title: The use of the WINROP screening algorithm for the prediction of retinopathy of prematurity in a Chinese population.
abstract: BACKGROUND
  Retinopathy of prematurity (ROP) is a gestational age (GA)-related illness that can lead to blindness in premature infants. Timely screening of premature infants could improve visual prognosis.
  
  
  OBJECTIVE
  To evaluate the WINROP algorithm as a method of predicting severe ROP in a Chinese population.
  
  
  METHODS
  590 infants with a GA <32 weeks were entered into an online surveillance system (www.winrop.com) that included ROP evaluations and weekly weight measurements from birth to a corrected GA of 40 weeks. If the rate of weight gain decreased to a certain degree, the algorithm signaled an alarm that the infant was at risk for developing sight-threatening ROP. Each infant was categorized as having no, mild, or severe ROP.
  
  
  RESULTS
  Among the 590 infants with a GA <32 weeks, an alarm was triggered in 85 infants (14.4%), 50 of which developed severe ROP and were identified in this alarm group. Twenty-seven infants triggered the alarm signal in the first week after birth and 7 infants triggered the alarm at birth. Seven of the infants developed proliferative ROP and the median GA at birth for these infants was 31 weeks.
  
  
  CONCLUSIONS
  The WINROP system had a sensitivity of 87.5% in a Chinese population for the early identification of infants that developed severe ROP. Postnatal weight gain may help predict ROP in lower birth weight infants.
authors.affiliation: Department of Pediatrics, Zhengzhou Children's Hospital, Zhengzhou, China.
authors.forename: Huiqing
authors.initials: H
authors.lastname: Sun
authors.forename: Wengqing
authors.initials: W
authors.lastname: Kang
authors.forename: Xiuyong
authors.initials: X
authors.lastname: Cheng
authors.forename: Chao
authors.initials: C
authors.lastname: Chen
authors.forename: Hong
authors.initials: H
authors.lastname: Xiong
authors.forename: Jing
authors.initials: J
authors.lastname: Guo
authors.forename: Chongchen
authors.initials: C
authors.lastname: Zhou
authors.forename: Yinghui
authors.initials: Y
authors.lastname: Zhang
authors.forename: Ann
authors.initials: A
authors.lastname: Hellström
authors.forename: Chatarina
authors.initials: C
authors.lastname: Löfqvist
authors.forename: Changlian
authors.initials: C
authors.lastname: Zhu
mesh_terms: D000465:Algorithms; D044466:Asian People; D015415:Biomarkers / Q000097:blood; D001724:Birth Weight; D002681:China / Q000453:epidemiology; D003936:Diagnosis, Computer-Assisted; D005260:Female; D005865:Gestational Age; D006801:Humans; D007230:Infant, Low Birth Weight; D007231:Infant, Newborn; D007234:Infant, Premature; D007334:Insulin-Like Growth Factor I / Q000378:metabolism; D020407:Internet; D008297:Male; D015997:Neonatal Screening / Q000379:methods*; D011237:Predictive Value of Tests; D011379:Prognosis; D012178:Retinopathy of Prematurity / Q000097:blood / Q000175:diagnosis* / Q000208:ethnology; D018570:Risk Assessment; D012307:Risk Factors; D012720:Severity of Illness Index; D015430:Weight Gain
chemical_list: D015415:Biomarkers; C583557:IGF1 protein, human; D007334:Insulin-Like Growth Factor I