A machine learning approach to predict mortality and neonatal persistent pulmonary hypertension in newborns with congenital diaphragmatic hernia. A retrospective observational cohort study

Congenital diaphragmatic hernia (CDH) has high morbidity and mortality rates. This study aimed to develop a machine learning (ML) algorithm to predict outcomes based on prenatal and early postnatal data. This retrospective observational cohort study involved infants with left-sided CDH, born from 2012 to 2020. We analyzed clinical and imaging data using three classification algorithms: XGBoost, Support Vector Machine, and K-Nearest Neighbors. Medical records of 165 pregnant women with CDH fetal diagnosis were reviewed. According to inclusion criteria, 50 infants with isolated left-sided CDH were enrolled. The mean o/eLHR was 37.32%, and the average gestational age at delivery was 36.5 weeks. Among these infants, 26 (52%) had severe persistent neonatal pulmonary hypertension (PPHN), while 24 (48%) had moderate or mild form; 37 survived (74%), and 13 did not (26%). The XGBoost model achieved 88% accuracy and 95% sensitivity for predicting mortality using ten features and 82% accuracy for PPHN severity with 14 features. The area under the ROC curve was 0.87 for mortality and 0.82 for PPHN severity. Conclusion: ML models show promise in predicting CDH outcomes and supporting clinical decisions. Future research should focus on more extensive studies to refine these algorithms and improve care management. Clinical trial registration: NCT04609163. (Table presented.)