# Retinopathy of prematurity: Accuracy of ROPScore and WINROP algorithms in a Brazilian population

## TL;DR

This study evaluates how well two algorithms predict severe retinopathy of prematurity in preterm Brazilian infants, finding one algorithm highly effective.

## Contribution

The study provides novel validation of ROPScore's performance in a Brazilian preterm infant population.

## Key findings

- ROPScore demonstrated 100% sensitivity and 100% negative predictive value for severe retinopathy of prematurity.
- WINROP had 78.9% sensitivity and 97.2% negative predictive value but lower specificity compared to ROPScore.
- ROPScore's high accuracy suggests it could optimize screening and reduce unnecessary exams in Brazil.

## Abstract

To assess the sensitivity and specificity of the retinopathy of prematurity
score (ROPScore) and weight, insulin-like growth factor-1, retinopathy of
prematurity algorithm in predicting the risk of developing severe
retinopathy of prematurity (prethreshold type 1) in a sample of preterm
infants in Brazil.

Retrospective analysis of medical records of preterm infants (n=288) with
birth weight of ≤1500 g and/or gestational age of 23-32 weeks in a
neonatal unit in Southern Brazil from May 2013 to December 2020 (92
months).

The incidence of confirmed severe retinopathy of prematurity was 6.6%.
ROPScore showed a 100% sensitivity, 44.6% specificity (95% confidence
interval [CI] 38.7-50.6), 11.3% positive predictive value (95% CI 6.5-16.1),
and 100% negative predictive value in predicting severe retinopathy of
prematurity. The weight, insulin-like growth factor-1, retinopathy of
prematurity algorithm demonstrated a 78.9% sensitivity (95% CI 60.6-97.3),
51.3% specificity (95% CI 45.3-57.3), 10.3% positive predictive value (95%
CI 5.3-15.2), and 97.2% negative predictive value (95% CI 94.5-99.9).

ROPScore identified all patients at risk for severe retinopathy of
prematurity. These findings support incorporating ROPScore into Brazilian
guidelines to optimize retinopathy of prematurity screening and reduce
unnecessary ophthalmologic examinations. Weight, insulin-like growth
factor-1, retinopathy of prematurity’s suboptimal performance in this
Brazilian sample highlights the need for country-specific algorithm
adjustments.