Assessment of WINROP algorithm as screening tool for preterm infants in Manitoba to detect retinopathy of prematurity

Ebtihal Ali1,2, Nasser Al-Shafouri3, Abrar Hussain2

  • 1Department of Community Health Sciences, University of Manitoba, Winnipeg, Manitoba.

Paediatrics & Child Health
|February 27, 2018
PubMed

Insights

The WINROP algorithm showed 90% sensitivity but only 60% specificity for detecting vision-threatening retinopathy of prematurity. This computer-based tool requires reassessment for clinical use in preterm infants.

Area of Science:

  • Neonatal care
  • Ophthalmology
  • Medical informatics

Background:

  • Early detection of retinopathy of prematurity (ROP) is crucial for preventing blindness in premature infants.
  • The WINROP algorithm, a computer-based tool, analyzes postnatal weight gain trends to predict ROP development.
  • Less invasive ROP detection methods are needed.

Purpose of the Study:

  • To evaluate the sensitivity and specificity of the WINROP algorithm for detecting vision-threatening ROP.
  • To assess the clinical utility of the WINROP algorithm in a Canadian neonatal intensive care unit population.

Main Methods:

  • Retrospective chart review of 215 preterm infants (<32 weeks gestation) from January 2008 to December 2013.
  • Infants' weekly body weight data were entered into the WINROP algorithm.
  • Paediatric ophthalmologist screening for ROP was used as the reference standard.

Main Results:

  • The WINROP algorithm demonstrated a sensitivity of 90% (P=0.021) for detecting vision-threatening ROP.
  • The specificity of the WINROP algorithm was found to be 60% (P=0.002).
  • Mean gestational age was 28.6 ± 1.8 weeks and mean birth weight was 1244 ± 294 g.

Conclusions:

  • The WINROP algorithm's sensitivity is insufficient for current clinical application in this population.
  • Further reassessment of the WINROP algorithm in contemporary infant populations is recommended.
  • The study highlights the need for improved predictive tools for ROP.
Abstract

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