ArticleAnnals of medicine2026
Improving early risk stratification for bronchopulmonary Dysplasia in preterm infants using machine learning: development and external validation.
Article in Annals of medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundDespite advances in perinatal care and improved survival of preterm infants, bronchopulmonary dysplasia (BPD) remains a major neonatal morbidity with long-term consequences. Accurate early risk prediction is therefore clinically important. Conventional models may be limited in capturing the complex and nonlinear interactions underlying BPD, whereas machine learning offers greater flexibility for high-dimensional clinical data. However, limited interpretability remains a barrier to clinical implementation.
methodThis retrospective study included 1,083 preterm infants with gestational age <32 weeks and birth weight <2,500 g admitted to the Affiliated Women's Hospital of Jiangnan University from January 2011 to December 2025. Data were split 7:3 into training and validation sets. Seven models were evaluated, with hyperparameters optimized by 10-fold cross-validation using AUC. Missing data were handled by complete case analysis. Performance was assessed using AUC-ROC, Brier score, and decision curve analysis (DCA), while SHAP and partial dependence plots (PDP) were used for interpretation. The optimal model was externally validated in an independent cohort of 1,000 neonates.
resultGBM showed the best internal performance, with an AUC of 0.880, accuracy of 0.793, sensitivity of 0.827, specificity of 0.759, and Brier score of 0.141. Birth weight, gestational age, and 1-minute Apgar score were the leading predictors. Neonatal anemia was strongly associated with BPD (aOR = 5.036, 95% CI: 3.35-7.57). PDP demonstrated a nonlinear association between birth weight and BPD risk, with a sharp increase between 1,250 and 1,500 g. DCA indicated favorable net clinical benefit across relevant threshold probabilities.
conclusionThe GBM model provides interpretable, individualized early BPD risk estimates using routinely available clinical variables and may support identification of high-risk preterm infants. It is intended for risk stratification rather than treatment guidance. Prospective multicenter validation is required before routine clinical implementation.
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