus delayed initiation (rapid arm patients initiating ≤90 d only).(DOCX)Click here for additional data file.S4 TableAbsolute and relative effect measure modification of primary outcome (initiated ≤90 d and suppressed by 10 mo).(DOCX)Click here for additional data file.S1 TextResearch protocol.(PDF)Click here for additional data file.S2 TextCONSORT statement.(PDF)Click here for additional data file.AbbreviationsALTalanine aminotransferaseaRRadjusted risk ratioARTantiretroviral therapyARVantiretroviralIQRinterquartile rangeCIconfidence intervalHRhazard ratioPHCprimary health clinicPOCpoint-of-careRapITRapid Initiation of TreatmentRDrisk differenceRRrelative riskTBtuberculosisReferences1\nSiednerMJ, NgCK, Bassett IV, KatzIT, BangsbergDR, TsaiAC. Trends in CD4 count at presentation to care and treatment initiation in sub-Saharan Africa, 2002–2013: a meta-analysis. Clin Infect Dis. 2014; 60:1120–1127. 10.1093/cid/ciu1137\n255161892\nPlazyM, Dray-SpiraR, Orne-GliemannJ, DabisF, Newell M-L. Continuum in HIV care from entry to ART initiation in rural KwaZulu-Natal, South Africa. Trop Med Int Health. 2014; 19:680–689. 10.1111/tmi.12301\n246549903\nClouseK, PettiforAE, MaskewM, BassettJ, VanRie A, BehetsF, et al\nPatient retention from HIV diagnosis through one year on antiretroviral therapy at a primary health care clinic in Johannesburg, South Africa. J Acquir Immune Defic Syndr. 2013; 62: 39–46.4\nLahuertaM, UeF, HoffmanS, ElulB, KulkarniSG, WuY, et al\nThe problem of late ART initiation in Sub-Saharan Africa: a transient aspect of scale-up or a long-term phenomenon?\nJ Health Care Poor Underserved. 2013; 24: 359–383. 10.1353/hpu.2013.0014\n233777395\nINSIGHT START Study Group. Initiation of antiretroviral therapy in early asymptomatic HIV infection. N Engl J Med. 2015; 373: 795–807. 10.1056/NEJMoa1506816\n261928736\nWorld Health Organization. Guideline on when to start antiretroviral therapy and on pre-exposure prophylaxis for HIV\nGeneva: World Health Organization; 2015.7\nGovindasamyD, FordN, KranzerK. Risk factors, barriers and facilitators for linkage to ART care in sub-Saharan Africa: a systematic review. AIDS. 2012; 26: 2059–2067. 10.1097/QAD.0b013e3283578b9b\n227812278\nSiednerMJ, LankowskiA, HabererJE, KembabaziA, EmenyonuN, TsaiAC, et al\nRethinking the “‘pre’” in pre-therapy counseling: no benefit of additional visits prior to therapy on adherence or viremia in Ugandans initiating ARVs. PLoS ONE. 2012; 7: e39894\n10.1371/journal.pone.0039894\n227619249\nWorld Health Organization. Global update on the health sector response to HIV, 2014\nGeneva: World Health Organization; 2014.10\nIngleSM, MayM, UebelK, TimmermanV, KotzeE, BachmannM, et al\nOutcomes in patients waiting for antiretroviral treatment in the Free State Province, South Africa: prospective linkage study. AIDS. 2010; 24: 2717–2725. 10.1097/QAD.0b013e32833fb71f\n2093555411\nMyerL, ZulligerR, PienaarD. Diversity of patient preparation activities before initiation of antiretroviral therapy in Cape Town, South Africa. Trop Med Int Heal. 2012; 17: 972–977. 10.1111/j.1365-3156.2012.03033.x\n12\nNational Department of Health. The South African Antiretroviral Treatment Guideline 2013\nPretoria: National Department of Health; 2013.13\nScottV, ZweigenthalV, JenningsK. Between HIV diagnosis and initiation of antiretroviral therapy: assessing the effectiveness of care for people living with HIV in the public primary care service in Cape Town, South Africa. Trop Med Int Heal. 2011; 16:1384–1391. 10.1111/j.1365-3156.2011.02842.x\n14\nGousN, ScottL, PotgieterJ, NtabeniL, EnslinS, NewmanR, et al\nFeasibility of performing multiple point of care testing for HIV anti-retroviral treatment initiation and monitoring from multiple or single fingersticks. PLoS ONE. 2013; 8: e85265\n10.1371/journal.pone.0085265\n2437687315\nJani IV, SitoeNE, ChongoPL, AlfaiER, QuevedoJI, TobaiwaO, et al\nAccurate CD4 T-cell enumeration and antiretroviral drug toxicity monitoring in primary healthcare clinics using point-of-care testing. AIDS. 2011; 25:807–812. 10.1097/QAD.0b013e328344f424\n2137853516\nMnyaniCN, McIntyreJA, MyerL. The reliability of point-of-care CD4 testing in identifying HIV-infected pregnant women eligible for antiretroviral therapy. J Acquir Immune Defic Syndr. 2012; 60: 260–264. 10.1097/QAI.0b013e318256b651\n2248758917\nWadeD, DaneauG, AboudS, VercauterenGH, UrassaWSK, KestensL, et al\nWHO multicenter evaluation of FACSCount CD4 and Pima CD4 t-cell count systems\u202f: instrument performance and misclassification of HIV-infected patients. J Acquir Immune Defic Syndr. 2014; 66:98–107.18\nScottLE, CampbellJ, WestermanL, KestensL, VojnovL, KohastsuL, et al\nA meta-analysis of the performance of the Pima CD4 for point of care testing. BMC Med. 2015; 13:168\n10.1186/s12916-015-0396-2\n2620886719\nMeyer-RathG, SchnippelK, LongL, MacleodW, SanneI, StevensW, et al\nThe impact and cost of scaling up GeneXpert MTB/RIF in South Africa. PLoS ONE. 2012; 7:e36966\n10.1371/journal.pone.0036966. 10.1371/journal.pone.0036966\n2269356120\nUNITAID. Tuberculosis diagnostics technology and market landscape\nGeneva: UNITAID; 2013.21\nFoxMP, MaskewM, MacPhailA. Cohort profile: the Themba Lethu Clinical Cohort, Johannesburg, South Africa. International Journal of Epidemiology. 2013; 42:430–439. 10.1093/ije/dys029\n2243486022\nHoffmannCJ, LewisJJ, DowdyDW, FieldingKL, GrantAD, MartinsonN, et al\nMortality associated with delays between clinic entry and ART initiation in resource-limited settings: results of a transition-state model. J Acquir Immune Defic Syndr. 2013; 63:105–111. 10.1097/QAI.0b013e3182893fb4\n2339245723\nJani IV, SitoeNE, AlfaiER, ChongoPL, QuevedoJI, RochaBM, et al\nEffect of point-of-care CD4 cell count tests on retention of patients and rates of antiretroviral therapy initiation in primary health clinics: An observational cohort study. Lancet. 2011; 378:1572–1579. 10.1016/S0140-6736(11)61052-0\n2195165624\nBurtleD, WelfareW, EldenS, MamvuraC, VandelanotteJ, PetherickE, et al\nIntroduction and evaluation of a “pre-ART care” service in Swaziland: an operational research study. BMJ Open. 2012; 2:e000195\n10.1136/bmjopen-2011-000195\n25\nBlackS, ZulligerR, MyerL, MarcusR, JenekerS, HonsBA, et al\nSafety, feasibility and efficacy of a rapid ART initiation in pregnancy pilot programme in Cape Town, South Africa. S Afr Med J. 2013; 103:557–562. 10.7196/SAMJ.6565\n2388573926\nMfinangaS, ChandaD, KivuyoSL, GuinnessL, BottomleyC, SimmsV, et al\nCryptococcal meningitis screening and community-based early adherence support in people with advanced HIV infection starting antiretroviral therapy in Tanzania and Zambia: an open-label, randomised controlled trial. Lancet. 2015; 385:2173–2182. 10.1016/S0140-6736(15)60164-7\n2576569827\nClouseK, Page-ShippL, DanseyH, MoatlhodiB, ScottL, BassettJ, et al\nImplementation of Xpert MTB/RIF for routine point-of-care diagnosis of tuberculosis at the primary care level. S Afr Med J. 2012; 102:805–807. 10.7196/SAMJ.5851\n23034211', 'title': ""Initiating Antiretroviral Therapy for HIV at a Patient's First Clinic Visit: The RapIT Randomized Controlled Trial."", 'date': '2016-05-11'}, '29136001': {'article_id': '29136001', 'content': ""PLoS MedPLoS MedplosplosmedPLoS Medicine1549-12771549-1676Public Library of ScienceSan Francisco, CA USA29136001568543710.1371/journal.pmed.1002433PMEDICINE-D-17-02016Research ArticleMedicine and health sciencesDiagnostic medicineHIV diagnosis and managementBiology and Life SciencesMicrobiologyMedical MicrobiologyMicrobial PathogensViral PathogensImmunodeficiency VirusesHIVMedicine and Health SciencesPathology and Laboratory MedicinePathogensMicrobial PathogensViral PathogensImmunodeficiency VirusesHIVBiology and Life SciencesOrganismsVirusesViral PathogensImmunodeficiency VirusesHIVBiology and Life SciencesOrganismsVirusesImmunodeficiency VirusesHIVBiology and life sciencesOrganismsVirusesRNA virusesRetrovirusesLentivirusHIVBiology and Life SciencesMicrobiologyMedical MicrobiologyMicrobial PathogensViral PathogensRetrovirusesLentivirusHIVMedicine and Health SciencesPathology and Laboratory MedicinePathogensMicrobial PathogensViral PathogensRetrovirusesLentivirusHIVBiology and Life SciencesOrganismsVirusesViral PathogensRetrovirusesLentivirusHIVBiology and Life SciencesImmunologyVaccination and ImmunizationAntiviral TherapyAntiretroviral TherapyMedicine and Health SciencesImmunologyVaccination and ImmunizationAntiviral TherapyAntiretroviral TherapyMedicine and Health SciencesPublic and Occupational HealthPreventive MedicineVaccination and ImmunizationAntiviral TherapyAntiretroviral TherapyResearch and Analysis MethodsDatabase and Informatics MethodsHealth InformaticsElectronic Medical RecordsMedicine and health sciencesEpidemiologyHIV epidemiologyResearch and Analysis MethodsResearch DesignSurvey ResearchQuestionnairesMedicine and Health SciencesDiagnostic MedicineClinical Laboratory SciencesClinical LaboratoriesPeople and PlacesGeographical LocationsAfricaMozambiqueA combination intervention strategy to improve linkage to and retention in HIV care following diagnosis in Mozambique: A cluster-randomized studyA combination intervention strategy to improve HIV care in Mozambiquehttp://orcid.org/0000-0001-6101-3073ElulBatyaConceptualizationFunding acquisitionMethodologyProject administrationSupervisionWriting – original draftWriting – review & editing12*LambMatthew R.ConceptualizationData curationFormal analysisFunding acquisitionMethodologyWriting – original draftWriting – review & editing12http://orcid.org/0000-0002-9748-9273LahuertaMariaMethodologyProject administrationSupervisionWriting – original draftWriting – review & editing12AbacassamoFatimaInvestigationProject administrationWriting – review & editing3AhouaLaurenceConceptualizationFunding acquisitionInvestigationMethodologyProject administrationWriting – review & editing1http://orcid.org/0000-0001-7915-8553KujawskiStephanie A.Data curationFormal analysisWriting – review & editing2TomoMariaMethodologyProject administrationWriting – review & editing3JaniIleshMethodologyWriting – review & editing41\nICAP at Columbia University, Mailman School of Public Health, Columbia University, New York, New York, United States of America2\nDepartment of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York, United States of America3\nCenter for Collaboration in Health, Maputo, Mozambique4\nInstituto Nacional de Saúde, Maputo, MozambiqueLewinSharon R.Academic EditorUniversity of Melbourne, AUSTRALIAI have read the journal's policy and the authors of this manuscript have the following competing interests: FA and MT were employees of the Center for Collaboration in Health which was providing technical support to the study health facilities at the time of the study.* E-mail: be2124@columbia.edu141120171120171411e100243396201710102017© 2017 Elul et al2017Elul et alThis is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.BackgroundConcerning gaps in the HIV care continuum compromise individual and population health. We evaluated a combination intervention strategy (CIS) targeting prevalent barriers to timely linkage and sustained retention in HIV care in Mozambique.Methods and findingsIn this cluster-randomized trial, 10 primary health facilities in the city of Maputo and Inhambane Province were randomly assigned to provide the CIS or the standard of care (SOC). The CIS included point-of-care CD4 testing at the time of diagnosis, accelerated ART initiation, and short message service (SMS) health messages and appointment reminders. A pre–post intervention 2-sample design was nested within the CIS arm to assess the effectiveness of CIS+, an enhanced version of the CIS that additionally included conditional non-cash financial incentives for linkage and retention. The primary outcome was a combined outcome of linkage to care within 1 month and retention at 12 months after diagnosis. From April 22, 2013, to June 30, 2015, we enrolled 2,004 out of 5,327 adults ≥18 years of age diagnosed with HIV in the voluntary counseling and testing clinics of participating health facilities: 744 (37%) in the CIS group, 493 (25%) in the CIS+ group, and 767 (38%) in the SOC group. Fifty-seven percent of the CIS group achieved the primary outcome versus 35% in the SOC group (relative risk [RR]CIS vs SOC = 1.58, 95% CI 1.05–2.39). Eighty-nine percent of the CIS group linked to care on the day of diagnosis versus 16% of the SOC group (RRCIS vs SOC = 9.13, 95% CI 1.65–50.40). There was no significant benefit of adding financial incentives to the CIS in terms of the combined outcome (55% of the CIS+ group achieved the primary outcome, RRCIS+ vs CIS = 0.96, 95% CI 0.81–1.16). Key limitations include the use of existing medical records to assess outcomes, the inability to isolate the effect of each component of the CIS, non-concurrent enrollment of the CIS+ group, and exclusion of many patients newly diagnosed with HIV.ConclusionsThe CIS showed promise for making much needed gains in the HIV care continuum in our study, particularly in the critical first step of timely linkage to care following diagnosis.Trial registrationClinicalTrials.gov NCT01930084In a cluster-randomized trial done in Mozambique, Batya Elul and colleagues study a combined intervention for linkage to and retention of people with HIV in care.Author summaryWhy was this study done?In sub-Saharan Africa, HIV testing, care, and treatment programs have been widely scaled up over the past decade, but suboptimal outcomes across the HIV care continuum—particularly with regards to timely linkage to and sustained retention in care—compromise their effectiveness.Patients experience multiple barriers to linkage to and retention in HIV care including health system barriers, structural barriers, and behavioral barriers, yet prior studies have largely evaluated individual interventions targeting a single barrier to care.Our study was designed specifically to examine the effectiveness of a combination intervention strategy (CIS) composed of several scalable evidence-based interventions targeting the multiple and prevalent health system, structural and behavioral barriers that patients face across the HIV continuum.What did the researchers do and find?We randomly assigned 10 primary health facilities in the city of Maputo and Inhambane Province in Mozambique to provide the standard of care (SOC) or the CIS, which included point-of-care CD4 testing at the time of diagnosis, accelerated ART initiation, and short message service (SMS) health messages and appointment reminders. A pre–post intervention 2-sample design was nested within the intervention arm to assess the effectiveness of CIS+, an enhanced version of the CIS that additionally included conditional non-cash financial incentives for linkage and retention.We enrolled 2,004 adults diagnosed with HIV in the voluntary counseling and testing clinics of participating health facilities, and compared the proportion who achieved a combined outcome of linkage to HIV care within 1 month of diagnosis and retention in care at 12 months across the 3 study groups.We found an increased likelihood of achieving the combined outcome in the CIS group compared to the SOC group, driven primarily by very large increases in same-day linkage, but no difference between the CIS+ and CIS groups.What do these findings mean?The CIS may help improve outcomes across the HIV care continuum in high-burden settings, particularly in the critical first step of timely linkage to care following diagnosis.Further research is needed to understand whether financial incentives can be optimized in this setting, given their effectiveness in enhancing other health outcomes.http://dx.doi.org/10.13039/100000200United States Agency for International DevelopmentAID-OAA-A-12-00027http://orcid.org/0000-0001-6101-3073ElulBatyahttp://dx.doi.org/10.13039/100000060National Institute of Allergy and Infectious DiseasesT32 AI114398http://orcid.org/0000-0001-7915-8553KujawskiStephanieThis study was funded by the United States Agency for International Development (USAID), USAID Award Number: AID-OAA-A-12-00027 and the National Institute of Allergy & Infectious Diseases of the National Institutes of Health, T32 AI114398 (SAK). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Data AvailabilityAll relevant data are within the paper and its Supporting Information files.Data AvailabilityAll relevant data are within the paper and its Supporting Information files.IntroductionAlthough the extraordinary scale-up of HIV testing, care, and treatment programs in sub-Saharan Africa over the past decade has resulted in more than 19 million persons accessing antiretroviral therapy (ART) [1], the effectiveness of these programs has been significantly hindered by high levels of attrition across the HIV care continuum. Observational studies and systematic reviews have repeatedly reported disturbing gaps in care as patients move from HIV testing clinics to HIV care clinics (i.e., linkage to care) and that patient dropout among those enrolled in HIV care is far too common, both before and after ART initiation (i.e., retention in care) [2–7]. Indeed, available data suggest that less than 1/3 of individuals who are diagnosed with HIV are successfully linked to and remain engaged in HIV care 12 months later [4,8].Barriers to timely linkage to and sustained retention in HIV care have been well documented, and include health system barriers (e.g., multiple HIV clinic visits for counseling and clinical and laboratory assessments prior to ART initiation), structural barriers (e.g., transport costs and distances, work and childcare constraints), and behavioral barriers (e.g., forgetting appointments, lack of understanding of required care) [9–14]. Prior studies have overwhelmingly evaluated individual interventions targeting a single barrier at a single point in the HIV care continuum such as mobile phone short message service (SMS) messaging to augment linkage to care following diagnosis, or point-of-care CD4 testing to enhance retention among patients enrolled in HIV care [15,16]. However, it is increasingly recognized that multi-component approaches composed of several practical, evidence-based interventions that simultaneously target the multiple and recurrent barriers that patients face as they navigate across the HIV care continuum are needed to maximize individual and population health [17,18]. Further, implementation science research that evaluates proposed multi-component approaches in real-world settings is needed to assess not only effectiveness, but also implementation outcomes including reach, adoption, and sustainability [19]. To this end, we designed a combination intervention strategy (CIS) composed of several scalable evidence-based interventions targeting prevalent health system, structural, and behavioral barriers across the HIV care continuum, and determined its effect on a combined outcome of linkage to and retention in HIV care among adults newly diagnosed with HIV in Mozambique, while also collecting information on its implementation and potential for broader scale-up [20]. Data regarding intervention feasibility and patient acceptability have been published [21], and thus we present here the effectiveness results. Because the interventions included in the CIS are expected to be implemented at the facility level, as opposed to targeted at specific individuals, should they be scaled up, we evaluated effectiveness using a cluster design, which best mirrors this implementation approach.MethodsA detailed description of the study protocol has been published [22].Ethics statementEthical approval was provided by Mozambique’s National Committee for Bioethics for Health and Columbia University’s institutional review board (IRB) (protocol AAAL1354). Informed written consent was obtained from all participants.Study designBetween April 22, 2013, and June 30, 2016, we conducted a 2-arm cluster-randomized study (effectiveness–implementation hybrid design, Type 1) [20] in health facilities in Maputo and Inhambane Province in Mozambique in order to assess the effectiveness of the CIS. Additionally, a pre–post intervention 2-sample design was nested within the intervention arm to assess the additional effectiveness of an enhanced version of the CIS, referred to as CIS+. Consequently, the standard of care (SOC) arm enrolled 1 cohort of patients, while the intervention arm enrolled 2 sequential cohorts of patients (CIS and CIS+). CIS+ participants were enrolled after CIS enrollment was completed at each facility randomized to the intervention arm.Study settingThe city of Maputo, the nation’s capital, has an area of 300 km2 and an estimated population of 1,225,868 [23], with an HIV prevalence of 16.9% among those aged 15 to 59 years [24]. The Maputo City Health Network has a total of 37 health facilities, 32 of which offered comprehensive HIV care and treatment services at the time of study implementation [25]. In contrast, Inhambane is a rural province, with an estimated 1,475,318 people spread across 68,615 km2 [23]. HIV prevalence among adults aged 15 to 59 years is 14.1% [24]. The ratio of doctors to population (5.96/100,000) is one of the lowest in the country [26]. Of the 135 health facilities in the province, 76 offered HIV care and treatment services when our study was initiated [25]. Suboptimal health facility infrastructure, long distances to facilities, and weak referral systems in the province are all believed to compromise health service uptake [26].RandomizationPrimary health facilities providing HIV testing, care, and treatment services and operated by the Ministry of Health with technical support from the Center for Collaboration in Health, a local PEPFAR implementing partner, were the unit of randomization. We focused on primary health facilities, rather than larger provincial hospitals, to reflect the increasingly decentralized nature of HIV service delivery in Mozambique. Ten facilities in Maputo (N = 4) and Inhambane Province (N = 6) were selected from the 66 primary health facilities receiving technical support from the Center for Collaboration in Health in those regions. Participating facilities were purposely chosen because they had the highest volume of adults testing HIV positive and enrolling in HIV care in the year prior to study start and thus were expected to have sufficient participants for appropriate power. Facilities were matched into pairs by region (Maputo or Inhambane), level of urbanicity (urban versus rural), and average number of patients testing HIV positive in voluntary counseling and testing (VCT) in the year prior to study initiation (high versus low), resulting in 5 matched pairs. Matched pairs were randomized by one of the authors (MRL) using a computerized random number generator to either the CIS arm or the SOC arm using matched-pair randomization. Sequences were concealed until interventions were assigned. The study was non-blinded.Study populationParticipants were enrolled in the SOC group beginning on April 22, 2013, and in the CIS group beginning on April 25, 2013. The last patient was enrolled in the SOC group on November 20, 2014, and the last patient in the CIS group was enrolled on February 11, 2015. Enrollment in the CIS+ group began after each clinic randomized to the intervention arm completed CIS enrollment, and ran from June 16, 2014, through June 30, 2015. All participants were followed for 12 months, with the last patient completing follow-up on June 30, 2016.Broad inclusion criteria were used to reflect as accurately as possible the population of adults newly diagnosed with HIV in VCT clinics at the participating health facilities. We focused on individuals newly diagnosed in VCT clinics, as opposed to those diagnosed in antenatal clinics and tuberculosis clinics, because the latter groups of patients typically follow a modified clinic flow. All adults testing HIV positive in the VCT clinics within the participating health facilities were informed of the study by HIV testing counselors following diagnosis, and those who were interested were referred to study staff for further information, eligibility screening, and consent procedures. Patients were excluded if they were less than 18 years of age, were pregnant, planned to move from their community of residence in the next 12 months, had enrolled in HIV care or initiated ART in the past 6 months, did not understand Portuguese or Xitsua, or were incapable of providing informed consent. Study participants agreed to be referred to HIV care and treatment services at the same facility where they were diagnosed (referred to as the “diagnosing facility”); to complete a baseline, 1-month, and 12-month interview; to be traced at their homes if they could not be reached by phone for follow-up interviews; to provide contact information for a family member or friend who could provide information on their vital status if they could not be located for a follow-up interview; and, if they enrolled in HIV care and treatment services at the diagnosing facility, to have their clinical data abstracted from the facility’s existing electronic medical records.Study interventionsStandard of careParticipants at health facilities randomized to receive the SOC were managed as per prevailing Ministry of Health guidelines [27]. Individuals diagnosed with HIV received post-test counseling in the VCT clinic and were referred verbally to HIV services, typically in the diagnosing facility. Patients presenting to the facility receptionist to schedule a clinical consultation for HIV care were referred to the laboratory for CD4 cell count, chemistry, and hematology testing, and provided with an appointment 2–4 weeks later to allow sufficient time for the laboratory results to be received. ART eligibility was determined at that first clinical consultation based on CD4 cell count ≤ 350 cells/mm3 and/or WHO stage 3/4. Those found to be eligible for ART received at least 1 individual counseling session before initiating treatment. For ART-eligible patients, the time interval between enrollment in HIV care and ART initiation was estimated at 1–2 months at the time the study started. Participants initiating ART were requested to return every 2 weeks for the first month, at 2 months, at 6 months, and every 6 months thereafter. ART-ineligible patients were instructed to return at 6 months for repeat clinical evaluation and laboratory testing.Combination intervention strategyAt facilities randomized to the intervention arm, we introduced 4 evidence-based interventions that simplified the clinic flow and encouraged linkage to and retention in care. These interventions targeted several known health system, structural, and behavioral barriers across the HIV care continuum, and were adapted for the on-the-ground realities—including practice norms, physical space, and available staffing—at the health facilities. First, we introduced Pima (Inverness Medical Innovations) CD4 assay machines in the VCT clinics to enable HIV testing counselors to provide real-time, point-of-care CD4 test results immediately following diagnosis, and thus addressed a health system barrier by reducing the number of visits required for CD4 testing. We also hypothesized that receipt of additional information on one’s health at the time of diagnosis would advance patient understanding of the need for care, a documented behavioral barrier [10,28]. All patients regardless of CD4 count were provided with a paper-based referral to on-site HIV services that included their CD4 count, and were instructed to present for their first clinical consultation within 1 week. Second, to address additional health system barriers, patients with Pima CD4 cell count ≤ 350 cells/mm3 were provided with accelerated ART initiation, with the ultimate goal of decreasing the HIV morbidity and mortality that contributes to significant attrition among ART-eligible patients [4]. These individuals received an individual ART preparatory counseling session in the VCT clinic immediately following CD4 testing, on the day of diagnosis. Facility receptionists were instructed to expedite appointments for these patients when they presented to schedule their clinical consultations. Although the patients were directed to the laboratory to have their blood drawn for baseline laboratory tests required by national ART guidelines, clinicians were encouraged to initiate ART at the first clinical visit rather than await the results of the laboratory tests unless the patient presented with comorbid conditions. Patients who initiated ART received a 2-week supply and followed the visit schedule dictated by national guidelines, similar to the SOC procedures. Once baseline laboratory results were available, they were reviewed by clinic staff, and if abnormalities were noted, the participant was contacted to return to the clinic. Third, participants received health messages and appointment reminders via SMS messaging to address behavioral barriers associated with deferring care engagement and forgetting appointments. The messages were sent from the central study office to the participant’s phone or to a friend or relative’s phone per participant preference, and did not refer to HIV or a specific health facility or reveal any personal information. The health messages encouraged participants to care for their health, and were sent weekly for 1 month following diagnosis and then monthly (e.g., “Hi. Your health is the most important thing. Please remember to come to the health center for health services.”). Appointment reminders were sent only to participants who linked to care at the diagnosing facility, and were sent 3–7 days before each scheduled clinic visit (e.g., “Hi. Your health is the most important thing. We expect to see you at your upcoming appointment scheduled for the day ___.”). Participants were not asked to confirm receipt or reply to the messages. Finally, patients in the CIS+ cohort received the CIS interventions plus a series of non-cash financial incentives (FIs) in the form of prepaid cellular air-time cards to offset structural barriers associated with the direct and indirect costs of coming to the health facility to receive HIV care. Air-time cards rather than cash were selected as the incentive based on discussion with the Ministry of Health. Each card was valued at approximately US$5 and was provided conditionally upon the following achievements: linkage to care within 1 month of diagnosis, retention in care 6 months after diagnosis, and retention in care 12 months after diagnosis, for a total of approximately US$15. Participants who completed each achievement received the card when presenting for routine services. Participants without cellular phones could opt to give them to a family member, sell them for cash, or trade them for other goods. Both the point-of-care CD4 testing and accelerated ART initiation interventions were provided by health facility staff to all individuals diagnosed with HIV in the VCT clinic regardless of whether they were enrolled in the study, while the SMS messages and FIs were provided by study staff and only to study participants.Data collection and outcomesSite assessmentsData on the configuration of HIV services at the 10 participating study sites were collected at the beginning and at the end of the study using a standardized site assessment form. The purpose of the site assessments was to identify important similarities and differences between participating health facilities, as well as to better understand how services at the site could impact study implementation.Baseline interviewParticipants completed closed-ended questionnaires administered by trained research assistants at the time of study enrollment. The questionnaire took about 30 minutes to complete, and gathered information on sociodemographic characteristics, social and family support, mental health, alcohol use, HIV testing history, HIV knowledge and beliefs, and anticipated stigma and barriers to care. Anticipated stigma was assessed through 6 items adapted from the 12-item anticipated HIV stigma index developed by Earnshaw and Chaudoir [29]. Stigma scores were summed, then dichotomized into 2 groups: highest (>75th percentile) versus lower anticipated stigma. Mental health was assessed via a 7-question evaluation based on the Kessler 10-item scale for psychological distress [30]. Mental health scores were summed, then dichotomized into 2 groups: highest (<75th percentile) versus lower level of distress. Perceived availability of social support was assessed with 4 questions adapted from a 9-item scale by Wortman and colleagues [31]. Social support scores were summed, then dichotomized into 2 groups: higher (>50th percentile) versus lower social support. Questions assessing HIV-related knowledge and attitudes were based on those used by one of the authors in a previous study [32]. HIV knowledge scores were summed, then dichotomized into 2 groups: higher (>50th percentile) versus lower knowledge. Baseline interview data were double-entered into a study database, and a computer program identified discrepant double-entered results for correction against the paper-based forms.Patient tracing and follow-up interviewsOne and 12 months after enrollment, up until June 30, 2016, trained research assistants contacted participants by phone to ascertain their vital status and HIV care status, and to administer follow-up questionnaires. If the participant could not be contacted by phone after 3 attempts, research assistants visited the participant’s home up to 3 times. Participants who were located completed closed-ended interviews that gathered updated information on key domains from the baseline questionnaire, as well as self-reported information on linkage to (1- and 12-month questionnaires) and retention in HIV care (12-month questionnaire only), reasons for linkage/non-linkage (1- and 12-month questionnaires) and retention/non-retention (12-month questionnaire only), ART status, hospitalizations, and anticipated stigma. In cases where the participant could not be located, research assistants contacted a friend or family member as specified by the participant at study enrollment. Research assistants did not refer to HIV or the health facility during contact tracing but rather attempted to determine whether the participant was alive or dead. For those whose vital status could not be determined through contact tracing, research assistants searched existing electronic medical records at other primary health facilities supported by the Center for Collaboration in Health in the same district to assess whether patients had enrolled in HIV care at another facility, and reviewed death registers at the municipal and provincial levels to ascertain their vital status. Similar data entry and reconciliation procedures to those used for the baseline interview data were used for the tracing and follow-up data.Abstraction of clinical data for patients linking to HIV care at the diagnosing facilityAs part of routine clinical practice for HIV patients, clinicians documented patient information at every clinic visit on national HIV care forms, and trained data clerks entered those data into an Access-based electronic medical record. In its role as a PEPFAR implementing partner supporting the study sites, the Center for Collaboration in Health assessed the completeness and accuracy of these electronic data every 4 months and initiated targeted interventions to enhance data quality if there was greater than 15% disagreement on key data elements between the electronic and paper-based systems. During the study period, research assistants reviewed the electronic medical records to identify study participants who had linked to care at their diagnosing facility. For those located, we extracted the complete electronic medical record, capturing information on visit dates, vital status, transfer status, ART status, laboratory test results, and opportunistic infections.OutcomesThe primary outcome was a combined outcome of linkage to HIV care within 1 month of diagnosis plus retention in care 12 months after diagnosis measured at the individual level. We used a combined outcome to reflect the fact that improvements are needed across the HIV care continuum in order to maximize individual and population health. Linkage to care was defined by at least 1 clinical consultation for HIV that included assessment of the patient’s medical history and a physical exam. Retention in care was defined by a clinic visit in the 90 days prior to the end of the 12-month study follow-up period, with no documentation that the patient had transferred to another facility or had died. We assessed the combined outcome from the perspective of the diagnosing health facility using data from the electronic medical records maintained by the HIV clinics. All study participants were included in these analyses, including those who did not complete follow-up interviews. Participants whose electronic medical records were not located were considered not to have achieved the combined outcome for this analysis. As a secondary approach, we evaluated the combined outcome from the perspective of the Mozambican health program by supplementing data from the electronic medical records with patient reports of linkage to and retention in care at HIV clinics at different health facilities (obtained during follow-up interviews) and information obtained from electronic medical records at other health facilities. In these analyses, participants whose self-reported linkage and retention status suggested they were linked to and/or retained at a health facility other than their diagnosing clinic were considered to have achieved the respective linkage/retention outcomes. Participants who either did not complete follow-up interviews or did not self-report linkage to or retention at another clinic maintained their initial outcome designation. All study participants were included in these analyses.Secondary outcomes included linkage to care at several predefined time points, ART eligibility assessment (defined as receipt of WHO staging and/or CD4 cell count), ART initiation, disease progression (defined as a new WHO stage 3/4 condition or hospitalization noted in the electronic medical records or self-reported during follow-up interviews), retention in care 6 and 12 months after diagnosis regardless of the timing of linkage, and death.Statistical analysisThe trial was designed and powered to measure outcomes at the individual level, with outcomes assessed within each cluster (5 clusters per arm). In our initial power calculations, we anticipated that an average of 200 patients per clinic (in the CIS and SOC arms) would be eligible for enrollment based on historical data on the annual number of adults testing positive in the VCT clinics at the participating health facilities. With 5 facilities per study arm, an average of 200 patients per facility, an intraclass correlation coefficient (ICC) of 0.05, and an alpha of 0.05 and assuming that 35% of participants in the SOC arm would achieve the primary outcome, we estimated that the study would have 80% power to detect as statistically significant 55% of participants in the CIS group achieving the primary outcome, and greater than 80% power to detect as statistically significant 75% of participants in the CIS+ group achieving the primary outcome. Because enrollment proceeded slower than originally planned, at study midpoint we assessed the implications for power if each health facility enrolled an average of 150 participants rather than 200. Our calculations revealed minimal change in power with this reduction in the number of participants per health facility. Calculations were performed using PASS 8.0 software for 2 independent proportions in a cluster randomization study design and a 2-sided Farrington and Manning Likelihood Score Test [33]. Our power estimations and statistical analyses did not take into account the pair matching prior to randomization but rather followed recommendations from Diehr et al. [34] to break matches in statistical analyses of clustered studies when the number of pairs is between 3 and 9.An intent-to-treat analysis determined the relative risk (RR) of achieving study outcomes between the CIS and SOC groups, and between the CIS+ and CIS groups. For analyses of the primary outcome, we used random-intercept multilevel log-Poisson models to account for clustering within health facilities with an empirical variance adjustment for small numbers of sampling units described by Morel et al. [35]. We also assessed whether the primary outcome differed after adjustment for patient-level factors by constructing propensity scores that estimated the probability of inclusion in the CIS, CIS+, and SOC groups by age, sex, region, education, income, employment status, marital status, religion, prior year history of being away from home for more than 1 month, travel time to clinic, tuberculosis status, past hospitalizations, diagnosis history, and whether another family member was known to be living with HIV. The propensity score was included as a covariate in the multivariable log-Poisson models (adjusted analyses). In post hoc analyses, we further estimated the likelihood of key subgroups achieving the primary outcome using interaction contrast ratios. The subgroups assessed included subgroups based on baseline age, sex, region of health facility, employment status, marital status, whether the participant was away from home for more than 1 month in the year prior to study enrollment, travel time to clinic, whether a household member was known to be HIV positive, and dichotomous variables based on scales for self-reported anticipated stigma, HIV knowledge, mental health, and perceived social support as described earlier. For analyses of secondary outcomes, log-Poisson models were used for dichotomous outcomes, and t tests and 2-way median tests as appropriate for continuous outcomes, adjusting for clustering but not for patient-level differences.ResultsHealth facility characteristicsAs noted above, 10 primary health facilities participated in the study, 4 in Maputo and 6 in Inhambane. At study start, the 5 health facilities randomized to the intervention arm reported that they had experienced disruptions of 3 or more days in VCT services in the prior 12 months, while only 1 facility randomized to the SOC arm reported experiencing a similar disruption. By study end, no facilities—whether in the intervention or SOC arm—had experienced such disruptions. Throughout the study, only intervention sites conducted point-of-care CD4 testing using Pima machines in the VCT clinic. Two SOC sites reported that they had Pima machines available in their laboratories but only used them to monitor CD4 counts after patients had enrolled in HIV care. None of the SOC sites used SMS messaging for health messages or appointment reminders on a routine basis for all patients, but 2 sites sent SMS appointment reminders for patients participating in community ART groups [36]. Though the 2013 national HIV treatment guidelines stipulate that 1 ART preparatory counseling session is required for ART-eligible patients, all the facilities participating in the study typically conducted 2 to 3 sessions prior to ART initiation, with a slight reduction in the number of sessions observed between study start and end.Enrollment and participant characteristicsFig 1 shows the enrollment, exclusion, and flow of the patients by study group. During the study period, 5,327 adults ≥18 years of age were diagnosed with HIV in the VCT clinics at the 10 study facilities. A total of 265 of those individuals were not referred to the study staff for further information on the study because they informed the HIV testing counselor that they were not interested in the study, were already receiving HIV services, or were not willing to be referred to the diagnosing health facility. Among the 5,062 who were referred to the study staff for further information, 3,058 did not meet study eligibility criteria. The main reasons for exclusion were inability to provide informed consent due to distress following diagnosis (19%), inability to understand Portuguese or Xitsua (12%), and refusal to be referred to the diagnosing health facility for HIV services (10%).10.1371/journal.pmed.1002433.g001Fig 1Flow chart for study participation.CIS, combination intervention strategy; SOC, standard of care; VCT, voluntary counseling and testing.A total of 2,004 adults ≥18 years of age enrolled in the study at the 10 health facilities: 744 (37%) in the CIS group, 493 (25%) in the CIS+ group, and 767 (38%) in the SOC group. The majority of participants were female (64%), and the median age of participants was 34 years of age, with no meaningful differences observed by study group (Table 1). More than half of the participants (53%) were living with a partner at the time of diagnosis, and 65% of participants had a primary or lower level of education. Most participants (74%) were employed, and 43% had a monthly income of less than 1,500 meticais (approximately US$50). One-quarter (27%) reported that another household member was living with HIV. While no serious adverse events were reported during the study period, there was 1 unanticipated event of a female participant reporting intimate partner violence. The Mozambican National Committee for Bioethics for Health and the Columbia University IRB were informed of this event, and the participant asked to remain in the study but to conduct all study interviews at the facility (i.e., no follow-up phone calls).10.1371/journal.pmed.1002433.t001Table 1Participant characteristics at study enrollment in the 3 study groups (N = 2,004).CharacteristicTotalN = 2,004CISN = 744CIS+N = 493SOCN = 767p-ValueRegionMaputo1,077 (54%)396 (53%)275 (56%)406 (53%)0.58Inhambane927 (46%)348 (47%)218 (44%)361 (47%)Sex0.50Female1,292 (64%)490 (66%)319 (65%)483 (63%)Male712 (36%)254 (34%)174 (35%)284 (37%)Age (years)34.2 (9.6)34.9 (9.8)33.8 (9.9)33.8 (9.3)0.04518–24265 (13%)90 (12%)70 (14%)105 (14%)0.1225–391,233 (62%)440 (59%)301 (61%)492 (64%)40–49348 (17%)148 (2%)87 (18%)113 (15%)50+158 (8%)66 (9%)35 (7%)57 (7%)Marital status<0.001Married/partner and living together1,068 (53%)376 (51%)255 (52%)437 (57%)Married/partner, but not living together222 (11%)101 (14%)86 (17%)35 (5%)Single713 (36%)266 (36%)152 (31%)295 (38%)Missing/refused1 (0%)1 (0%)0 (0%)0 (0%)Education0.003None164 (8%)59 (8%)33 (7%)72 (9%)Primary1,149 (57%)442 (59%)256 (52%)451 (59%)Secondary471 (24%)164 (22%)130 (26%)177 (23%)Above secondary219 (11%)78 (1%)74 (15%)67 (9%)Missing/refused1 (0%)1 (0%)0 (0%)0 (9%)Employment0.46Employed1,473 (74%)537 (72%)361 (73%)575 (75%)Unemployed531 (26%)207 (28%)132 (27%)192 (25%)Monthly income<0.001≤1,500 meticais871 (43%)342 (46%)165 (33%)364 (47%)>1,500 meticais936 (47%)343 (46%)271 (55%)322 (42%)Missing/refused197 (1%)59 (8%)57 (12%)81 (11%)Another household member has HIV0.28Yes550 (27%)187 (25%)144 (29%)219 (29%)No913 (46%)361 (49%)219 (44%)333 (43%)Don’t know539 (27%)196 (26%)130 (26%)213 (28%)Missing/refused2 (0%)0 (0%)0 (0%)2 (0%)Data given as N (percent).CIS, combination intervention strategy; SOC, standard of care.Intervention effect on linkage to and retention in HIV care at the diagnosing facilityAs shown in Table 2, the CIS was associated with statistically significant improvements in the combined outcome of linkage to care within 1 month of diagnosis and retention in care 12 months following diagnosis when compared to the SOC. Analyses using data from electronic medical records to examine linkage to and retention at the diagnosing health facility showed that 57% of participants in the CIS group achieved the primary outcome versus 35% of those in the SOC group (RRCIS vs SOC = 1.58, 95% CI 1.05–2.39). Post hoc calculation of the ICC for the primary outcome according to the methods of Snijders and Bosker for binary outcome data [37] estimated an ICC of 0.066, similar to but slightly higher than the assumed ICC of 0.05 used in power and sample size estimation. These results were robust to adjustment for patient-level differences (adjusted RR [aRR]CIS vs SOC = 1.55, 95% CI 1.07–2.25). As shown in Fig 2, the greatest intervention effects were observed among young adults age 18–24 years (RRCIS vs SOC = 2.39, 95% CI 1.51–3.80, p-value for interaction between age and treatment arm = 0.07), those in Maputo (RRCIS vs SOC = 2.31, 95% CI 1.90–2.79, p-value for interaction between region and treatment arm < 0.0001), those who did not report that another household member was living with HIV (RRCIS vs SOC = 1.81: 95% CI 1.52–2.16, p-value for interaction between household member with HIV and treatment arm = 0.11), and those reporting high levels of anticipated stigma at enrollment (RRCIS vs SOC = 1.95, 95% CI 1.53–2.49, p-value for interaction between stigma and treatment arm = 0.10).10.1371/journal.pmed.1002433.g002Fig 2Relative risk of the CIS compared to the SOC on the primary outcome at the diagnosing health facility by patient characteristics.a Fifteen patients with missing information were excluded from this estimate. A description of the variables examined and categories used are provided in the Methods section.10.1371/journal.pmed.1002433.t002Table 2Linkage to and retention in HIV care: CIS versus SOC and CIS+ versus CIS.CategoryOutcomeCISN = 744CIS+N = 493SOCN = 767RR1 (95% CI), p-ValueaRR2 (95% CI), p-ValueNPercentNPercentNPercentCIS versus SOCCIS+ versus CISCIS versus SOCCIS+ versus CISPrimary outcomeAt diagnosing facilityLinked to care within 1 month of diagnosis and retained 12 months after diagnosis42557%27355%26835%1.58 (1.05–2.39)p = 0.030.96 (0.81–1.16)p = 0.661.55 (1.07–2.25)p = 0.040.94 (0.76–1.18)p = 0.52At any health facilityLinked to care within 1 month of diagnosis and retained 12 months after diagnosis54774%36073%36347%1.47 (1.08–2.01)p = 0.020.98 (0.85–1.15)p = 0.911.46 (1.05–2.04)p = 0.030.96 (0.83–1.11)p = 0.52Secondary outcomesLinkage at diagnosing facilitySame day as HIV test65989%45793%12016%9.13 (1.65–50.40)p = 0.021.04 (0.92–1.20)p = 0.38N/AWithin 1 week of HIV test67891%46194%34946%2.43 (0.70–8.41)p = 0.141.03 (0.91–1.16)p = 0.59N/AWithin 1 month of HIV test70394%46795%48263%1.48 (0.93–2.35)p = 0.091.00 (0.89–1.13)p = 0.96N/AWithin 12 months of HIV test71696%46795%59277%1.23 (1.03–1.48)p = 0.030.98 (0.87–1.11)p = 0,74N/ARetention at diagnosing facility6 months after diagnosis46262%32265%40553%1.18 (1.00–1.39)p = 0.061.05 (0.88–1.26)p = 0.48N/A12 months after diagnosis43558%27355%34144%1.32 (1.12–1.54)p = 0.0040.95 (0.79–1.13)p = 0.45N/A1RR accounts for clustering within sites using random-intercept log-Poisson regression with empirical standard error estimates.2aRR adjusts for patient-level differences using propensity scores.aRR, adjusted relative risk; CIS, combination intervention strategy; N/A, not applicable; RR, relative risk; SOC, standard of care.Eighty-nine percent of participants in the CIS group linked to the diagnosing facility on the same day as diagnosis compared to 16% (RRCIS vs SOC = 9.13, 95% CI 1.65–50.40) in the SOC group, 91% within 1 week compared to 46% (RRCIS vs SOC = 2.43, 95% CI 0.70–8.41), and 94% within 1 month compared to 63% (RRCIS vs SOC = 1.48, 95% CI 0.93–2.35). By 12 months, nearly all CIS participants (96%) had linked to care compared to 77% (RRCIS vs SOC = 1.23, 95% CI 1.03–1.48) of SOC participants. Among those linking to care, the median (interquartile range [IQR]) time from diagnosis to linkage was 0 days (0–0) in the CIS group and 3 days (1–26) in the SOC group (median test p < 0.001 for CIS versus SOC). The effect of the intervention on retention in care, regardless of the timing of linkage, was more modest but statistically significant (6-month retention: 62% CIS versus 53% SOC, RRCIS vs SOC = 1.18, 95% CI 1.00–1.39; 12-month retention: 58% CIS versus 44% SOC, RRCIS vs SOC = 1.32, 95% CI 1.12–1.54).In analyses restricted to the participants initiating ART, the median (IQR) time from diagnosis to ART initiation in the CIS and SOC groups was 32 (12–135), and 63 (33–230) days, respectively, while the median (IQR) time from enrollment in HIV care to ART initiation was 32 (11–127), and 50 (15–205) days, respectively. Median time from ART eligibility to ART initiation for the CIS, CIS+, and SOC groups was 21 (9–40), and 25 (11–56) days, respectively.There was no additional benefit of adding FIs to the CIS, with 55% (RRCIS+ vs CIS = 0.96, 95% CI 0.81–1.16; aRRCIS+ vs CIS = 0.94, 95% CI 0.76–1.18) of those in the CIS+ group achieving the primary outcome; 95% (RRCIS+ vs CIS = 1.00, 95% CI 0.83–1.13) linking to HIV care within 1 month of diagnosis, regardless of retention at 12 months; and 55% (RRCIS+ vs CIS = 0.95, 95% CI 0.79–1.13) being retained in care 12 months after diagnosis, regardless of the timing of linkage to care.Intervention effect on linkage to and retention in care at any health facilityAnalyses supplementing data from electronic medical records from participating facilities with data from patient interviews and other health facilities in the study regions to examine linkage to and retention at any health facility showed similar effects of the intervention package. A total of 74% (RRCIS vs SOC = 1.47, 95% CI 1.08–2.01) of participants in the CIS group and 47% in the SOC group were found to have linked to HIV care at any health facility within 1 month of diagnosis and were retained in HIV care 12 months after diagnosis (Table 2). Adjustment for patient-level differences did not result in any change in this finding (aRRCIS vs SOC = 1.46, 95% CI 1.05–2.04). Inclusion of FIs in the CIS also showed no additional benefit for linkage to and retention at any health facility, with 73% (RRCIS+ vs CIS = 0.98, 95% CI 0.85–1.15; aRRCIS+ vs CIS = 0.96, 95% CI 0.83–1.11) of those in the CIS+ group known to have linked to and been retained in HIV care at any health facility compared to the CIS group.Intervention effect on ART eligibility and initiation, disease progression, and deathData from electronic medical records at study sites indicated that compared to patients in the SOC group, patients in the CIS group were more likely to ever have their ART eligibility assessed (100% versus 76.9%, RRCIS vs SOC = 1.29, 95% CI 1.08–1.54), be identified as ART eligible (75% versus 60%, RRCIS vs SOC = 1.24, 95% CI 1.07–1.43), and initiate ART (65% versus 54%, RRCIS vs SOC = 1.20, 95% CI 1.00–1.43) (Table 3). Very few participants were diagnosed with a new WHO stage 3/4 event at the diagnosing facility or self-reported a hospitalization in the 12 months after HIV diagnosis. Those in the CIS group had a non-significantly but modestly decreased risk compared to those in the SOC group (1% versus 3%, RRCIS vs SOC = 0.38, 95% CI 0.07–2.03), while similar results were observed between the CIS and CIS+ groups (1% versus 1%, RRCIS+ vs CIS = 0.65, 95% CI 0.12–3.64). Neither the CIS nor the CIS+ interventions had a significant effect on mortality within 12 months of diagnosis, with 6%, 5%, and 7% of participants in the CIS, CIS+, and SOC groups, respectively, known to have died during study follow-up (RRCIS vs SOC = 0.87, 95% CI 0.40–1.91; RRCIS+ vs CIS = 0.88, 95% CI 0.45–1.74). The CIS also did not have a significant impact on mortality before (3%, RRCIS vs SOC = 0.78, 95% CI 0.46–1.32) or after ART initiation (3%, RRCIS vs SOC = 0.96, 95% CI 0.26–3.48); participants in the CIS+ group were less likely to die, though non-significantly so, before initiating ART compared to those in the CIS group (1% versus 3%, RRCIS+ vs CIS = 0.34, 95% CI 0.09–1.29).10.1371/journal.pmed.1002433.t003Table 3ART determination and initiation, disease progression, and death: CIS versus SOC and CIS+ versus CIS.\xa0OutcomeCIS(N = 744)CIS+(N = 493)SOC(N = 767)RR1 (95% CI), p-valueNPercentNPercentNPercentCIS versus SOC1CIS+ versus CIS1ART eligibility assessed744100%493100%59077%1.29 (1.08–1.54)p = 0.011.00 (0.89–1.12)p = 1.00Identified as ART eligible55775%37275%46460%1.24 (1.07–1.43)p = 0.011.01 (0.85–1.19)p = 0.91Initiated ART48465%33267%41654%1.20 (1.00–1.43)p = 0.051.03 (0.88–1.22)p = 0.59New WHO stage 3/4 or hospitalization71%31%233%0.38 (0.07–2.03)p = 0.220.65 (0.12–3.64)p = 0.53Death within 12 months466%275%547%0.87 (0.40–1.91)p = 0.690.88 (0.45–1.74)p = 0.63Death before ART initiation223%51%294%0.78 (0.46–1.32)p = 0.310.34 (0.09–1.29)p = 0.09Death after ART initiation243%224%253%0.96 (0.26–3.48)p = 0.941.38 (0.62–3.07)p = 0.331RR accounts for clustering within sites using random-intercept log-Poisson regression with empirical standard error estimates.ART, antiretroviral therapy; CIS, combination intervention strategy; RR, relative risk; SOC, standard of care.DiscussionWe conducted a cluster-randomized study in Mozambique to examine the effectiveness of a multi-component approach to increase linkage to and retention in HIV care—2 critical elements of the HIV care continuum—among adults newly diagnosed with HIV. The operational model of the CIS that we evaluated addresses known structural, biomedical, and behavioral barriers across the HIV care continuum and was composed of evidence-based, practical, and scalable interventions, including CD4 testing in VCT clinics with immediate turnaround of results, accelerated ART initiation for eligible individuals, and SMS health messages and appointment reminders. An enhanced version of the CIS additionally included FIs. In the spirit of implementation science, 2 of the interventions were implemented by existing health facility staff, rather than study staff, providing information on the real-world successes and challenges associated with the CIS that can be extrapolated to a range of settings with similar implementation contexts.Our study showed that participants receiving the CIS were 1.58 times more likely to link to HIV care at their diagnosing facility within 1 month of diagnosis and be retained in care at that same facility 12 months following diagnosis, representing not only a statistically significant but also a programmatically meaningful improvement. Particularly impressive gains were observed in timely linkage to care at the diagnosing facility: 89% of CIS participants linked to care on the day of diagnosis, representing a greater than 5-fold improvement compared to the SOC, and nearly universal linkage (96%) was achieved within 1 month of diagnosis. Notably, the intervention effect was greatest in subpopulations documented to have particularly poor outcomes across the HIV care continuum, including young adults [38,39] and those with high stigma perceptions [40–42]. The intervention also had beneficial effects on other important milestones in the HIV care continuum in the 12 months following diagnosis, including the likelihood of patients having their ART eligibility assessed and initiating ART. While the intervention significantly increased retention in HIV care at both 6 and 12 months following diagnosis, retention in the CIS group remained concerningly low and far short of what is needed to end the HIV epidemic in Mozambique and other high-burden countries.We found no additional gain in effectiveness from adding FIs to the CIS. Prior studies examining the effect of FIs in enhancing outcomes across the HIV care continuum among people living with HIV have shown inconsistent results. Studies from India, Uganda, and Democratic Republic of the Congo reported reductions in time to ART initiation and improvements in retention with the provision of incentives, while in the United States, randomized trials did not show any effect of FIs on linkage to care or viral load suppression [43–47]. While 89% of participants in the current study reported that the type of FI provided and the amount of the FIs (i.e., mobile phone air-time vouchers worth approximately US$5 at 3 points in time) were adequate, it is possible that the FIs were not sufficiently optimized to affect behaviors. Indeed, as reported elsewhere, patient reactions to the FIs were surprisingly tepid, with only 21% reporting it to be the “most useful” intervention for retention in care 12 months following diagnosis [21]. Additionally, fidelity to the FI component of the intervention package was imperfect, with, for example, 86% of participants eligible to receive the first incentive actually receiving it, which may have further limited the effect of this intervention [21]. However, given the benefits of FIs in other health sectors [48–50], further research is needed to understand whether and how they may be optimized to enhance outcomes across the HIV care continuum.This study has several important strengths. It is among the first studies to evaluate the impact of a multi-component approach on 2 important HIV care and treatment indicators: timely linkage to care following an HIV diagnosis and sustained retention in care. Improving performance for these 2 elements of the HIV care continuum is critical for realizing the individual and population benefits of HIV programming in sub-Saharan Africa. Further, while studies have examined the effectiveness of multi-component intervention packages that include FIs on HIV care outcomes [51,52], this study is the first to our knowledge to use a design that permits estimation of the additional benefit of including FIs as part of such a package.Our study also had limitations. First, in alignment with recent recommendations for implementation science studies [19], we used existing electronic medical records in the HIV clinics at the study sites to ascertain outcomes at the diagnosing facility, but such records may have limited data quality. However, data quality assessments were conducted regularly during the study period and ensured at least 85% concurrence between paper-based and electronic medical records on key data elements. Second, aside from the FI, we cannot unpack the effect of individual intervention components. Third, the relevance of point-of-care CD4 count testing may change as countries adopt “treatment for all” strategies, although our results suggest that providing people living with HIV with additional information on their health status immediately following diagnosis may be important in facilitating same-day linkage to care and likely same-day ART initiation. Fourth, the CIS+ cohort was enrolled once the target sample size had been reached in the CIS cohort, thus introducing the potential for secular trends to have biased the comparison of the CIS and CIS+ packages. However, because we found no difference in the primary outcome between the CIS+ and CIS groups, secular trends would have had to have operated in the direction of reducing overall linkage and retention for this bias to result in the failure to observe an additional benefit of FIs for linkage and retention. While this is plausible, we do not have any evidence that a substantial reduction in overall linkage and retention occurred over the relatively limited time frame of the study. Finally, while the study was implemented in 2 contrasting settings within Mozambique, study facilities were located primarily in urban and semi-urban areas within the city of Maputo and Inhambane Province, which may limit generalizability. Indeed, settings with lower education and cell phone coverage than those included in our study may experience greater challenges implementing the SMS health messages and appointment reminders. Similarly, while we set broad inclusion criteria, we did exclude people who did not understand Portuguese or Xitsua, were planning on leaving the community, or were not willing to receive services at the diagnosing facility, all factors that may have reduced generalizability. Finally, due to slower-than-expected enrollment, we enrolled fewer participants in the CIS+ group than intended, which decreased our power to detect statistically significant differences in study outcomes between the CIS+ and CIS groups. However, as the proportion achieving the combined outcome in the 2 groups was extremely similar (CIS 57% versus CIS+ 55%), it is unlikely that the inability to detect significant differences was primarily due to lack of power.ConclusionMulti-component intervention strategies have been proposed to address troubling gaps in the HIV care continuum [17,18]. To our knowledge, this is amongst the first studies to rigorously evaluate such an approach. The CIS we examined, comprising 3 evidence-based, practical, and scalable interventions, holds great promise as an approach to make much needed gains in the HIV care continuum in sub-Saharan Africa, particularly in the critical first step of timely linkage to care following diagnosis.Supporting informationS1 TextStudy protocol.(PDF)Click here for additional data file.S2 TextCONSORT checklist.(DOCX)Click here for additional data file.S1 DataData file.(CSV)Click here for additional data file.S2 DataData codebook.(XLSX)Click here for additional data file.We are grateful to the study participants, study staff, and participating health facilities for their contributions to this research. We also thank Antonia Mussa, Deborah Horowitz, Margaret McNairy, and Violante Viola for their inputs during study development and launch.AbbreviationsaRRadjusted relative riskARTantiretroviral therapyCIScombination intervention strategyFIfinancial incentiveICCintraclass correlation coefficientIQRinterquartile rangeIRBinstitutional review boardRRrelative riskSMSshort message serviceSOCstandard of careVCTvoluntary counseling and testingReferences1Joint United Nations Programme on HIV/AIDS. Fact sheet July 2017. Geneva: Joint United Nations Programme on HIV/AIDS; 2017 [cited 2017 Oct 18]. Available from: http://www.unaids.org/sites/default/files/media_asset/UNAIDS_FactSheet_en.pdf.2McGrathN, GlynnJR, SaulJ, KranzerK, JahnA, MwaunguluF, et al\nWhat happens to ART-eligible patients who do not start ART? Dropout between screening and ART initiation: a cohort study in Karonga, Malawi. BMC Public Health. 2010;10:601\ndoi: 10.1186/1471-2458-10-601\n209398723Hoffman S, Charalambous S, Churchyard G, Martinson N, Chaisson R. Delayed ART initiation and risk of death. 18th Conference on Retroviruses and Opportunistic Infections; 2011 Feb 27–Mar 2; Boston, MA, US.4RosenS, FoxMP. Retention in HIV care between testing and treatment in sub-Saharan Africa: a systematic review. PLoS Med. 2011;8(7):e1001056\ndoi: 10.1371/journal.pmed.1001056\n218114035KranzerK, GovindasamyD, FordN, JohnstonV, LawnSD. Quantifying and addressing losses along the continuum of care for people living with HIV infection in sub-Saharan Africa: a systematic review. J Int AIDS Soc. 2012;15(2):17383\ndoi: 10.7448/IAS.15.2.17383\n231997996MugglinC, EstillJ, WandelerG, BenderN, EggerM, GsponerT, et al\nLoss to programme between HIV diagnosis and initiation of antiretroviral therapy in sub-Saharan Africa: systematic review and meta-analysis. Trop Med Int Health. 2012;17(12):1509–20. doi: 10.1111/j.1365-3156.2012.03089.x\n229941517PlazyM, Orne-GliemannJ, DabisF, Dray-SpiraR. Retention in care prior to antiretroviral treatment eligibility in sub-Saharan Africa: a systematic review of the literature. BMJ Open. 2015;5(6):e006927\ndoi: 10.1136/bmjopen-2014-006927\n261091108FoxMP, RosenS. Patient retention in antiretroviral therapy programs up to three years on treatment in sub-Saharan Africa, 2007–2009: systematic review. Trop Med Int Health. 2010;15(Suppl 1):1–15.9GengEH, BangsbergDR, MusinguziN, EmenyonuN, BwanaMB, YiannoutsosCT, et al\nUnderstanding reasons for and outcomes of patients lost to follow-up in antiretroviral therapy programs in Africa through a sampling-based approach. J Acquir Immune Defic Syndr. 2010;53(3):405–11. doi: 10.1097/QAI.0b013e3181b843f0\n1974575310GovindasamyD, FordN, KranzerK. Risk factors, barriers and facilitators for linkage to antiretroviral therapy care: a systematic review. AIDS. 2012;26(16):2059–67. doi: 10.1097/QAD.0b013e3283578b9b\n2278122711Rabkin M, editor. High patient retention rates in a multinational HIV/AIDS treatment program: the Columbia University mother-to-child-plus experience. 17th Conference on Retroviruses and Opportunistic Infections; 2010 Feb 16–19; San Francisco, CA, US.12YuJK, ChenSC, WangKY, ChangCS, MakombeSD, SchoutenEJ, et al\nTrue outcomes for patients on antiretroviral therapy who are “lost to follow-up” in Malawi. Bull World Health Organ. 2007;85(7):550–4. doi: 10.2471/BLT.06.037739\n1776850413LankowskiAJ, SiednerMJ, BangsbergDR, TsaiAC. Impact of geographic and transportation-related barriers on HIV outcomes in sub-Saharan Africa: a systematic review. AIDS Behav. 2014;18(7):1199–223. doi: 10.1007/s10461-014-0729-8\n2456311514Ochieng-OokoV, OchiengD, SidleJE, HoldsworthM, Wools-KaloustianK, SiikaAM, et al\nInfluence of gender on loss to follow-up in a large HIV treatment programme in western Kenya. Bull World Health Organ. 2010;88(9):681–8. doi: 10.2471/BLT.09.064329\n2086507315SiednerMJ, SantorinoD, HabererJE, BangsbergDR. Know your audience: predictors of success for a patient-centered texting app to augment linkage to HIV care in rural Uganda. J Med Internet Res. 2015;17(3):e78\ndoi: 10.2196/jmir.3859\n2583126916JaniIV, SitoeNE, AlfaiER, ChongoPL, QuevedoJI, RochaBM, et al\nEffect of point-of-care CD4 cell count tests on retention of patients and rates of antiretroviral therapy initiation in primary health clinics: an observational cohort study. Lancet. 2011;378(9802):1572–9. doi: 10.1016/S0140-6736(11)61052-0\n2195165617DonnellD, BaetenJM, KiarieJ, ThomasKK, StevensW, CohenCR, et al\nHeterosexual HIV-1 transmission after initiation of antiretroviral therapy: a prospective cohort analysis. Lancet. 2010;375(9731):2092–8. doi: 10.1016/S0140-6736(10)60705-2\n2053737618AnglemyerA, RutherfordGW, HorvathT, BaggaleyRC, EggerM, SiegfriedN. Antiretroviral therapy for prevention of HIV transmission in HIV-discordant couples. Cochrane Database Syst Rev. 2013;2013(4):CD009153.19GengEH, PeirisD, KrukME. Implementation science: relevance in the real world without sacrificing rigor. PLoS Med. 2017;14(4):e1002288\ndoi: 10.1371/journal.pmed.1002288\n2844143520CurranGM, BauerM, MittmanB, PyneJM, StetlerC. Effectiveness-implementation hybrid designs: combining elements of clinical effectiveness and implementation research to enhance public health impact. Med Care. 2012;50(3):217–26. doi: 10.1097/MLR.0b013e3182408812\n2231056021SuttonR, LahuertaM, AbacassamoF, AhouaL, TomoM, LambMR, et al\nFeasibility and acceptability of health communication interventions within a combination intervention strategy for improving linkage and retention in HIV care in Mozambique. J Acquir Immune Defic Syndr. 2017;74(Suppl 1):S29–36.2793060922ElulB, LahuertaM, AbacassamoF, LambMR, AhouaL, McNairyML, et al\nA combination strategy for enhancing linkage to and retention in HIV care among adults newly diagnosed with HIV in Mozambique: study protocol for a site-randomized implementation science study. BMC Infect Dis. 2014;14:549\ndoi: 10.1186/s12879-014-0549-5\n2531199823Instituto Nacional de Estatística. Estatísticas e indicadores sociais 2013–2014. Maputo (Mozambique): Instituto Nacional de Estatística; 2015.24Instituto Nacional de Saúde, Instituto Nacional de Estatística, ICF. Moçambique: inquérito de indicadores de imunizaçao, malária e HIV/SIDA em Moçambique (IMASIDA) 2015—relatório de indicadores básicos de HIV. Rockville (Maryland): DHS Program; 2017 [cited 2017 Oct 18]. Available from: https://dhsprogram.com/pubs/pdf/PR85/PR85.pdf.25Centro de Colaboração em Saúde. Semi-annual report for the Center for Disease Control and Prevention. Maputo (Mozambique): Centro de Colaboração em Saúde; 2016.26Mozambique Ministry of Health. Annual report 2010, Inhambane Province. Maputo (Mozambique): Mozambique Ministry of Health; 2010.27Direcção Nacional de Assistência Médica. Guia de tratamento antiretroviral e infecções oportunistas no adulto, adolescente, grávida e criança. Maputo (Mozambique): Mozambique Ministry of Health; 2014.28LayerEH, KennedyCE, BeckhamSW, MbwamboJK, LikindikokiS, DavisWW, et al\nMulti-level factors affecting entry into and engagement in the HIV continuum of care in Iringa, Tanzania. PLoS ONE. 2014;9(8):e104961\ndoi: 10.1371/journal.pone.0104961\n2511966529EarnshawVA, ChaudoirSR. From conceptualizing to measuring HIV stigma: a review of HIV stigma mechanism measures. AIDS Behav. 2009;13(6):1160–77. doi: 10.1007/s10461-009-9593-3\n1963669930KesslerRC, AndrewsG, ColpeLJ, HiripiE, MroczekDK, NormandSL, et al\nShort screening scales to monitor population prevalences and trends in non-specific psychological distress. Psychol Med. 2002;32(6):959–76. 1221479531KesslerRC, PriceRH, WortmanCB. Social factors in psychopathology: stress, social support, and coping processes. Annu Rev Psychol. 1985;36:531–72. doi: 10.1146/annurev.ps.36.020185.002531\n388389332ElulB, BasingaP, Nuwagaba-BiribonwohaH, SaitoS, HorowitzD, NashD, et al\nHigh levels of adherence and viral suppression in a nationally representative sample of HIV-infected adults on antiretroviral therapy for 6, 12 and 18 months in Rwanda. PLoS ONE. 2013;8(1):e53586\ndoi: 10.1371/journal.pone.0053586\n2332646233DonnerA, KlarN. Design and analysis of cluster randomization trials in health research. London: Arnold; 2000.34DiehrP, MartinDC, KoepsellT, CheadleA. Breaking the matches in a paired t-test for community interventions when the number of pairs is small. Stat Med. 1995;14(13):1491–504. 748118735MorelJG, BokossaMC, NeerchalNK. Small sample correction for the variance of GEE estimators. Biom J. 2003;45(4):395–409.36JobartehK, ShiraishiRW, MalimaneI, Samo GudoP, DecrooT, AuldAF, et al\nCommunity ART support groups in Mozambique: the potential of patients as partners in care. PLoS ONE. 2016;11(12):e0166444\ndoi: 10.1371/journal.pone.0166444\n2790708437SnijdersTAB, BoskerRJ. Multilevel analysis: an introduction to basic and advanced mulitlevel modeling. Thousand Oaks (California): Sage; 1999.38LambMR, FayorseyR, Nuwagaba-BiribonwohaH, ViolaV, MutabaziV, AlwarT, et al\nHigh attrition before and after ART initiation among youth (15–24 years of age) enrolled in HIV care. AIDS. 2014;28(4):559–68. doi: 10.1097/QAD.0000000000000054\n2407666139NachegaJB, HislopM, NguyenH, DowdyDW, ChaissonRE, RegensbergL, et al\nAntiretroviral therapy adherence, virologic and immunologic outcomes in adolescents compared with adults in southern Africa. J Acquir Immune Defic Syndr. 2009;51(1):65–71. doi: 10.1097/QAI.0b013e318199072e\n1928278040MallS, MiddelkoopK, MarkD, WoodR, BekkerLG. Changing patterns in HIV/AIDS stigma and uptake of voluntary counselling and testing services: the results of two consecutive community surveys conducted in the Western Cape, South Africa. AIDS Care. 2013;25(2):194–201. doi: 10.1080/09540121.2012.689810\n2269460241MeibergAE, BosAE, OnyaHE, SchaalmaHP. Fear of stigmatization as barrier to voluntary HIV counselling and testing in South Africa. East Afr J Public Health. 2008;5(2):49–54. 1902441042KatzIT, RyuAE, OnuegbuAG, PsarosC, WeiserSD, BangsbergDR, et al\nImpact of HIV-related stigma on treatment adherence: systematic review and meta-synthesis. J Int AIDS Soc. 2013;16(3 Suppl 2):18640.2424225843SolomonSS, SrikrishnanAK, VasudevanCK, AnandS, KumarMS, BalakrishnanP, et al\nVoucher incentives improve linkage to and retention in care among HIV-infected drug users in Chennai, India. Clin Infect Dis. 2014;59(4):589–95. doi: 10.1093/cid/ciu324\n2480338144Emenyonu N, Thirumurthy N, Muyindike W, Mwebesa B, Ragland K, Geng E, et al., editors. Cash transfers to cover clinic transportation costs improve retention in care in an HIV treatment program in rural Uganda. 17th Conference on Retroviruses and Opportunistic Infections; 2010 Feb 16–19; San Francisco, CA, US.45El-Sadr WM, Branson BM, Beauchamp G, Hall HI, Torian LV, Zingman BS, et al. Effect of financial incentives on linkage to care and viral suppression: HPTN 065. Abstract number 29. Conference on Retroviruses and Opportunistic Infections; 2015 Feb 23–26; Seattle, Washington, US.46YotebiengM, ThirumurthyH, MoraccoKE, EdmondsA, TabalaM, KawendeB, et al\nConditional cash transfers to increase retention in PMTCT care, antiretroviral adherence, and postpartum virological suppression: a randomized controlled trial. J Acquir Immune Defic Syndr. 2016;72(Suppl 2):S124–9.2735549947MetschLR, FeasterDJ, GoodenL, MathesonT, StitzerM, DasM, et al\nEffect of patient navigation with or without financial incentives on viral suppression among hospitalized patients with HIV infection and substance use: a randomized clinical trial. JAMA. 2016;316(2):156–70. doi: 10.1001/jama.2016.8914\n2740418448FiszbeinA, SchadyN, FerreiraFHG, GroshM, KeleherN, OlintoP, et al\nConditional cash transfers: reducing present and future poverty. Washington (DC): World Bank; 2009.49RanganathanM, LagardeM. Promoting healthy behaviours and improving health outcomes in low and middle income countries: a review of the impact of conditional cash transfer programmes. Prev Med. 2012;55(Suppl):S95–105.2217804350RasellaD, AquinoR, SantosCA, Paes-SousaR, BarretoML. Effect of a conditional cash transfer programme on childhood mortality: a nationwide analysis of Brazilian municipalities. Lancet. 2013;382(9886):57–64. doi: 10.1016/S0140-6736(13)60715-1\n2368359951SiednerMJ, SantorinoD, LankowskiAJ, KanyesigyeM, BwanaMB, HabererJE, et al\nA combination SMS and transportation reimbursement intervention to improve HIV care following abnormal CD4 test results in rural Uganda: a prospective observational cohort study. BMC Med. 2015;13:160\ndoi: 10.1186/s12916-015-0397-1\n2614972252McNairy M, Lamb M, Gachuhi A, Nuwagaba-Biribonwoha H, Burke S, Mazibuko S, et al. Link4Health: a cluster randomized-controlled trial evaluating the effectiveness of a combination strategy for linkage to and retention in HIV care in Swaziland. International AIDS Conference; 2016 Jul 18–22; Durban, South Africa."", 'title': 'A combination intervention strategy to improve linkage to and retention in HIV care following diagnosis in Mozambique: A cluster-randomized study.', 'date': '2017-11-15'}, '29112963': {'article_id': '29112963', 'content': ""PLoS MedPLoS MedplosplosmedPLoS Medicine1549-12771549-1676Public Library of ScienceSan Francisco, CA USA29112963567537610.1371/journal.pmed.1002420PMEDICINE-D-17-02007Research ArticleBiology and Life SciencesMicrobiologyMedical MicrobiologyMicrobial PathogensViral PathogensImmunodeficiency VirusesHIVMedicine and Health SciencesPathology and Laboratory MedicinePathogensMicrobial PathogensViral PathogensImmunodeficiency VirusesHIVBiology and Life SciencesOrganismsVirusesViral PathogensImmunodeficiency VirusesHIVBiology and Life SciencesOrganismsVirusesImmunodeficiency VirusesHIVBiology and life sciencesOrganismsVirusesRNA virusesRetrovirusesLentivirusHIVBiology and Life SciencesMicrobiologyMedical MicrobiologyMicrobial PathogensViral PathogensRetrovirusesLentivirusHIVMedicine and Health SciencesPathology and Laboratory MedicinePathogensMicrobial PathogensViral PathogensRetrovirusesLentivirusHIVBiology and Life SciencesOrganismsVirusesViral PathogensRetrovirusesLentivirusHIVBiology and Life SciencesImmunologyVaccination and ImmunizationAntiviral TherapyAntiretroviral TherapyMedicine and Health SciencesImmunologyVaccination and ImmunizationAntiviral TherapyAntiretroviral TherapyMedicine and Health SciencesPublic and Occupational HealthPreventive MedicineVaccination and ImmunizationAntiviral TherapyAntiretroviral TherapySocial SciencesEconomicsFinancePeople and PlacesGeographical LocationsAfricaSwazilandMedicine and Health SciencesHealth CareHealth Education and AwarenessMedicine and health sciencesEpidemiologyHIV epidemiologyMedicine and Health SciencesHealth CarePatientsMedicine and health sciencesDiagnostic medicineHIV diagnosis and managementEffectiveness of a combination strategy for linkage and retention in adult HIV care in Swaziland: The Link4Health cluster randomized trialLink4Health: A combination intervention to improve HIV carehttp://orcid.org/0000-0001-7853-633XMcNairyMargaret L.ConceptualizationData curationFormal analysisFunding acquisitionInvestigationMethodologyProject administrationSupervisionValidationWriting – original draftWriting – review & editing12*LambMatthew R.ConceptualizationData curationFormal analysisInvestigationMethodologyWriting – original draftWriting – review & editing13GachuhiAverie B.Data curationProject administrationSupervisionWriting – original draftWriting – review & editing1Nuwagaba-BiribonwohaHarrietData curationProject administrationSupervisionWriting – original draftWriting – review & editing13BurkeSeanData curationInvestigationProject administrationSupervisionWriting – review & editing1MazibukoSikhatheleConceptualizationInvestigationProject administrationWriting – review & editing4http://orcid.org/0000-0003-1155-2735OkelloVelephiConceptualizationSupervisionWriting – review & editing4http://orcid.org/0000-0003-2028-4779EhrenkranzPeterConceptualizationSupervisionWriting – original draftWriting – review & editing5http://orcid.org/0000-0002-0180-1649SahaboRubenConceptualizationProject administrationSupervisionWriting – review & editing1http://orcid.org/0000-0003-3735-9781El-SadrWafaa M.ConceptualizationFunding acquisitionMethodologyProject administrationSupervisionValidationWriting – original draftWriting – review & editing131\nICAP at Columbia University, New York, New York, United States of America2\nDepartment of Medicine, Weill Cornell Medical College, New York, New York, United States of America3\nDepartment of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York, United States of America4\nMinistry of Health, Kingdom of Swaziland, Mbabane, Swaziland5\nBill and Melinda Gates Foundation, Seattle, Washington, United States of AmericaDeeksSteven G.Academic EditorSan Francisco General Hospital, UNITED STATESThe authors have declared that no competing interests exists.* E-mail: mm3780@columbia.edu71120171120171411e100242012620172992017© 2017 McNairy et al2017McNairy et alThis is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.BackgroundGaps in the HIV care continuum contribute to poor health outcomes and increase HIV transmission. A combination of interventions targeting multiple steps in the continuum is needed to achieve the full beneficial impact of HIV treatment.Methods and findingsLink4Health, a cluster-randomized controlled trial, evaluated the effectiveness of a combination intervention strategy (CIS) versus the standard of care (SOC) on the primary outcome of linkage to care within 1 month plus retention in care at 12 months after HIV-positive testing. Ten clusters of HIV clinics in Swaziland were randomized 1:1 to CIS versus SOC. The CIS included point-of-care CD4+ testing at the time of an HIV-positive test, accelerated antiretroviral therapy (ART) initiation for treatment-eligible participants, mobile phone appointment reminders, health educational packages, and noncash financial incentives. Secondary outcomes included each component of the primary outcome, mean time to linkage, assessment for ART eligibility, ART initiation and time to ART initiation, viral suppression defined as HIV-1 RNA < 1,000 copies/mL at 12 months after HIV testing among patients on ART ≥6 months, and loss to follow-up and death at 12 months after HIV testing. A total of 2,197 adults aged ≥18 years, newly tested HIV positive, were enrolled from 19 August 2013 to 21 November 2014 (1,096 CIS arm; 1,101 SOC arm) and followed for 12 months. The median participant age was 31 years (IQR 26–39), and 59% were women. In an intention-to-treat analysis, 64% (705/1,096) of participants at the CIS sites achieved the primary outcome versus 43% (477/1,101) at the SOC sites (adjusted relative risk [RR] 1.52, 95% CI 1.19–1.96, p = 0.002). Participants in the CIS arm versus the SOC arm had the following secondary outcomes: linkage to care regardless of retention at 12 months (RR 1.08, 95% CI 0.97–1.21, p = 0.13), mean time to linkage (2.5 days versus 7.5 days, p = 0.189), retention in care at 12 months regardless of time to linkage (RR 1.48, 95% CI 1.18–1.86, p = 0.002), assessment for ART eligibility (RR 1.20, 95% CI 1.07–1.34, p = 0.004), ART initiation (RR 1.16, 95% CI 0.96–1.40, p = 0.12), mean time to ART initiation from time of HIV testing (7 days versus 14 days, p < 0.001), viral suppression among those on ART for ≥6 months (RR 0.97, 95% CI 0.88–1.07, p = 0.55), loss to follow-up at 12 months after HIV testing (RR 0.56, 95% CI 0.40–0.79, p = 0.002), and death (N = 78) within 12 months of HIV testing (RR 0.80, 95% CI 0.46–1.35, p = 0.41). Limitations of this study include a small number of clusters and the inability to evaluate the incremental effectiveness of individual components of the combination strategy.ConclusionsA combination strategy inclusive of 5 evidence-based interventions aimed at multiple steps in the HIV care continuum was associated with significant increase in linkage to care plus 12-month retention. This strategy offers promise of enhanced outcomes for HIV-positive patients.Trial registrationClinicalTrials.gov NCT01904994.In a cluster-randomized trial done in Swaziland, Margaret McNairy and colleagues test a combined intervention for linking and retaining adults with HIV infection in care.Author summaryWhy was this study done?Linkage to care, retention in care, and achievement of viral load suppression on antiretroviral therapy (ART) among HIV-positive adults are necessary in order to achieve optimal health outcomes in terms of reduced morbidity and mortality and to decrease the risk of HIV transmission to others.Barriers to linkage to and retention in care are multifactorial and include both individual- and health system-level factors.We hypothesized that a multicomponent strategy using a combination of evidence-based interventions was needed to address the multiple gaps in linkage to and retention in care across the HIV care continuum.What did the researchers do and find?Ten clusters of affiliated HIV clinics in Swaziland were randomized to receive the standard of care (SOC; 1,101 participants) or a combination intervention strategy (CIS; 1,096 participants). The CIS included provision of participants with point-of-care CD4+ count testing at time of HIV testing, accelerated ART initiation among eligible patients, mobile phone appointment reminders, health educational packages, and noncash financial incentives.Participants were followed for 12 months from the time of testing HIV positive, and the primary study outcome was prompt linkage to care within 1 month of testing HIV-positive plus retention in care at 12 months after testing HIV positive. Secondary outcomes included additional steps in the HIV care continuum.We found that participants receiving care at HIV clinics randomized to the CIS study arm, as compared to the SOC study arm, were significantly more likely to achieve the primary outcome of prompt linkage to care plus 12-month retention (64% in the CIS arm versus 43% in the SOC arm, relative risk [RR] 1.52, 95% CI 1.19–1.96, p = 0.002).We also found that participants at CIS sites versus SOC sites had faster linkage to care, were more likely to be assessed for ART initiation, and had faster time to ART start. However, we did not find significant differences in viral suppression or mortality at 12-months after testing HIV positive.What do these findings mean?The Link4Health study showed that a CIS was 50% more effective than the SOC on prompt linkage to HIV care plus 12-month retention after HIV-testing and that the effect appeared to be primarily driven by enhanced retention in care.Limitations of this study include a small number of clusters and the inability to evaluate the contribution of each of the components of the strategy to the effect noted.The combination strategy used in this study could be easily adapted to other resource-limited settings and may be relevant to the challenges faced in engaging HIV-positive vulnerable and key populations.http://dx.doi.org/10.13039/100006492Division of Intramural Research, National Institute of Allergy and Infectious DiseasesRO1A1100059http://orcid.org/0000-0003-3735-9781El-SadrWafaa M.Gates FoundationOPP1145477http://orcid.org/0000-0001-7853-633XMcNairyMargaret L.This study was funded by the National Institutes of Health (NIH), NIH Award Number: RO1A1100059, and the Gates Foundation OPP1145477. No funding bodies had any role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Data AvailabilityData are from the Link4Health study, which may be contacted at icap-partnerships@columbia.edu. Deidentified data are uploaded as S1 Table.Data AvailabilityData are from the Link4Health study, which may be contacted at icap-partnerships@columbia.edu. Deidentified data are uploaded as S1 Table.IntroductionAchieving the desired impact of HIV treatment, as measured by individual health outcomes and reduced transmission to others, is contingent upon completing all steps in the HIV care continuum, from identifying all individuals who are living with HIV and linking those found to be HIV positive to HIV care to retaining them in lifelong care and on antiretroviral therapy (ART) [1]. Over the past decade, the scale-up of HIV programs has been substantial, with over 18 million persons having initiated ART by the end of 2015 in low- and middle-income countries and an associated substantial decrease in HIV-related morbidity and mortality, as well as evidence of a decrease in HIV incidence in many of the most severely affected countries [2]. However, in order to achieve epidemic control, further optimization of the HIV care continuum is needed so as to achieve the Joint United Nations Programme on HIV/AIDS (UNAIDS) 90/90/90 targets, which require that 90% of individuals with HIV are aware of their diagnosis, that 90% of those aware of their HIV infection are initiated on ART, and that 90% of those on treatment achieve and maintain viral suppression [3].Findings from HIV programs indicate that linkage to and retention in HIV care currently fall far short of the desired goals [4–6]. Linkage of HIV-positive individuals to HIV care varies from less than half of individuals linking to care within 6 months of an HIV-positive test to 72% who ever linked [5,7,8]. Once linked to care, less than half of HIV-positive patients are retained in care prior to initiation of ART, with only two-thirds of ART-eligible individuals initiating ART [5,7]. Lastly, only approximately three-quarters of patients initiated on ART have been noted to be retained in care at 12 months, with retention decreasing over the ensuing follow-up years [4].Barriers to linkage to and retention in care are multifactorial and include both individual- and health system-level factors such as stigma, fear of disclosure, distance and cost of travel to clinic, attitudes of providers, and cumbersome clinic procedures with long waiting times [9,10]. Previous studies that aimed to overcome such barriers have largely focused on the assessment of 1 intervention primarily targeting a single step in the HIV care continuum [11–14]. We postulated that in order to address the multiple gaps across the care continuum, a multi-component intervention strategy is needed, with each component targeting one or more steps in the HIV care continuum.The Link4Health study evaluated the effectiveness of a combination intervention strategy (CIS) utilizing 5 evidence-based interventions that address structural, behavioral, and biomedical barriers across the continuum of care, to improve linkage to and retention in care among newly identified HIV-positive adults in Swaziland.MethodsEthicsThe study was approved by the institutional review boards at Columbia University and the Swaziland Scientific and Ethics Committee.Study designA detailed description of the study methods was previously reported [15]. In brief, Link4Health was an implementation science study using a cluster site-randomized trial design. The study unit of randomization consisted of a public secondary-level HIV clinic paired with its largest affiliated public primary-level HIV clinic. Ten study units were selected from a total of 11 existing secondary-level HIV clinics in the country, based on clinic patient volume. Study units were pair matched, first by implementing partner (matching the 2 study units from implementing partner A) and then by location (urban [4] versus rural [4]) and clinic size, based on the estimated number of adults testing HIV positive in the 2 years prior to study implementation (<50 versus >50 per month for rural study units and <100 versus >100 per month for urban study units). Matched study units were randomized by a computerized random number generator to the CIS or standard of care (SOC) study arm. A cluster design was chosen to avoid disruption of service delivery, enable better fit within the routine workings at the clinical site, and allow the clinic staff to more easily implement the study. The study staff and clinic providers at each study unit were not blinded to the assigned arm for the site.Study setting and populationSwaziland is located in Southern Africa and has the world’s highest HIV prevalence, with HIV as the leading cause of death in the country [16]. The estimated adult (age 18–49 years) HIV prevalence is 31%, and the estimated incidence is 2.4% (95% CI 2.1–2.8) [17, 18]. The country has made impressive strides in responding to the epidemic, with nearly 70% of persons estimated to be living with HIV having initiated ART as of 2015 [19]. Nevertheless, historic rates of linkage to and retention in care at 12 months after ART initiation remain suboptimal [20]. Data available from Swaziland at the time of the initiation of the study showed that among 1,105 adults who tested HIV positive at community testing venues, only 37% linked to HIV care within 12 months of HIV testing [21]. Retention among adults at 36 months after ART start was 68% in 2011 per national estimates [22].Inclusion criteria were as follows: adults aged ≥18 years, newly tested HIV positive, and willing to receive HIV care at the study unit and consent to study procedures. Exclusion criteria were as follows: planning on leaving the community during the study, prior enrollment in HIV care or initiation of ART in the past 6 months, currently on ART, reports a current pregnancy, or not able to speak English or SiSwati.Study interventionsAll adults who tested HIV positive at participating sites were informed of the study by their providers. Interested individuals were referred to a study nurse who provided further information, confirmed eligibility, and, if eligible, obtained written consent. All consenting participants provided baseline information and thereafter were managed based on the study arm to which the clinic was randomly assigned.SOCParticipants at study units randomized to the SOC arm were managed according to country guidelines. These guidelines recommend that individuals identified as HIV positive receive post-test counseling and be referred to an HIV clinic using a national referral form [21]. Thereafter, upon presentation at their first HIV clinic visit, such individuals are to present their referral form, receive a clinical assessment, and have blood drawn for a CD4+ count test as well as hematology and chemistry tests and are instructed to return in 1–2 weeks for receipt of their results. Upon return, those eligible for ART according to then prevailing national guidelines (i.e., with a CD4+ count ≤ 350 cells/mm3) are to receive the first of 3 counseling sessions. Patients who are prescribed ART are instructed to return to the clinic every month for 6 months and then every 3 months, if they are stable on treatment. Patients who are ineligible for ART are instructed to return to clinic every 3 months for follow-up. Peer counselors are encouraged to call patients within 7 days of a missed clinic appointment. All patients are prescribed cotrimoxazole prophylaxis, and condoms, and health informational materials are to be made available in the clinics.CISParticipants at clinics randomized to the CIS arm received a multicomponent strategy of 5 evidence-based interventions, targeting structural, biomedical, and behavioral barriers, which are described briefly below (Table 1) [15]. All components of the combination strategy utilized in this study were selected based on evidence of their effectiveness, feasibility, and suitability to patients in diverse healthcare settings.10.1371/journal.pmed.1002420.t001Table 1Comparison of combination intervention strategy (CIS) to standard of care (SOC) procedures.InterventionStandard of care (SOC)Combination intervention strategy (CIS)Type of interventionStep targeted in HIV care continuumPoint-of-care CD4+ count testing• Point-of-care CD4 assays available in some primary care clinics and some secondary health centers/hospitals for patients enrolled in HIV care but not at the HIV testing site• CD4+ count (Cyflow and FACS Caliber) after linkage to HIV care in the clinic or lab• Turnaround time approximately 2 weeks• Point-of-care CD4 assays at the HIV testing site at the time of HIV testing• Turnaround time immediateStructural and biomedicalLinkage, ART eligibility assessment, and ART initiationAccelerated ART initiationART initiation per national guidelines for patients with CD4+ count ≤ 350 cells/mm3 or WHO Stage III/VI• Requires 3 counseling sessions and receipt of baseline lab tests• Initiation 2 weeks to 1 month from determination of ART eligibility• Accelerated ART initiation for patients with point-of-care CD4+ count ≤ 350 cells/mm3 within 1 week from testing• Two counseling sessions (1 at the time of HIV testing and the other at the first HIV clinic visit) and collection of blood for other baseline lab tests• Initiation of ART for eligible patients prior to return of results with use of a checklistStructural and biomedicalART initiation and retentionCellular phone visit reminders• Telephone call within 7 days of missed visit for ART patients only• SMS (or voice if illiterate) visit reminders 3 days prior to each scheduled visit• SMS (or voice if illiterate) reminder within 7 days after a missed visit for all patientsBehavioralLinkage and retentionHealth education packages• Cotrimoxazole was prescribed for all patients once enrolled in HIV care• Condoms available• A health education package was provided approximately every 3 months at visits. Packages included condoms, soap, cotrimoxazole, a pill box, and pictorial education about use of materials and HIVBiomedical and behavioralRetentionNoncash financial incentive• None• Noncash financial incentive (mobile airtime) were provided for those linked to care within 1 month of testing and completion of 6- and 12-month visitsStructuralLinkage and retentionAbbreviations: ART, antiretroviral therapy; SMS, short message service.The first intervention was provision of point-of-care (POC) CD4+ count testing performed immediately after an HIV-positive test, in the same physical location as the HIV testing site, with the aim of improving linkage to care, assessment for ART eligibility, and prompt ART initiation. Several studies have reported higher linkage and ART initiation rates with POC CD4+ count testing as compared to traditional CD4+ count testing [23–26].The second intervention of accelerated ART initiation for eligible patients (CD4+ count ≤ 350 cells/mm3 or WHO stage III/IV) involved 2 rather than 3 counseling sessions and recommended ART initiation within the first week after linkage to care. Delays in ART initiation among those eligible for treatment have been shown to be associated with increased morbidity and mortality [27]. Late ART initiation is also associated with a longer period of increased infectiousness due to ongoing viral replication [28]. In this study, initiation of ART promptly rather than waiting for the return of baseline safety laboratory test results was strongly encouraged, and a checklist was made available to the providers to assist in identifying those potential participants at risk for renal insufficiency who would require waiting for the serum creatinine results prior to ART initiation.The third intervention involved use of short-message-service (SMS) appointment reminders, sent from a central server, which aimed at improving linkage to and retention in care among participants. SMS reminders were sent 3 days prior to an appointment and after a missed appointment. The message did not contain any information relating to HIV status. SMS communications have been used in HIV care and other chronic disease management to improve health communication and patient adherence [29–37].The fourth intervention was a health education package that included health information and supplies such as soap, a toothbrush, and a pill box, which also aimed to improve both linkage to and retention in care. A package of different materials and information was given every 3 months. A similar intervention has been evaluated in Uganda and was associated with high rates of cotrimoxazole use, condom use, and HIV testing of family members [38].Lastly, financial incentives of modest amount were provided that served to reimburse participants for expenses or lost wages or transport costs for clinic attendance [39]. This intervention was selected because there has been great interest in the use of financial incentives as a structural intervention to achieve positive health behaviors including retention in care [39–45]. A noncash type of financial incentive was provided in the form of a prepaid mobile phone card valued at US$8 and was given to participants upon linkage to care within 1 month of HIV testing and at completion of 6 and 12 months in follow-up care.Data collection and study measuresAll participants completed a baseline questionnaire, at the time of study enrollment, which solicited information on sociodemographic characteristics, HIV disease history, barriers to care, travel time to clinic, depression, social and family support, and HIV-related knowledge. Follow-up questionnaires were conducted at 1 and 12 months after enrollment, at the participant’s home or a prespecified location in the community, to collect information on changes in sociodemographic characteristics, self-reported linkage to care and retention, preferences about the study interventions, and vital status, if the latter was not known. Clinical data including CD4+ count, WHO Stage, date of ART initiation, ART regimen, and clinic and pharmacy visit dates were abstracted from participants’ medical charts—the data source for the primary outcome. These data were collected between 3–6 months after the participant reached the end of the study follow-up period. If the participant’s medical record was missing, he/she was assumed to have not achieved the primary outcome. Death was ascertained via medical record reviews or at the time of the 1- or 12-month interview. Viral load measurement was done using dried blood samples (DBSs) (HIV-1 RNA Abbott m2000rt system) collected at the time of the 12-month questionnaire at the participant’s home or a prespecified location [46].Study outcomesThe primary outcome was a combined outcome of linkage to HIV care within 1 month of HIV testing plus retention in care at 12 months from HIV testing among participants at the individual level. Linkage to care was defined by participant attendance of at least 1 visit to an HIV clinic with completion of an intake assessment including medical history and physical exam. Retention in care at 12 months after HIV testing was defined as no documented death and a clinic visit at the study unit within 90 days prior to the end of the study follow-up period. Participants with a missing medical record at the time of medical record abstraction were considered nonretained.Secondary endpoints included evaluation of the effectiveness of the CIS compared to the SOC with regard to the following: each component of the primary outcome described above, time to linkage, ART eligibility, ART initiation, time to ART initiation, viral suppression defined as HIV-1 RNA < 1,000 copies/mL at 12 months among patients on ART for at least 6 months, and death and loss to follow-up at 12 months after HIV testing. Death and transfer status were ascertained from medical records and through the 12-month follow-up visit questionnaire. Linkage and retention at clinics other than the assigned study unit were assessed in a sensitivity analysis using self-reported linkage and retention data from the 1- and 12-month questionnaires.Statistical analysisThe study sample size was calculated by estimating that 35% of the participants in the SOC study arm would achieve the primary outcome (assuming that 50% link to HIV care within 1 month of testing and that 70% of those linking within 1 month are retained at 12 months after testing). We estimated that approximately 2,750 adults would be eligible for study enrollment based on historic HIV testing volume and the proportion of individuals testing HIV positive at the study units in the year prior to the study start. Assuming 80% of eligible individuals would consent to enrollment, we estimated an average enrollment of 220 participants per study unit or 2,200 in total (1,110 per study arm). With this sample size and 5 study units per study arm, we then estimated the minimum difference in the primary outcome we could detect with 80% power, 2-sided alpha of 0.05, assuming an interclass correlation coefficient (ICC) of 0.05. In a post hoc analysis, we estimated the ICC of the primary outcome using the method outlined by Snijders and Bosker for binary outcome data [47].An intent-to-treat analysis compared the relative risk (RR) of achieving the primary outcome between study arms, with each having 5 clusters per arm. Within study unit clustering was accounted for using random-intercept multilevel models. For dichotomous outcomes, log-Poisson models with robust standard error were used. For continuous outcomes, random-intercept linear regression models were used. Assessment of potential confounding despite cluster randomization was performed by constructing multivariable random-intercept regression models including covariates found statistically different between treatment arms at an alpha of 0.01. Additionally, we conducted a per-protocol analysis comparing the RR of achieving the primary outcome among participants who received the full package of the CIS for the duration of study participation. Sensitivity analysis assessed any changes to the intent-to-treat analysis after including self-reported linkage and retention obtained from follow-up surveys. In post hoc analyses, assessment of the RR for achieving the primary outcome by key subgroups was done using interaction contrast ratios.ResultsStudy populationOf the 10 study units included in this study, 6 were located in urban areas, and 4 in rural areas. At study units randomized to the CIS study arm, a total of 1,234 individuals were screened for eligibility, with 1,096 (89%) enrolled in the study from 19 August 2013 to 21 November 2014 (Fig 1). At study units assigned to the SOC study arm, a total of 1,316 were screened, with 1,101 (84%) enrolled. Study refusal differed by study arm, with 23 refusals (1.9%) in the CIS arm and 114 refusals (8.7%) in the SOC arm (p < 0.001). Reasons for ineligibility are shown in Fig 1, with 111 participants ineligible in the CIS arm compared to 101 in the SOC arm.10.1371/journal.pmed.1002420.g001Fig 1Flow diagram of study enrollment.ART, antiretroviral therapy; CIS, combination intervention strategy; SOC, standard of care; SU, study unit.Among 2,197 participants included in this analysis, 1,294 (59%) were female, and the median age was 31 years (IQR 26–39), with 445 (20%) of the participants being young adults aged 18–24 years (Table 2). Forty-five percent reported no education or only primary schooling; approximately half were unemployed. Median individual weekly income was US$9 (IQR US$0–US$37). Eighty-four percent reported living in the current residence for more than 1 year, with 16% reporting travel away from home for over a 1-month duration in the past year. The median travel time from residence to HIV clinic was 30 minutes (IQR 20–50). The majority (80%) were diagnosed with HIV through a voluntary counseling and testing site, with the remainder having received HIV testing through provider-initiated testing and counseling at clinics within the study units. Over half (54%) of the participants reported that this was their first HIV test, while 89% indicated that it was their first positive HIV test.10.1371/journal.pmed.1002420.t002Table 2Participant characteristics at HIV testing (N = 2,197).CharacteristicsCIS armSOC armTotal\xa0\xa0N%N%N%\xa01,096\xa01,101\xa02,197\xa0Female\xa065760%63758%1,29459%Age (years)Median (IQR)32 (26–40)30 (25–39)31 (26–39)18–2421019%23521%44520%25–3961256%60455%1,21655%40–4915814%16615%32415%>5011611%959%21110%Missing/refused\xa0\xa010%10%EducationNone/primary47844%51947%99745%Secondary or higher61756%58153%1,19855%Missing/refused10%10%20%Weekly incomeMedian (IQR)US$9 (US$0-US$37)US$14 (US$0-US$37)US$9 (US$0-US$37)Unemployed\xa062457%53148%1,15553%Married\xa040036%40837%80837%Number of living children020619%20719%41319%1 to 364559%68062%1,32560%>324322%21419%45721%Missing/refused20%00%20%Lives alone\xa011611%16015%27613%Away from home >1 month in past year\xa017916%17015%34916%Time at current residence1 year or less16415%19217%35616%Greater than 1 year93085%90682%1,83684%Missing/refused20%30%50%Travel time to clinicMedian (IQR) time minutes30 (20–45)30 (20–60)30 (20–50)<30 minutes69063%58453%1,27458%31–60 minutes33030%32329%65330%>60 minutes626%19117%25311%Missing/refused141%30%171%Currently on TB treatment\xa081%141%221%HIV testing siteVCT93785%82074%1,75780%PITC15915%28025%43920%Missing/refused00%10%10%First HIV test\xa064259%53949%1,18154%First positive HIV test\xa096788%97889%1,94589%Household member with HIV\xa042739%34832%77535%Alcohol consumption in the last 7 daysEvery day161%182%342%Some days23521%23421%46921%Never84577%84977%1,69477%Abbreviations: CIS, combination intervention strategy; PITC, provider-initiated testing and counselling; SOC, standard of care; TB, tuberculosis; VCT, voluntary HIV counselling and testing.Primary outcomeIn the intent-to-treat analysis, 705 (64%) participants at sites randomized to the CIS study arm and 477 (43%) participants at sites randomized to the SOC study arm achieved the primary outcome of linkage to HIV care within 1 month of HIV-positive testing plus retention in HIV care at 12 months after HIV testing, for an RR of 1.48 (95% CI 1.37–1.61, p < 0.001). Accounting for clustering within study units, the RR was 1.52 (95% CI 1.19–1.96, p = 0.002) (Fig 2, Table 3). Additionally, adjusting for covariates significant in the bivariate analyses listed in Table 2 did not appreciably change the results. A total of 64 (6%) of participants in the CIS arm and 144 (13%) of participants in the SOC arm did not have a medical record and were classified as “not linked” to HIV care.10.1371/journal.pmed.1002420.g002Fig 2Proportion of participants who achieved the primary outcome of linkage to HIV care within 1 month of HIV testing plus retention in HIV care at 12 months after HIV testing by study arm (combination intervention strategy [CIS] and standard of care [SOC]).10.1371/journal.pmed.1002420.t003Table 3Primary and secondary outcomes for the combination intervention strategy (CIS) and standard of care (SOC) study arms.CIS group (N = 1,096)SOC group (N = 1,101)Relative risk (RR)N%N%RR95% CIp-ValuePrimary outcomeIntention to treat70564%47743%1.48(1.37–1.61)<0.001Intention to treat accounting for clustering170564%47743%1.52(1.19–1.96)0.002Intention to treat accounting for clustering and differences in covariates1,370564%47743%1.50(1.12–1.99)0.009Per protocol1,267269%44743%1.68(1.32–2.15)<0.001Sensitivity analysis1,476169%55751%1.41(1.13–1.74)0.004Secondary outcomesLinkageLinked to care (ever)1103294%95787%1.08(0.97–1.21)0.13Mean (SD) time from HIV testing to linkage2.5 days (19.5)7.5 days (46.6)0.189ART eligibilityAssessed for ART eligibility11,096100%92084%1.20(1.07–1.34)0.004Became ART eligible183376%72165%1.18(1.01–1.37)0.038Mean (SD) time from HIV testing to ART eligibility assessment50 (0)6.3 (35.5)<0.001ART initiation*Initiated ART (ever)1*71065%63558%1.16(0.96–1.40)0.12Median (IQR) time from testing HIV positive to ART initiation among ART eligible, days67.0 (3.0–21.0)14.0 (7.0–31.0)<0.001Retention regardless of time to linkage and ART statusRetained 12 months after HIV testing172066%49845%1.48(1.18–1.86)0.002Viral suppressionViral suppression (HIV-1 RNA < 1,000 copies/ml) among participants on ART for ≥6 months (N = 477 CIS and N = 451 SOC)1,741988%40690%0.97(0.88–1.07)0.55Deaths within 12 months of HIV testingTotal deaths1353%434%0.80(0.46–1.35)0.41Death before ART initiation1101%232%0.44(0.19–1.01)0.05Death after ART initiation1252%202%1.18(0.57–2.47)0.63Transfers within 12 months of HIV testingTotal transfers1232%262%0.88(0.44–1.77)0.71Transfers before ART initiation171%192%0.37(0.16–0.85)0.02Transfers after ART initiation1161%71%2.10(0.72–6.18)0.16Lost to follow-up within 12 months of HIV testingTotal lost to follow-up131829%53449%0.56(0.40–0.79)0.002Lost to follow-up before ART initiation124022%35732%0.60(0.40–0.89)0.014Lost to follow-up after ART initiation1787%17716%0.51(0.31–0.85)0.0131 Accounting for within-study unit clustering using random intercept log-Poisson regression models with robust standard error.2 The per-protocol analysis compared all patients in the SOC arm to those in the CIS arm self-reporting receipt of all interventions: point-of-care (POC) CD4+ count, accelerated antiretroviral therapy (ART) initiation (if eligible), health education package, short message service (SMS), and financial incentives. A total of 937 of the 1,096 patients in the CIS arm were included. Patients were excluded for the following: missing PIMA (2), ART counseling session #1 (24), ART counseling session #2 (14), first health education package (7), second health education package (12), third health education package (4), fourth health education package (2), financial incentive for linkage to care (86), second financial incentive (8), or third financial incentive (4).3 Additionally adjusting for covariates significantly different between groups at an alpha of 0.1: employment status, number of children, whether the participant lives alone, HIV testing location, family member with HIV, travel time to clinic, and whether this was the participant’s first HIV test.4 The sensitivity analysis considers participants linked to HIV care or retained in HIV care if they are recorded as linked and retained in their medical records or if they self-reported linkage or retention in the 1- and/or 12-month study questionnaire.5 All participants in the SOC arm were assessed for ART eligibility at the time of testing HIV positive. Of the SOC participants, 920/1,101 (84%) were assessed at enrollment into HIV care or clinical follow-up.6 Time to ART initiation measured from date of HIV-positive test to ART initiation among those becoming ART eligible. The p-values are Wilcoxon tests of differences between medians.7 The proportion of viral load suppression (<1,000 copies/ml) among participants who were on ART for ≥6 months with available viral loads is reported in the table. Among all participants who were on ART for ≥6 months, 85% (419/493) in the CIS arm and 89% (406/458) in the SOC arm had viral suppression.* In the CIS arm, 85% of those ART eligible initiated ART. In the SOC arm, 88% of those eligible initiated ART.The RR in the per-protocol analysis accounting for clustering for achieving the primary outcome was 1.68 (95% CI 1.32–2.15, p = 0.003) (Table 3). The RR in the sensitivity analysis, accounting for clustering, which included participants who self-reported linkage and retention in the 1- and 12-month surveys at a clinic other than 1 with their assigned study unit, was 1.41 (95% CI 1.13–1.74, p = 0.004), respectively (Table 3). Using this approach, we calculated an ICC of 0.086, similar to but slightly higher than the assumed ICC used in power and sample size estimation.The CIS strategy was delivered according to the study protocol to 937 (85%) of the 1,096 participants enrolled in study units assigned to the CIS. Reasons for not receiving all of the CIS strategy intervention components included missing POC CD4+ count testing (<1% CIS participants), missing an ART counseling session per accelerated ART procedures (3%), missing receipt of 1 healthcare bag (2%), and missing receipt of 1 financial incentive (9%). There was heterogeneity in the primary outcome across the 5 pairs of matched study units. The proportion of participants who achieved the primary outcome in study units randomized to the CIS ranged from 49% to 82%, while this ranged from 22% to 57% in the study units randomized to SOC.Secondary outcomesA similar proportion of participants linked to care anytime during the study follow-up period in both study arms: 1,032 (94%) in the CIS arm as compared to 957 (87%) in the SOC arm (RR 1.08, 95% CI 0.97–1.21, p = 0.13), with no significant differences in linkage within the same day or 1 month after testing (Table 3). The mean time to linkage to care was shorter in the CIS arm versus the SOC study arm but was not statistically different (2.5 compared to 7.5 days, p = 0.189). However, among those who ever linked to care (1,032 in the CIS study arm and 957 in the SOC study arm), significantly fewer patients (13%) in CIS sites versus SOC sites (18%) did not return for subsequent visits after the first clinic visit (p = 0.008).Assessment for ART eligibility through either a CD4+ count or WHO staging was done for all participants in the CIS arm as compared to 84% of participants in the SOC arm (RR 1.20, 95% CI 1.07–1.34, p = 0.004). The mean time to ART eligibility assessment was 0 days in the CIS study arm compared to 6.3 days in the SOC arm (p < 0.001). The median CD4+ count among 1,096 participants in the CIS arm who had POC CD4+ count testing done at the time of HIV testing was 311 cells/mm3 (IQR 159–443). Among the 907 (82%) participants in the SOC arm who linked to HIV care and had a CD4+ count done, the median CD4 count was 285 cells/mm3 (155–444) (p = 0.07).A total of 710 participants (85% of ART-eligible participants) in the CIS arm as compared to 635 (88% among ART-eligible participants) in the SOC arm initiated ART within the study follow-up period (RR 1.16, 95% CI 0.96–1.40, p = 0.12) (Table 3). The median time from HIV testing to ART initiation among eligible patients was 7.0 days (IQR 3.0–21.0) as compared to 14.0 days (IQR 7.0–13.0) in the CIS and SOC study arms, respectively (p < 0.001).Retention in care, regardless of time to linkage or ART status, at 12 months was significantly greater in participants in the CIS as compared to the SOC study arm, with an RR of 1.48 (95% CI 1.18–1.86, p = 0.002). Loss to follow-up during pre-ART care (RR = 0.60, 95% CI 0.40–0.89, p = 0.014) and after ART initiation (RR = 0.51, 95% CI 0.31–0.85, p = 0.013) was significantly lower in the CIS arm as compared to the SOC arm.For participants on ART for at least 6 months during follow-up regardless of retention status, viral load data were available for 97% (N = 477/493) of participants in the CIS arm and 98% (N = 451/458) in the SOC arm. Viral suppression among participants on ART ≥6 months with available viral loads was similar by study arm at 88% in CIS and 90% in SOC (RR 0.97, 95% CI 0.88–1.07, p = 0.55).There were 78 deaths (3.6% of the study population) that occurred during follow-up, and this did not differ by study arm (35 deaths [3%] in the CIS study arm versus 43 deaths [4%] in the SOC arm, with an RR of 0.80, 95% CI 0.46–1.35, p = 0.40) (Table 3). However, there was nonsignificantly lower mortality among participants prior to ART initiation in the CIS arm (10 deaths) compared to the SOC arm (23 deaths), with an RR of 0.44 (95% CI 0.19–1.01, p = 0.05). Fig 3 compares the CIS study arm versus the SOC study arm across the HIV care continuum from linkage to care within 1 month of testing through retention in care at 12 months after testing HIV positive.10.1371/journal.pmed.1002420.g003Fig 3HIV care continuum comparing the combination intervention strategy (CIS) study arm versus the standard of care (SOC) study arm.In post hoc analyses, we examined achievement of the primary outcome between study arms by key subgroups. The effect of the CIS, as compared to the SOC, was consistent across all prespecified subgroups, including by age, sex, income, employment, marital status, travel away from home in the past year, travel time to clinic, past HIV testing history, household members with HIV, and type of clinic (Fig 4, S1 Table).10.1371/journal.pmed.1002420.g004Fig 4Primary outcome by subgroups of participants.USD, US dollars; yrs, years.DiscussionIn this cluster-randomized study, a novel combination strategy, inclusive of 5 evidence-based interventions, was 50% more effective than the SOC in enhancing linkage to care plus retention in care among HIV-positive individuals. The robustness of this outcome is supported by the consistent findings in the per-protocol analysis, in sensitivity analyses, and across subgroups of participants. In addition, the combination strategy was associated with improvements across multiple steps of the care continuum, with an increased proportion of participants who were assessed for ART eligibility, decreased time to ART eligibility assessment, decreased time to ART initiation, increased retention at 12 months after HIV testing regardless of time to linkage and ART status, and decreased mortality among participants prior to ART initiation. However, high rates of viral suppression were similar among ART patients by study arm.In our study, the effect noted on the primary outcome appeared to be largely driven by enhanced retention rather than by the linkage-to-care component. This finding may be due to the high proportion of participants in both study arms who linked to care within 1 month of HIV testing in both arms of the study (87% in the SOC arm and 92% in the CIS arm), and thus, our sample size was insufficient to show a difference between the arms. The high proportion of participant linkage was likely influenced by a national campaign to improve linkage that was implemented during the study period [21].The combination strategy significantly reduced loss to follow-up among participants regardless of whether they were in pre-ART care or on treatment. Loss to follow-up, in both study arms, was higher among pre-ART participants, as compared to participants who had initiated ART. This is consistent with findings from other studies, including those from a large study of 390,603 HIV-positive adults in Kenya, Mozambique, Rwanda, and Tanzania, in which 34.8% of all patients who had not initiated ART were lost from care at 12 months, compared to 5.8% among patients on ART [6]. While the pre-ART care phase should largely be minimized with the release of the recent WHO guidelines that recommend offering ART to all HIV-positive individuals irrespective of CD4+ count or WHO disease stage, evidence suggests that retention in care and on ART remains a challenge even in the context of “treatment for all” [48]. For example, while adoption of Option B+, which entails initiation of ART for all HIV-positive pregnant women, has been associated with an increase in the number of pregnant women on ART, loss to follow-up has remained a challenge. Among 21,939 HIV-infected pregnant women who started ART as per Option B+ in Malawi, 17% were lost to follow-up at 6 months after treatment start, with a 5-fold higher loss to follow-up compared to those who initiated ART at a more advanced stage of HIV disease [49]. Thus, the findings from our study remain relevant even though the study was conducted at a time when a CD4+ count threshold was recommended for ART initiation.In this study, viral suppression was high among all participants on ART for a minimum of 6 months, irrespective of study arm. This confirms the potency of the first-line regimen, consisting of tenofovir, lamivudine, and efavirenz or nevirapine, and suggests that participants were highly adherent to their medications. These findings build upon those from the Population-based HIV Impact Assessment Project surveys that were conducted recently in Malawi, Zambia, Zimbabwe, and Swaziland, which included nationally representative samples of individuals in which 87% of HIV-positive adults who reported being on ART were virally suppressed (HIV-1 RNA < 1,000 copies/ml) [50,51]. Findings from this project survey in Swaziland showed that among adults who were aware of their HIV-positive status and who indicated being on ART, 92% had a suppressed viral load [52]. The finding of similarly high proportions of viral suppression among participants in both arms of our study suggests that the sample size was insufficient to detect a difference. In addition, it is important to note that the interventions used in this study were not designed with a focus specifically on enhancing medication adherence and viral suppression. Design of future combination strategies may prioritize the use of interventions that focus specifically on enhancing adherence to ART, such as the use of financial incentives to improve viral suppression [53].Every effort was made to ascertain accurate loss to follow-up and mortality outcomes in our study. It should be noted that reporting of accurate loss to follow-up and mortality outcomes by HIV programs has been a controversial topic. This is due to the fact that when tracing was done for individuals reported as lost to follow-up by HIV programs, a substantial proportion were found to have either died or transferred care to another health facility [54]. We feel confident that it is unlikely that such misclassification occurred in our study as home tracing was conducted for all study participants to ascertain their outcomes at the end of the 12-month follow-up period. While the study was not powered to detect a difference between the study arms in terms of mortality, the combination strategy appeared to have a meaningful—albeit not statistically significant—effect, with as much as 50% lower mortality noted among pre-ART patients. This may be due to better retention in care among participants in the intervention arm. Poor retention in care has been demonstrated to be associated with increased mortality, likely due to missed clinic visits that deprive patients of clinical and laboratory assessments for diagnosis of early complications and delay prompt initiation of ART [55].We observed substantial heterogeneity in the primary outcome across clinics in both the CIS and SOC study arms. This may reflect clinic-level differences such as clinic size and location. For example, the CIS study unit with the lowest achievement of the study’s primary outcome was the largest clinic in urban Swaziland. It is possible that individuals who test HIV positive at such a large clinic may seek ongoing care at clinics closer to their homes. Other reasons could be differences in patient-level factors, such as sex, age, and immunological status, which warrant further analyses.To date, most intervention studies to address gaps in the HIV care continuum have focused on 1 step in the continuum, largely that of ART initiation. The Rapid Initiation of Treatment trial showed that single-visit ART initiation that included POC CD4+ count testing was associated with significantly higher ART initiation (97%) compared to the standard of care (72%) [56]. The START-ART trial was a stepped-wedge cluster-randomized trial of 20 clinics in Uganda that evaluated an intervention aimed at improving ART initiation among eligible patients; this intervention was associated with a higher proportion of patients initiating ART (80%) within 14 days after determination of ART eligibility compared to 38% in the control group [13]. Finally, the Same Day ART Initiation Study in Haiti, which evaluated the effect of same-day ART initiation on the day of HIV diagnosis among asymptomatic HIV-positive adults with CD4+ count ≤ 500 cells/uL and WHO stage I or II disease, noted that a higher proportion (53%) of participants randomized to same-day ART initiation were retained in care at 12 months with viral suppression compared to those in the standard of care arm (44%) [14].Our study had several strengths, including the use of a pragmatic approach consistent with implementation science design. Specifically, it utilized broad eligibility criteria, was conducted within established health facilities, tested feasible interventions that were delivered primarily by available staff rather than research staff, and assessed the primary outcome largely through routinely available data. In addition, the study included the majority of clinics in Swaziland and involved cluster-randomized design rather than randomization of individual participants, which allowed for ease of implementation and avoided disruption of services within the clinics. The study also uniquely assessed the effect of the delivery of multiple interventions packaged in 1 strategy aimed at multiple steps in the HIV care continuum. Thus, implementation of the study strategy has the potential to achieve not only prompt ART initiation but also better retention in care and on ART, consequently enhancing individual and society benefits from the “treat all” approach.The primary limitations of this study included a limited number of clusters, although it was inclusive of all the available clusters in the country. At the time of study initiation, there were only 11 secondary health facilities offering HIV services in Swaziland, and we selected 10 of these for participation in this study. Consequently, it is possible that the cluster randomization did not evenly distribute all determinants of linkage and retention other than the study interventions between treatment arms. While it is encouraging that analyses adjusting for individual-level differences between treatment arms did not appreciably change the results, we cannot definitively rule out residual confounding as a potential explanation of the findings. In addition, the design focused on evaluation of a package of interventions as 1 strategy and, thus, it did not allow for evaluation of the effectiveness of individual components of the combination approach. Another limitation was use of self-reported linkage and retention at other clinics to ascertain undocumented transfers to other clinics outside of the study unit.ConclusionsThe Link4Health study demonstrated that a combination strategy of evidence-based interventions, aimed at gaps in various steps of the HIV care continuum, was highly effective in enhancing linkage of HIV-positive individuals to care plus increasing their retention in care and on ART. The study also showed that once participants initiated ART, viral load suppression was high irrespective of the study arm. Cost effectiveness and qualitative analyses are ongoing in order to inform decision makers considering adoption of this strategy. Our findings offer an effective strategy that can advance the quality of HIV programs in Swaziland and that can be adapted to other similar contexts.Supporting informationS1 TextConsolidated Standards of Reporting Trials (CONSORT) statement.(DOCX)Click here for additional data file.S1 DataLink4Health deidentified dataset.(XLSX)Click here for additional data file.S1 TablePrimary outcome by prespecified participant subgroup.(DOCX)Click here for additional data file.AbbreviationsARTantiretroviral therapyCIScombination intervention strategyCONSORTConsolidated Standards of Reporting TrialsDBSdried blood sampleICCinterclass correlation coefficientPITCprovider-initiated testing and counsellingPOCpoint of careRRrelative riskSMSshort message serviceSOCstandard of careSUstudy unitTBtuberculosisUNAIDSJoint United Nations Programme on HIV/AIDSVCTvoluntary HIV counselling and testingReferences1McNairyML, El-SadrWM. The HIV care continuum: no partial credit given. AIDS. 2012;26(14):1735–8. doi: 10.1097/QAD.0b013e328355d67b .226148882UNAIDS. UNAIDS Website. Accessed May 24, 2016 at: http://www.unaids.org/en/resources/presscentre/pressreleaseandstatementarchive/2015/july/20150714_PR_MDG6report.3UNAIDS. 90-90-90 An ambitious treatment target to help end the AIDS epidemic. Accessed May 1, 2016 at: http://www.unaids.org/sites/default/files/media_asset/90-90-90_en_0.pdf.4FoxMP, RosenS. Retention of Adult Patients on Antiretroviral Therapy in Low- and Middle-Income Countries: Systematic Review and Meta-analysis 2008–2013. J Acquir Immune Defic Syndr. 2015;69(1):98–108. doi: 10.1097/QAI.0000000000000553 ; PubMed Central PMCID: PMC4422218.259424615MugglinC, EstillJ, WandelerG, BenderN, EggerM, GsponerT, et al\nLoss to programme between HIV diagnosis and initiation of antiretroviral therapy in sub-Saharan Africa: systematic review and meta-analysis. Trop Med Int Health. 2012;17(12):1509–20. Epub 2012/09/22. doi: 10.1111/j.1365-3156.2012.03089.x ; PubMed Central PMCID: PMC3895621.229941516McNairyML, LambMR, AbramsEJ, ElulB, SahaboR, HawkenMP, et al\nUse of a Comprehensive HIV Care Cascade for Evaluating HIV Program Performance: Findings From 4 Sub-Saharan African Countries. J Acquir Immune Defic Syndr. 2015;70(2):e44–51. doi: 10.1097/QAI.0000000000000745 .263754667FoxMP, ShearerK, MaskewM, Meyer-RathG, ClouseK, SanneI. Attrition through Multiple Stages of Pre-Treatment and ART HIV Care in South Africa. PLoS ONE. 2014;9(10):e110252\ndoi: 10.1371/journal.pone.0110252 ; PubMed Central PMCID: PMC4203772.253300878IwujiCC, Orne-GliemannJ, LarmarangeJ, OkesolaN, TanserF, ThiebautR, et al\nUptake of Home-Based HIV Testing, Linkage to Care, and Community Attitudes about ART in Rural KwaZulu-Natal, South Africa: Descriptive Results from the First Phase of the ANRS 12249 TasP Cluster-Randomised Trial. PLoS Med. 2016;13(8):e1002107\ndoi: 10.1371/journal.pmed.1002107 ; PubMed Central PMCID: PMC4978506.275046379GovindasamyD, FordN, KranzerK. Risk factors, barriers and facilitators for linkage to antiretroviral therapy care: a systematic review. AIDS. 2012;26(16):2059–67. Epub 2012/07/12. doi: 10.1097/QAD.0b013e3283578b9b .2278122710HallBJ, SouKL, BeanlandR, LackyM, TsoLS, MaQ, et al\nBarriers and Facilitators to Interventions Improving Retention in HIV Care: A Qualitative Evidence Meta-Synthesis. AIDS Behav. 2016;21(6):1755–67. doi: 10.1007/s10461-016-1537-0 .2758208811FoxMP, RosenS, GeldsetzerP, BarnighausenT, NegussieE, BeanlandR. Interventions to improve the rate or timing of initiation of antiretroviral therapy for HIV in sub-Saharan Africa: meta-analyses of effectiveness. J Int AIDS Soc. 2016;19(1):20888 10.7448/IAS.19.1.20888. 27507249; PubMed Central PMCID: PMC4978859. doi: 10.7448/IAS.19.1.20888\n2750724912GovindasamyD, MeghijJ, Kebede NegussiE, BaggaleyRC, FordN, KranzerK. Interventions to improve or facilitate linkage to or retention in pre-ART (HIV) care and initiation of ART in low- and middle-income settings—a systematic review. J Int AIDS Soc. 2014;17:19032\ndoi: 10.7448/IAS.17.1.19032 ; PubMed Central PMCID: PMC4122816.2509583113AmanyireG, SemitalaFC, NamusobyaJ, KaturamuR, KampiireL, WallentaJ, et al\nEffects of a multicomponent intervention to streamline initiation of antiretroviral therapy in Africa: a stepped-wedge cluster-randomised trial. Lancet HIV. 2016;3(11):e539–e48. doi: 10.1016/S2352-3018(16)30090-X .2765887314KoenigSP, DorvilN, DevieuxJG, Hedt-GauthierBL, RiviereC, FaustinM, et al\nSame-day HIV testing with initiation of antiretroviral therapy versus standard care for persons living with HIV: A randomized unblinded trial. PLoS Med. 2017;14(7):e1002357\ndoi: 10.1371/journal.pmed.1002357 ; PubMed Central PMCID: PMC5526526.2874288015McNairyML, GachuhiAB, LambMR, Nuwagaba-BiribonwohaH, BurkeS, EhrenkranzP, et al\nThe Link4Health study to evaluate the effectiveness of a combination intervention strategy for linkage to and retention in HIV care in Swaziland: protocol for a cluster randomized trial. Implementation science: IS. 2015;10:101\ndoi: 10.1186/s13012-015-0291-4 ; PubMed Central PMCID: PMC4506770.2618915416Institute for Health Metrics and Evaluation. IHME Health Data Swaziland. Accessed May 30, 2017 at: http://www.healthdata.org/swaziland.17JustmanJ, ReedJB, BicegoG, DonnellD, LiK, BockN, et al\nSwaziland HIV Incidence Measurement Survey (SHIMS): a prospective national cohort study. Lancet HIV. 2016;4(2):e83–e92. doi: 10.1016/S2352-3018(16)30190-4 .2786399818CDC In Swaziland. Swaziland Health Factsheet. Accessed November 15 2013 at: https://www.cdc.gov/globalhealth/countries/swaziland/pdf/swaziland_factsheet.pdf.19The World Band. Swaziland HIV Data. Accessed December 20 2016 at: http://data.worldbank.org/indicator/SH.HIV.ARTC.ZS.20AuldAF, KamiruH, AzihC, BaughmanAL, Nuwagaba-BiribonwohaH, EhrenkranzP, et al\nImplementation and Operational Research: Evaluation of Swaziland's Hub-and-Spoke Model for Decentralizing Access to Antiretroviral Therapy Services. J Acquir Immune Defic Syndr. 2015;69(1):e1–e12. doi: 10.1097/QAI.0000000000000547 .2594246521MacKellarDA, WilliamsD, StorerN, OkelloV, AzihC, DrummondJ, et al\nEnrollment in HIV Care Two Years after HIV Diagnosis in the Kingdom of Swaziland: An Evaluation of a National Program of New Linkage Procedures. PLoS ONE. 2016;11(2):e0150086\ndoi: 10.1371/journal.pone.0150086 ; PubMed Central PMCID: PMC4766101.2691084722The Kingdom of Swaziland Ministry of Health. Annual HIV Programs Report 2015. Mbabane, Swaziland: Swaziland Ministry of Health, 2015.23LarsonBA, BrennaA, McNamaraL, LongL, RosenS, SanneI, et al\nLost Opportunities to complete CD4+ lymphocyte testing among patients who tested positive for HIV in South Africa. Bull World Health Organ. 2010;88(9):675–80. doi: 10.2471/BLT.09.068981 .2086507224JaniIV, SitoeNE, AlfaiER, ChongPL, QuevedoJI, RochaBM, et al\nEffect of point-of-care CD4 cell count tests on retention of patients and rates of antiretroviral therapy initiation in primary health clinics: an observational cohort study. Lancet. 2011;378(9802):1572–9. doi: 10.1016/S0140-6736(11)61052-0 .2195165625FaalM, NaidooN, GlenncrossDK, VenterWD, OsihR. Providing immediate CD4 count results at HIV testing improves ART initiation. J Acquir Immune Defic Syndr. 2011;58(3):e54–9. doi: 10.1097/QAI.0b013e3182303921 .2185735626LarsonBA, SchnippelK, NdibongoB, XuluT, BrennanA, LongL, et al\nRapid point-of-care CD4 testing at mobile HIV testing sites to increase linkage to care: an evaluation of a pilot program in South Africa. J Acquir Immune Defic Syndr. 2012;61(2):e13–7. doi: 10.1097/QAI.0b013e31825eec60 ; PubMed Central PMCID: PMC3458178.2265965027LahuertaM, UeF, HoffmanS, ElulB, KulkarniSG, WuY, et al\nThe problem of late ART initiation in Sub-Saharan Africa: a transient aspect of scale-up or a long-term phenomenon?\nJ Health Care Poor Underserved. 2013;24(1):359–83. doi: 10.1353/hpu.2013.0014 ; PubMed Central PMCID: PMC3655523.2337773928CohenMS, ChenYQ, McCauleyM, GambleT, HosseinipourMC, KumarasamyN, et al\nPrevention of HIV-1 infection with early antiretroviral therapy. N Engl J Med. 2011;365(6):493–505. Epub 2011/07/20. doi: 10.1056/NEJMoa1105243 ; PubMed Central PMCID: PMC3200068.2176710329ChangL, KagaayiJ, NakigoziG, PackerAH, SerwaddaD, QuinnTC, et al\nResponding to the human resource crisis: peer health workers, mobile phones, and HIV care in Rakai, Uganda. AIDS Patient Care STDS. 2008;22(3):173–4. doi: 10.1089/apc.2007.0234 PubMed Central PMCID 2674572 1829075030DownerSR, MearJG, Da CostaAC, SethuramanK. SMS text messaging improves outpatient attendance. Aust Health Rev. 8\n2006;30(3):389–96. .1687909831FjeldsoeBS, MarshallAL, MillerYD. Behavior change interventions delivered by mobile telephone short-message service. Am J Prev Med. 2\n2009;36(2):165–73. doi: 10.1016/j.amepre.2008.09.040 .1913590732HabererJE, KiwanukaJ, NanseraD, WilsonIB, BangsbergDR. Challenges in using mobile phones for collection of antiretroviral therapy adherence data in a resource-limited setting. AIDS Behav. Dec\n2010;14(6):1294–301. doi: 10.1007/s10461-010-9720-1 .2053260533LesterRT, RitvoP, MillsEJ, KaririA, KaranjaS, ChungMH, et al\nEffects of a mobile phone short message service on antiretroviral treatment adherence in Kenya (WelTel Kenya1): a randomised trial. Lancet. 11\n27\n2010;376(9755):1838–45. doi: 10.1016/S0140-6736(10)61997-6 .2107107434LiewSM, TongSF, LeeVK, NgCJ, LeongKC, TengCL. Text messaging reminders to reduce non-attendance in chronic disease follow-up: a clinical trial. Br J Gen Pract. 12\n2009;59(569):916–20. doi: 10.3399/bjgp09X472250 .1971254435Mukund BahadurKC, MurrayPJ. Cell phone short messaging service (SMS) for HIV/AIDS in South Africa: a literature review. Stud Health Technol Inform. 2010;160(Pt 1):530–5. .2084174336Pop-ElechesC, ThirumurthyH, HabyarimanaJP, ZivinJG, GoldsteinMP, de WalqueD, et al\nMobile phone technologies improve adherence to antiretroviral treatment in a resource-limited setting: a randomized controlled trial of text message reminders. AIDS. Mar 27\n2011;25(6):825–34. doi: 10.1097/QAD.0b013e32834380c1 .2125263237ShetA, de CostaA. India calling: harnessing the promise of mobile phones for HIV healthcare. Trop Med Int Health. 2011;16(2):214–6. doi: 10.1111/j.1365-3156.2010.02678.x .2137121438ColindresP, MerminJ, EzatiE, KambabaziS, BuyungoP, SekabembeL, et al\nUtilization of a basic care and prevention package by HIV-infected persons in Uganda. AIDS Care. 2008;20(2):139–45. Epub 2007/09/27. doi: 10.1080/09540120701506804 .1789619639GiuffridaA, TorgersonDJ. Should we pay the patient? Review of financial incentives to enhance patient compliance. BMJ. 1997;315(7110):703–7. Epub 1997/10/07. ; PubMed Central PMCID: PMC2127496.931475440VolppKG, JohnLK, TroxelAB, NortonL, FassbenderJ, LoewensteinG. Financial incentive-based approaches for weight loss: a randomized trial. JAMA. 2008;300(22):2631–7. Epub 2008/12/11. doi: 10.1001/jama.2008.804 .1906638341VolppKG, LoewensteinG, TroxelAB, DoshiJ, PriceM, LaskinM, et al\nA test of financial incentives to improve warfarin adherence. BMC Health Serv Res. 2008;8:272 Epub 2008/12/24. doi: 10.1186/1472-6963-8-272 ; PubMed Central PMCID: PMC2635367.1910278442VolppKG, TroxelAB, PaulyMV, GlickHA, PuigA, AschDA, et al\nA randomized, controlled trial of financial incentives for smoking cessation. N Engl J Med. 2009;360(7):699–709. Epub 2009/02/14. doi: 10.1056/NEJMsa0806819 .1921368343CharnessG, GneezyU. Incentives to exercise. Econometrica. 2009;77(3):909–31.44MarcusAC, KaplanCP, CraneLA, BerekJS, BernsteinG, GunningJE, et al\nReducing loss-to-follow-up among women with abnormal Pap smears. Results from a randomized trial testing an intensive follow-up protocol and economic incentives. Med Care. 1998;36(3):397–410. Epub 1998/04/01. .952096345MalotteCK, RhodesF, MaisKE. Tuberculosis screening and compliance with return for skin test reading among active drug users. Am J Public Health. 1998;88(5):792–6. Epub 1998/05/20. ; PubMed Central PMCID: PMC1508952.958574746WHO. Technical and Operational Considerations for Implementing HIV Viral Load Testing. Geneva: WHO, 2014.47SnijdersTA, BoskerRJ. Multilevel analysis: An introduction to basic and advanced mulitlevel modeling. Thousand Oaks, California: Sage; 1999.48WHO. Guideline on when to start antiretroviral therapy and on pre-exposure prophylaxis for HIV. Geneva: WHO, 2015 9\n2015.49TenthaniL, HaasAD, TweyaH, JahnA, van OosterhoutJJ, ChimbwandiraF, et al\nRetention in care under universal antiretroviral therapy for HIV-infected pregnant and breastfeeding women ('Option B+') in Malawi. AIDS. 2014;28(4):589–98. doi: 10.1097/QAD.0000000000000143 ; PubMed Central PMCID: PMC4009400.2446899950Columbia University. The Population HIV Impact Assessment (PHIA) Project. Accessed May 31 2017 at: www.phia.icap.columbia.edu.51Justman J. Real Progress in the HIV Epidemic: PHIA Findings from Zimbabwe, Malawi, and Zambia. Oral Abstract. Conference of Retroviruses and Opportunistic Infections February 13–15, 2017; Seattle, WA2017.52Nkambule R, Nuwagaba-Biribownwoha H, Mnisi Z, Ao T, Duong Y, Patel H, et al. Substantial progress in confronting the HIV epidemic in Swaziland: first evidence of national impact. Abstract 204LB. International AIDS Society 2017; July 24, 2017; Paris, France 2017.53El-SadrWM, DonnellD, BeauchampG, HallHI, TorianLV, ZingmanB, et al\nFinancial Incentives for Linkage to Care and Viral Suppression Among HIV-Positive Patients: A Randomized Clinical Trial (HPTN 065). JAMA. 2017;177(8):1083–92. doi: 10.1001/jamainternmed.2017.2158 .2862870254GengEH, GliddenDV, BwanaMB, MusinguziN, EmenyonuN, MuyindikeW, et al\nRetention in care and connection to care among HIV-infected patients on antiretroviral therapy in Africa: estimation via a sampling-based approach. PLoS ONE. 2011;6(7):e21797\ndoi: 10.1371/journal.pone.0021797 ; PubMed Central PMCID: PMC3144217.2181826555GiordanoTP, GiffordAL, WhiteACJr., Suarez-AlmazorME, RabeneckL, HartmanC, et al\nRetention in care: a challenge to survival with HIV infection. Clin Infect Dis. 2007;44(11):1493–9. Epub 2007/05/08. doi: 10.1086/516778 .1747994856RosenS, MaskewM, FoxMP, NyoniC, MongwenyanaC, MaleteG, et al\nInitiating Antiretroviral Therapy for HIV at a Patient's First Clinic Visit: The RapIT Randomized Controlled Trial. PLoS Med. 2016;13(5):e1002015\ndoi: 10.1371/journal.pmed.1002015 .27163694"", 'title': 'Effectiveness of a combination strategy for linkage and retention in adult HIV care in Swaziland: The Link4Health cluster randomized trial.', 'date': '2017-11-08'}, '28542080': {'article_id': '28542080', 'content': 'Lack of accessible laboratory infrastructure limits HIV antiretroviral therapy (ART) initiation, monitoring, and retention in many resource-limited settings. Point-of-care testing (POCT) is advocated as a mechanism to overcome these limitations. We executed a pragmatic, prospective, randomized, controlled trial comparing the impact of POCT vs. standard of care (SOC) on treatment initiation and retention in care.\nSelected POC technologies were embedded at 3 primary health clinics in South Africa. Confirmed HIV-positive participants were randomized to either SOC or POC: SOC participants were venesected and specimens referred to the laboratory with patient follow-up as per algorithm (∼3 visits); POC participants had phlebotomy and POCT immediately on-site using Pima CD4 to assess ART eligibility followed by hematology, chemistry, and tuberculosis screening with the goal of receiving same-day adherence counseling and treatment initiation. Participant outcomes measured at recruitment 6 and 12 months after initiation.\nFour hundred thirty-two of 717 treatment eligible participants enrolled between May 2012 and September 2013: 198 (56.7%) SOC; 234 (63.6%) POC. Mean age was 37.4 years; 60.5% were female. Significantly more participants were initiated using POC [adjusted prevalence ratio (aPR) 0.83; 95% confidence interval (CI): 0.74 to 0.93; P < 0.0001], the median time to initiation was 1 day for POC and 26.5 days for SOC. The proportion of patients in care and on ART was similar for both arms at 6 months (47 vs. 50%) (aPR 0.96; 95% CI: 0.79 to 1.16) and 12 months (32 vs. 32%) (aPR 1.05; 95% CI: 0.80 to 1.38), with similar mortality rates. Loss to follow-up at 12 months was higher for POC (36% vs. 51%) (aPR 0.82; 95% CI: 0.65 to 1.04).\nAdoption of POCT accelerated ART initiation but once on treatment, there was unexpectedly higher loss to follow-up on POC and no improvement in outcomes at 12 months over SOC.', 'title': 'Multidisciplinary Point-of-Care Testing in South African Primary Health Care Clinics Accelerates HIV ART Initiation but Does Not Alter Retention in Care.', 'date': '2017-05-26'}}",0.666666667,"Public Health, Epidemiology & Health Systems"
 21,"Is ART uptake at 12 months higher, lower, or the same when comparing rapid ART to standard initiation?",higher,moderate,yes,"['28742880', '27658873', '29509839', '27163694']",31206168,2019,"{'28742880': {'article_id': '28742880', 'content': 'PLoS MedPLoS MedplosplosmedPLoS Medicine1549-12771549-1676Public Library of ScienceSan Francisco, CA USA28742880552652610.1371/journal.pmed.1002357PMEDICINE-D-17-00266Research ArticleBiology and Life SciencesImmunologyVaccination and ImmunizationAntiviral TherapyAntiretroviral TherapyMedicine and Health SciencesImmunologyVaccination and ImmunizationAntiviral TherapyAntiretroviral TherapyMedicine and Health SciencesPublic and Occupational HealthPreventive MedicineVaccination and ImmunizationAntiviral TherapyAntiretroviral TherapyBiology and Life SciencesMicrobiologyMedical MicrobiologyMicrobial PathogensViral PathogensImmunodeficiency VirusesHIVMedicine and Health SciencesPathology and Laboratory MedicinePathogensMicrobial PathogensViral PathogensImmunodeficiency VirusesHIVBiology and Life SciencesOrganismsVirusesViral PathogensImmunodeficiency VirusesHIVBiology and Life SciencesOrganismsVirusesImmunodeficiency VirusesHIVBiology and life sciencesOrganismsVirusesRNA virusesRetrovirusesLentivirusHIVBiology and Life SciencesMicrobiologyMedical MicrobiologyMicrobial PathogensViral PathogensRetrovirusesLentivirusHIVMedicine and Health SciencesPathology and Laboratory MedicinePathogensMicrobial PathogensViral PathogensRetrovirusesLentivirusHIVBiology and Life SciencesOrganismsVirusesViral PathogensRetrovirusesLentivirusHIVMedicine and health sciencesDiagnostic medicineHIV diagnosis and managementBiology and Life SciencesMicrobiologyMedical MicrobiologyMicrobial PathogensViral PathogensImmunodeficiency VirusesHIVHIV-1Medicine and Health SciencesPathology and Laboratory MedicinePathogensMicrobial PathogensViral PathogensImmunodeficiency VirusesHIVHIV-1Biology and Life SciencesOrganismsVirusesViral PathogensImmunodeficiency VirusesHIVHIV-1Biology and Life SciencesOrganismsVirusesImmunodeficiency VirusesHIVHIV-1Biology and life sciencesOrganismsVirusesRNA virusesRetrovirusesLentivirusHIVHIV-1Biology and Life SciencesMicrobiologyMedical MicrobiologyMicrobial PathogensViral PathogensRetrovirusesLentivirusHIVHIV-1Medicine and Health SciencesPathology and Laboratory MedicinePathogensMicrobial PathogensViral PathogensRetrovirusesLentivirusHIVHIV-1Biology and Life SciencesOrganismsVirusesViral PathogensRetrovirusesLentivirusHIVHIV-1Medicine and Health SciencesHealth CareHealth Care ProvidersMedical DoctorsPhysiciansPeople and PlacesPopulation GroupingsProfessionsMedical DoctorsPhysiciansPeople and placesGeographical locationsNorth AmericaCaribbeanHaitiBiology and Life SciencesMicrobiologyVirologyViral Transmission and InfectionViral LoadMedicine and health sciencesDiagnostic medicineHIV clinical manifestationsSame-day HIV testing with initiation of antiretroviral therapy versus standard care for persons living with HIV: A randomized unblinded trialSame-day HIV testing and antiretroviral therapy initiationhttp://orcid.org/0000-0001-7464-275XKoenigSerena P.ConceptualizationFunding acquisitionInvestigationMethodologySupervisionWriting – original draftWriting – review & editing12*DorvilNancyInvestigationMethodologyProject administrationSupervisionWriting – review & editing1DévieuxJessy G.ConceptualizationFunding acquisitionInvestigationMethodologySupervisionWriting – original draftWriting – review & editing3http://orcid.org/0000-0002-9689-5413Hedt-GauthierBethany L.ConceptualizationFormal analysisFunding acquisitionMethodologySoftwareSupervisionValidationVisualizationWriting – review & editing4RiviereCynthiaInvestigationMethodologyProject administrationSupervisionWriting – review & editing1FaustinMikerlyneInvestigationMethodologyProject administrationSupervisionWriting – review & editing1LavoileKerlyneInvestigationMethodologyProject administrationSupervisionWriting – review & editing1PerodinChristianFormal analysisInvestigationMethodologySoftwareValidationVisualizationWriting – review & editing1ApollonAlexandraConceptualizationInvestigationMethodologyProject administrationSupervisionWriting – review & editing1DuvergerLimatheInvestigationMethodologyProject administrationSupervisionWriting – review & editing1McNairyMargaret L.MethodologyWriting – review & editing56HennesseyKelly A.Formal analysisMethodologySoftwareValidationVisualizationWriting – review & editing1SouroutzidisAriadneFormal analysisMethodologySoftwareValidationVisualizationWriting – review & editing7CremieuxPierre-YvesFormal analysisMethodologySoftwareValidationVisualizationWriting – review & editing7SeverePatriceConceptualizationFunding acquisitionInvestigationMethodologyProject administrationSupervisionWriting – review & editing1PapeJean W.ConceptualizationFunding acquisitionInvestigationMethodologyProject administrationSupervisionWriting – review & editing151\nHaitian Study Group for Kaposi’s Sarcoma and Opportunistic Infections (GHESKIO), Port-au-Prince, Haiti2\nDivision of Global Health Equity, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America3\nAIDS Prevention Program, Florida International University, Miami, Florida, United States of America4\nDepartment of Global Health and Social Medicine, Harvard Medical School, Harvard University, Boston, Massachusetts, United States of America5\nCenter for Global Health, Department of Medicine, Weill Cornell Medical College, Cornell University, New York, New York, United States of America6\nDivision of General Internal Medicine, Department of Medicine, Weill Cornell Medical College, Cornell University, New York, New York, United States of America7\nAnalysis Group, Boston, Massachusetts, United States of AmericaGengElvin H.Academic EditorUniversity of California, San Francisco, UNITED STATESThe authors have declared that no competing interests exist.* E-mail: skoenig@bwh.harvard.edu257201772017147e100235724120171662017© 2017 Koenig et al2017Koenig et alThis is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.\nThe science of rapid start—From the when to the how of antiretroviral initiation\nBackgroundAttrition during the period from HIV testing to antiretroviral therapy (ART) initiation is high worldwide. We assessed whether same-day HIV testing and ART initiation improves retention and virologic suppression.Methods and findingsWe conducted an unblinded, randomized trial of standard ART initiation versus same-day HIV testing and ART initiation among eligible adults ≥18 years old with World Health Organization Stage 1 or 2 disease and CD4 count ≤500 cells/mm3. The study was conducted among outpatients at the Haitian Group for the Study of Kaposi’s Sarcoma and Opportunistic infections (GHESKIO) Clinic in Port-au-Prince, Haiti. Participants were randomly assigned (1:1) to standard ART initiation or same-day HIV testing and ART initiation. The standard group initiated ART 3 weeks after HIV testing, and the same-day group initiated ART on the day of testing. The primary study endpoint was retention in care 12 months after HIV testing with HIV-1 RNA <50 copies/ml. We assessed the impact of treatment arm with a modified intention-to-treat analysis, using multivariable logistic regression controlling for potential confounders. Between August 2013 and October 2015, 762 participants were enrolled; 59 participants transferred to other clinics during the study period, and were excluded as per protocol, leaving 356 in the standard and 347 in the same-day ART groups. In the standard ART group, 156 (44%) participants were retained in care with 12-month HIV-1 RNA <50 copies, and 184 (52%) had <1,000 copies/ml; 20 participants (6%) died. In the same-day ART group, 184 (53%) participants were retained with HIV-1 RNA <50 copies/ml, and 212 (61%) had <1,000 copies/ml; 10 (3%) participants died. The unadjusted risk ratio (RR) of being retained at 12 months with HIV-1 RNA <50 copies/ml was 1.21 (95% CI: 1.04, 1.38; p = 0.015) for the same-day ART group compared to the standard ART group, and the unadjusted RR for being retained with HIV-1 RNA <1,000 copies was 1.18 (95% CI: 1.04, 1.31; p = 0.012). The main limitation of this study is that it was conducted at a single urban clinic, and the generalizability to other settings is uncertain.ConclusionsSame-day HIV testing and ART initiation is feasible and beneficial in this setting, as it improves retention in care with virologic suppression among patients with early clinical HIV disease.Trial registrationThis study is registered with ClinicalTrials.gov number NCT01900080In a randomized unblinded trial in Port-au-Prince, Haiti, Serena Koenig and colleagues investigate whether initiating ART on the day of HIV diagnosis improved retention in care and viral suppression.Author summaryWhy was this study done?Multiple visits for counseling, laboratory testing, and other procedures to prepare patients for initiation of antiretroviral therapy (ART) are burdensome and contribute to the high rate of attrition during the period from HIV testing to ART initiation.The World Health Organization (WHO) recently changed their guidelines to recommend ART for all persons living with HIV, facilitating ART initiation.This study was conducted to determine if ART initiation on the day of HIV diagnosis could improve treatment initiation rates, retention in care, and HIV viral suppression for patients with asymptomatic or minimally symptomatic HIV disease.What did the researchers do and find?We randomly assigned patients who presented for HIV testing at a clinic in Port-au-Prince, Haiti to standard ART initiation or same-day HIV testing and ART initiation (356 in the standard and 347 in the same-day groups).The standard group had 3 weekly visits with a social worker and physician and then started ART 21 days after the date of HIV diagnosis; the same-day ART group initiated ART on the day of HIV diagnosis.All participants in the same-day ART group and 92% of participants in the standard group initiated ART.At 12 months after HIV testing, a higher proportion of participants in the same-day ART group were retained in care (80% versus 72%), and a higher proportion were retained in care with viral load <50 copies/ml (53% versus 44%) and viral load <1,000 copies/ml (61% versus 52%).What do these findings mean?This study demonstrates that it is feasible to initiate ART on the day of HIV diagnosis for patients with early HIV clinical disease and that same-day treatment leads to increased ART uptake, retention in care, and viral suppression.Though same-day ART initiation improves outcomes, retention in care and viral suppression remain suboptimal, so further interventions to maximize long-term outcomes will be essential.The study is limited by being conducted at 1 clinic in urban Haiti. Further study will be necessary to determine if this strategy will be effective in other settings.http://dx.doi.org/10.13039/100000060National Institute of Allergy and Infectious DiseasesR01AI104344http://orcid.org/0000-0001-7464-275XKoenigSerena P.This project was supported by the National Institute of Allergy and Infectious Diseases, grant number R01AI104344. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Data AvailabilityWe have included the anonymized dataset as a Supporting Information file (S1 Data).Data AvailabilityWe have included the anonymized dataset as a Supporting Information file (S1 Data).IntroductionThe Joint United Nations Programme on HIV/AIDS (UNAIDS) 90-90-90 targets state that 90% of HIV-infected persons know their status, 90% initiate antiretroviral therapy (ART), and 90% achieve virologic suppression by the year 2020 to curb the AIDS epidemic [1]. In 2015, the World Health Organization (WHO) updated their guidelines to recommend ART for all persons living with HIV based on evidence that earlier treatment improves outcomes and decreases transmission [2–4]. To achieve these goals, patients must be promptly linked to HIV services, initiated on ART, and retained in lifelong care [5].Attrition rates are particularly high during the period from HIV testing to ART initiation, with one-quarter to one-third of patients lost in the process of starting ART [6–9]. Even if many of these patients re-engage in care at a later date, they will return with more advanced disease. Though there are many factors that contribute to pretreatment attrition, the current standard of care in most settings, which requires multiple sequential visits for HIV testing and counseling, laboratory testing, and adherence counseling prior to ART initiation, creates barriers to treatment initiation. As of June 2016, WHO guidelines note inadequate evidence to support a recommendation of same-day HIV testing and ART initiation [2]. However, the availability of point-of-care tests, the fact that CD4 cell counts are no longer necessary prior to ART initiation, and the provision of same-day counseling can accelerate treatment initiation, potentially reducing attrition [10–12]. We conducted a randomized trial in Haiti to determine whether same-day HIV testing and ART initiation, as compared with standard ART initiation, improves retention in care with viral suppression.MethodsStudy design and settingWe conducted an unblinded, randomized controlled trial of standard ART initiation versus same-day HIV testing and ART initiation among HIV-infected adults at the Haitian Group for the Study of Kaposi’s Sarcoma and Opportunistic infections (GHESKIO) in Port-au-Prince, Haiti. Haiti is the poorest country in the Western Hemisphere, with adult HIV prevalence of 1.7% [13,14]. GHESKIO is a Haitian nongovernmental organization and the largest provider of HIV care in the Caribbean, treating up to 700 patients per day for HIV and/or tuberculosis (TB). All care is provided free of charge. The study was approved by the institutional review boards at Partners Healthcare, GHESKIO, Weill Cornell Medical College, and Florida International University. See supporting information files S1 Text for the study protocol and S2 Text for the CONSORT checklist.ParticipantsParticipants were recruited from the HIV voluntary counseling and testing center at GHESKIO from August 2013 to October 2015. They received HIV testing and posttest counseling; those with a positive HIV test were referred for same-day physician evaluation, CD4 count (FACS Count, Becton-Dickinson, Franklin Lakes, New Jersey), WHO staging, and chest radiograph. Patients were eligible for study inclusion if they were infected with HIV-1, ≥18 years of age, and had WHO Stage 1 or 2 disease and CD4 count ≤500 cells/mm3. Initially, enrollment was limited to patients with CD4 count ≤350 cells/mm3, but in February 2014, the cutoff was increased to ≤500 cells/mm3 in response to revised WHO and Haitian guidelines [15]. Patients were excluded if they were already aware of their HIV diagnosis, had received ART previously, were pregnant or breastfeeding, lived outside of the greater Port-au-Prince metropolitan area, planned to transfer care during the study period, or failed to demonstrate preparedness on an ART readiness survey, which was administered by a social worker prior to study enrollment. The survey includes a 5-point scale, with respondents ranking their preparedness from “not at all ready” to “completely ready” in response to 7 questions. Study inclusion required a response of “somewhat ready” or “completely ready” for all 7 questions (S3 Text) [16].Randomization and maskingAfter the patients had provided written informed consent, the study team performed a screening evaluation for study exclusion criteria, and eligible participants were enrolled and randomized on the day of HIV testing. Participants were randomly assigned with the use of a computer-generated random-number list to either standard ART or same-day ART initiation in a 1:1 ratio, with allocation concealment. The randomization sequence was generated by a computer in the GHESKIO data management unit by a data manager who had no other involvement in study procedures. Participants were enrolled in the study and assigned to groups by a study physician. Participants, site personnel, and study statisticians were not masked to group assignment.ProceduresAfter randomization, the standard group participants received ART initiation procedures that mirror national guidelines. Participants were referred to return on Day 7 for baseline laboratory tests (creatinine, alanine aminotransferase, aspartate aminotransferase, complete blood count, purified protein derivative [PPD]), physician evaluation, and counseling with a social worker. On Day 10, they received interpretation of PPD results, and on Days 14 and 21, they were seen by a physician and social worker for additional counseling, test results, and ongoing evaluations for opportunistic infections. Participants started ART on Day 21 and had an additional social worker and physician visit at Week 5 (Fig 1). The ART regimen was the same as that for nonstudy patients at GHESKIO. First-line therapy included a single combination tablet including tenofovir disoproxil fumarate, lamivudine, and efavirenz.10.1371/journal.pmed.1002357.g001Fig 1Study interventions for the standard ART and same-day ART groups.The same-day ART group had identical laboratory tests as the standard ART group, a 30-minute counseling session with a social worker, and physician evaluation, and then initiated the same ART regimen as the standard ART group. They returned on Day 3 for physician and social worker visits and receipt of baseline laboratory test results; those with creatinine clearance <50 mL/minute as calculated by the Cockcroft-Gault equation were switched from tenofovir to zidovudine or abacavir. They returned on Days 10 and 17 for additional physician and social worker visits and on Day 24 for a physician visit. The same number of scheduled physician visits and counseling sessions were provided to each group so that the only difference in care was in the schedule of visits during the first 5 weeks of the study and the timing of ART initiation.All care was delivered by GHESKIO clinic staff, and the same providers (physicians, nurses, social workers, pharmacists, and field workers) cared for both groups. A counseling manual was followed with an outline for the social workers to follow at each scheduled counseling visit; these were identical between groups, except for the timing of ART initiation, and each session took about 30 minutes. All counseling was provided for individual patients, rather than for groups. The counseling sessions were audiotaped and systematically evaluated for quality control purposes. If a participant in either group missed a study visit that included a scheduled social worker counseling session, the counseling was provided at the next visit.Participants in both groups had monthly physician visits throughout the follow-up period and received the same package of services provided to all HIV-infected patients at GHESKIO, including prophylactic treatment with trimethoprim-sulfamethoxazole and isoniazid. Field workers phoned patients who missed a visit and attempted a home visit for those not reachable by phone. Participants received a transportation subsidy of 100 Haitian gourdes (US$1.70) per visit.OutcomesThe primary endpoint was retention in care with HIV-1 RNA <50 copies/ml at 12 months after HIV testing. Retention was defined as attending the 12-month visit (1 clinic visit between 12 and 15 months after HIV testing). Lost to follow-up (LTFU) was defined as failure to attend the 12-month visit. Deaths were ascertained by review of medical records or report from family members. A National Institutes of Health Division of AIDS Expedited Adverse Event Form was filled out within 48 hours after the study team became aware of any death. Transfers were ascertained by confirmation that the participant was receiving care at a different site. Secondary outcomes include survival, ART initiation, retention in care with HIV-1 RNA <1,000 copies/ml at 12 months after HIV testing, adherence as measured by pharmacy refill records and self-report, and cost and cost-effectiveness of standard and same-day ART; the adherence and cost-effectiveness evaluations will be reported in separate manuscripts.Statistical analysisDemographic, clinical, and laboratory data from the electronic medical record and study forms were de-identified, entered into an Excel spreadsheet, and exported into Stata v14 software (StataCorp, 2011, College Station, Texas) for analysis. After study completion, all participants who were LTFU were recontacted to determine their vital status.The study was powered to detect a 10% absolute difference in the rate of retention with virologic suppression between the 2 groups at 12 months after enrollment (65% in the standard and 75% in the same-day ART group). At the α = 0.05 significance level, we estimated that we would need to enroll 349 participants per group (698 in total) to achieve 80% power to detect this difference. Because patients who transferred during the study period were excluded, we increased the total sample size to 762 participants. For all analyses, a modified intention-to-treat approach was used, in which all patients were analyzed according to their assignment group, excluding patients who transferred to another facility during the follow-up period, according to protocol.Baseline characteristics were summarized using simple frequencies and proportions and medians with interquartile ranges (IQRs) stratified by treatment arm. Among participants who died, baseline CD4 count was compared using the Wilcoxon rank-sum test. We compared the proportion of participants who were retained in care with HIV-1 RNA <50 copies/ml (primary endpoint), retained with HIV-1 RNA <1,000 copies/ml, retained regardless of HIV-1 RNA, initiated ART, and died (secondary endpoints) at 12 months after enrollment using a chi-square test. We conducted multivariable logistic regression including all covariates listed in Table 1 to control for any residual confounding. We present unadjusted and adjusted risk ratios (RR) with 95% confidence intervals. Because of the change in enrollment criteria mid-study, we conducted a sensitivity analysis that included only the participants who met the original enrollment criteria of CD4 count ≤350 cells/mm3. In response to a reviewer’s request, we also plotted retention in care, regardless of viral load, for both groups and compared the distributions with the log-rank test. The study is registered with ClinicalTrials.gov number NCT01900080.10.1371/journal.pmed.1002357.t001Table 1Baseline characteristics of study participants by group.CharacteristicStandard Group (n = 356)Same-Day ART Group (n = 347)Age (years)—Median (IQR)37 (30, 45)37 (29, 46)Female sex—no. (%)181 (51)166 (48)Education—no. (%)\xa0\xa0\xa0\xa0No school90 (25)93 (27)\xa0\xa0\xa0\xa0Primary school110 (31)111 (32)\xa0\xa0\xa0\xa0Secondary school or more156 (44)143 (41)Income—no. (%)\xa0\xa0\xa0\xa0No income92 (26)90 (26)\xa0\xa0\xa0\xa0>$0 to $1/day176 (49)159 (46)\xa0\xa0\xa0\xa0>$1 to $2/day67 (19)76 (22)\xa0\xa0\xa0\xa0>$2/day21 (6)22 (6)Marital status—no. (%)\xa0\xa0\xa0\xa0Single71 (20)71 (20)\xa0\xa0\xa0\xa0Currently married/living with partner222 (62)211 (61)\xa0\xa0\xa0\xa0Formerly married63 (18)65 (19)WHO Stage—no. (%)\xa0\xa0\xa0\xa0WHO Stage 1117 (33)101 (29)\xa0\xa0\xa0\xa0WHO Stage 2239 (67)246 (71)CD4 count (cells/mm3)—Median (IQR)247 (150, 349)249 (143, 336)Body mass index—Median (IQR)*21.6 (19.7, 23.9)20.9 (19.3, 23.5)* Body mass index differed significantly between the 2 groups (p = 0.025).ART, antiretroviral therapy; IQR, interquartile range, WHO, World Health Organization.ResultsA total of 821 patients were screened, and 762 were enrolled in the study and underwent randomization (Fig 2). After randomization, 59 participants (28 in the standard ART and 31 in same-day ART group) transferred to another clinic and were excluded from all analyses, as per protocol. The median age was 37 years old (IQR: 30–45 years), 347 (49%) were women, and the median CD4 count was 248 cells/mm3 (IQR: 148, 345).10.1371/journal.pmed.1002357.g002Fig 2Screening, randomization, and follow-up.Of the 356 participants in the standard group, 256 (72%) were retained in care, 20 (6%) died, and 80 (23%) were LTFU (Table 2). Among the 256 participants retained in the standard ART group, 156 (61% of retained and 44% overall) had HIV-1 RNA <50 copies/ml. Of the 347 participants in the same-day ART group, 277 (80%) were retained in care, 10 (3%) died, and 60 (17%) were LTFU. Among the 277 participants retained in the same-day ART group, 184 (66% of retained and 53% overall) had HIV-1 RNA <50 copies/ml. The unadjusted RR of being retained in care at 12 months and achieving HIV-1 RNA <50 copies/ml was 1.21 (95% CI: 1.04, 1.38; p = 0.015) for the same-day ART group compared to the standard group (Table 3); the adjusted RR for this comparison was 1.24 (95% CI: 1.06, 1.41; p = 0.008).10.1371/journal.pmed.1002357.t002Table 2Study outcomes by group.OutcomeStandard ART Group (n = 356)Same-Day ART Group (n = 347)Unadjusted Risk Difference (95% CI)p-valuePrimary OutcomeRetained in care at 12 months with VL <50 copies/ml156 (43.8%)184 (53.0%)9.2% (1.8%, 16.6%)0.015†Secondary OutcomesRetained in care at 12 months with VL <1,000 copies/ml184 (51.7%)212 (61.1%)9.4% (2.1%, 16.7%)0.012‡Retained in care at 12 months, regardless of VL results256 (71.9%)277 (79.8%)7.9% (1.6%, 14.2%)0.014††Died20 (5.6%)10 (2.9%)Lost to follow-up80 (22.5%)60 (17.3%)† p-value comparing the proportion of all patients who were retained in care with viral load <50 copies/ml between the 2 arms.‡ p-value comparing the proportion of all patients who were retained in care with viral load <1,000 copies/ml between the 2 arms.†† p-value comparing the proportion of all patients who were retained in care between the 2 arms.ART, antiretroviral therapy; VL, viral load.10.1371/journal.pmed.1002357.t003Table 3Unadjusted and adjusted risk ratios of study outcomes.UnadjustedAdjusted for All Baseline Co-variatesRR95% CIp-valueRR95% CIp-valueRetained in care with viral load <50 copies/mlStandard ART Group1.01.0Same-Day ART Group1.21(1.04, 1.38)0.0151.24(1.06, 1.41)0.008Retained in care with viral load <1,000 copies/mlStandard ART Group1.01.0Same-Day ART Group1.18(1.04, 1.31)0.0121.20(1.05, 1.33)0.008Mortality during study periodStandard ART Group1.01.0Same-Day ART Group0.51(0.24, 1.08)0.0730.43(0.19, 0.94)0.033ART, antiretroviral therapy; RR, risk ratio.In the standard ART group, 184 (72% of retained and 52% overall) participants who were retained in care had HIV-1 RNA <1,000 copies/ml. In the same-day ART group, 212 (77% of retained and 61% overall) participants who were retained in care had HIV-1 RNA <1,000 copies/ml. The unadjusted RR of being retained in care at 12 months and achieving HIV-1 RNA <1,000 copies/ml was 1.18 (95% CI: 1.04, 1.31; p = 0.012) for the same-day ART group compared to the standard ART group (Table 3); the adjusted RR for this comparison was 1.20 (95% CI: 1.05, 1.33; p = 0.008). In the sensitivity analysis that included only participants who met the original enrollment criteria (CD4 count ≤350 cells/mm3), the adjusted RR of being retained in care at 12 months and achieving HIV-1 RNA <50 copies/ml was 1.19 (95% CI: 0.99, 1.38; p = 0.060), and the adjusted RR of being retained in care at 12 months and achieving HIV-1 RNA < 1,000 copies/ml was 1.18 (95% CI: 1.01, 1.34; p = 0.035).Vital status at the end of the study was known for 328 (92%) participants in the standard ART group and 329 (95%) in the same-day ART group. The unadjusted RR for mortality was 0.51 (95% CI: 0.24, 1.08; p = 0.073) for the same-day group compared to the standard group; the adjusted RR for this comparison was 0.43 (95% CI: 0.19, 0.94; p = 0.033). In the sensitivity analysis that included only participants with CD4 count ≤350 cells/mm3, the adjusted RR for mortality was 0.41 (95% CI: 0.18, 0.93; p = 0.033). Among the participants who died, the median baseline CD4 count was 100 cells/mm3 (IQR: 45, 192) in the standard and 207 cells/mm3 (IQR: 112, 291) in the same-day ART group (p = 0.078). Eight of 20 (40%) deaths in the standard ART group occurred in participants who were LTFU prior to ART, 8 (40%) deaths occurred in those LTFU after starting ART, and 4 (20%) occurred while in care; the causes of death for those in care were stroke, trauma, and cancer in 3, and the fourth had pain and died after seeing a traditional healer. Three of the 10 (30%) deaths in the same-day ART group occurred in participants who were LTFU after starting ART; among the 7 (70%) participants who died while in care, 1 of each died of stroke, pneumonia, malaria, renal failure, and sudden death, and 2 died of gastroenteritis. No deaths for those in care were attributed to immune reconstitution syndrome or an opportunistic infection that was missed at ART initiation. In Fig 3, the Kaplan-Meier curve plots the retention in care, regardless of viral load, for both groups. The log-rank test comparing the curves between the standard and same-day ART group indicates a significant difference (p = 0.028).10.1371/journal.pmed.1002357.g003Fig 3Retention in care by study group.In the same-day ART group, 344 of 347 (99%) participants started ART on the day of HIV testing, and the remaining 3 patients started ART within the subsequent week. During the Day 3 follow-up visit, 13 patients (4%) in the same-day ART group had adjustments in their ART regimens (replacement of tenofovir with zidovudine or abacavir) because they had creatinine clearance <50 mL/minute on baseline testing. In the standard group, 281 (79%) participants initiated ART by Day 28, the end of the time window for the 3-week ART initiation visit. Thirty-six (10%) standard group participants initiated ART from Day 29 to Day 90, and 12 (3%) initiated ART after Day 90 due to late or missed visits. Twenty-seven (8%) standard group participants never started ART during the study period because they were LTFU or died prior to initiating treatment. Isoniazid prophylaxis was initiated for 337 (95%) participants in the standard group and 340 (98%) in the same-day group. Eight cases of TB were diagnosed during the first 3 months after ART initiation; 6 of these occurred in the standard group and 2 in the same-day ART group.DiscussionThe results of this randomized controlled trial show that among HIV-infected adults with early WHO Stage disease and CD4 count ≤500 cells/mm3, same-day HIV testing and ART initiation, as compared to standard care, improves retention in care with virologic suppression and, in the multivariable analysis, decreases mortality. These results are important given recent WHO 2016 guidelines stating the lack of evidence in support of same-day ART initiation.Our findings suggest that ART initiation as soon as possible after HIV testing may be beneficial for clinically stable patients. In resource-poor settings with fragile delivery systems, such as Haiti, the provision of immediate support by care providers at the time of HIV diagnosis can have both structural and individual impact. In addition to making treatment initiation logistically easier for patients, we believe that same-day counseling and ART initiation increase the sense of hope, optimism, and overall connectedness to the healthcare system for patients, which have been shown to be important for retention [17–20].Our findings are consistent with the results of the RapIT study, a randomized trial that included participants in South Africa with WHO Stage 3 or 4 disease or CD4 count ≤350 cells/mm3 [11]. Participants in the standard group in that study generally started ART at the sixth visit, and 72% of participants in the rapid group started ART on the day of study enrollment. Rapid ART initiation resulted in a 17% improvement in retention and 13% improvement in viral suppression. A stepped-wedge cluster-randomized trial in Uganda found an increase in ART initiation within 2 weeks after eligibility by implementing a multicomponent intervention to streamline ART initiation that included training healthcare workers, providing point-of-care CD4 count testing platforms, eliminating mandatory multiple preinitiation sessions, and giving feedback to facilities on their ART initiation rates [21]. A weighted proportion of 80% in the intervention group had started ART within 2 weeks after eligibility compared with 38% in the control group. A cohort study of same-day ART initiation in pregnant women in South Africa also found high rates of treatment initiation, with 91% initiating ART on the day of referral to the service [22]. In the intervention group of the Sustainable East Africa Research on Community Health (SEARCH) HIV test-and-treat study, a cluster-randomized controlled trial conducted in Kenya and Uganda, HIV-infected patients who were identified through community testing were referred to HIV care upon diagnosis and then offered immediate ART initiation; retention was high (89%) among patients newly linking to care [23].At ART initiation, it is critical that patients are ready to start lifelong therapy, that TB screening is conducted, and that renal function is evaluated to avoid the use of tenofovir in patients with renal insufficiency. In this study, ART readiness was remarkably high, with over 99% of patients screened for the study reporting they were ready to start lifelong ART. This is a particularly significant and timely finding for the provision of recommended universal ART because the majority of people living with HIV have early clinical disease, and there has been prior concern that healthier patients may be less willing to accept lifelong therapy [4]. Most patients with early clinical disease do not have TB symptoms (cough, fever, night sweats, or weight loss), so they do not require further work up to exclude TB, according to WHO guidelines [2]. With the exclusion of patients with a baseline chest x-ray that was suspicious for TB, we found that less than 1% of participants in the same-day ART group had TB that was missed at the time of ART initiation. We found that 4% of participants in the same-day ART group had creatinine clearance <50 mL/minute; ART regimens were adjusted on Day 3 for these patients.Both groups in our study received high-level care, with multiple counseling and physician visits in the first month, followed by monthly physician visits. At the time the study was started, this was the standard of care in Haiti. However, this standard has shifted over the past few years towards decreased frequency of visits and nonphysician providers [2,24–27]. We believe that same-day ART can be provided with fewer follow-up visits if proper counseling is provided during the early period after ART initiation. However, clinic-level procedures play a major role in the effectiveness of accelerated ART initiation strategies, as illustrated in Malawi, where among nearly 22,000 pregnant women who started ART for mother-to-child prevention, LTFU rates ranged from 0% to 58% between facilities and were highest among women who initiated ART on the day of HIV testing at large clinics [28].Though lower than anticipated, retention in both groups in our study was higher than reports of standard ART initiation from other resource-poor settings. Two studies from South Africa found that approximately one-third of patients remained in care from HIV testing through 12 months of ART, and systematic reviews of African studies have found high rates of pre-ART attrition [6,8,29,30]. In Haiti, data on pre-ART outcomes are limited, but 12-month retention after ART initiation is 73% nationwide [31]. We attribute the higher retention in our study in large part to faster ART initiation, even in the standard group, compared to many other HIV programs. We surmise that retention would have been lower in the standard group if there had been longer delays in ART initiation [5,11,30].The rates of retention with viral suppression in our study are lower than those reported from clinical trial cohorts, including at GHESKIO. In the GHESKIO Clinical Trials Unit, with a median monthly average of 483 subjects participating in NIH-funded clinical trials, retention is 97%. We attribute the lower retention and viral suppression rates in our study to 2 major reasons. First, nearly all patients meeting WHO stage and CD4 criteria were enrolled in the study on the day of HIV testing, including those with substantial barriers to retention in care and adherence. In contrast, over one-third of patients are generally lost to care prior to ART initiation or enrollment in clinical trials [6,8,29,30]. Second, the care that was provided in this study was similar to that received by nonstudy patients at GHESKIO, with the aim of producing findings that could be reproduced in other resource-poor settings. In order to achieve the UNAIDS 90-90-90 targets, it will be important to evaluate reasons for attrition and implement new strategies to improve retention in care. One approach that has been successful in a cohort of nonresearch patients at GHESKIO has been expedited follow-up care, with fewer visits of shorter duration for clinically stable patients [32]. Streamlined care has also been associated with high rates of retention in the SEARCH study, which is described above [23].Our study was conducted in a large urban clinic, which may limit the generalizability of our findings. In addition, though our study included patients with early clinical disease, the CD4 counts in our population were lower than would be expected with the provision of universal ART. It is possible that patients with higher CD4 counts may experience less benefit from same-day ART. It is also noteworthy that we conducted a chest x-ray prior to enrollment; if same-day ART is provided without a chest x-ray, it is possible that TB cases will be missed. Our study was not blinded. All participants in both groups received the same number of visits and the same retention plan, but we cannot exclude the possibility that awareness of study group impacted provider behavior.In conclusion, in a population of asymptomatic or minimally symptomatic HIV-infected patients, same-day HIV testing and ART initiation decreased mortality and improved the rate of retention in care with virologic suppression compared with standard ART initiation. Furthermore, human and material resources provided to each group were similar, so same-day ART is not expected to increase treatment costs. The new WHO recommendations to provide ART to all HIV-infected patients should facilitate same-day test and treat.Supporting informationS1 TextStudy protocol.(DOCX)Click here for additional data file.S2 TextCONSORT checklist.(DOC)Click here for additional data file.S3 TextHIV medication readiness scale.(PDF)Click here for additional data file.S1 DataAnonymized dataset.(XLSX)Click here for additional data file.Presented in part at the 21st International AIDS Conference, Durban, South Africa, July 18 to 22, 2016. We thank all of the patients who participated in this study and all of the GHESKIO staff who cared for them. We thank Drs. Paul Farmer, Daniel Fitzgerald, Martin Hirsch, Warren Johnson, Daniel Kuritzkes, and Paul Sax for expert advice on study design and Kaya Hedt and Anshul Saxena for manuscript formatting and preparation. We also thank Drs. Carlos del Rio, Kenneth Mayer, and Larry Moulton for serving on the data safety monitoring board and providing oversight of the study.AbbreviationsARTantiretroviral therapyGHESKIOHaitian Group for the Study of Kaposi’s Sarcoma and Opportunistic infectionsIQRinterquartile rangeLTFUlost to follow-upPPDpurified protein derivativeRRrisk ratioSEARCHSustainable East Africa Research on Community HealthUNAIDSThe Joint United Nations Programme on HIV/AIDSWHOWorld Health OrganizationReferences1UNAIDS Fast-Track, Ending the AIDS Epidemic by 2030. Accessed May 24, 2017 at: http://www.unaids.org/en/resources/campaigns/World-AIDS-Day-Report-2014.2Consolidated Guidelines on the Use of Antiretroviral Drugs for Treating and Preventing HIV Infection. Recommendations for a Public Health Approach. Second Edition, World Health Organization, 2016. Accessed May 24, 2017 at: http://www.who.int/hiv/pub/arv/arv-2016/en/.3The INSIGHT START Study Group, LundgrenJD, BabikerAG, GordinF, EmeryS, GrundB, et al\nInitiation of Antiretroviral Therapy in Early Asymptomatic HIV Infection. New Engl J Med. 2015;373(9):795–807. doi: 10.1056/NEJMoa1506816\n261928734The TEMPRANO ANRS 12136 Study Group. A Trial of Early Antiretrovirals and Isoniazid Preventive Therapy in Africa. New Engl J Med. 2015;373(9):808–22. doi: 10.1056/NEJMoa1507198\n261931265FoxMP, RosenS. Retention of Adult Patients on Antiretroviral Therapy in Low- and Middle-Income Countries: Systematic Review and Meta-analysis 2008–2013. J Acquir Immune Defic Syndr. 2015;69(1):98–108. doi: 10.1097/QAI.0000000000000553\n259424616ClouseK, PettiforAE, MaskewM, BassettJ, Van RieA, BehetsF, et al\nPatient retention from HIV diagnosis through one year on antiretroviral therapy at a primary health care clinic in Johannesburg, South Africa. J Acquir Immune Defic Syndr. 2013;62(2):e39–46. doi: 10.1097/QAI.0b013e318273ac48\n230114007ZachariahR, Tayler-SmithK, ManziM, MassaquoiM, MwagombaB, van GriensvenJ, et al\nRetention and attrition during the preparation phase and after start of antiretroviral treatment in Thyolo, Malawi, and Kibera, Kenya: implications for programmes?\nTrans Roy Soc Trop Med Hyg. 2011;105(8):421–30. doi: 10.1016/j.trstmh.2011.04.014\n217242198RosenS, FoxMP. Retention in HIV care between testing and treatment in sub-Saharan Africa: a systematic review. PLoS Med. 2011;8(7):e1001056\ndoi: 10.1371/journal.pmed.1001056\n218114039KoenigSP, BernardD, DevieuxJG, AtwoodS, McNairyML, SevereP, et al\nTrends in CD4 Count Testing, Retention in Pre-ART Care, and ART Initiation Rates over the First Decade of Expansion of HIV Services in Haiti. PLoS ONE. 2016;11(2):e0146903\ndoi: 10.1371/journal.pone.0146903\n2690179510SiednerMJ, LankowskiA, HabererJE, KembabaziA, EmenyonuN, TsaiAC, et al\nRethinking the ""pre"" in pre-therapy counseling: no benefit of additional visits prior to therapy on adherence or viremia in Ugandans initiating ARVs. PLoS ONE. 2012;7(6):e39894\ndoi: 10.1371/journal.pone.0039894\n2276192411RosenS, MaskewM, FoxMP, NyoniC, MongwenyanaC, MaleteG, et al\nInitiating Antiretroviral Therapy for HIV at a Patient\'s First Clinic Visit: The RapIT Randomized Controlled Trial. PLoS Med. 2016;13(5):e1002015\ndoi: 10.1371/journal.pmed.1002015\n2716369412JaniIV, SitoeNE, AlfaiER, ChongoPL, QuevedoJI, RochaBM, et al\nEffect of point-of-care CD4 cell count tests on retention of patients and rates of antiretroviral therapy initiation in primary health clinics: an observational cohort study. Lancet. 2011;378(9802):1572–9. doi: 10.1016/S0140-6736(11)61052-0\n2195165613UNAIDS—Haiti profile. Accessed May 24, 2017 at: http://www.unaids.org/en/regionscountries/countries/haiti.14International Human Development Indicators, Haiti Country Profile. United Nations Development Program. Accessed May 24, 2017 at: http://hdr.undp.org/en/countries/profiles/HTI.15Consolidated Guidelines on the Use of Antiretroviral Drugs for Treating and Preventing HIV Infection. Recommendations for a Public Health Approach. World Health Organization, 2013. Accessed May 24, 2017 at: http://www.who.int/hiv/pub/guidelines/arv2013/en/.16BalfourL, TascaGA, KowalJ, CoraceK, CooperCL, AngelJB, et al\nDevelopment and validation of the HIV Medication Readiness Scale. Assessment. 2007;14(4):408–16. doi: 10.1177/1073191107304295\n1798665817WareNC, WyattMA, GengEH, KaayaSF, AgbajiOO, MuyindikeWR, et al\nToward an understanding of disengagement from HIV treatment and care in sub-Saharan Africa: a qualitative study. PLoS Med. 2013;10(1):e1001369\ndoi: 10.1371/journal.pmed.1001369\n2334175318BernaysS, RhodesT, BarnettT. Hope: a new way to look at the HIV epidemic. AIDS. 2007;21\nSuppl 5:S5–11.19BarnettT, WestonM. Wealth, health, HIV and the economics of hope. AIDS. 2008;22\nSuppl 2:S27–34.20MasquillierC, WoutersE, MortelmansD, Booysen FleR. Families as catalysts for peer adherence support in enhancing hope for people living with HIV/AIDS in South Africa. J Int AIDS Soc. 2014;17:18802\ndoi: 10.7448/IAS.17.1.18802\n2470279721AmanyireG, SemitalaFC, NamusobyaJ, KaturamuR, KampiireL, WallentaJ, et al\nEffects of a multicomponent intervention to streamline initiation of antiretroviral therapy in Africa: a stepped-wedge cluster-randomised trial. Lancet HIV. 2016;3(11):e539–e48. doi: 10.1016/S2352-3018(16)30090-X\n2765887322MyerL, ZulligerR, BlackS, PienaarD, BekkerLG. Pilot programme for the rapid initiation of antiretroviral therapy in pregnancy in Cape Town, South Africa. AIDS Care. 2012;24(8):986–92. doi: 10.1080/09540121.2012.668173\n2251956123BrownLB, HavlirDV, AyiekoJ, MwangwaF, OwaraganiseA, KwarisiimaD, et al\nHigh levels of retention in care with streamlined care and universal test and treat in East Africa. AIDS. 2016;30(18):2855–64. doi: 10.1097/QAD.0000000000001250\n2760329024SanneI, OrrellC, FoxMP, ConradieF, IveP, ZeineckerJ, et al\nNurse versus doctor management of HIV-infected patients receiving antiretroviral therapy (CIPRA-SA): a randomised non-inferiority trial. Lancet. 2010;376(9734):33–40. doi: 10.1016/S0140-6736(10)60894-X\n2055792725LongL, BrennanA, FoxMP, NdibongoB, JaffrayI, SanneI, et al\nTreatment outcomes and cost-effectiveness of shifting management of stable ART patients to nurses in South Africa: an observational cohort. PLoS Med. 2011;8(7):e1001055\ndoi: 10.1371/journal.pmed.1001055\n2181140226HumphreysCP, WrightJ, WalleyJ, MamvuraCT, BaileyKA, NtshalintshaliSN, et al\nNurse led, primary care based antiretroviral treatment versus hospital care: a controlled prospective study in Swaziland. BMC Health Serv Res. 2010;10:229\ndoi: 10.1186/1472-6963-10-229\n2068795527FairallL, BachmannMO, LombardC, TimmermanV, UebelK, ZwarensteinM, et al\nTask shifting of antiretroviral treatment from doctors to primary-care nurses in South Africa (STRETCH): a pragmatic, parallel, cluster-randomised trial. Lancet. 2012;380(9845):889–98. doi: 10.1016/S0140-6736(12)60730-2\n2290195528TenthaniL, HaasAD, TweyaH, JahnA, van OosterhoutJJ, ChimbwandiraF, et al\nRetention in care under universal antiretroviral therapy for HIV-infected pregnant and breastfeeding women (\'Option B+\') in Malawi. AIDS. 2014;28(4):589–98. doi: 10.1097/QAD.0000000000000143\n2446899929FoxMP, ShearerK, MaskewM, Meyer-RathG, ClouseK, SanneI. Attrition through Multiple Stages of Pre-Treatment and ART HIV Care in South Africa. PLOS ONE. 2014;9(10):e110252\ndoi: 10.1371/journal.pone.0110252\n2533008730MugglinC, EstillJ, WandelerG, BenderN, EggerM, GsponerT, et al\nLoss to programme between HIV diagnosis and initiation of antiretroviral therapy in sub-Saharan Africa: systematic review and meta-analysis. Trop Med Int Health. 2012;17(12):1509–20. doi: 10.1111/j.1365-3156.2012.03089.x\n2299415131Bulletin de Surveillance, Epidemiologique VIH/SIDA, Programme National de Lutte contre les IST/VIH/SIDA, Juin, 2016.32Guiteau Moise C, Bellot C, Hennessey K, Rivera V, Severe P, Aubin D, et al. Retention of clinically stable ART patients in a rapid model of care in Haiti. Conference on Retroviruses and Opportunistic Infections (CROI), Boston, MA, USA, 2016.', 'title': 'Same-day HIV testing with initiation of antiretroviral therapy versus standard care for persons living with HIV: A randomized unblinded trial.', 'date': '2017-07-26'}, '27658873': {'article_id': '27658873', 'content': ""In Africa, up to 30% of HIV-infected patients who are clinically eligible for antiretroviral therapy (ART) do not start timely treatment. We assessed the effects of an intervention targeting prevalent health systems barriers to ART initiation on timing and completeness of treatment initiation.\nIn this stepped-wedge, non-blinded, cluster-randomised controlled trial, 20 clinics in southwestern Uganda were randomly assigned in groups of five clinics every 6 months to the intervention by a computerised random number generator. This procedure continued until all clinics had crossed over from control (standard of care) to the intervention, which consisted of opinion-leader-led training and coaching of front-line health workers, a point-of-care CD4 cell count testing platform, a revised counselling approach without mandatory multiple pre-initiation sessions, and feedback to the facilities on their ART initiation rates and how they compared with other facilities. Treatment-naive, HIV-infected adults (aged ≥18 years) who were clinically eligible for ART during the study period were included in the study population. The primary outcome was ART initiation 14 days after first clinical eligibility for ART. This study is registered with ClinicalTrials.gov, number NCT01810289.\nBetween April 11, 2013, and Feb 2, 2015, 12\u2008024 eligible patients visited one of the 20 participating clinics. Median CD4 count was 310 cells per μL (IQR 179-424). 3753 of 4747 patients (weighted proportion 80%) in the intervention group had started ART by 2 weeks after eligibility compared with 2585 of 7066 patients (38%) in the control group (risk difference 41·9%, 95% CI 40·1-43·8). Vital status was ascertained in a random sample of 208 patients in the intervention group and 199 patients in the control group. Four deaths (2%) occurred in the intervention group and five (3%) occurred in the control group.\nA multicomponent intervention targeting health-care worker behaviour increased the probability of ART initiation 14 days after eligibility. This intervention consists of widely accessible components and has been tested in a real-world setting, and is therefore well positioned for use at scale.\nNational Institute of Allergy and Infectious Diseases (NIAID) and the President's Emergency Fund for AIDS Relief (PEPFAR)."", 'title': 'Effects of a multicomponent intervention to streamline initiation of antiretroviral therapy in Africa: a stepped-wedge cluster-randomised trial.', 'date': '2016-10-30'}, '29509839': {'article_id': '29509839', 'content': 'Home-based HIV testing is a frequently used strategy to increase awareness of HIV status in sub-Saharan Africa. However, with referral to health facilities, less than half of those who test HIV positive link to care and initiate antiretroviral therapy (ART).\nTo determine whether offering same-day home-based ART to patients with HIV improves linkage to care and viral suppression in a rural, high-prevalence setting in sub-Saharan Africa.\nOpen-label, 2-group, randomized clinical trial (February 22, 2016-September 17, 2017), involving 6 health care facilities in northern Lesotho. During home-based HIV testing in 6655 households from 60 rural villages and 17 urban areas, 278 individuals aged 18 years or older who tested HIV positive and were ART naive from 268 households consented and enrolled. Individuals from the same household were randomized into the same group.\nParticipants were randomly assigned to be offered same-day home-based ART initiation (n\u2009=\u2009138) and subsequent follow-up intervals of 1.5, 3, 6, 9, and 12 months after treatment initiation at the health facility or to receive usual care (n\u2009=\u2009140) with referral to the nearest health facility for preparatory counseling followed by ART initiation and monthly follow-up visits thereafter.\nPrimary end points were rates of linkage to care within 3 months (presenting at the health facility within 90 days after the home visit) and viral suppression at 12 months, defined as a viral load of less than 100 copies/mL from 11 through 14 months after enrollment.\nAmong 278 randomized individuals (median age, 39 years [interquartile range, 28.0-52.0]; 180 women [65.7%]), 274 (98.6%) were included in the analysis (137 in the same-day group and 137 in the usual care group). In the same-day group, 134 (97.8%) indicated readiness to start ART that day and 2 (1.5%) within the next few days and were given a 1-month supply of ART. At 3 months, 68.6% (94) in same-day group vs 43.1% (59) in usual care group had linked to care (absolute difference, 25.6%; 95% CI, 13.8% to 36.3%; P\u2009<\u2009.001). At 12 months, 50.4% (69) in the same-day group vs 34.3% (47) in usual care group achieved viral suppression (absolute difference, 16.0%; 4.4%-27.2%; P\u2009=\u2009.007). Two deaths (1.5%) were reported in the same-day group, none in usual care group.\nAmong adults in rural Lesotho, a setting of high HIV prevalence, offering same-day home-based ART initiation to individuals who tested positive during home-based HIV testing, compared with usual care and standard clinic referral, significantly increased linkage to care at 3 months and HIV viral suppression at 12 months. These findings support the practice of offering same-day ART initiation during home-based HIV testing.\nclinicaltrials.gov Identifier: NCT02692027.', 'title': 'Effect of Offering Same-Day ART vs Usual Health Facility Referral During Home-Based HIV Testing on Linkage to Care and Viral Suppression Among Adults With HIV in Lesotho: The CASCADE Randomized Clinical Trial.', 'date': '2018-03-07'}, '27163694': {'article_id': '27163694', 'content': 'PLoS MedPLoS MedplosplosmedPLoS Medicine1549-12771549-1676Public Library of ScienceSan Francisco, CA USA27163694486268110.1371/journal.pmed.1002015PMEDICINE-D-15-03455Research ArticleBiology and Life SciencesImmunologyVaccination and ImmunizationAntiviral TherapyAntiretroviral TherapyMedicine and Health SciencesImmunologyVaccination and ImmunizationAntiviral TherapyAntiretroviral TherapyMedicine and Health SciencesPublic and Occupational HealthPreventive MedicineVaccination and ImmunizationAntiviral TherapyAntiretroviral TherapyBiology and Life SciencesMicrobiologyMedical MicrobiologyMicrobial PathogensViral PathogensImmunodeficiency VirusesHIVMedicine and Health SciencesPathology and Laboratory MedicinePathogensMicrobial PathogensViral PathogensImmunodeficiency VirusesHIVBiology and Life SciencesOrganismsVirusesViral PathogensImmunodeficiency VirusesHIVBiology and Life SciencesOrganismsVirusesImmunodeficiency VirusesHIVBiology and life sciencesOrganismsVirusesRNA virusesRetrovirusesLentivirusHIVBiology and Life SciencesMicrobiologyMedical MicrobiologyMicrobial PathogensViral PathogensRetrovirusesLentivirusHIVMedicine and Health SciencesPathology and Laboratory MedicinePathogensMicrobial PathogensViral PathogensRetrovirusesLentivirusHIVBiology and Life SciencesOrganismsVirusesViral PathogensRetrovirusesLentivirusHIVPeople and placesGeographical locationsAfricaSouth AfricaBiology and Life SciencesAnatomyBody FluidsBloodBlood CountsMedicine and Health SciencesAnatomyBody FluidsBloodBlood CountsBiology and Life SciencesPhysiologyBody FluidsBloodBlood CountsMedicine and Health SciencesPhysiologyBody FluidsBloodBlood CountsMedicine and Health SciencesHematologyBloodBlood CountsMedicine and Health SciencesHealth CareHealth Care ProvidersNursesPeople and PlacesPopulation GroupingsProfessionsNursesBiology and Life SciencesMicrobiologyVirologyViral Transmission and InfectionViral LoadMedicine and Health SciencesInfectious DiseasesBacterial DiseasesTuberculosisMedicine and Health SciencesTropical DiseasesTuberculosisMedicine and Health SciencesPharmaceuticsDrug TherapyInitiating Antiretroviral Therapy for HIV at a Patient’s First Clinic Visit: The RapIT Randomized Controlled TrialSingle-Visit ART InitiationRosenSydney\n1\n\n2\n*MaskewMhairi\n2\nFoxMatthew P.\n2\n\n3\nNyoniCynthia\n2\nMongwenyanaConstance\n2\nhttp://orcid.org/0000-0003-1473-880XMaleteGiven\n2\nSanneIan\n2\nhttp://orcid.org/0000-0001-5800-1960BokabaDorah\n4\nSaulsCeleste\n2\nhttp://orcid.org/0000-0002-1180-8764RohrJulia\n1\nLongLawrence\n2\n\n1\nDepartment of Global Health, Boston University School of Public Health, Boston, Massachusetts, United States of America\n\n2\nHealth Economics and Epidemiology Research Office, Department of Internal Medicine, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa\n\n3\nDepartment of Epidemiology, Boston University School of Public Health, Boston, Massachusetts, United States of America\n\n4\nHealth Department, City of Johannesburg, Johannesburg, South Africa\nBinagwahoAgnesAcademic Editor\nRwanda Ministry of Health, RWANDA\nThe authors have declared that no competing interests exist.Conceived and designed the experiments: SR LL MM IS MPF. Performed the experiments: CN CM DB CS JR. Analyzed the data: MM GM SR. Wrote the first draft of the manuscript: SR MM. Contributed to the writing of the manuscript: SR MM LL MPF. Enrolled patients: CN. Agree with the manuscript’s results and conclusions: SR MM LL MPF CN CM GM IS DB CS JR. All authors have read, and confirm that they meet, ICMJE criteria for authorship.* E-mail: sbrosen@bu.edu105201652016135e1002015171120152232016© 2016 Rosen et al2016Rosen et alThis is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.BackgroundHigh rates of patient attrition from care between HIV testing and antiretroviral therapy (ART) initiation have been documented in sub-Saharan Africa, contributing to persistently low CD4 cell counts at treatment initiation. One reason for this is that starting ART in many countries is a lengthy and burdensome process, imposing long waits and multiple clinic visits on patients. We estimated the effect on uptake of ART and viral suppression of an accelerated initiation algorithm that allowed treatment-eligible patients to be dispensed their first supply of antiretroviral medications on the day of their first HIV-related clinic visit.Methods and FindingsRapIT (Rapid Initiation of Treatment) was an unblinded randomized controlled trial of single-visit ART initiation in two public sector clinics in South Africa, a primary health clinic (PHC) and a hospital-based HIV clinic. Adult (≥18 y old), non-pregnant patients receiving a positive HIV test or first treatment-eligible CD4 count were randomized to standard or rapid initiation. Patients in the rapid-initiation arm of the study (“rapid arm”) received a point-of-care (POC) CD4 count if needed; those who were ART-eligible received a POC tuberculosis (TB) test if symptomatic, POC blood tests, physical exam, education, counseling, and antiretroviral (ARV) dispensing. Patients in the standard-initiation arm of the study (“standard arm”) followed standard clinic procedures (three to five additional clinic visits over 2–4 wk prior to ARV dispensing). Follow up was by record review only. The primary outcome was viral suppression, defined as initiated, retained in care, and suppressed (≤400 copies/ml) within 10 mo of study enrollment. Secondary outcomes included initiation of ART ≤90 d of study enrollment, retention in care, time to ART initiation, patient-level predictors of primary outcomes, prevalence of TB symptoms, and the feasibility and acceptability of the intervention. A survival analysis was conducted comparing attrition from care after ART initiation between the groups among those who initiated within 90 d. Three hundred and seventy-seven patients were enrolled in the study between May 8, 2013 and August 29, 2014 (median CD4 count 210 cells/mm3). In the rapid arm, 119/187 patients (64%) initiated treatment and were virally suppressed at 10 mo, compared to 96/190 (51%) in the standard arm (relative risk [RR] 1.26 [1.05–1.50]). In the rapid arm 182/187 (97%) initiated ART ≤90 d, compared to 136/190 (72%) in the standard arm (RR 1.36, 95% confidence interval [CI], 1.24–1.49). Among 318 patients who did initiate ART within 90 d, the hazard of attrition within the first 10 mo did not differ between the treatment arms (hazard ratio [HR] 1.06; 95% CI 0.61–1.84). The study was limited by the small number of sites and small sample size, and the generalizability of the results to other settings and to non-research conditions is uncertain.ConclusionsOffering single-visit ART initiation to adult patients in South Africa increased uptake of ART by 36% and viral suppression by 26%. This intervention should be considered for adoption in the public sector in Africa.Trial RegistrationClinicalTrials.gov NCT01710397, and South African National Clinical Trials Register DOH-27-0213-4177.In the RapIT randomized controlled trial, Sydney Rosen and colleagues investigate whether accelerated initiation of antiretroviral therapy can improve viral suppression for HIV patients in South Africa.Author SummaryWhy Was This Study Done?One of the most persistent operational challenges facing antiretroviral therapy (ART) programs for HIV/AIDS in sub-Saharan Africa is late presentation of patients for care and high rates of attrition from care between HIV testing and ART initiation.One reason for this is that starting ART in many countries is a lengthy and burdensome process, imposing long waits and multiple clinic visits on patients; in South Africa, the country with the world’s largest HIV treatment program, patients must typically make five or six clinic visits, starting with an HIV test, before they receive medications.There have not yet been any controlled evaluations of an integrated, rapid HIV treatment initiation algorithm that allows patients to initiate ART in a single clinic visit, so the RapIT trial was done to find out if “same-day initiation” of ART would increase the number of patients starting treatment and improve overall health outcomes, compared to current practices.What Did the Researchers Do and Find?We randomly assigned 377 adult patients at two public clinics in Johannesburg, South Africa, who had provided consent to participate in the study to one of two groups.Patients in the group assigned to receive rapid treatment initiation were offered the chance to start treatment on the same day as their first clinic visit, using rapid, point-of-care laboratory tests and an accelerated sequence of other steps, including a physical exam, education, and counseling.Patients in the group assigned to receive standard treatment initiation followed the standard schedule for treatment initiation used by the clinics, which usually required three to five additional clinic visits over a 2–4 wk period.After the study enrollment visit, patients were followed up by reviewing their regular clinic medical records, to determine how many did start treatment and how many were still in care and had good outcomes, as indicated by a suppressed viral load, 10 mo later.We found that 97% of patients in the rapid initiation group had started ART by 90 d after study enrollment—three-quarters of them on the same day—compared to 72% of patients in the standard initiation group.By 10 mo after study enrollment, 64% of patients in the rapid group had good outcomes compared to 51% in the standard group.Rapid initiation group patients spent roughly two and a half hours in the clinic to complete all the steps required before they got their medications.What Do These Findings Mean?The RapIT (Rapid Initiation of Treatment) trial showed that it is possible to initiate nearly all eligible patients on HIV therapy, and to do so in a much shorter time interval than previously required.By showing that offering the opportunity to start treatment on the spot, without delay, overcomes many barriers patients would otherwise face, this study demonstrates that same-day ART initiation is an effective strategy for improving health outcomes.More patients in the rapid initiation group dropped out of care after starting treatment than in the standard initiation group; although the rapid initiation group still had better health outcomes overall, adherence support after starting treatment remains essential.The findings of this study are limited because the study took place in only two clinics in one part of South Africa and was carried out by study staff, not by regular clinic staff.Based on this study’s results, consideration could be given to accelerating the process of ART initiation in many different settings and for different types of patients.http://dx.doi.org/10.13039/100000060National Institute of Allergy and Infectious Diseases1U01AI100015RosenSydneyFunding for this study was provided by the U.S. National Institutes of Health (National Institute of Allergy and Infectious Diseases) under the terms of grant 1U01AI100015 to Boston University. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Data AvailabilityData will be made publicly available in the Dryad repository (http://www.datadryad.org/) after the protocol has been closed (anticipated closure June 2018). Until then, data will remain under the supervision of the University of the Witwatersrand Human Research Ethics Committee (HREC). Requests should be sent to the HREC Research Administrator at: https://www.wits.ac.za/research/about-our-research/ethics-and-research-integrity/human-research-ethics-committee-medical/.Data AvailabilityData will be made publicly available in the Dryad repository (http://www.datadryad.org/) after the protocol has been closed (anticipated closure June 2018). Until then, data will remain under the supervision of the University of the Witwatersrand Human Research Ethics Committee (HREC). Requests should be sent to the HREC Research Administrator at: https://www.wits.ac.za/research/about-our-research/ethics-and-research-integrity/human-research-ethics-committee-medical/.IntroductionOne of the most persistent operational challenges facing antiretroviral therapy (ART) programs for HIV/AIDS in sub-Saharan Africa is late presentation of patients for care and high rates of attrition from care between HIV testing and ART initiation, with baseline median CD4 cell counts remaining well below 200 cells/mm3 in the region despite steadily rising eligibility thresholds [1]. Even among those who have been diagnosed and found to be treatment-eligible, loss to care before starting ART has consistently been estimated at a third to a quarter of patients [2,3]. While many of those who drop out of care prior to ART initiation will make their way back at a later time, they will almost certainly have lower CD4 counts and more symptoms of illness than when they first tested positive. Some will be very sick or die before treatment can be started, and those who do eventually start will have a poorer prognosis on treatment than if they had begun treatment earlier [4,5]. Offering ART to all who test positive regardless of CD4 count, as is now recommended by the World Health Organization [6], will make little difference if those who test positive fail to initiate treatment.There are likely many causes of loss to care before treatment initiation, but one reason observed is that starting ART in many countries is a lengthy and burdensome process, requiring long waits and multiple clinic visits [7,8]. In South Africa, the country with the world’s largest HIV treatment program [9], the process typically includes an HIV test (visit 1), determination of treatment eligibility (visit 2), adherence education and counseling and baseline blood tests (visits 3, 4, and 5), and physical examination and dispensing of antiretrovirals (ARVs) (visit 6). The proliferation of visits has three main causes. First, clinic receipt of printed test results from centralized laboratories typically takes several days, if not longer. Second, a belief remains that to ensure adherence, patients must participate in multiple preparatory educational and counseling sessions [2,10,11]. And third, clinics have had little motivation to accelerate the initiation process for patients who are not critically ill, as standard performance indicators do not include the proportion of eligible patients who actually initiate ART, nor the time required to do so.If patients are deterred from starting treatment by the complexity of the process, then one strategy for reducing loss of patients prior to ART initiation and encouraging earlier treatment initiation may be to shorten the time period, reduce the number of visits, and simplify the steps required before medications are dispensed. This strategy depends critically on two factors: a clinic’s willingness and ability to adjust its schedules and procedures to compress and accelerate the required steps, and the availability of rapid, point-of care (POC) laboratory assays that eliminate delays in receiving whatever lab results are required for initiation. There have not yet been any rigorous, controlled evaluations of an integrated, rapid HIV treatment initiation algorithm incorporating procedural changes and POC tests for adult, non-pregnant patients. We therefore conducted a randomized controlled trial of rapid ART initiation that allowed patients in public sector clinics in Johannesburg, South Africa to have treatment eligibility determined, all treatment preparation steps performed, and ARV medications dispensed on the day of their first HIV-related clinic visit.MethodsRapIT (Rapid Initiation of Treatment) was an unblinded, individually randomized, controlled trial of a service delivery intervention. It was approved by the Institutional Review Board of Boston University Medical Campus (H-31880) and the Human Research Ethics Committee (Medical) of the University of the Witwatersrand (M120843) and is registered with ClinicalTrials.gov, number NCT01710397.Study Sites, Infrastructure, and StaffingRapIT was conducted at two public sector outpatient clinics. Site 1 is a primary health clinic serving an urban informal settlement population on the edge of Johannesburg. Site 2 is a large, hospital-based HIV clinic serving an urban formal and informal population within Johannesburg. Both sites follow South African national treatment guidelines for ART initiation, ARV regimens, and monitoring [12]. During the period of study enrollment, May 8, 2013–August 29, 2014, the prevailing threshold for ART eligibility was a CD4 count ≤ 350 cells/mm3 or a WHO Stage 3/4 clinical condition. Requirements for care prior to initiating ART are not standardized in South Africa [13], but both sites generally required four to five clinic visits between HIV testing and dispensing the first month’s supply of ARVs.At each site, a small clinic room with security bars, running water, and basic furnishings was designated for study equipment and supplies, POC instruments, and files. As all the POC instruments were designed as desktop devices, no separate laboratory was needed. An outdoor booth for safe collection of sputum samples from tuberculosis (TB) suspects was constructed at Site 1 and made available for both study arms; existing facilities for this purpose were used at Site 2. Clinical procedures were performed by study nurses with the same level of clinical certification as existing primary health care nurses at the sites. Non-clinical procedures (consent, questionnaire, education, counseling, patient flow management) were implemented by study assistants with qualifications comparable to those of experienced lay counselors at the sites. All study staff received study and instrument-specific training. A small stipend (R1000/month, equivalent to US$86 at the exchange rate at the time of the study) was paid to clinic lay counselors at Site 1 and a messenger at Site 2 who assisted by referring potential study participants to the study assistant.Study PopulationThe study enrolled adult (≥18 y old), non-pregnant patients who presented to have an HIV test, provide a blood sample for a CD4 count if already known to be HIV-infected, or receive the results of the patient’s first treatment-eligible CD4 count. During pre-screening and screening, patients who had previously been found to be eligible for ART, were already on ART or reported receiving it in the past 12 mo, indicated that they intended to seek HIV care during the next 12 mo at a different clinic, were judged by clinic or study staff to be physically or emotionally unable to provide consent or participate in all study procedures, or did not meet other study inclusion criteria were excluded. Potential participants whose visit purpose was to have an HIV test were enrolled; those found post-enrollment not to be eligible for ART were subsequently withdrawn upon determination of ineligibility. Potential participants whose visit purpose was to receive a CD4 count result and were not eligible for treatment on the basis of that CD4 count were not enrolled.Participants were individually randomized 1:1 to either rapid treatment initiation or standard-of-care treatment initiation, using block randomization in blocks of 6. Sealed, opaque envelopes containing the allocations were prepared by the local principal investigator and numbered sequentially. The envelopes were kept in sequential, numbered order at the study sites. After obtaining written informed consent, the study assistant opened the next sequentially numbered envelope to reveal the allocation.Study Design and ProceduresProcedures for each study arm are illustrated in Fig 1. Standard-of-care treatment initiation followed existing procedures at the sites as closely as possible. Study staff interaction with participants was limited to screening for study eligibility, obtaining written informed consent, administering a questionnaire, and referring patients to clinic staff for either a blood draw for a CD4 count or a next visit appointment if the patient already had results of a CD4 count in hand. After referral, patients in the standard-initiation arm of the study were followed passively, through medical record review, and had no further interaction with the study. Standard-of-care procedures for ART initiation at both study sites included a CD4 count to determine eligibility, TB symptom screening followed by a TB test and TB treatment initiation if required, pre-initiation blood tests (hemoglobin, creatinine, and alanine aminotransferase (ALT)), group and individual counseling and education sessions, and a physical examination. All samples for laboratory tests were sent to centralized public sector laboratories, requiring patients to make separate clinic visits to provide biological samples and to receive results. Once ART eligibility was determined, initiation typically required three to four more clinic visits over a period of 2–4 wk. Patients who were very ill or found to have low CD4 counts could be “fast-tracked,” with the schedule shown in Fig 1 completed in as little as one week.10.1371/journal.pmed.1002015.g001Fig 1Standard initiation of treatment and rapid initiation procedures and visit schedule.For patients randomized to rapid initiation, all the same procedures were performed, but the use of a compressed and accelerated schedule and rapid laboratory instruments at point of care allowed them all to be completed in a single visit (Box 1). Patients offered rapid initiation typically completed each step in order, with little or no waiting time in between unless a TB test was required, which entailed a wait to process the sample. Patients who enrolled in the study too late in the day for all steps to be completed before the clinic closed were asked to return the next day to finish study procedures. Patients who were randomized to rapid initiation but did not have time to participate on the day of enrollment or wished to delay for other reasons were given up to 30 d to return and be initiated under rapid procedures. Those returning beyond 30 d were offered standard initiation by the clinic.Box 1. Rapid Initiation ProceduresCD4 countPatients who enrolled in the study and did not already have CD4 count results from a test performed within the previous 6 mo were given a rapid CD4 count using the Alere Pima CD4 Test (http://alerehiv.com/hiv-monitoring/alere-pima-cd4/) with venous blood draw. This test, previously evaluated in several studies in Africa [14–18], provides a CD4 count result from a capillary or venous blood sample in 20 min. Following the test, patients with a CD4 count ≤ 350 cells mm3 or evident physical symptoms or complaints that suggested a Stage 3 or 4 condition continued with study procedures. Those not eligible for ART were withdrawn from the study at this point and referred to the clinic for standard pre-ART monitoring.TB symptom screen and testWhile awaiting CD4 count results, a TB symptom screen was administered using South Africa’s four-question screening tool. All patients who reported symptoms were then asked to provide a sputum sample, which was immediately processed using the Cepheid Xpert MTB/RIF test (http://www.cepheid.com/us/cepheid-solutions/clinical-ivd-tests/critical-infectious-diseases/xpert-mtb-rif). This is the technology currently used for TB diagnosis in the public sector throughout South Africa, but it is located in centralized laboratories rather than at point of care [19]. It generates a TB diagnosis in 90 min [20]. Two sputum samples were run simultaneously to increase the reliability of results. Any patient who received a positive Xpert test was escorted to the clinic TB nurse to initiate TB treatment, which under national guidelines required a delay of at least 2 wk before ART could be initiated. Patients initiated on TB treatment were asked to return 2 wk later to complete rapid ART initiation on a second visit.Baseline testsOnce eligibility for ART was established, pre-initiation blood tests (hemoglobin, creatinine, and ALT) were run on a point-of-care Reflotron Plus instrument (Roche, http://www.roche-diagnostics.co.in/Products/Pages/ReflotronPlusDry.aspx)[14] using the same blood sample dawn for the CD4 count. This instrument takes approximately 2 min to complete each test. A standard clinic urine dipstick pregnancy test was also conducted for female patients of child-bearing age.Physical examA standard physical examination was conducted by the study nurse to identify any specific conditions or concerns prior to initiating ART. Initiation was delayed in patients found to have conditions that required referral to a hospital or consultation with the clinic’s doctor.Education sessionA condensed version of HIV/ART/adherence education was developed using the study clinics’ materials and provided to study participants. It was delivered in a one-on-one session by the study counselor in approximately 20 min.Counseling sessionAfter completing all tests, physical examination, and education session, each patient met individually with the study nurse, who reviewed results with the patient and provided an opportunity for the patient to ask any remaining questions and confirm that she or he was indeed ready for treatment initiation.Dispensing of ARVsThe study nurses, like other qualified nurses in South Africa, were authorized to write prescriptions for ARVs, which could then be filled directly by the nurse from study room stock (Site 1) or at the on-site clinic pharmacy (Site 2). Study patients at Site 2 were served at the pharmacy immediately, rather than being required to wait in pharmacy queues to fill prescriptions. Once the initial 4 wk supply of ARVs was dispensed, study interaction with rapid group patients ceased. Patients were asked to return to the clinic for monitoring and prescription refill by clinic staff in 1 mo, consistent with routine practice.After the enrollment visit, or completion of rapid initiation procedures for patients in the rapid-initiation arm of the study (“rapid arm”) who delayed initiation but returned to complete it within 30 d, the study team had no further contact with study patients. Patients who started ART in either arm received standard-of care treatment management from the clinic, which called for monitoring visits and medication refills at 1, 2, 3, 6, and 12 mo after initiation, with a routine viral load test at the 6 mo visit.Outcomes and DataThe primary, protocol-defined outcome for the study was viral suppression (≤400 copies/ml) within 10 mo of study enrollment, a time period selected to capture the 6 mo routine monitoring visit called for by national guidelines. Ten months was selected as the endpoint to allow patients to take up to 3 mo to initiate ART and to be up to 1 mo late for the 6 mo routine visit. Because the study sites occasionally omitted the 6 mo viral load and performed the test only at 12 mo, we considered a patient with a suppressed viral load test result any time from 3 to 12 mo after study enrollment to have achieved viral suppression. In this analysis, missing viral load test results were regarded as failures; only patients with recorded, suppressed viral load results were defined as virally suppressed. To account for the possibility that viral load results could be missing due to clinic oversight in not ordering the test, rather than patient default, and to investigate the possibility that rapid initiation merely shifts attrition from before to after treatment initiation, we also report the secondary outcome of retention in care at 10 mo after study enrollment, with retention defined as any HIV-related clinic visit in months 5–10 after study enrollment, regardless of viral load.Although viral suppression was the primary outcome assessed, the pathway by which the study aimed to increase suppression was reduction of attrition between HIV testing and treatment initiation. We therefore report initiation of treatment within 90 d of study enrollment as a secondary outcome, with initiation defined as being dispensed a first month’s supply of ARVs. We also report uptake of treatment within 180 d, as a CD4 count result is considered to be valid under South African guidelines for 6 mo—after that, a patient must have a new CD4 count to establish eligibility for ART. Finally, we report the distribution of time (d) to treatment initiation in each group.Other secondary outcomes evaluated in the study included the feasibility of the intervention, as indicated by the ability of both study sites to implement the accelerated algorithm; acceptability of the intervention, as measured by the proportion of patients offered rapid initiation who accepted it; patient-level predictors of the primary outcome; and, in the rapid arm, the prevalence of TB symptoms and confirmed TB disease and ART initiation among patients with TB.After the enrollment visit, all data collection for both groups was by passive medical record review. Both study sites routinely utilized an electronic medical record system called TherapyEdge-HIV, into which patient data were entered retrospectively by data clerks from paper files (Site 1) or by a combination of clinicians in real time and data clerks from paper files (Site 2)[21]. This record system improved the completeness of the follow-up dataset used in the study. In instances of incomplete follow-up data—for example, if the database reported a clinic visit 6 mo after ART initiation but contained no viral load test result—study staff searched the clinics’ paper files and registers and the online data portal of the National Health Laboratory Service to determine if any additional information existed but had not been recorded in the clinics’ databases. The study team had no further contact with study participants after the enrollment visit so as not to have any influence on retention in care, a study outcome.Data AnalysisWe designed the study to detect a 20% difference in viral suppression rates between the arms at 10 mo after study enrollment. With an α of 0.05, power of 90%, 1:1 randomization, and an uncorrected Fisher’s exact test, we estimated that we would need to enroll at least 124 HIV positive ART-eligible participants per group (248 total). We increased this to a maximum of 200 per group (400 total) to allow for stratification by site, sex, or age.Characteristics at study enrollment of all randomized participants who met ART initiation and study inclusion criteria were summarized using simple proportions and medians with interquartile ranges (IQR) stratified by treatment arm. For the remaining analyses, we excluded patients who were found after randomization not to be eligible for ART or not to meet study inclusion criteria. We compared the proportions of patients achieving each dichotomized study outcome and present crude risk ratios (RR) and risk differences (RD) with 95% confidence intervals (CI) stratified by group. Baseline predictors of outcomes that appeared imbalanced by treatment arm were also adjusted for using log-linear regression models to estimate adjusted risk ratios (aRR). We estimated time to treatment initiation in days using a cumulative incidence curve. To investigate whether attrition after initiation of ART differed between the study arms, we performed a survival analysis comparing attrition from care after ART initiation among those who initiated within 90 d between the groups. Person-time accrued from ART initiation date to the earliest of loss to follow up, transfer, or 10 mo of follow up, and hazard ratios of attrition from care were estimated with Cox proportional hazards models. A stratified analysis was performed to detect effect measure modification by site or patient-level factors. Finally, to confirm that no imbalance was created by excluding patients after randomization for reasons other than ineligibility for ART or evidence of a previous eligible CD4 count, we conducted sensitivity analysis incorporating the excluded patients and assigning each a negative outcome.ResultsBetween May 8, 2013, and August 29, 2014, 603 patients were screened for study eligibility and 463 provided written informed consent and were enrolled in the study (Fig 2). Of the 140 screened but excluded prior to randomization, 109 did not meet study eligibility criteria, including 43 who resided outside study clinic catchment areas or intended to seek further care elsewhere; 24 who were determined by the study assistant to be too ill for consent and study procedures; 16 who were not eligible on the basis of a prior CD4 count, were ineligible for ART, or were already on ART; 12 who were determined by the study assistant to be too emotionally upset to provide consent; 9 who did not speak any of the languages spoken by the study team; 3 who were found to be pregnant; and 2 who were excluded for other reasons. An additional 31 patients refused participation; of these, 18 were in a hurry and did not have time for study procedures, six did not wish to participate in the study, five stated that they would prefer standard care, and two were not willing to initiate therapy. Follow-up ended 10 mo after the last patient was enrolled (June 28, 2015).10.1371/journal.pmed.1002015.g002Fig 2Study enrollment and randomization.Characteristics of patients in each study arm at time of enrollment are reported in Table 1. There were no important differences between the study arms in the variables shown. Just over half the participants were female and the median age was 35 y. The median CD4 count was less than 200 cells/mm3. Age, sex, and CD4 count characteristics of the study sample were similar to those of the overall non-pregnant patient populations initiating ART at the study clinics in 2014.10.1371/journal.pmed.1002015.t001Table 1Baseline characteristics of study sample (n = 463).VariableStandard armRapid arm\nn (randomized participants)229234Enrollment site (n)\xa0\xa0\xa0\xa0Site 1 (primary health clinic)124126\xa0\xa0\xa0\xa0Site 2 (hospital-based HIV clinic)105108Age (median, IQR)35.8 (29.5–41.6)34.2 (29.0–40.1)Sex (% female)132 (58%)129 (55%)CD4 count (cells/mm3) (median, IQR)195 (103–322)224 (128–327)Purpose of clinic visit (%)\xa0\xa0\xa0\xa0Have HIV test (diagnosed today)100 (44%)90 (38%)\xa0\xa0\xa0\xa0Provide blood sample for CD4 count8 (4%)10 (4%)\xa0\xa0\xa0\xa0Receive first CD4 count results109 (47%)112 (48%)\xa0\xa0\xa0\xa0Other11 (5%)22 (10%)Reason for treatment eligibility (%)\xa0\xa0\xa0\xa0CD4 count below threshold182 (79%)183 (78%)\xa0\xa0\xa0\xa0Clinical condition Stage 3 or 43 (1%)4 (2%)\xa0\xa0\xa0\xa0Excluded (not eligible for treatment or study)44 (20%)47 (20%)Household composition\xa0\xa0\xa0\xa0Live alone (% yes)36 (16%)41 (18%)\xa0\xa0\xa0\xa0# other persons in house (median, IQR)2 (1–4)2 (1–3)Household type (%)\xa0\xa0\xa0\xa0Formal house or flat146 (63%)165 (71%)\xa0\xa0\xa0\xa0Informal dwelling or shack83 (37%)69 (29%)Travel time to clinic (minutes) (median, IQR)18 (9–24)15 (9–27)Employment status (%)\xa0\xa0\xa0\xa0Employed formally68 (30%)90 (38%)\xa0\xa0\xa0\xa0Work informally62 (27%)54 (23%)\xa0\xa0\xa0\xa0Unemployed, seeking work91 (40%)84 (36%)\xa0\xa0\xa0\xa0Unemployed, not seeking work8 (3%)6 (3%)Marital status (%)\xa0\xa0\xa0\xa0Married or long-term partner173 (76%)157 (67%)\xa0\xa0\xa0\xa0Single, no long-term partner41 (18%)57 (24%)\xa0\xa0\xa0\xa0Other (widowed, divorced)15 (6%)20 (9%)Reasons for excluding patients during the study screening process are reported in Fig 2. The 603 patients screened represent a subset of those pre-screened by clinic counselors and then referred to the study assistant for screening. While pre-screening data, which were collected by the counselors and not by study staff, are of uncertain quality, they do provide some indication of the proportion of all patients presenting at clinics who could be eligible for rapid initiation. At Site 1, for which the pre-screening data are more complete, a total of 2,636 patients presenting at the clinic’s HIV counseling and testing service were pre-screened. More than half of these were HIV-negative (1,468/2,636, 56%) or known to have CD4 counts above the eligibility threshold or already on ART (114/2,636, 4%). Of the remaining 1,054, 325 (31%) were referred for study screening. Another 293/1,054 (28%) were judged by the counselors not to meet study protocol eligibility criteria (age, residence location, language, not first CD4 count) but would likely have been eligible for the intervention if it were offered as routine care. A fifth (225/1,054, 21%) were regarded by the counselors as too sick for study participation (not necessarily for ART initiation) and were referred to a clinic doctor or nurse for immediate care; it is not clear if they would have been eligible for the intervention or not. The remainder (20%) included patients who refused study participation (36/1,054, 3%) or refused any further care (12/1,054, 1%), were deemed too upset or emotionally distressed to participate (25/1,054, 2%), were referred directly to the clinic’s HIV or TB nurse rather than the study assistant (75/1,054, 7%), or were in a hurry or had no reason stated (63/1,254, 6%).Among 463 patients screened and found eligible for study participation, 234 patients were randomized to rapid initiation and 229 to standard initiation (Fig 2). Upon completion of a CD4 count, which occurred after randomization for those who did not already have one in hand, 37 patients in each group were determined not to be eligible for ART under South African guidelines and were excluded from further data collection and from the analysis. An additional 12 patients were excluded after randomization, for reasons indicated in Fig 2. One hundred and ninety patients in the standard group and 187 in the rapid group (n = 377 total) were offered full study procedures and are included in the analysis below, with sensitivity analysis incorporating the six who were excluded after randomization for a reason other than ineligibility for ART or evidence of a prior eligible CD4 count.The protocol-defined primary outcome for the study was viral suppression within 10 mo of study enrollment. As presented in Table 2, viral suppression by 10 mo was 64% (119/187) in the rapid arm and 51% (96/190) in the standard arm, indicating a risk difference of 13% (3%–33%) and a crude relative risk of 1.26 (1.05–1.50).10.1371/journal.pmed.1002015.t002Table 2ART initiation, 10-mo retention in care, and 10-mo viral suppression.OutcomeStandard arm(%)n = 190Rapid arm(%)n = 187Crude risk difference(95% CI)Crude relative risk(95% CI)Initiated ≤ 90 d and suppressed by 10 mo (primary outcome)96 (51%)119 (64%)13% (3%–23%)1.26 (1.05–1.50)\xa0\xa0\xa0\xa0Of those\nnot\ninitiated ≤ 90 d and suppressed by 10 mo\n\n94 (49%)\n\n68 (36%)\n\n\xa0\xa0\xa0\xa0\xa0\xa0Not initiated\n\n54 (28%)\n\n5 (3%)\n\n\xa0\xa0\xa0\xa0\xa0\xa0Initiated but not suppressed\n\n40 (21%)\n\n63 (34%)\n\n\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0Of those initiated but not suppressed:\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0Retained, unsuppressed viral load test reported\n\n11 (6%)\n\n17 (9%)\n\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0Retained, no viral load test reported\n\n14 (7%)\n\n16 (9%)\n\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0Transferred to another clinic\n\n1 (1%)\n\n6 (3%)\n\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0Died\n\n3 (2%)\n\n0 (0%)\n\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0Lost to follow-up\n\n11 (6%)\n\n24 (13%)\nInitiated ≤ 90 d136 (72%)182 (97%)25% (19%–33%)1.36 (1.24–1.49)Initiated ≤ 90 d and retained at 10 mo (secondary outcome)121 (64%)151 (81%)17% (5%–23%)1.27 (1.12–1.44)\xa0\xa0\xa0\xa0Of those not initiated ≤ 90 d and retained at 10 mo:\n69 (36%)\n\n36 (19%)\n\xa0\xa0\xa0\xa0\xa0\xa0Initiated but not retained\n\n15 (8%)\n\n31 (17%)\n\xa0\xa0\xa0\xa0\xa0\xa0Not initiated\n\n54 (28%)\n\n5 (3%)\nBy 90 d after study enrollment, 97% (182/187) of participants in the rapid arm and 72% (136/190) of participants in the standard arm had initiated ART, equating to a risk difference of 25% (95% CI 19%–33%) and a crude relative risk of 1.36 (1.24–1.49) (Table 2). In adjusted analysis (S1 Table), neither age, sex, nor baseline CD4 count affected these values. By 180 d, one additional patient in the rapid arm and two in the standard arm had initiated, leaving four patients in the rapid arm and 52 in the standard arm who did not initiate within the period of validity of their CD4 count results. In the rapid arm, all four were referred to a clinic nurse or doctor for clinical confirmation of TB and did not return for ART initiation. In the standard arm, 73% (38/52) of the patients who did not initiate within 180 d made no further visits to the site after the visit in which they were enrolled in the study.\nFig 3 shows the cumulative incidence of treatment initiation in each study arm over the 180 d following enrollment. In the rapid arm, 72% (135/187) of patients started ART on the same day as study enrollment, an additional 7% (13/187) on the next day, and 96% (179/187) within 1 mo. In the standard arm, 58% of patients initiated within one month. The median (IQR) time to initiation in the standard arm for the subset who did initiate within 90 d (n = 136) was 17 (11–26) d. For rapid arm patients who did not initiate on the same day (n = 48), the reasons for delay were the need for clinical confirmation of TB or a Stage 3 or 4 condition or for TB treatment (25/48, 52%), insufficient time to complete all steps on the same day (6/48, 13%), patient preferences (5/48, 10%), lack of electricity in the clinic (2/48, 4%), and unknown reasons (10/48, 21%). Time to treatment initiation in the standard arm was shorter for patients who already had CD4 count results available upon study enrollment (median days 16, [IQR 11–22]) compared to those who enrolled in the study at the time of having an HIV test (22 [IQR 10–35]); the median for both types of patients in the rapid arm was 0 d (i.e., same-day initiation).10.1371/journal.pmed.1002015.g003Fig 3Time to ART initiation, by study arm.Cumulative incidence of ART initiation in each study arm, by number of days since study enrollment.All patients in the rapid arm had the opportunity to initiate treatment on the day of study enrollment (same-day initiation) unless one of the reasons for delay listed above pertained to them. To explore whether a delay in initiation was associated with different post-initiation outcomes, we compared patients who did initiate on the same day to those who delayed for any reason. There were no differences in either the primary outcome of viral suppression or the secondary outcome of retention in care between these two groups of patients (S3 Table). Because this analysis was limited to rapid arm patients, however, it is not a randomized comparison and should be interpreted with caution.Retention in care, defined as making a clinic visit between months 5 and 10 after study enrollment, was 81% (151/187) in the rapid arm and 64% (121/190) in the standard arm, for a risk difference of 17% (5%–23%) and a crude relative risk of 1.27 (1.12–1.44). Table 2 also indicates that 86% (31/36) of patients in the rapid arm who were not retained were lost from care after ART initiation, compared to just 22% (15/69) in the standard arm; the fall-off in the standard arm, in contrast, was mainly among those who never initiated (54/69, 78%). Although there was less loss to follow-up after initiation in the standard arm (15/190, 8% versus 31/187, 17%), this was more than offset by the higher pre-initiation loss in the standard arm (54/190, 28% versus 5/187, 3%), resulting in an overall increase in retention of 17%. Among the patients lost to care after initiation (15 in the standard arm and 31 in the rapid arm), a large majority of patients who initiated ART but were not retained in care either never came back after their initiation visit (40% of patients in the standard arm (6/15) and 45% in the rapid arm (14/31)) or came back just once (47% (7/15) and 35% (11/31), respectively), suggesting that most of these patients were never “established” on ART.To explore further the rate of loss to care, we estimated attrition from care within the first 10 mo after initiation among the subsample of 318 patients who did initiate ART within 90 d. In the standard arm, during 1,250 mo of total person-time, 22/136 (16%) dropped out of care after ART initiation, for an attrition rate of 1.8 per 100 person-months. In the rapid arm, during 1,626 mo of total person-time, 30/182 (16%) dropped out of care, for a rate of 1.8 per 100 person-months. The hazard of attrition within the first 10 mo after ART initiation among those who initiated within 90 d did not differ between the treatment arms (HR 1.06; 95% CI 0.61–1.84). We note that this result is subject to selection bias and confounding, however, due to the exclusion of those who did not start treatment within 90 d.In pooled analysis of both study arms, none of the variables presented in Table 1 predicted any of the outcomes reported above, with three exceptions (S2 Table). A slightly higher proportion of patients with baseline CD4 counts below 100 cells/mm3 initiated ART, but this difference did not persist through retention or viral suppression at 10 mo. As might be expected, patients who enrolled in the study at the time of receiving their CD4 count results (thus their second HIV-related clinic visit overall), rather than at the time of having an HIV test, were slightly more likely to achieve all three outcomes, though only for retention in care was this difference statistically significant. Finally, patients who reported being employed at the time of study enrollment, while no more likely to initiate ART, had significantly better retention in care and viral suppression than did those who reported being unemployed.In stratified analysis (S4 Table) we observed non-significant differences in effect sizes for the primary outcome (viral suppression at 10 mo) by sex, age group, and study site. A larger effect was seen among men aged <35 y (risk difference [95% CI] 34% [12%–55%]), while little effect was seen among men or women ≥35 (5% [-9%–19%]). The effect size was also greater at the primary health clinic (21% [8%–34%]), while little effect was seen at the hospital-based HIV clinic (2% [-12%–17%]). As noted, these differences were not statistically significant, and the study was not powered to detect differences among subgroups.In the rapid arm, for which TB diagnostic data were available, 29/187 patients (16%) presented with TB symptoms and were tested for TB using Xpert MTB/RIF. Four patients (17% of those with symptoms and 2% of all rapid arm patients) had a confirmed TB diagnosis. All four initiated ART within the 90-d outcome defined above, with a range of 11–54 d between study enrollment and ART initiation.The results of the sensitivity analysis incorporating the six patients who were excluded after randomization for reasons other than ART eligibility or prior CD4 count, and assigning each a negative outcome, did not differ substantively from the findings presented above, with a relative risk of viral suppression by 10 mo of 1.22 [1.02–1.46].Rapid initiation, using the procedures described above and as implemented by the study, appeared acceptable to patients at the time it was offered and feasible to implement at both study sites. We were not able to assess acceptability after patients received the intervention, as the study had no post-initiation interaction with those enrolled, and thus can surmise acceptability only on the basis of acceptance of the intervention. The study refusal rate was very low (31/603, 5%); nearly four out of five (148/187, 79%) patients offered the intervention accepted initiation on the same day or the next day, and rapid arm patients consistently expressed appreciation for the opportunity to start immediately.All steps in the rapid initiation process were completed on the same day as study enrollment for 72% (135/187) of those in the rapid arm, demonstrating the feasibility of the intervention, at least within the context of the study. From provision of informed consent (study enrollment) to dispensing of the first supply of ARV medications, rapid initiation took a median of 2.4 (IQR 2.1–2.8) hours for those who initiated on the same day as study enrollment. This interval was shorter for patients who already had CD4 count results in hand at study enrollment (median 2.25 hours). It was longer (median 4.5 hours) for those who required a TB test and did initiate ART on the same day, but 15/20 patients requiring TB tests did not initiate on the same day. The only obstacle encountered in implementing rapid procedures was fairly frequent power outages, a common occurrence in South Africa, at Site 1, which did not have a generator for backup power supply. Most rapid instrument tests could not be performed during power outages. The rapid test instruments otherwise performed well throughout the study, and no major delays or problems arose in the acceleration of clinic procedures.DiscussionIn this randomized controlled trial, we evaluated the effectiveness of an accelerated ART initiation algorithm that combined compressed and accelerated clinic procedures with point-of-care laboratory testing technologies that allowed eligible patients to initiate ART in a single clinic visit. This intervention increased the proportion of patients eligible for ART at study enrollment who initiated ART within 90 d by 25%, to 97% of all eligible patients and 100% of patients who were not delayed for TB treatment. By 10 mo after study enrollment, the intervention increased viral suppression among all treatment-eligible patients by 13% and retention in care by 17%. It was feasible and appeared acceptable at both study sites.The trial demonstrated that it is possible to initiate nearly all eligible patients on ART, and to do so in a much shorter time interval than previously required. The net benefit for overall viral suppression was clinically meaningful and may underestimate the true benefits of the intervention. Both the study sites were relatively well-managed, public sector clinics, resulting in a higher rate of ART initiation in the standard arm (72%) than is found elsewhere in the country, for example in rural KwaZulu Natal Province where the rate was 59% [2]. In addition, we observed a larger effect at Site 1, the primary health clinic, than at Site 2, the hospital-based HIV clinic. Primary health clinics, which have fewer resources than hospital-based clinics but treat 85% of HIV patients in South Africa, may struggle more with loss to follow-up before treatment initiation than do hospital-based clinics, creating a greater opportunity for a service delivery intervention like RapIT to be effective. The potential for reaching younger men, who have been among the least likely to access ART under standard care [22], is another important potential benefit of rapid initiation. Additional research is needed to explore further the non-significant differences in effect that we observed in our study.The patients who likely benefited most from RapIT were those who would not otherwise have initiated treatment at all, or who would have waited until they were sick enough to compromise their prognosis on treatment. In the standard arm, most patients who did not start treatment did not return to the study clinics for even one more visit, underscoring the importance of taking full advantage of the first visit to get as many patients started on treatment as possible. For those who would have initiated treatment, just not as soon, there is some evidence that even relatively short delays may be harmful. A recent modeling exercise using South African data estimated that compared to immediate initiation, a delay in initiating ART of 70 d would lead to a 34% increase in 12-mo mortality [22]. Delaying treatment initiation thus both deters some patients from starting at all and jeopardizes outcomes for those who do start.We hypothesize that the delays and multiple visits patients must endure before starting ART directly deter treatment initiation. Patients who cannot afford transport fare for multiple visits, have childcare obligations at home, or risk job or wage loss if they miss too many days of work may be directly deterred from returning. Others may simply grow impatient or lose their courage or motivation, particularly if they are asymptomatic when diagnosed. These patients are likely to drift away and only return when their CD4 counts are lower and symptoms have started, or to die before treatment can be started. Our results suggest that offering the opportunity to start treatment on the spot, without delay, overcomes these barriers, without risking poorer outcomes later on.Among patients who did initiate ART, post-initiation loss to care was higher in the rapid arm than the standard arm. This difference disappeared in the survival analysis, which controlled for number of months on ART but does not reflect the benefits of randomization. We speculate that some patients who did not want or were not ready for treatment chose to accept immediate initiation simply because it was offered or they wanted to participate in the study. For these patients, attrition from care was simply shifted from before ART initiation to after. While the intervention was successful in increasing the overall proportion of treatment-eligible patients with successful outcomes (viral suppression and/or retention in care), the rate of post-initiation attrition is a reminder that early retention in care and adherence support once patients start treatment remain high priorities for further research and intervention.Other studies have gauged the impact on treatment uptake of a single POC technology [23] or changes in service delivery [24], but we found only one prior report of a “single-visit initiation” intervention that was similar, to some degree, to RapIT. That study enrolled pregnant women initiating ART for prevention of mother-to-child transmission in South Africa and found very high uptake of ART among women offered rapid initiation, but it did not have a comparison arm to allow an effect to be estimated [25]. A study in Tanzania and Zambia compared the effect of community support on a two-visit algorithm and reported 99% uptake of ART in both study arms [26]. Taken together, these studies imply that accelerating ART initiation is effective in a wide range of settings.Nothing in the rapid initiation procedures used in this study differed fundamentally from existing clinic procedures. The intervention was delivered by study nurses and counselors with the same qualifications as existing clinic staff, though with study-specific training and supervision. The intervention imposed no major burdens on site management, though managerial acquiescence to the study and operational flexibility were needed to adjust the schedule and content of patient visits, staff responsibilities, and record keeping to allow for rapid initiation [27]. The main technical training required was in the use of the POC test instruments, which also required a secure location within the clinic, temperature control, and electricity.Although South Africa has better clinic infrastructure than do many other countries in the region, the RapIT intervention does not require anything that most urban and many rural clinics cannot provide. We speculate that the RapIT intervention would be feasible and potentially even more effective in other high HIV prevalence areas, where patients travel farther to reach clinics and results from centralized laboratories take even longer to return. As the new WHO guidelines are adopted, moreover, laboratory test results may not be required prior to ART initiation for patients who are asymptomatic, reducing the need for POC technology.The generalizability of our results is limited in several ways. The study was conducted in only two clinics in one province of one country. The trial intervention was delivered by study staff; it is uncertain if clinic staff delivering the same intervention will achieve the same outcomes (and whether their outcomes will be better or worse than those observed in the trial). As is typical in individually randomized trials of service delivery interventions, the possibility exists that quality of care in the standard arm was improved by the presence of the study, as clinic staff providing care for the standard arm may have been motivated by the study to make treatment initiation more efficient. If this occurred, the effect reported here would understate the true improvement in ART initiation that could be expected under routine implementation. As with many studies in which retention in care is an endpoint, we do not know the true outcomes of study patients who were not retained nor whether rapid arm patients who were not retained and who agreed to start treatment solely due to the presence of the study, and would otherwise not have done so, are at increased risk of developing ARV resistance. Finally, as reported above, rapid initiation under the study algorithm took 2–3 hours to complete, making same-day initiation impractical for patients who arrive late in the day (and for clinics with large numbers of such patients).We also do not know how clinic and patient characteristics will affect the net cost and cost-effectiveness of the intervention. Most of the changes introduced in the RapIT intervention entailed only adjustments in schedules and staff time, and we speculate that these will not result in a major net change to service delivery costs. The POC instruments used in the trial require an up-front investment, but it may be possible to initiate ART in a single visit without any POC instruments if there is no CD4 count threshold for initiation, patients with TB symptoms are identified and managed separately, and ARV regimen adjustments are routinely made at the first refill visit, rather than before initiation. Costs saved by patients, who must make just one clinic visit rather than four or five, should also be taken into account.The RapIT intervention as designed and implemented showed clinically meaningful improvements in ART uptake and viral suppression, providing “proof of principle” for a single-visit treatment initiation algorithm. Follow-on studies are needed to evaluate effectiveness and cost-effectiveness in routine practice in a variety of settings, and variations on the algorithm could also be considered. The RapIT trial has demonstrated that accelerating ART initiation can be effective and feasible in this setting and appeared acceptable to patients to whom it was offered; the next challenge will be adapting it to the range of settings and conditions found in clinics throughout Africa.Supporting InformationS1 TableStudy outcomes adjusted for baseline CD4 count, age, and sex.(DOCX)Click here for additional data file.S2 TableCrude patient-level predictors of treatment uptake, viral suppression, and retention in care.(DOCX)Click here for additional data file.S3 TableStudy outcomes stratified by immediate versus delayed initiation (rapid arm patients initiating ≤90 d only).(DOCX)Click here for additional data file.S4 TableAbsolute and relative effect measure modification of primary outcome (initiated ≤90 d and suppressed by 10 mo).(DOCX)Click here for additional data file.S1 TextResearch protocol.(PDF)Click here for additional data file.S2 TextCONSORT statement.(PDF)Click here for additional data file.AbbreviationsALTalanine aminotransferaseaRRadjusted risk ratioARTantiretroviral therapyARVantiretroviralIQRinterquartile rangeCIconfidence intervalHRhazard ratioPHCprimary health clinicPOCpoint-of-careRapITRapid Initiation of TreatmentRDrisk differenceRRrelative riskTBtuberculosisReferences1\nSiednerMJ, NgCK, Bassett IV, KatzIT, BangsbergDR, TsaiAC. Trends in CD4 count at presentation to care and treatment initiation in sub-Saharan Africa, 2002–2013: a meta-analysis. Clin Infect Dis. 2014; 60:1120–1127. 10.1093/cid/ciu1137\n255161892\nPlazyM, Dray-SpiraR, Orne-GliemannJ, DabisF, Newell M-L. Continuum in HIV care from entry to ART initiation in rural KwaZulu-Natal, South Africa. Trop Med Int Health. 2014; 19:680–689. 10.1111/tmi.12301\n246549903\nClouseK, PettiforAE, MaskewM, BassettJ, VanRie A, BehetsF, et al\nPatient retention from HIV diagnosis through one year on antiretroviral therapy at a primary health care clinic in Johannesburg, South Africa. J Acquir Immune Defic Syndr. 2013; 62: 39–46.4\nLahuertaM, UeF, HoffmanS, ElulB, KulkarniSG, WuY, et al\nThe problem of late ART initiation in Sub-Saharan Africa: a transient aspect of scale-up or a long-term phenomenon?\nJ Health Care Poor Underserved. 2013; 24: 359–383. 10.1353/hpu.2013.0014\n233777395\nINSIGHT START Study Group. Initiation of antiretroviral therapy in early asymptomatic HIV infection. N Engl J Med. 2015; 373: 795–807. 10.1056/NEJMoa1506816\n261928736\nWorld Health Organization. Guideline on when to start antiretroviral therapy and on pre-exposure prophylaxis for HIV\nGeneva: World Health Organization; 2015.7\nGovindasamyD, FordN, KranzerK. Risk factors, barriers and facilitators for linkage to ART care in sub-Saharan Africa: a systematic review. AIDS. 2012; 26: 2059–2067. 10.1097/QAD.0b013e3283578b9b\n227812278\nSiednerMJ, LankowskiA, HabererJE, KembabaziA, EmenyonuN, TsaiAC, et al\nRethinking the “‘pre’” in pre-therapy counseling: no benefit of additional visits prior to therapy on adherence or viremia in Ugandans initiating ARVs. PLoS ONE. 2012; 7: e39894\n10.1371/journal.pone.0039894\n227619249\nWorld Health Organization. Global update on the health sector response to HIV, 2014\nGeneva: World Health Organization; 2014.10\nIngleSM, MayM, UebelK, TimmermanV, KotzeE, BachmannM, et al\nOutcomes in patients waiting for antiretroviral treatment in the Free State Province, South Africa: prospective linkage study. AIDS. 2010; 24: 2717–2725. 10.1097/QAD.0b013e32833fb71f\n2093555411\nMyerL, ZulligerR, PienaarD. Diversity of patient preparation activities before initiation of antiretroviral therapy in Cape Town, South Africa. Trop Med Int Heal. 2012; 17: 972–977. 10.1111/j.1365-3156.2012.03033.x\n12\nNational Department of Health. The South African Antiretroviral Treatment Guideline 2013\nPretoria: National Department of Health; 2013.13\nScottV, ZweigenthalV, JenningsK. Between HIV diagnosis and initiation of antiretroviral therapy: assessing the effectiveness of care for people living with HIV in the public primary care service in Cape Town, South Africa. Trop Med Int Heal. 2011; 16:1384–1391. 10.1111/j.1365-3156.2011.02842.x\n14\nGousN, ScottL, PotgieterJ, NtabeniL, EnslinS, NewmanR, et al\nFeasibility of performing multiple point of care testing for HIV anti-retroviral treatment initiation and monitoring from multiple or single fingersticks. PLoS ONE. 2013; 8: e85265\n10.1371/journal.pone.0085265\n2437687315\nJani IV, SitoeNE, ChongoPL, AlfaiER, QuevedoJI, TobaiwaO, et al\nAccurate CD4 T-cell enumeration and antiretroviral drug toxicity monitoring in primary healthcare clinics using point-of-care testing. AIDS. 2011; 25:807–812. 10.1097/QAD.0b013e328344f424\n2137853516\nMnyaniCN, McIntyreJA, MyerL. The reliability of point-of-care CD4 testing in identifying HIV-infected pregnant women eligible for antiretroviral therapy. J Acquir Immune Defic Syndr. 2012; 60: 260–264. 10.1097/QAI.0b013e318256b651\n2248758917\nWadeD, DaneauG, AboudS, VercauterenGH, UrassaWSK, KestensL, et al\nWHO multicenter evaluation of FACSCount CD4 and Pima CD4 t-cell count systems\u202f: instrument performance and misclassification of HIV-infected patients. J Acquir Immune Defic Syndr. 2014; 66:98–107.18\nScottLE, CampbellJ, WestermanL, KestensL, VojnovL, KohastsuL, et al\nA meta-analysis of the performance of the Pima CD4 for point of care testing. BMC Med. 2015; 13:168\n10.1186/s12916-015-0396-2\n2620886719\nMeyer-RathG, SchnippelK, LongL, MacleodW, SanneI, StevensW, et al\nThe impact and cost of scaling up GeneXpert MTB/RIF in South Africa. PLoS ONE. 2012; 7:e36966\n10.1371/journal.pone.0036966. 10.1371/journal.pone.0036966\n2269356120\nUNITAID. Tuberculosis diagnostics technology and market landscape\nGeneva: UNITAID; 2013.21\nFoxMP, MaskewM, MacPhailA. Cohort profile: the Themba Lethu Clinical Cohort, Johannesburg, South Africa. International Journal of Epidemiology. 2013; 42:430–439. 10.1093/ije/dys029\n2243486022\nHoffmannCJ, LewisJJ, DowdyDW, FieldingKL, GrantAD, MartinsonN, et al\nMortality associated with delays between clinic entry and ART initiation in resource-limited settings: results of a transition-state model. J Acquir Immune Defic Syndr. 2013; 63:105–111. 10.1097/QAI.0b013e3182893fb4\n2339245723\nJani IV, SitoeNE, AlfaiER, ChongoPL, QuevedoJI, RochaBM, et al\nEffect of point-of-care CD4 cell count tests on retention of patients and rates of antiretroviral therapy initiation in primary health clinics: An observational cohort study. Lancet. 2011; 378:1572–1579. 10.1016/S0140-6736(11)61052-0\n2195165624\nBurtleD, WelfareW, EldenS, MamvuraC, VandelanotteJ, PetherickE, et al\nIntroduction and evaluation of a “pre-ART care” service in Swaziland: an operational research study. BMJ Open. 2012; 2:e000195\n10.1136/bmjopen-2011-000195\n25\nBlackS, ZulligerR, MyerL, MarcusR, JenekerS, HonsBA, et al\nSafety, feasibility and efficacy of a rapid ART initiation in pregnancy pilot programme in Cape Town, South Africa. S Afr Med J. 2013; 103:557–562. 10.7196/SAMJ.6565\n2388573926\nMfinangaS, ChandaD, KivuyoSL, GuinnessL, BottomleyC, SimmsV, et al\nCryptococcal meningitis screening and community-based early adherence support in people with advanced HIV infection starting antiretroviral therapy in Tanzania and Zambia: an open-label, randomised controlled trial. Lancet. 2015; 385:2173–2182. 10.1016/S0140-6736(15)60164-7\n2576569827\nClouseK, Page-ShippL, DanseyH, MoatlhodiB, ScottL, BassettJ, et al\nImplementation of Xpert MTB/RIF for routine point-of-care diagnosis of tuberculosis at the primary care level. S Afr Med J. 2012; 102:805–807. 10.7196/SAMJ.5851\n23034211', 'title': ""Initiating Antiretroviral Therapy for HIV at a Patient's First Clinic Visit: The RapIT Randomized Controlled Trial."", 'date': '2016-05-11'}}",0.25,"Public Health, Epidemiology & Health Systems"
@@ -272,11 +272,11 @@ question_id,question,answer,evidence_certainty,fulltext_required,relevant_source
 269,"Is the rate of caesarean delivery higher, lower, or the same when comparing Bishop score to transvaginal ultrasound (TVUS)?",no difference,moderate,no,"['15660437', '21484904']",26068943,2015,"{'15660437': {'article_id': '15660437', 'content': 'To compare transvaginal ultrasound with the Bishop score in assessment of cervical ripening for choice of induction agent.\nEighty women were randomized to have preinduction cervical assessment for choice of induction agent based on either Bishop score or transvaginal ultrasound. The primary outcome measure was the percentage of women who were administered prostaglandin as a preinduction agent. The criteria for considering the cervix as unripe and thus for using prostaglandin were either a Bishop score < 6 or a cervical length > 30 mm with cervical wedging of < 30% of the total cervical length. Secondary outcome measures included interval to active phase, interval to delivery and rate of Cesarean section.\nWhile 85% of women received prostaglandin in the Bishop score group, only 50% of them did in the transvaginal ultrasound group (P = 0.001). The interval to active phase, interval to delivery and rate of Cesarean section were similar in both groups.\nWith the suggested cut-off values of a Bishop score < 6 or a cervical length > 30 mm and wedging < 30%, the use of transvaginal ultrasound instead of Bishop score for preinduction cervical assessment to choose induction agent significantly reduces the need for intracervical prostaglandin treatment without adversely affecting the success of induction.', 'title': 'Bishop score and transvaginal ultrasound for preinduction cervical assessment: a randomized clinical trial.', 'date': '2005-01-22'}, '21484904': {'article_id': '21484904', 'content': 'To compare sonographically measured cervical length with the Bishop score in determining the requirement for prostaglandin administration for preinduction cervical ripening in nulliparae at term.\nOne hundred and fifty-four women with singleton pregnancies at term who were scheduled for induction of labor were randomly assigned to receive prostaglandin for preinduction cervical ripening based on the Bishop score or sonographic cervical length. A cervix unfavorable for treatment with prostaglandin for preinduction cervical ripening was defined as having either a Bishop score of ≤ 4 or a cervical length of ≥ 28 mm. The primary outcome measures were induction success (defined as an ability to achieve the active phase of labor) and the percentage of patients treated with prostaglandin for preinduction cervical ripening.\nThe two groups were similar with respect to maternal demographics, gestational age, cervical length, and Bishop score. The rates of induction success and Cesarean delivery, the interval to active phase of labor, and the interval to delivery were also similar in the two groups. However, in the transvaginal ultrasound group (n = 77), prostaglandin was administered to only 36% of the nulliparae compared with 75% of those in the Bishop score group (n = 77) (P < 0.0001).\nIn comparison with the Bishop score, the use of sonographic cervical length for assessing the cervix prior to induction of labor can reduce the need for prostaglandin administration by approximately 50% without adversely affecting the outcome of induction in nulliparae at term if the cut-off values used are a Bishop score of ≤ 4 and a cervical length of ≥ 28 mm.', 'title': 'Comparison between sonographic cervical length and Bishop score in preinduction cervical assessment: a randomized trial.', 'date': '2011-04-13'}}",0.5,Obstetrics & Gynecology
 270,"Is birth weight higher, lower, or the same when comparing caffeinated instant coffee and decaffeinated instant coffee? ",no difference,low,no,['17259189'],26058966,2015,"{'17259189': {'article_id': '17259189', 'content': ""To estimate the effect of reducing caffeine intake during pregnancy on birth weight and length of gestation.\nRandomised double blind controlled trial.\nDenmark.\n1207 pregnant women drinking at least three cups of coffee a day, recruited before 20 weeks' gestation.\nCaffeinated instant coffee (568 women) or decaffeinated instant coffee (629 women).\nBirth weight and length of gestation.\nData on birth weight were obtained for 1150 liveborn singletons and on length of gestation for 1153 liveborn singletons. No significant differences were found for mean birth weight or mean length of gestation between women in the decaffeinated coffee group (whose mean caffeine intake was 182 mg lower than that of the other group) and women in the caffeinated coffee group. After adjustment for length of gestation, parity, prepregnancy body mass index, and smoking at entry to the study the mean birth weight of babies born to women in the decaffeinated group was 16 g (95% confidence interval -40 to 73) higher than those born to women in the caffeinated group. The adjusted difference (decaffeinated group-caffeinated group) of length of gestation was -1.31 days (-2.87 to 0.25).\nA moderate reduction in caffeine intake in the second half of pregnancy has no effect on birth weight or length of gestation.\nClinical Trials NCT00131690 [ClinicalTrials.gov]."", 'title': 'Effect of reducing caffeine intake on birth weight and length of gestation: randomised controlled trial.', 'date': '2007-01-30'}}",1.0,Obstetrics & Gynecology
 271,"Is all-cause mortality higher, lower, or the same when comparing the placement of transjugular intrahepatic portosystemic shunts (TIPS) placement with conventional treatment?",uncertain effect,low,no,"['12454841', '10841872']",38235907,2024,"{'12454841': {'article_id': '12454841', 'content': 'The transjugular intrahepatic portosystemic shunt (TIPS) has been shown to be more effective than repeated paracentesis plus albumin in the control of refractory ascites. However, its effect on survival and healthcare costs is still uncertain.\nSeventy patients with cirrhosis and refractory ascites were randomly assigned to TIPS (35 patients) or repeated paracentesis plus intravenous albumin (35 patients). The primary endpoint was survival without liver transplantation. Secondary endpoints were complications of cirrhosis and costs.\nTwenty patients treated with TIPS and 18 treated with paracentesis died during the study period, whereas 7 patients in each group underwent liver transplantation (mean follow-up 282 +/- 43 vs. 325 +/- 61 days, respectively). The probability of survival without liver transplantation was 41% at 1 year and 26% at 2 years in the TIPS group, as compared with 35% and 30% in the paracentesis group (P = 0.51). In a multivariate analysis, only baseline blood urea nitrogen levels and Child-Pugh score were independently associated with survival. Recurrence of ascites and development of hepatorenal syndrome were lower in the TIPS group compared with the paracentesis group, whereas the frequency of severe hepatic encephalopathy was greater in the TIPS group. The calculated costs were higher in the TIPS group than in the paracentesis group.\nIn patients with refractory ascites, TIPS lowers the rate of ascites recurrence and the risk of developing hepatorenal syndrome. However, TIPS does not improve survival and is associated with an increased frequency of severe encephalopathy and higher costs compared with repeated paracentesis plus albumin.', 'title': 'Transjugular intrahepatic portosystemic shunting versus paracentesis plus albumin for refractory ascites in cirrhosis.', 'date': '2002-11-28'}, '10841872': {'article_id': '10841872', 'content': 'In patients with cirrhosis and ascites, creation of a transjugular intrahepatic portosystemic shunt may reduce the ascites and improve renal function. However, the benefit of this procedure as compared with that of large-volume paracentesis is uncertain.\nWe randomly assigned 60 patients with cirrhosis and refractory or recurrent ascites (Child-Pugh class B in 42 patients and class C in 18 patients) to treatment with a transjugular shunt (29 patients) or large-volume paracentesis (31 patients). The mean (+/-SD) duration of follow-up was 45+/-16 months among those assigned to shunting and 44+/-18 months among those assigned to paracentesis. The primary outcome was survival without liver transplantation.\nAmong the patients in the shunt group, 15 died and 1 underwent liver transplantation during the study period, as compared with 23 patients and 2 patients, respectively, in the paracentesis group. The probability of survival without liver transplantation was 69 percent at one year and 58 percent at two years in the shunt group, as compared with 52 percent and 32 percent in the paracentesis group (P=0.11 for the overall comparison, by the log-rank test). In a multivariate analysis, treatment with transjugular shunting was independently associated with survival without the need for transplantation (P=0.02). At three months, 61 percent of the patients in the shunt group and 18 percent of those in the paracentesis group had no ascites (P=0.006). The frequency of hepatic encephalopathy was similar in the two groups. Of the patients assigned to paracentesis in whom this procedure was unsuccessful, 10 received a transjugular shunt a mean of 5.5+/-4 months after randomization; 4 had a response to this rescue treatment.\nIn comparison with large-volume paracentesis, the creation of a transjugular intrahepatic portosystemic shunt can improve the chance of survival without liver transplantation in patients with refractory or recurrent ascites.', 'title': 'A comparison of paracentesis and transjugular intrahepatic portosystemic shunting in patients with ascites.', 'date': '2000-06-08'}}",0.0,Internal Medicine & Subspecialties
-272,"Is visual acuity at 6 to 12 months higher, lower, or the same when comparing macular hole and vitrectomy?",higher,moderate,no,"['14769600', '9006420', '8644802']",25965055,2015,"{'14769600': {'article_id': '14769600', 'content': 'To determine the benefits of idiopathic full-thickness macular hole (FTMH) surgery compared with observation and to evaluate the use of autologous serum as an intraoperative adjunct.\nA randomized clinical trial was performed to evaluate the anatomic and visual benefits of FTMH surgery for lesions of 9 months or less symptom duration and visual acuity of 20/60 or less. We compared surgery with natural history and determined whether use of intraoperative adjunctive autologous serum improves the surgical outcome. Eyes were randomized to (1). observation, (2). vitrectomy, or (3). vitrectomy plus serum and were followed for 24 months to assess anatomic status and visual function.\nIn total, 185 eyes of 174 patients were enrolled. In the observation group, spontaneous closure of the FTMH occurred in 7 (11.5%) of 61 patients, with little or no change in overall acuity levels in 24 months. In contrast, the surgical groups had an overall closure rate of 80.6% (100/124) at 24 months, with 45% of eyes achieving Snellen acuity of 20/40 or greater. Surgical eyes had better median near acuity than observation eyes by 6 lines (N5 vs N14). Use of autologous serum did not seem to affect anatomic or visual results. At 24 months, 72 (58.1%) of 124 surgical eyes had undergone cataract extraction.\nSurgery for FTMH is safe and effective and is associated with significant visual improvement compared with the natural history. Autologous serum application does not enhance the results of surgery.', 'title': 'Surgery for idiopathic full-thickness macular hole: two-year results of a randomized clinical trial comparing natural history, vitrectomy, and vitrectomy plus autologous serum: Morfields Macular Hole Study Group RAeport no. 1.', 'date': '2004-02-11'}, '9006420': {'article_id': '9006420', 'content': 'To prospectively assess the risks and benefits of vitrectomy surgery for eyes with stage 3 or 4 macular holes.\nA multicentered, controlled, randomized clinical trial.\nCommunity- and university-based ophthalmology clinics.\nOne hundred twenty patients (129 eyes) with stage 3 or 4 macular holes.\nStandardized macular hole surgery vs observation alone.\nFour measures of best-corrected visual function, standardized photographic evaluation of the extent of hole closure, evaluation of lens opacification, and determination of adverse events. Outcomes were determined at 6 months after randomization.\nCompared with observation alone, a significant benefit due to surgery was found in the rate of hole closure (4% vs 69%, P < .001). After adjusting for baseline visual acuity, hole duration, and maximum hole diameter, a significant benefit due to surgery was found in visual acuity for the Bailey-Lovie Word Reading (P = .02) and the Potential Acuity Meter (P < .01) tests; a marginally significant benefit due to surgery was found in visual acuity for the Early Treatment Diabetic Retinopathy Study chart (P = .05). Although the proportion of eyes achieving a change in visual acuity of 2 or more lines on the Early Treatment Diabetic Retinopathy Study chart was significantly greater for the surgery group vs the observed group (11 [19%] of 59 eyes vs 3 [5%] of 58 eyes, adjusted P = .05), 20 (34%) of 59 eyes randomized to surgery had a loss in visual acuity of 1 or more lines. Compared with the observation group, eyes randomized to surgery had higher nuclear sclerosis scores (2.4 vs 1.3, P < .001). Fourteen adverse events were noted in the surgery group; none were noted in the observed group.\nSome visual benefit of vitrectomy surgery for macular holes exists, despite a notable incidence of adverse events. The large variability in visual acuity outcome in the surgical group may be because of complications or progressive cataract. A study of the long-term outcome after macular hole surgery is needed.', 'title': 'Vitrectomy for the treatment of full-thickness stage 3 or 4 macular holes. Results of a multicentered randomized clinical trial. The Vitrectomy for Treatment of Macular Hole Study Group.', 'date': '1997-01-01'}, '8644802': {'article_id': '8644802', 'content': 'To determine the risks and benefits of vitrectomy surgery in eyes with stage 2 macular holes.\nA multicentered, controlled, randomized clinical trial was performed with participation of 16 community and university-based ophthalmology clinics. Thirty-six eyes with stage 2 macular holes and 12 months of follow-up were studied. Pars plana vitrectomy with separation of the posterior hyaloid membrane and intraocular injection of perfluoropropane (C3F8) was followed by postoperative face-down positioning for two weeks. This protocol was compared with observation alone. Outcome variables included anatomic closure of the macular hole, macular hole size, and four standardized measures of vision.\nAt 12 months, 15 (71%) of 21 eyes randomly assigned to observation progressed to stages 3 or 4, compared with three (20%) of 15 eyes randomly assigned to surgery (P < .006). Compared with eyes randomly assigned to observation, eyes randomly assigned to surgery had significantly smaller hole diameters (P < .01) and significantly better visual acuity outcomes, as measured by the Word Reading (P = .02) and Potential Acuity Meter (P = .002) charts. No significant differences were found for the Early Treatment Diabetic Retinopathy Study chart and Contrast Sensitivity test.\nCompared with observation alone, surgical intervention in stage 2 macular holes resulted in a significantly lower incidence of hole enlargement and appeared to be associated with better outcome in some measures of visual acuity.', 'title': 'Prospective randomized trial of vitrectomy or observation for stage 2 macular holes. Vitrectomy for Macular Hole Study Group.', 'date': '1996-06-01'}}",0.0,Surgery
+272,"Is visual acuity at 6 to 12 months higher, lower, or the same when comparing vitrectomy to observation?",higher,moderate,no,"['14769600', '9006420', '8644802']",25965055,2015,"{'14769600': {'article_id': '14769600', 'content': 'To determine the benefits of idiopathic full-thickness macular hole (FTMH) surgery compared with observation and to evaluate the use of autologous serum as an intraoperative adjunct.\nA randomized clinical trial was performed to evaluate the anatomic and visual benefits of FTMH surgery for lesions of 9 months or less symptom duration and visual acuity of 20/60 or less. We compared surgery with natural history and determined whether use of intraoperative adjunctive autologous serum improves the surgical outcome. Eyes were randomized to (1). observation, (2). vitrectomy, or (3). vitrectomy plus serum and were followed for 24 months to assess anatomic status and visual function.\nIn total, 185 eyes of 174 patients were enrolled. In the observation group, spontaneous closure of the FTMH occurred in 7 (11.5%) of 61 patients, with little or no change in overall acuity levels in 24 months. In contrast, the surgical groups had an overall closure rate of 80.6% (100/124) at 24 months, with 45% of eyes achieving Snellen acuity of 20/40 or greater. Surgical eyes had better median near acuity than observation eyes by 6 lines (N5 vs N14). Use of autologous serum did not seem to affect anatomic or visual results. At 24 months, 72 (58.1%) of 124 surgical eyes had undergone cataract extraction.\nSurgery for FTMH is safe and effective and is associated with significant visual improvement compared with the natural history. Autologous serum application does not enhance the results of surgery.', 'title': 'Surgery for idiopathic full-thickness macular hole: two-year results of a randomized clinical trial comparing natural history, vitrectomy, and vitrectomy plus autologous serum: Morfields Macular Hole Study Group RAeport no. 1.', 'date': '2004-02-11'}, '9006420': {'article_id': '9006420', 'content': 'To prospectively assess the risks and benefits of vitrectomy surgery for eyes with stage 3 or 4 macular holes.\nA multicentered, controlled, randomized clinical trial.\nCommunity- and university-based ophthalmology clinics.\nOne hundred twenty patients (129 eyes) with stage 3 or 4 macular holes.\nStandardized macular hole surgery vs observation alone.\nFour measures of best-corrected visual function, standardized photographic evaluation of the extent of hole closure, evaluation of lens opacification, and determination of adverse events. Outcomes were determined at 6 months after randomization.\nCompared with observation alone, a significant benefit due to surgery was found in the rate of hole closure (4% vs 69%, P < .001). After adjusting for baseline visual acuity, hole duration, and maximum hole diameter, a significant benefit due to surgery was found in visual acuity for the Bailey-Lovie Word Reading (P = .02) and the Potential Acuity Meter (P < .01) tests; a marginally significant benefit due to surgery was found in visual acuity for the Early Treatment Diabetic Retinopathy Study chart (P = .05). Although the proportion of eyes achieving a change in visual acuity of 2 or more lines on the Early Treatment Diabetic Retinopathy Study chart was significantly greater for the surgery group vs the observed group (11 [19%] of 59 eyes vs 3 [5%] of 58 eyes, adjusted P = .05), 20 (34%) of 59 eyes randomized to surgery had a loss in visual acuity of 1 or more lines. Compared with the observation group, eyes randomized to surgery had higher nuclear sclerosis scores (2.4 vs 1.3, P < .001). Fourteen adverse events were noted in the surgery group; none were noted in the observed group.\nSome visual benefit of vitrectomy surgery for macular holes exists, despite a notable incidence of adverse events. The large variability in visual acuity outcome in the surgical group may be because of complications or progressive cataract. A study of the long-term outcome after macular hole surgery is needed.', 'title': 'Vitrectomy for the treatment of full-thickness stage 3 or 4 macular holes. Results of a multicentered randomized clinical trial. The Vitrectomy for Treatment of Macular Hole Study Group.', 'date': '1997-01-01'}, '8644802': {'article_id': '8644802', 'content': 'To determine the risks and benefits of vitrectomy surgery in eyes with stage 2 macular holes.\nA multicentered, controlled, randomized clinical trial was performed with participation of 16 community and university-based ophthalmology clinics. Thirty-six eyes with stage 2 macular holes and 12 months of follow-up were studied. Pars plana vitrectomy with separation of the posterior hyaloid membrane and intraocular injection of perfluoropropane (C3F8) was followed by postoperative face-down positioning for two weeks. This protocol was compared with observation alone. Outcome variables included anatomic closure of the macular hole, macular hole size, and four standardized measures of vision.\nAt 12 months, 15 (71%) of 21 eyes randomly assigned to observation progressed to stages 3 or 4, compared with three (20%) of 15 eyes randomly assigned to surgery (P < .006). Compared with eyes randomly assigned to observation, eyes randomly assigned to surgery had significantly smaller hole diameters (P < .01) and significantly better visual acuity outcomes, as measured by the Word Reading (P = .02) and Potential Acuity Meter (P = .002) charts. No significant differences were found for the Early Treatment Diabetic Retinopathy Study chart and Contrast Sensitivity test.\nCompared with observation alone, surgical intervention in stage 2 macular holes resulted in a significantly lower incidence of hole enlargement and appeared to be associated with better outcome in some measures of visual acuity.', 'title': 'Prospective randomized trial of vitrectomy or observation for stage 2 macular holes. Vitrectomy for Macular Hole Study Group.', 'date': '1996-06-01'}}",0.0,Surgery
 273,"Is the number of antibiotics used by patients higher, lower, or the same when comparing the use of written information to usual care?",lower,moderate,no,['19640941'],27886368,2016,"{'19640941': {'article_id': '19640941', 'content': 'BMJBMJbmjThe BMJ0959-81381756-1833BMJ Publishing Group Ltd.196409412718088fran61680510.1136/bmj.b2885ResearchInfectious DiseasesClinical Trials (Epidemiology)General Practice / Family MedicineImmunology (Including Allergy)Child HealthAsthmaPneumonia (Respiratory Medicine)Effect of using an interactive booklet about childhood respiratory tract infections in primary care consultations on reconsulting and antibiotic prescribing: a cluster randomised controlled trial FrancisNick Amedical research council health services fellow12ButlerChristopher Cprofessor of primary care medicine, head of department of primary care and public health1HoodKerenzareader in statistics, director of south east Wales trials unit12SimpsonSharonsenior research fellow12WoodFionalecturer1NuttallJacquelinesenior trial manager121Department of Primary Care and Public Health, School of Medicine, Cardiff University, Neuadd Meirionnydd, Heath Park, Cardiff CF14 4XN2South East Wales Trials Unit, School of Medicine, Cardiff UniversityCorrespondence to: N Francis francisna@cf.ac.uk20092972009339b2885432009This is an open-access article distributed under the terms of the Creative Commons Attribution Non-commercial License, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited, the use is non commercial and is otherwise in compliance with the license. See: http://creativecommons.org/licenses/by-nc/2.0/ and http://creativecommons.org/licenses/by-nc/2.0/legalcode.Objective To establish whether an interactive booklet on respiratory tract infections in children reduces reconsultation for the same illness episode, reduces antibiotic use, and affects future consulting intentions, while maintaining parental satisfaction with care.Design Pragmatic cluster randomised controlled trial. Setting 61 general practices in Wales and England.Participants 558 children (6 months to 14 years) presenting to primary care with an acute respiratory tract infection (7 days or less). Children with suspected pneumonia, asthma or a serious concomitant illness, or needing immediate hospital admission were excluded. Three withdrew and 27 were lost to follow-up, leaving 528 (94.6%) with main outcome data.Interventions Clinicians in the intervention group were trained in the use of an interactive booklet on respiratory tract infections and asked to use the booklet during consultations with recruited patients (and provide it as a take home resource). Clinicians in the control group conducted their consultations as usual.Main outcome measures The proportion of children who attended a face-to-face consultation about the same illness during the two week follow-up period. Secondary outcomes included antibiotic prescribing, antibiotic consumption, future consulting intentions, and parental satisfaction, reassurance, and enablement.Results Reconsultation occurred in 12.9% of children in the intervention group and 16.2% in the control group (absolute risk reduction 3.3%, 95% confidence interval −2.7% to 9.3%, P=0.29). Using multilevel modelling (at the practice and individual level) to account for clustering, no significant difference in reconsulting was noted (odds ratio 0.75; 0.41 to 1.38). Antibiotics were prescribed at the index consultation to 19.5% of children in the intervention group and 40.8% of children in the control group (absolute risk reduction 21.3%, 95% confidence interval 13.7 to 28.9), P<0.001). A significant difference was still present after adjusting for clustering (odds ratio 0.29; 0.14 to 0.60). There was also a significant difference in the proportion of parents who said they would consult in the future if their child developed a similar illness (odds ratio 0.34; 0.20 to 0.57). Satisfaction, reassurance, and parental enablement scores were not significantly different between the two groups.Conclusions Use of a booklet on respiratory tract infections in children within primary care consultations led to important reductions in antibiotic prescribing and reduced intention to consult without reducing satisfaction with care.Trial registration Current Controlled Trials ISRCTN46104365IntroductionRespiratory tract infections are the most common reason for patients to consult in primary care, with children consulting more than any other age group.1 One in five children who consult for a respiratory tract infection returns during the same illness episode, and many of these visits are unnecessary.2\n3 Unnecessary re-consulting represents an opportunity cost and can increase the pressure on clinicians to prescribe antibiotics. Acute cough in children alone is estimated to cost the NHS at least £31.5m (€36.8m;$51.4m), with most of this cost arising from consultations with general practitioners.4Complications of respiratory tract infections are rare, and there is little or no benefit from treatment with antibiotics.5\n6\n7\n8\n9 Guidelines of the National Institute for Health and Clinical Excellence (NICE) recommend against the immediate use of antibiotics for most children who have respiratory tract infections, and promote effective communication and information provision including an indication of the likely duration of illness.10 Nevertheless, antibiotics continue to be overprescribed for these illnesses,11\n12 with children receiving more antibiotics than any other age group.13 Prescribing for non-specific upper respiratory tract infections, which declined in the late 1990s, is once again increasing.14 Unnecessary antibiotic use wastes healthcare resources, encourages further consulting in the future for similar illnesses,15 contributes to the problem of antibiotic resistance, and unnecessarily exposes patients to risk of adverse effects.16Parental beliefs, fears, and expectations play an important part in both consulting behaviour and determining whether an antibiotic is prescribed.17 Parents fear serious illness, and worry that they will not be able to recognise the symptoms.18 Few are aware of the likely risks and benefits from antibiotic treatment and the normal duration of illness.19 Providing information on recognising the signs of serious illness and the likely duration of illness can reduce anxiety, increase confidence, and empower parents to manage their child’s illness without needing to consult a healthcare professional. A nurse administered educational intervention aimed at helping parents cope with ear pain in the United States resulted in a reduction in consultations for ear pain over the following year.20 Patient information leaflets for adults with lower respiratory tract infections that describe expected duration of illness and suggest simple self-help measures reduce reconsultations21 and antibiotic prescribing.22Communication within the consultation is central to addressing parental concerns and expectations, and helps parents to manage their child’s illness effectively and safely. Clinicians seldom explicitly ask parents about their expectations about antibiotic treatment,23 and overestimate the expectation for antibiotics.24 When clinicians believe that patients (and parents) expect antibiotics, they are more likely to prescribe them.17 Clinicians often tell parents that their child should recover in a few days, although children usually have symptoms for substantially longer than this.25 Setting realistic expectations about the likely duration of illness could reduce parental anxiety and rates of visits. Furthermore, parents value a thorough examination, explanation, reassurance, and advice or guidance more than a prescription for antibiotics.24\n26We therefore set out to establish whether training clinicians in the use of an interactive booklet, designed to enhance communication within the consultation, and act as a take home resource for parents, would have an effect on rates of reconsultation and antibiotic prescribing. A cluster design was needed, since the intervention was partly directed at the clinicians in the practice. Clinicians who had received training in use of the booklet, and through its use had learnt from its content, would therefore have been unable to not use this knowledge in each consultation where a patient was recruited.MethodsThe methods for this cluster randomised controlled trial have been described in detail elsewhere,27 and are summarised below.Half of all general practices from nine local health boards in Wales (n=147) were randomly selected to be sent information about the study (our research group was conducting another randomised controlled trial assessing a related intervention and the other practices were sent information about that study). This procedure was followed by attempts to contact a general practitioner or practice manager in each practice. Telephone contact with a general practitioner or practice manager was successful for 81 practices. Sixty two practices in Wales agreed to take part, although only 49 of these returned a practice agreement and were subsequently randomised. Of the 49 randomised practices, 36 recruited study participants. In England, four primary care research networks agreed to help recruit practices. The total number of practices approached in these networks is not available. However, 38 practices in England verbally agreed to take part, 34 of these returned a practice agreement and were randomised, and 25 of the randomised practices recruited participants. Practices were randomised by a statistician using block randomisation with random block sizes and stratification by practice list size, antibiotic prescribing rate for 2005, and country.Participating clinicians were asked to recruit sequential eligible children (6 months to 14 years) consulting with a respiratory tract infection (cough, cold, sore throat, earache for seven days or less) and their parents. Exclusions included children with asthma and those with serious ongoing medical conditions such as malignancy or cystic fibrosis.Sample size calculationWe calculated that we would need 524 participants recruited from 60 clusters (practices) in order to show a reduction in the proportion of children who reconsult from 20% to 10%, with 80% power, at a 5% significance level, and with an intracluster coefficient of 0.04. Our aim was to ask 60 practices to recruit ten children each (total of 600 participants) which would allow for loss to follow-up and missing data.The interventionThe intervention consisted of an eight page booklet on respiratory tract infections in children, designed to be used within the consultation and then provided to parents as a take home resource (see www.equipstudy.com). Online training on the use of the booklet was also provided for clinicians. The study booklet was developed through a multistage process which has been described elsewhere.28 The online training described the content and aims of the booklet, and encouraged its use within the consultation to facilitate the use of certain communication skills, mainly exploring the parent’s main concerns, asking about their expectations, and discussing prognosis, treatment options, and any reasons that should prompt reconsultation. Clinicians in practices randomised to the control group were asked to conduct the consultation in their usual manner.MeasuresBaseline data, including age, duration of illness, and symptoms, were collected by participating clinicians at the time of recruitment. We asked clinicians to collect non-identifiable data for all potentially eligible patients (including those who were not approached, those who were approached but were ineligible, and those who declined participation) to assess for possible selection bias. Follow-up was via a telephone administered questionnaire with the child’s parent or guardian, 14 days after recruitment. Where a participant’s parent could not be contacted on day 14, further attempts were made for at least three days. If still unsuccessful, the telephone number was checked with the relevant general practice and with directory inquiries, and if this approach was unsuccessful then a self completion questionnaire was sent to the parents. Follow-up measurements included reported consultations in primary and secondary care in the two weeks after recruitment, prescriptions for and use of antibiotics, intention to consult with a similar illness in the future, parental enablement (using a modification of the patient enablement instrument29), satisfaction with the index consultation, reassurance, and value of any information given to them during the index consultation. Telephone interviewers were blinded to treatment group and were asked to record any subsequent unblinding of allocation (such as a parent talking about receiving a booklet).Primary outcome was a reconsultation during the two weeks after the index consultation. Antibiotic prescribing, antibiotic consumption, future consulting intentions, parental satisfaction, perception of the usefulness of information received, reassurance, and enablement were secondary outcomes.AnalysisData were analysed using Stata version 9 and MLwiN version 2.10. After checks for missing data and ranges, and double entry of a 10% sample of the case report forms, Stata was used to obtain summary statistics and undertake univariate analyses. “Satisfaction” and “usefulness of information received” were measured using five point items, but their response distributions were highly skewed. For this reason, these items were transformed into binary outcomes, split into “very satisfied” or “satisfied” versus ‘‘neutral”, ‘‘dissatisfied” or “very dissatisfied” and “very useful” or “useful” versus “neutral”, “unhelpful” or “very unhelpful”. Similarly, the outcome “reassured” was transformed from a three response item into a binary outcome, split into “very reassured” versus “a little reassured” or “not reassured”. The enablement score was calculated in the standard way, but since one item had been excluded, possible scores ranged from 0 to 10. Enablement scores had a skewed distribution and were therefore converted into a binary outcome using a mid-range cutpoint of 5.The primary analysis was intention to treat, conducted by fitting two level (practice and patient) random intercept logistic regression models using MLwiN. Similar models were fitted for the secondary outcomes.After the initial analyses, sensitivity analyses were done by adding the stratifying variables (practice size, practice prescribing status, and country), age, duration of illness, and any symptoms found to be significantly associated in univariate analyses at the 10% level into each model as covariates. Exploratory analyses were conducted by including factors likely to affect reconsulting and antibiotic prescribing into these two models. The interaction factors were then examined to look for subgroup effects.ResultsEighty-three practices were randomised, and 61 of these recruited a total of 558 eligible patients between October 2006 and April 2008 (fig 1).Fig 1 Study profile. *One patient from the control group was subsequently found to have longstanding asthma and was therefore determined (after consultation with the trial steering committee) to have been “recruited in error” and has not been included as a recruited patientIntervention and control practices, and randomised practices that did and did not recruit participants, were similar in terms of list size, antibiotic prescribing history, and location (Wales or England) (table 1). Patients recruited by intervention and control practices were similar in terms of age, sex, duration of illness, and symptoms (table 1). Patients were recruited by intervention and control practices at a similar rate (fig 2). We achieved a follow-up rate of 94.6% (93.4% intervention, 95.8% control) for the primary outcome data. Telephone interviewers reported becoming aware of the participant’s treatment group in 34 of 509 interviews (6.7%).Fig 2 Recruitment rates in the two groupsTable 1\u2002Baseline characteristics of randomised recruiting and non-recruiting practices, participating clinicians, and patients, by treatment groupInterventionControlRandomised, non-recruiting practices1111Median (IQR) list size7000 (3680 to 12 000)8300 (4300 to 9200)No (%) above average prescribing practice5 (45.5)3 (27.3)No (%) of practices in England 3 (27.3)6 (54.6)Recruiting practices3031Median (IQR) list size6750 (4400 to 9000)6800 (3700 to 8700)No (%) above average prescribing9 (30.0)10 (32.3)No (%) of practices in England 14 (46.7)11 (35.5)Median (IQR) cluster size 9.5 (5 to 10)10 (7 to 10)Participating clinicians5553No (%) of nurses 5 (9.1)11 (20.8)Proportion of patients recruited by a nurse11.4%19.4%Patients274284Mean (SD) age (years)5.1 (3.9)5.3 (3.8)Male 45.3%53.5%Duration of illness, days (SD)3.2 (1.7)3.3 (1.8)No (%) with symptom:\u2003Cough173 (63.4)167 (58.8)\u2003Earache74 (27.1)69 (24.3)\u2003Runny nose85 (31.1)97 (34.2)\u2003Sore throat89 (32.6)112 (39.4)\u2003Fever103 (37.7)109 (38.4)\u2003Looks unwell36 (13.2)48 (16.9)IQR=interquartile range. No=number.The number and proportion of patients experiencing each outcome, and odds ratios (with 95% confidence intervals) for the primary and secondary outcomes are shown in table 2. There was no significant difference between the intervention and control groups in the odds of reconsulting in primary care during the two weeks after registration. Children in the intervention group were significantly less likely to receive a prescription for antibiotics at the index consultation, less likely to take antibiotics during the first two weeks, and their parents were less likely to report that they would consult in the future if their child had a similar illness. There were no significant differences in terms of satisfaction, level of reassurance, parental enablement, or the parent’s rating of the “usefulness of any information received in the consultation.” Similar results were found at the univariate level, with a non-significant difference in reconsulting (absolute risk reduction 3.3%, 95% confidence interval −2.7% to 9.3%, P=0.29), and significant differences in antibiotic prescribing (absolute risk reduction 21.3%, number needed to treat 4.7, P<0.001), antibiotic consumption (absolute risk reduction 20.6%, 95% confidence interval 12.7% to 28.5%, number needed to treat 4.9, P<0.001), and future consulting intentions (21.1%, 13.1% to 29.2%, 4.7, P<0.001).Table 2\u2002Effect of the intervention on patient outcomesNo (%) experiencing the outcomeOdds ratio from multilevel modelling (95% CI)InterventionControlOutcomes with data collected from telephone administered and postal questionnairesNo of patients256272Primary outcome: primary care reconsultation within first two weeks* (intracluster correlation coefficient=0.06)33 (12.9)44 (16.2)0.75 (0.41 to 1.38)Antibiotic prescribed at index consultation (intracluster correlation coefficient=0.24)50 (19.5)111 (40.8)0.29 (0.14 to 0.60)Outcomes with data collected from telephone administered questionnaire onlyNo of patients246263Antibiotics taken within first two weeks (including antibiotics prescribed after index consultation)55 (22.4)111 (43.0)0.35 (0.18 to 0.66)Parent intends to consult if their child has similar illness in future136 (55.3)201 (76.4)0.34 (0.20 to 0.57)Parental enablement score (≥5)99 (40.2)94† (35.9)1.20 (0.84 to 1.73)Satisfaction‡222 (90.2)246 (93.5)0.64 (0.33 to 1.22)Reassurance§177 (72.0)198 (75.3)0.84 (0.57 to 1.25)Usefulness of information received¶210 (85.4)224 (85.2)1.01 (0.60 to 1.68)*Parental report that child attended a face to face consultation with a primary care clinician in their general practice, or with an out-of-hours provider, in the two weeks after registration.†No=262 for this group as one parent was unable to complete enablement questions because of language problems.‡Proportion of parents who reported being very satisfied or satisfied with the consultation.§ Proportion of parents who reported feeling very reassured after their consultation.¶Proportion of parents who reported that information they received in the consultation was very useful or useful.There was no significant intervention effect when telephone consultations were counted as reconsultations along with face to face primary care consultations (odds ratio 0.81; 0.47 to 1.42), or when consultations at accident and emergency departments were included along with primary care consultations (0.85; 0.48 to 1.51). The intervention had a similar effect size on the antibiotic outcomes of receiving a prescription for antibiotics for immediate use at the index consultation (excluding prescriptions for delayed use; 0.26; 0.11 to 0.62) and receiving an antibiotic prescription at any point in the two week follow -up (0.31; 0.16 to 0.62).The sensitivity analyses for the main reconsultation outcome and the antibiotic prescribing outcome did not result in any meaningful changes to the results (that is, there were no significant intervention effects in the sensitivity analyses for the reconsultation outcome and similar significant effects for all analyses with the antibiotic prescribing outcome).Subgroup analysesNo significant interaction effects were seen in the reconsultation models. In the antibiotic prescribing model, the intervention was more effective in above-average prescribing practices (table 3). There were no other significant interaction effects.Table 3\u2002Effect of practice prescribing history and study intervention on probability of being prescribed an antibioticPractice antibiotic prescribing historyHigher (above national average for 2005)Lower (below national average for 2005)\u2003Intervention16.3%15.4%\u2003Control64.1%27.3%Values show probability of being prescribed an antibiotic, calculated from coefficients derived from multilevel modelling.Adverse eventsSeven patients (three in the intervention group and four in the control group) were subsequently admitted to hospital or observed in a paediatric assessment unit. One patient in the control group had a longstanding diagnosis of asthma, and as such was excluded (after discussion in the trial steering committee). The longest hospital admission (two nights) was a patient in the intervention group who had febrile convulsions. The remaining admissions were one night or less.Comparing recruited and non-recruited patientsNinety three patients were not recruited into the study (50 were ineligible, 27 declined participation, and 16 were not recruited because of a lack of time in the consultation or for other unspecified reasons. There were no significant differences between recruited and non-recruited patients in terms of age or presenting symptoms; however, cough was more common in non-recruited patients (71.9% v 61.0%). Of the non-recruited patients, there was no significant difference in cough between the intervention and control groups. Previous duration of illness was higher in non-recruited patients than in recruited patients, although this finding was not surprising, since a duration of illness of more than seven days was a common reason for exclusion from the study.DiscussionClinicians’ use of an interactive booklet on respiratory tract infections in children within primary care consultations resulted in a significant reduction in antibiotic prescribing and consumption and high levels of parental satisfaction. Use of the intervention did not result in a significant reduction in the proportion of children who reconsulted in the two weeks after the index consultation. However, fewer parents in the intervention group said that they would consult in the future should their child develop a similar illness. No significant differences were seen in terms of parental satisfaction, reassurance, enablement, or perception of the usefulness of any information received about their child’s illness.Strengths and limitations of the studyThis was an adequately powered randomised controlled trial. Practices were recruited from throughout Wales and England and were broadly representative of UK general practice. The results of this study are therefore likely to be highly generalisable to UK general practice. The stratified randomisation procedure helped ensure practices in both groups were similar in terms of size, location, and historical antibiotic prescribing rate. We achieved the target sample for both clusters (general practices) and patients, with a high follow-up rate.Cluster randomised designs can increase risk of selection bias. Our intervention was aimed not only at individual patients, but also at the primary care clinicians (through the online training). For this reason, an individually randomised trial was not possible: once trained in new consulting skills, clinicians cannot switch back to their untrained state. Selection bias can occur at the level of the cluster (that is, through differential dropout) or the individual. There were 11 practices in each arm of the trial that did not recruit any participants. Of these 22 practices, there were no significant differences in terms of list size, historical prescribing rate, or proportion located in Wales or England. We asked all participating clinicians to identify sequential eligible patients, and to record non-identifiable data for all those who were not recruited, in order to look for evidence of selection bias at the individual level. We found no important differences in the patients who were and were not recruited or between the patients who were not recruited by clinicians in the intervention and control groups. Similar recruitment rates in the two groups also suggest minimal selection bias.The non-significant difference in scores of parental enablement and usefulness of information received are surprising and seem inconsistent with the significant reduction in the proportion of parents stating that they would consult with a future similar illness. The patient enablement instrument was designed for first person use in routine general practice consultations and might not have been sensitive enough for measuring changes in parental enablement two weeks after the consultation.Clinicians in the control group might have altered their behaviour (towards providing more information than usual) as a result of their participation in the study, which could have attenuated any effect that changes in the behaviour of doctors in intervention practices might have had on parental satisfaction, enablement, and usefulness of information received. We are exploring the effects of the intervention on parental knowledge and beliefs in a qualitative process evaluation.Neither clinicians nor participants were blinded as to study group. As our intervention was directed partly at clinicians, a change in their behaviour was both expected and desirable. However, we need to distinguish between changes related to use of the intervention and changes associated with an awareness of being observed (Hawthorne effect). All participating clinicians were provided with information about the aims of the study. However, antibiotic use was listed fourth in a long list of outcome measures and is therefore unlikely to have resulted in meaningful changes in prescribing behaviour. The effect of the intervention on antibiotic prescribing was not modified by practice location, which, given that many practices in Wales would be aware of the research group’s interest in antibiotic prescribing, suggests that the Hawthorne effect was unlikely to be playing a significant part. Clinicians did not have any involvement in measuring outcomes, and are therefore unlikely to have contributed to ascertainment bias. Children (and their parents) were not blinded to treatment group. However, practices were instructed not to inform participants of the group to which they had been assigned before obtaining consent. Telephone interviewers were blinded to trial group in 93% of all interviews.Although we recognise that interventions are not always delivered as planned in pragmatic trials, we did not measure treatment fidelity because we wanted the assessors to remain blinded to study group where possible. However, suboptimum fidelity of intervention delivery is likely to dilute the treatment effect and therefore could have led to a type II error regarding reconsultations, but is unlikely to have led to a type I error regarding the positive findings.There were unequal numbers of nurses in the study groups (and thus patients recruited by nurses). We believe that this discrepancy was due to chance. We found no association between clinician’s profession (doctor or nurse) and either reconsultations or antibiotic prescribing, either at a univariate level or using multilevel modelling, and therefore believe that this did not have an important effect on our results.Comparison with other published workOur findings are consistent with those of Macfarlane and colleagues who found that the use of a leaflet on lower respiratory tract infection in adults resulted in a reduction in antibiotic use by nearly 25%.22 However, Macfarlane and colleagues have also shown a reduction in reconsultations from use of a leaflet,21 whereas our results did not show a significant reduction. This finding might be because the underlying reconsultation rate in our study was lower than that found by Macfarlane and lower than that used in our sample size calculation. This lower rate could indicate societal changes in knowledge or beliefs over time and might be much closer to a desirable level of reconsulting, and therefore more difficult to reduce. Certainly the 3.3% absolute difference found in our study was substantially smaller than the 10% reduction we had considered to be clinically important. Although we did not identify any studies that used a booklet designed specifically for use in consultations on respiratory tract infections in children, studies that have evaluated sending information booklets on minor illnesses to patients’ homes have generally found little effect on consultation rates.30\n31\n32\n33\n34 A recent study in the United States found that a sustained, multifaceted intervention, conducted over three years and aimed at reducing antibiotic prescribing in young children, resulted in minimal reduction in antibiotic use beyond underlying trends.35 The intervention in this study included several printed and web-based educational materials but did not encourage interactive use of the material within the consultation. Use of a leaflet for patients with lower respiratory tract infections resulted in an increase in reconsultations in the first month, and no significant difference in use of antibiotics or satisfaction.36 However, the leaflet in this study was brief, was not designed for interactive use in the consultation, and was provided in addition to verbal information about the natural history. A further United States study found that providing patients with a pack containing a pamphlet, a sticker, and a thermometer was associated with reduced consultation rates. However, this study was limited by non-random allocation and post allocation exclusion of patients.37 Another study where parents were randomised to receive written materials on either antibiotic use or injury prevention found no reduction in antibiotic use in the families who received the intervention.38 This finding could indicate the need to provide parents with positive messages (how best to manage the illness) rather than negative ones (don’t use antibiotics).Interpretation of the resultsWe found a statistically non-significant reduction in the proportion of children who reconsulted in the intervention group, which was considerably smaller than the 10% difference that was specified as clinically meaningful.We did demonstrate statistically and clinically significant reductions in antibiotic prescribing and consumption, which have important implications for policy makers, practitioners, and ultimately patients. How the reduction in prescribing was mediated is not yet clear, but it was possibly through a combined effect on clinician and parental behaviour. Clinicians probably recognised the importance of changing their prescribing behaviour and felt that they had the resources to effectively achieve this.A significant reduction in the proportion of parents who said that they would consult if their child had a similar illness in the future is encouraging and suggests that use of the intervention could have an effect on future consulting.No differences were recorded in terms of satisfaction, reassurance, value of information received, or parental enablement. Reassuringly, a high level of satisfaction was reported in the intervention group despite the significant reduction in antibiotic prescribing.The routine use of this intervention in primary care should now be considered along with other effective interventions such as delayed prescribing.39 The magnitude of the reduction in antibiotic prescribing achieved suggests that its use could have important implications for patients, and, as a result of the threat posed by increasing antimicrobial resistance, for public health. Furthermore, the booklet and online training could be produced and distributed fairly cheaply. Its use also seems to be safe and result in high levels of parental satisfaction. However, like any complex intervention, the precise elements that contributed to its effectiveness are unclear. The intervention not only provided parents with a take-home resource, it also aimed to modify the consultation process (especially communication within the consultation), which could have had an effect on consultation length. We are currently exploring which aspects of the intervention led to its effectiveness, the impact of its use on consultation length, its effects on long term consulting rates, and its economic impact. For example, we do not know if another booklet or leaflet on the same subject would result in a similar effect, or whether the training programme or the interactive use of the booklet was important. In the meantime, higher prescribing clinicians, or those who would like to reduce their prescribing but feel that they lack the tools to achieve this, might wish to consider use of this intervention.What is already known on this topicRespiratory tract infections in children are largely self limiting and benefit very little from antibiotic treatment. However, consultation rates continue to be high and antibiotics are still frequently prescribed.What this study addsProviding primary care clinicians with a carefully developed booklet on respiratory tract infections in children, and training in its use within the consultation, reduces antibiotic prescribing by around two thirds. Satisfaction among parents receiving this intervention was high, and no significant difference was found between those receiving the intervention and those receiving usual care.Use of this intervention seems to have little effect on reconsulting for the same illness episode, but does reduce future consulting intentions.Clinicians should consider the use of this intervention in routine consultations with children with respiratory tract infections.We are grateful to all the patients, parents, and clinicians who participated in the development of the intervention and the trial. We gratefully acknowledge the support received from the Primary Care Research Network and from all participating research networks. We thank the administrative staff in the Department of Primary Care and Public Health and the South-East Wales Trials Unit who worked hard to ensure the success of the study. We also gratefully acknowledge the contribution made by members of the independent trial steering committee.Contributors: CB conceived the study. NF, FW, CB, KH, and SS developed the intervention. CB, NF, KH, and SS wrote the protocol. All contributors sat on the study management group. NF managed the trial and NF and JN conducted the telephone interviews. NF wrote the first draft of the paper and all authors made subsequent contributions. NF is the guarantor.Funding: We gratefully acknowledge funding from the Medical Research Council and the Welsh Assembly Government in the form of a joint Health Services Fellowship for NF. Funding for the development of the training website was from an educational grant from Pfizer UK. The South-East Wales Trials Unit is funded by the Welsh Office for Research and Development. All authors declare that this work was conducted independently from the study funders.Sponsorship: This study was sponsored by Cardiff University.Competing interests: None declared.Ethical approval: This study was approved by the South East Wales Local Research Ethics Committee (Reference number 04/WSE04/109).1McCormick A, Fleming D, Charlton J. Morbidity statistics from general practice. Fourth national study 1991-1992. London: HMSO, 1995.2Butler CC, Robling M, Prout H, Hood K, Kinnersley P. Management of suspected acute viral upper respiratory tract infection in children with intranasal sodium cromoglicate: a randomised controlled trial. Lancet2002;359:2153-8.120909803Stott NC. Management and outcome of winter upper respiratory tract infections in children aged 0-9 years. BMJ1979;1:29-31.7609444Hollinghurst S, Gorst C, Fahey T, Hay AD, Hollinghurst S, Gorst C, et al. Measuring the financial burden of acute cough in pre-school children: a cost of illness study. BMC Family Practice2008;9:10.182374235Arroll B, Kenealy T. Antibiotics for the common cold and acute purulent rhinitis. Cochrane Database Syst Rev 2005;3:CD000247.160348506Del Mar C, Glasziou PP, Spinks A. Antibiotics for sore throat. Cochrane Database Syst Rev 2006;4:CD000023.170541267Smith SM, Fahey T, Smucny J, Becker Lorne A. Antibiotics for acute bronchitis. Cochrane Database Syst Rev2004;4:CD000245.154949948Spurling GKP, Fonseka K, Doust J, Del Mar C. Antibiotics for bronchiolitis in children. Cochrane Database Syst Rev2007;1:CD005189.172535459Glasziou PP, Del Mar C, Sanders S, Hayem M. Antibiotics for acute otitis media in children. Cochrane Database Syst Rev2004;1:CD000219.1497395110National Institute for Health and Clinical Excellence. Prescribing of antibiotics for self-limiting respiratory tract infections in adults and children in primary care (Clinical guideline 69). 2008. www.nice.org.uk/CG69.11Ashworth M, Charlton J, Cox K, Gulliford M, Latinovic R, Rowlands G. Why has antibiotic prescribing for respiratory illness declined in primary care? A longitudinal study using the General Practice Research Database. J Public Health2004;26:268-74.12Shapiro E. Injudicious antibiotic use: an unforeseen consequence of the emphasis on patient satisfaction? Clin Ther2002;24:197-204.1183383213Akkerman AE, van der Wouden JC, Kuyvenhoven MM, Dieleman JP, Verheij TJM. Antibiotic prescribing for respiratory tract infections in Dutch primary care in relation to patient age clinical entities. J Antimicrob Chemother2004;54:1116-21.1554697314Thompson PL, Spyridis N, Sharland M, Gilbert RE, Saxena S, Long PF, et al. Changes in clinical indications for community antibiotic prescribing for children in the UK from 1996-2006: will the new NICE prescribing guidance on upper respiratory tract infections be ignored? Arch Dis Child2008; 10.1136/adc.2008.147579.15Little P, Gould C, Williamson I, Warner G, Gantley M, Kinmonth AL. Reattendance and complications in a randomised trial of prescribing strategies for sore throat: the medicalising effect of prescribing antibiotics. BMJ1997;315:350-2.927045816Butler CC, Hillier S, Roberts Z, Dunstan F, Howard A, Palmer S. Antibiotic-resistant infections in primary care are symptomatic for longer and increase workload: outcomes for patients with E. coli UTIs. Br J Gen Pract2006;56:686-92.1695400117Cockburn J, Pit S. Prescribing behaviour in clinical practice: patients’ expectations and doctors’ perceptions of patients’ expectations—a questionnaire study. BMJ1997;315:520-3.932930818Kai J. What worries parents when their preschool children are acutely ill, and why: a qualitative study. BMJ1996;313:983-6.889242019Braun BL, Fowles JB. Characteristics and experiences of parents and adults who want antibiotics for cold symptoms. Arch Fam Med2000;9:589-95.1091030420McWilliams DB, Jacobson RM, Van Houten HK, Naessens JM, Ytterberg KL. A program of anticipatory guidance for the prevention of emergency department visits for ear pain. Arch Pediatr Adolesc Med2008;162:151-6.1825024021Macfarlane JT, Holmes WF, Macfarlane RM. Reducing reconsultations for acute lower respiratory tract illness with an information leaflet: A randomized controlled study of patients in primary care. Br J Gen Pract1997;47:719-22.951951822Macfarlane J, Holmes W, Gard P, Thornhill D, Macfarlane R, Hubbard R. Reducing antibiotic use for acute bronchitis in primary care: blinded, randomised controlled trial of patient information leaflet. BMJ2002;324:91-4.1178645423Butler CC, Rollnick S, Kinnersley P, Jones A, Stott N. Reducing antibiotics for respiratory tract symptoms in primary care: consolidating ‘why’ and considering ‘how’. Br J Gen Pract1998;48:1865-70.1019851224Butler CC, Rollnick S, Pill R, Maggs-Rapport F, Stott N. Understanding the culture of prescribing: Qualitative study of general practitioners’ and patients’ perceptions of antibiotics for sore throats. BMJ1998;317:637-42.972799225Butler CC, Kinnersley P, Hood K, Robling M, Prout H, Rollnick S, et al. Clinical course of acute infection of the upper respiratory tract in children: cohort study. BMJ2003;327:1088-9.1460493226Kallestrup P, Bro F. Parents’ beliefs and expectations when presenting with a febrile child at an out-of-hours general practice clinic. Br J Gen Pract2003;53:43-4.1256427627Francis N, Hood K, Simpson S, Wood F, Nuttall J, Butler C. The effect of using an interactive booklet on childhood respiratory tract infections in consultations: study protocol for a cluster randomised controlled trial in primary care. BMC Family Practice2008;9:23.1843585728Francis N, Wood F, Simpson S, Hood K, Butler CC. Developing an ‘interactive’ booklet on respiratory tract infections in children for use in primary care consultations. Patient Educ Couns2008;73:286-93.1872330629Howie J, Heaney D, Maxwell M, Walker J. A Comparison of a Patient Enablement Instrument (PEI) against two established satisfaction scales as an outcome measure of primary care consultations. Fam Pract1998;15:165-71.961348630Morrell DC, Avery AJ, Watkins CJ. Management of minor illness. BMJ1980;280:769-71.737065131Hansen BW. A randomized controlled trial on the effect of an information booklet for young families in Denmark. Patient Educ Couns1990;16:147-50.229076932Usherwood TP. Development and randomized controlled trial of a booklet of advice for parents. Br J Gen Pract1991;41:58-62.203173733Heaney D, Wyke S, Wilson P, Elton R, Rutledge P, Sommerville A, et al. Assessment of impact of information booklets on use of healthcare services: randomised controlled trial. BMJ2001;322:1-5.1114112834Little P, Somerville J, Williamson I, Warner G, Moore M, Wiles R, et al. Randomised controlled trial of self management leaflets and booklets for minor illness provided by post. BMJ2001;322:1214-7.1135877535Finkelstein JA, Huang SS, Kleinman K, Rifas-Shiman SL, Stille CJ, Daniel J, et al. Impact of a 16-community trial to promote judicious antibiotic use in Massachusetts. Pediatrics2008;121:e15-23.1816653336Little P, Rumsby K, Kelly J, Watson L, Moore M, Warner G, et al. Information leaflet and antibiotic prescribing strategies for acute lower respiratory tract infection: a randomized controlled trial. JAMA2005;293:3029-35.1597256537Roberts CR, Imrey PB, Turner JD, Hosokawa MC, Alster JM. Reducing physician visits for colds through consumer education. JAMA1983;250:1986-9.635296738Taylor JA, Kwan-Gett TSC, McMahon EM, Jr. Effectiveness of a parental educational intervention in reducing antibiotic use in children: a randomized controlled trial. Pediatr Infect Dis J2005;24:489-93.1593355639Spurling GKP, Del Mar C, Dooley L, Foxlee R. Delayed antibiotics for respiratory infections. Cochrane Database Syst Rev 2007;3:CD004417.17636757Cite this as: BMJ 2009;339:b2885', 'title': 'Effect of using an interactive booklet about childhood respiratory tract infections in primary care consultations on reconsulting and antibiotic prescribing: a cluster randomised controlled trial.', 'date': '2009-07-31'}}",1.0,"Public Health, Epidemiology & Health Systems"
 274,"Is primary patency rate higher, lower, or the same when comparing endovascular group and the surgery group?",no difference,moderate,no,['16102611'],31868929,2019,"{'16102611': {'article_id': '16102611', 'content': 'The aim of this prospective randomized study was to evaluate the relative risks and advantages of using the Hemobahn graft for popliteal artery aneurysm (PAA) treatment compared with open repair (OR). The primary end point was patency rate; secondary end points were hospital stay and length of surgical procedure.\nThe study was a prospective, randomized clinical trial carried out at a single center from January 1999 to December 2003. Inclusion criteria were an aneurysmal lesion in the popliteal artery with a diameter > or = 2 cm at the angio-computed tomography (CT) scan, and proximal and distal neck of the aneurysm with a length of > 1 cm to offer a secure site of fixation of the stent graft. Exclusion criteria were age < 50 years old, poor distal runoff, contraindication to antiplatelet, anticoagulant, or thrombolytic therapy, and symptoms of nerve and vein compression. The enrolled patients were thereafter prospectively randomized in a 1-to-1 ratio between OR (group A) or endovascular therapy (ET) (group B). The follow-up protocol consisted of duplex ultrasound scan and ankle-brachial index (ABI) measured during a force leg flexion at 1, 3, and 6 months. Group B patients underwent an angio-CT scan and plain radiography of the knee with leg flexion (> 120 degrees) at 6 and 12 months, and then yearly.\nBetween January 1999 and December 2003, 30 PAAs were performed: 15 OR (group A) and 15 ET (group B). Bypass and exclusion of the PAA was the preferred method of OR; no perioperative graft failure was observed. Twenty stent grafts were placed in 15 PAAs. Endograft thrombosis occurred in one patient (6.7%) in the postoperative period. The mean follow-up period was 46.1 months (range, 12 to 72 months) for group A and 45.9 months (range, 12 to 65 months) for group B. Kaplan-Meier analysis showed a primary patency rate of 100% at 12 months for OR and 86.7% at 12 months with a secondary patency rate of 100% at 12 and 36 months for ET. No statistical differences were observed at the log-rank test. The mean operation time (OR, 155.3 minutes; ET, 75.4 minutes) and hospital stay (OR, 7.7 days; ET, 4.3 days) were statistically longer for OR compared with ET (P < .01).\nWe can conclude, with the power limitation of the study, that PAA treatment can be safely performed by using either OR or ET. ET has several advantages, such as quicker recovery and shorter hospital stay.', 'title': 'Open repair versus endovascular treatment for asymptomatic popliteal artery aneurysm: results of a prospective randomized study.', 'date': '2005-08-17'}}",1.0,Surgery
 275,"Is visual acuity higher, lower, or the same when comparing part-time occlusion (with any necessary glasses) to glasses alone?",higher,,no,"['16751033', '15838439']",25051925,2014,"{'16751033': {'article_id': '16751033', 'content': 'To compare 2 hours of daily patching (combined with 1 hour of concurrent near visual activities) with a control group of spectacle wear alone (if needed) for treatment of moderate to severe amblyopia in children 3 to 7 years old.\nProspective randomized multicenter clinical trial (46 sites).\nOne hundred eighty children 3 to 7 years old with best-corrected amblyopic-eye visual acuity (VA) of 20/40 to 20/400 associated with strabismus, anisometropia, or both who had worn optimal refractive correction (if needed) for at least 16 weeks or for 2 consecutive visits without improvement.\nRandomization either to 2 hours of daily patching with 1 hour of near visual activities or to spectacles alone (if needed). Patients were continued on the randomized treatment (or no treatment) until no further improvement was noted.\nBest-corrected VA in the amblyopic eye after 5 weeks.\nImprovement in VA of the amblyopic eye from baseline to 5 weeks averaged 1.1 lines in the patching group and 0.5 lines in the control group (P = 0.006), and improvement from baseline to best measured VA at any visit averaged 2.2 lines in the patching group and 1.3 lines in the control group (P<0.001).\nAfter a period of treatment with spectacles, 2 hours of daily patching combined with 1 hour of near visual activities modestly improves moderate to severe amblyopia in children 3 to 7 years old.', 'title': 'A randomized trial to evaluate 2 hours of daily patching for strabismic and anisometropic amblyopia in children.', 'date': '2006-06-06'}, '15838439': {'article_id': '15838439', 'content': 'To plan a future randomized clinical trial, we conducted a pilot study to determine whether children randomized to near or non-near activities would perform prescribed activities. A secondary aim was to obtain a preliminary estimate of the effect of near versus non-near activities on amblyopic eye visual acuity, when combined with 2 hours of daily patching.\nSixty-four children, 3 to less than 7 years of age, with anisometropic, strabismic, or combined amblyopia (20/40 to 20/400) were randomly assigned to receive either 2 hours of daily patching with near activities or 2 hours of daily patching without near activities. Parents completed daily calendars for 4 weeks recording the activities performed while patched and received a weekly telephone call in which they were asked to describe the activities performed during the previous 2 hours of patching. Visual acuity was assessed at 4 weeks.\nThe children assigned to near visual activities performed more near activities than those assigned to non-near activities (by calendars, mean 1.6 +/- 0.5 hours versus 0.2 +/- 0.2 hours daily, P < 0.001; by telephone interviews, 1.6 +/- 0.4 hours versus 0.4 +/- 0.5 hours daily, P < 0.001). After 4 weeks of treatment, there was a suggestion of greater improvement in amblyopic eye visual acuity in those assigned to near visual activities (mean 2.6 lines versus 1.6 lines, P = 0.07). The treatment group difference in visual acuity was present for patients with severe amblyopia but not moderate amblyopia.\nChildren patched and instructed to perform near activities for amblyopia spent more time performing those near activities than children who were instructed to perform non-near activities. Our results suggest that performing near activities while patched may be beneficial in treating amblyopia. Based on our data, a formal randomized amblyopia treatment trial of patching with and without near activities is both feasible and desirable.', 'title': 'A randomized pilot study of near activities versus non-near activities during patching therapy for amblyopia.', 'date': '2005-04-20'}}",0.5,Other
-276,"Is overall survival higher, lower, or the same when comparing Ppost-mastectomy radiotherapy (PMRT) to control?",higher,moderate,no,['17306393'],37327075,2023,"{'17306393': {'article_id': '17306393', 'content': 'Numerous consensus reports recommend that postmastectomy radiotherapy (RT) in addition to systemic therapy is indicated in high-risk patients with 4+ positive nodes, but not in patients with 1-3 positive nodes. A subgroup analysis of the DBCG 82 b&c trials was performed to evaluate the loco-regional recurrence rate and survival in relation to number of positive nodes.\nIn the DBCG 82 b&c trials 3083 pre- and postmenopausal high-risk women were randomized to postoperative RT in addition to adjuvant systemic therapy. Since many patients had relatively few lymph nodes removed (median 7), the present analysis was limited to 1152 node positive patients with 8 or more nodes removed.\nThe overall 15-year survival rate in the subgroup was 39% and 29% (p=0.015) after RT and no RT, respectively. RT reduced the 15-year loco-regional failure rate from 51% to 10% (p<0.001) in 4+ positive node patients and from 27% to 4% (p<0.001) in patients with 1-3 positive nodes. Similarly, the 15-year survival benefit after RT was significantly improved in both patients with 1-3 positive nodes (57% vs 48%, p=0.03) and in patients with 4+ positive nodes (21% vs 12%, p=0.03).\nThe survival benefit after postmastectomy RT was substantial and similar in patients with 1-3 and 4+ positive lymph nodes. Furthermore, it was not strictly associated with the risk of loco-regional recurrence, which was most pronounced in patients with 4+ positive nodes. The indication for RT seems therefore to be at least equally beneficial in patients with 1-3 positive nodes, and future consensus should be modified accordingly.', 'title': 'Is the benefit of postmastectomy irradiation limited to patients with four or more positive nodes, as recommended in international consensus reports? A subgroup analysis of the DBCG 82 b&c randomized trials.', 'date': '2007-02-20'}}",1.0,Oncology & Hematology
+276,"Is overall survival higher, lower, or the same when comparing post-mastectomy radiotherapy (PMRT) to control?",higher,moderate,no,['17306393'],37327075,2023,"{'17306393': {'article_id': '17306393', 'content': 'Numerous consensus reports recommend that postmastectomy radiotherapy (RT) in addition to systemic therapy is indicated in high-risk patients with 4+ positive nodes, but not in patients with 1-3 positive nodes. A subgroup analysis of the DBCG 82 b&c trials was performed to evaluate the loco-regional recurrence rate and survival in relation to number of positive nodes.\nIn the DBCG 82 b&c trials 3083 pre- and postmenopausal high-risk women were randomized to postoperative RT in addition to adjuvant systemic therapy. Since many patients had relatively few lymph nodes removed (median 7), the present analysis was limited to 1152 node positive patients with 8 or more nodes removed.\nThe overall 15-year survival rate in the subgroup was 39% and 29% (p=0.015) after RT and no RT, respectively. RT reduced the 15-year loco-regional failure rate from 51% to 10% (p<0.001) in 4+ positive node patients and from 27% to 4% (p<0.001) in patients with 1-3 positive nodes. Similarly, the 15-year survival benefit after RT was significantly improved in both patients with 1-3 positive nodes (57% vs 48%, p=0.03) and in patients with 4+ positive nodes (21% vs 12%, p=0.03).\nThe survival benefit after postmastectomy RT was substantial and similar in patients with 1-3 and 4+ positive lymph nodes. Furthermore, it was not strictly associated with the risk of loco-regional recurrence, which was most pronounced in patients with 4+ positive nodes. The indication for RT seems therefore to be at least equally beneficial in patients with 1-3 positive nodes, and future consensus should be modified accordingly.', 'title': 'Is the benefit of postmastectomy irradiation limited to patients with four or more positive nodes, as recommended in international consensus reports? A subgroup analysis of the DBCG 82 b&c randomized trials.', 'date': '2007-02-20'}}",1.0,Oncology & Hematology
 277,"Is the enrollment of children into health insurance higher, lower, or the same when comparing people offered health insurance information to control?",higher,moderate,no,['16322168'],25425010,2014,"{'16322168': {'article_id': '16322168', 'content': ""Lack of health insurance adversely affects children's health. Eight million US children are uninsured, with Latinos being the racial/ethnic group at greatest risk for being uninsured. A randomized, controlled trial comparing the effectiveness of various public insurance strategies for insuring uninsured children has never been conducted.\nTo evaluate whether case managers are more effective than traditional methods in insuring uninsured Latino children.\nRandomized, controlled trial conducted from May 2002 to August 2004.\nA total of 275 uninsured Latino children and their parents were recruited from urban community sites in Boston.\nUninsured children were assigned randomly to an intervention group with trained case managers or a control group that received traditional Medicaid and State Children's Health Insurance Program (SCHIP) outreach and enrollment. Case managers provided information on program eligibility, helped families complete insurance applications, acted as a family liaison with Medicaid/SCHIP, and assisted in maintaining coverage.\nObtaining health insurance, coverage continuity, the time to obtain coverage, and parental satisfaction with the process of obtaining insurance for children were assessed. Subjects were contacted monthly for 1 year to monitor outcomes by a researcher blinded with respect to group assignment.\nOne hundred thirty-nine subjects were assigned randomly to the intervention group and 136 to the control group. Intervention group children were significantly more likely to obtain health insurance (96% vs 57%) and had approximately 8 times the adjusted odds (odds ratio: 7.78; 95% confidence interval: 5.20-11.64) of obtaining insurance. Seventy-eight percent of intervention group children were insured continuously, compared with 30% of control group children. Intervention group children obtained insurance significantly faster (mean: 87.5 vs 134.8 days), and their parents were significantly more satisfied with the process of obtaining insurance.\nCommunity-based case managers are more effective than traditional Medicaid/SCHIP outreach and enrollment in insuring uninsured Latino children. Case management may be a useful mechanism to reduce the number of uninsured children, especially among high-risk populations."", 'title': 'A randomized, controlled trial of the effectiveness of community-based case management in insuring uninsured Latino children.', 'date': '2005-12-03'}}",1.0,"Public Health, Epidemiology & Health Systems"
 278,"Is clinical pregancy rate higher, lower, or the same when comparing the group who underwent ovarian cyst aspiration to the conservatively managed group?",no difference,low,no,"['2120087', '16253965']",25502626,2014,"{'2120087': {'article_id': '2120087', 'content': 'This study was designed to ascertain whether any benefit would be derived from aspirating ovarian cysts identified before ovarian stimulation in patients undergoing in vitro fertilization. Thirty-seven patients who had ovarian cysts were categorized into two groups: group A (n = 14) with baseline ovarian cysts and group B (n = 23) with ovarian cysts that developed during pituitary suppression with the gonadotropin-releasing hormone analog. Each group was prospectively randomized into two subgroups depending on whether the ovarian cysts were aspirated or not. In group A, there was a significantly greater number of follicles and oocytes in the ovaries in which cysts were aspirated. However, there was no significant difference in the total number of follicles, oocytes retrieved and fertilized, or in the final outcome. In group B, there was no significant difference in folliculogenesis between the aspirated and nonaspirated subgroups. These observations suggest that the presence of a baseline ovarian cyst may reduce folliculogenesis but do not support routine cyst aspiration if the patient has two functional ovaries.', 'title': 'Ovarian cyst aspiration and the outcome of in vitro fertilization.', 'date': '1990-10-01'}, '16253965': {'article_id': '16253965', 'content': 'The formation of functional ovarian cysts has been recognized as one of the side effects of GnRH agonist administration. The formation of cysts during IVF treatment may be of no clinical significance or may negatively influence its outcome. The objective of this study was to determine the incidence of ovarian cyst formation following GnRH agonist administration and to examine their effect on IVF outcome.\nA prospective study of 1317 IVF patients who developed one or more functional ovarian cysts of >or=15 mm following GnRH agonist treatment was performed. Transvaginal ultrasonographic-guided cyst aspiration was carried out in 76 randomly allocated patients out of 122 patients who were found to have functional ovarian cysts before starting ovarian stimulation with gonadotropins.\nThe incidence of follicular cyst formation was 9.3%. Cyst cycles in comparison with non-cyst cycles had significantly elevated day 3 basal FSH (mean+/-SD of 8.3+/-3.2 versus 5.3+/-2.6 mIU/ml, P<0.05) and required more ampoules of gonadotropins (46.3+/-16.5 versus 35+/-14.6, P<0.01). Furthermore, they showed a statistically significant decrease in the quality and number of oocytes retrieved, fertilization rate, number and quality of embryos, implantation and pregnancy rates, with a significant increase in cancellation and abortion rates. Patients with bilateral cysts had a significantly lower number of oocytes and embryos retrieved, with a lower proportion of metaphase II oocytes. They also had a higher proportion of poor quality embryos. Cyst aspiration was not associated with a significant difference in the above parameters.\nThe incidence of cyst formation during GnRH agonist treatment is lower than previously reported. In such cases, the quality of oocytes and embryos were significantly compromised, with a significant increase in the cycle cancellation rate and a decrease in the implantation and pregnancy rates. Neither conservative management nor cyst aspiration improved the IVF outcome.', 'title': 'Ovarian cyst formation following GnRH agonist administration in IVF cycles: incidence and impact.', 'date': '2005-10-29'}}",0.5,Obstetrics & Gynecology
 279,"Is the incidence rate of Frey's syndrome higher, lower, or the same when comparing sternocleidomastoid muscle flap procedure to control?",no difference,low,no,"['12464202', '15871587']",31578708,2019,"{'12464202': {'article_id': '12464202', 'content': ""Parotid surgery can cause postoperative facial nerve dysfunction, cosmetic impairment, and Frey's syndrome. Thirty-six patients listed for superficial parotidectomy were entered into a prospective randomised trial to find out if the use of a sternocleidomastoid flap could reduce the incidence of these complications. Partial facial nerve paresis was seen at 3 months in five patients in whom flaps were raised compared with six among those who did not have flaps (P=0.025). There was no difference between the two groups at 1 year. The flap was not associated with an improvement in either subjective (P=0.13) or objective (P=0.12) appearance measured on visual analogue scales. Eight patients in whom flaps were raised described symptoms suggestive of Frey's syndrome, compared with nine patients in whom a flap was not raised (P=0.31). Overall 19 of those who had a flap and 11 of those who did not had a positive starch-iodine test (P=0.21)."", 'title': 'Prospective randomised trial of the benefits of a sternocleidomastoid flap after superficial parotidectomy.', 'date': '2002-12-05'}, '15871587': {'article_id': '15871587', 'content': ""We studied the incidence of Frey's syndrome and facial contour deformity in two groups of patients who had undergone superficial parotidectomy. One group was made up of 12 patients who were randomized to undergo reconstruction of the surgical defect with a sternocleidomastoid muscle flap; the other 12 patients did not receive a flap. All 24 patients were evaluated via a short questionnaire, the starch-iodine test, and a visual examination. On the questionnaire, none of the 24 patients said they experienced abnormal facial sweating, flushing, or warmth while eating, although 6 of the 12 patients in the nonflap group had a mildly positive starch-iodine test. No patient in the flap group had a positive test. The difference between the two groups was statistically significant (p < 0.05). No statistically significant difference was seen between the two groups with respect to cosmetic results."", 'title': ""Sternocleidomastoid muscle flap reconstruction during parotidectomy to prevent Frey's syndrome and facial contour deformity."", 'date': '2005-05-06'}}",0.5,Surgery