XGBoost prediction of adverse neurodevelopmental outcomes in hypoxic-ischemic encephalopathy neonates.

Early prediction of neurodevelopmental (ND) impairment in neonates with hypoxic-ischemic encephalopathy (HIE) is essential for timely intervention, particularly in infants treated with therapeutic hypothermia (TH). We developed an Extreme Gradient Boosting-based prediction model using neonatal brain magnetic resonance imaging volumetrics and clinical features in 89 full-term HIE infants treated with TH. ND impairment was defined as a Bayley-III composite score <85 at 18 to 24 months. Model performance was evaluated using stratified 5-fold cross-validation, and interpretability was assessed using SHapley Additive exPlanations. The model achieved strong performance (mean area under the receiver operating characteristic curve of 0.952, sensitivity of 0.800, and specificity of 0.914). Key predictors included magnetic resonance imaging severity, regional brain volume reductions, lower 5-minute Apgar scores, and higher Gross Motor Function Classification System levels. SHapley Additive exPlanations demonstrated patient-specific risk profiles. This interpretable AI model enables accurate, individualized early risk stratification of adverse ND outcomes in term HIE infants treated with TH.