Risk factor-based models to predict severe retinopathy of prematurity in preterm Thai infants

Abstract

Purpose: To develop prediction models for severe retinopathy of prematurity (ROP) based on risk factors in preterm Thai infants to reduce unnecessary eye examinations in low-risk infants. Methods: This retrospective cohort study included preterm infants screened for ROP in a tertiary hospital in Bangkok, Thailand, between September 2009 and December 2020. A predictive score model and a risk factor-based algorithm were developed based on the risk factors identified by a multivariate logistic regression analysis. Validity scores, and corresponding 95% confidence intervals (CIs), were reported. Results: The mean gestational age and birth weight (standard deviation) of 845 enrolled infants were 30.3 (2.6) weeks and 1264.9 (398.1) g, respectively. The prevalence of ROP was 26.2%. Independent risk factors across models included gestational age, birth weight, no antenatal steroid use, postnatal steroid use, duration of oxygen supplementation, and weight gain during the first 4 weeks of life. The predictive score had a sensitivity (95% CI) of 92.2% (83.0, 96.6), negative predictive value (NPV) of 99.2% (98.1, 99.6), and negative likelihood ratio (NLR) of 0.1. The risk factor-based algorithm revealed a sensitivity of 100% (94, 100), NPV of 100% (99, 100), and NLR of 0. Similar validity was observed when “any oxygen supplementation” replaced “duration of oxygen supplementation.” Predictive score, unmodified, and modified algorithms reduced eye examinations by 71%, 43%, and 16%, respectively. Conclusions: Our risk factor-based algorithm offered an efficient approach to reducing unnecessary eye examinations while maintaining the safety of infants at risk of severe ROP. Prospective validation of the model is required.