Retinopathy of prematurity (ROP), the leading cause of preventable childhood blindness worldwide, is traditionally detected by eye examinations performed by ophthalmologists on infants at risk for ROP. Because of the low diagnostic yield of these examinations for identifying infants that require treatment of ROP, various statistical prediction models have been developed to identify infants at high ROP risk who require frequent eye examinations and infants at low risk who require less-frequent or no ROP examinations.1 These prediction models use statistical modeling approaches of various complexity, usually including birth weight (BW), gestational age (GA), and postnatal factors, such as oxygen exposure or postnatal weight gain. The model performance is usually evaluated using sensitivity, specificity, or the reduction in number of infants examined for detecting the ROP outcome of interest (eg, severe ROP, ROP requiring treatment, type 1 ROP). Most prediction models were developed from a small numbers of infants who have the ROP outcomes of interest, potentially leading to overfitting or optimistic estimates of model performance. When applying prediction models to an independent cohort through external validation, their performance usually becomes poorer. Because no prediction model works well universally, research on developing and validating robust ROP prediction models for clinical use continues to be of interest.
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Ying G. A Prediction Model for Retinopathy of Prematurity—Is It Ready for Prime Time? JAMA Ophthalmol. 2020;138(1):29–30. doi:10.1001/jamaophthalmol.2019.4608
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