Abstract / Summary
Background/Objectives: Retinopathy of prematurity (ROP) screening must balance timely detection with feasibility, particularly in resource-limited settings. We aimed to develop a pragmatic, locally derived prediction model to support prioritization of ophthalmologic assessment among Thai neonates already eligible for ROP screening. Methods: We conducted a retrospective cohort study of neonates undergoing their first planned ROP screening examination at Chiang Rai Prachanukroh Hospital (2011–2015) and Lampang Hospital (2021–2022). Prespecified predictors included gestational age, birth weight, multiple pregnancy, respiratory distress syndrome, neonatal anemia, neonatal sepsis, necrotizing enterocolitis, bronchopulmonary dysplasia, duration of oxygen exposure, and duration of mechanical ventilation. A multivariable logistic regression model was developed. Performance was evaluated using discrimination, calibration, decision curve analysis, and bootstrap internal validation with 1000 resamples. Results: Among 681 infants, 50 (7.3%) had any ROP detected at the index examination. The apparent area under the receiver operating characteristic curve was 0.901 (95% CI, 0.856–0.946), and the optimism-corrected estimate was 0.883 (bootstrap 95% CI, 0.841–0.928). Optimism-corrected calibration-in-the-large was 0.008, the expected-to-observed ratio was 1.005, and the calibration slope was 0.866. At the sensitivity-constrained exploratory cut-off of 1.5%, sensitivity was 96.0% and specificity was 53.4%. The observed ROP risk was 14.0% in the higher-risk group and 0.6% in the lower-risk group. Decision curve analysis suggested net benefit across threshold probabilities up to 0.20. Conclusions: The model showed good discrimination and may support risk stratification and prioritization among infants already eligible for ROP screening. The 1.5% cut-off remains exploratory, and independent external validation, preferably in prospective contemporary cohorts, is required before clinical implementation.