title: Postnatal Growth and Retinopathy of Prematurity Study: Rationale, Design, and Subject Characteristics.
abstract: Postnatal-growth-based predictive models demonstrate strong potential for improving the low specificity of retinopathy of prematurity (ROP) screening. Prior studies are limited by inadequate sample size. We sought to study a sufficiently large cohort of at-risk infants to enable development of a model with highly precise estimates of sensitivity for severe ROP.
  
  
  
  The Postnatal Growth and ROP (G-ROP) Study was a multicenter retrospective cohort study of infants at 30 North American hospitals during 2006-2012. A total of 65 G-ROP-certified abstractors submitted data to a secure, web-based database. Data included ROP examination findings, treatments, complications, daily weight measurements, daily oxygen supplementation, maternal/infant demographics, medical comorbidities, surgical events, and weekly nutrition. Data quality was monitored with system validation rules, data audits, and discrepancy algorithms.
  
  
  
  Of 11,261 screened infants, 8334 were enrolled, and 2927 had insufficient data due to transfer, discharge, or death. Of the enrolled infants, 90% (7483) had a known ROP outcome and were included in the study. Median birth weight was 1070 g (range 310-3000g) and mean gestational age 28 weeks (range 22-35 weeks). Severe ROP (Early Treatment of Retinopathy type 1 or 2) developed in 931 infants (12.5%).
  
  
  
  Successful incorporation of a predictive model into ROP screening requires confidence that it will capture cases of severe ROP. This dataset provides power to estimate sensitivity with half-confidence interval width of less than 0.5%, determined by the high number of severe ROP cases. The G-ROP Study represents a large, diverse cohort of at-risk infants undergoing ROP screening. It will facilitate evaluation of growth-based algorithms to improve efficiency of ROP screening.
authors.affiliation: a The Children's Hospital of Philadelphia , Philadelphia , PA , USA.|b Scheie Eye Institute , Perelman School of Medicine at the University of Pennsylvania , Philadelphia , PA , USA.
authors.forename: Gil
authors.initials: G
authors.lastname: Binenbaum
authors.affiliation: a The Children's Hospital of Philadelphia , Philadelphia , PA , USA.
authors.forename: Lauren A
authors.initials: LA
authors.lastname: Tomlinson
mesh_terms: D000465:Algorithms; D001724:Birth Weight; D005260:Female; D005865:Gestational Age; D006801:Humans; D007231:Infant, Newborn; D007234:Infant, Premature; D008297:Male; D008954:Models, Biological; D015997:Neonatal Screening; D011237:Predictive Value of Tests; D012178:Retinopathy of Prematurity / Q000175:diagnosis* / Q000503:physiopathology; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D015430:Weight Gain
keywords: Predictive model; prematurity; retinopathy of prematurity; screening