WINROP algorithm for prediction of sight threatening retinopathy of prematurity: Initial experience in Indian preterm

Gaurav Sanghi1, Anil Narang2, Sunny Narula3

  • 1Department of Vitreo-Retina, Sangam Netralaya, Mohali, Punjab, India.

Insights

The WINROP tool shows high sensitivity (90.32%) in detecting sight-threatening retinopathy of prematurity (ROP) in Indian preterm infants. Further algorithm refinement may enhance its clinical utility for ophthalmologists and neonatologists.

Area of Science:

  • Ophthalmology
  • Neonatology
  • Medical Technology

Background:

  • Retinopathy of prematurity (ROP) is a leading cause of blindness in preterm infants.
  • Early detection and treatment are crucial for preventing vision loss.
  • The WINROP tool utilizes weight gain patterns to predict ROP risk.

Purpose of the Study:

  • To evaluate the efficacy of the WINROP online monitoring tool.
  • To assess WINROP's ability to detect sight-threatening Type 1 ROP in Indian preterm infants.

Main Methods:

  • Seventy preterm infants (<32 weeks gestation) were enrolled.
  • Birth weight, gestational age, and weekly weight data were input into the WINROP algorithm.
  • Sensitivity, specificity, positive, and negative predictive values were calculated.

Main Results:

  • 31 (44.28%) infants developed Type 1 ROP.
  • WINROP signaled an alarm in 74.28% of all infants and 90.32% of those with Type 1 ROP.
  • The negative predictive value was 83.3%, while specificity was 38.46%.

Conclusions:

  • WINROP demonstrated high sensitivity in detecting Type 1 ROP in this Indian cohort.
  • The study is the first in India to use a weight gain-based algorithm for ROP prediction.
  • Population-specific algorithm adjustments could improve WINROP's clinical utility.
Abstract

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