Abstract Purpose To develop and validate time-updated, corrected gestational age (CGA)–specific prediction models for identifying treatment-requiring retinopathy of prematurity (ROP) using routinely available systemic factors, and to assess longitudinal changes in predictor combinations and model performance with advancing CGA. Study design Retrospective cohort study. Methods Preterm infants who met institutional ROP screening criteria from a single tertiary center were included, excluding those with aggressive ROP. Multivariable logistic regression models were independently developed at CGA 28, 30, 32, and 34 weeks using routinely available systemic variables summarized from birth to each CGA. For each CGA-specific analysis, infants treated before the target CGA were excluded. Exhaustive subset selection based on the Akaike information criterion identified CGA-specific optimal predictor sets. Model performance was evaluated using stratified five-fold cross-validation with out-of-fold predictions, and discrimination was assessed by the area under the receiver operating characteristic curve (AUC). Results Predictor combinations selected by the optimal models differed across CGAs, indicating longitudinal changes in the predictive relevance of systemic factors. Growth-related and inflammatory indices were repeatedly selected, while overall predictor composition evolved with advancing CGA. Discrimination improved at later CGAs, with cross-validated AUCs of 0.650, 0.716, 0.809, and 0.842 at CGA 28, 30, 32, and 34 weeks, respectively. Conclusion These findings suggest that CGA-specific prediction models may help characterize the evolving risk of treatment-requiring ROP. However, prospective multicenter validation and further refinement are required before clinical application.
Sugimoto et al. (Sat,) studied this question.