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

Abstract

Purpose: 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.

Methods: 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).

Results: 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).

Conclusion: 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.