Development and Validation of a Prenatal Prediction Model for Neonatal Hyperbilirubinemia Based on Maternal Factors: Revealing Heterogeneous Risk Profiles Across Gestational Age Subgroups

Background: Neonatal hyperbilirubinemia is common, and current risk assessment depends largely on postnatal monitoring. A prediction model using only antenatal maternal factors could enable earlier identification, especially if it accounts for differences across gestational age (GA) subgroups. Methods: This single-center case-control study enrolled 1967 subjects. After screening for significant differences in baseline characteristics and excluding multicollinearity, independent predictors were identified via univariate and multivariate logistic regression. Several machine learning models were developed and evaluated based on their area under the curve (AUC), sensitivity, specificity, accuracy, etc. Interaction analysis was performed, and subgroup-specific models were built for early-term and full-term subgroups. The SHAP analysis was employed to rank feature importance. Results: Key independent antenatal predictors included GA, mode of delivery, maternal hypothyroidism, infection, mean corpuscular hemoglobin concentration (MCHC), alkaline phosphatase (AKP), albumin (ALB), and total bilirubin (TBIL). A significant interaction was found between GA and ALB ( P for interaction=0.004). Logistic regression was selected as the optimal model (AUC: 0.661[0.617– 0.704]) for its generalizability and clinical utility. Subgroup analysis revealed distinct risk factor patterns; for instance, the SHAP analysis indicated that ALB and mode of delivery were the two most significant predictive factors in the full-term group, while mode of delivery and platelets were the key factors in the early-term group. Conclusion: This study developed a predictive model for neonatal hyperbilirubinemia using an exploratory analysis of routine antenatal maternal factors. We demonstrated that GA significantly modified risk profiles, necessitating stratified assessment. However, the clinical relevance of certain non-traditional laboratory predictors requires further validation.