Risk stratification and prediction of emergency delivery in early-onset preeclampsia using machine learning

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BACKGROUND: Early-onset preeclampsia (PE) poses significant risks for maternal and fetal outcomes, particularly when emergency delivery is required. This study aimed to develop machine learning models to predict emergency delivery within 48 h of PE diagnosis, facilitating timely clinical interventions. METHODS: We analyzed a retrospective cohort of 648 singleton pregnancies diagnosed with PE at Fujian Maternal and Child Health Hospital from 2014 to 2024, with gestational ages ranging from 28 to 34 weeks. Patients were stratified into emergency delivery (≤ 48 h post-diagnosis, n = 174) and non-emergency groups (n = 474). Feature selection was performed via univariate analysis, collinearity testing, and logistic regression, yielding 16 predictors. Eight machine learning models (logistic regression, naïve Bayes, XGBoost, LightGBM, SVM, GBDT, MLP, elastic net) were trained and evaluated for discriminative power and calibration. SHAP analysis was employed to interpret model predictions. RESULTS: XGBoost demonstrated superior performance (testing AUROC: 0.908) with excellent calibration, while GBDT achieved high AUROC (0.931) but poorer calibration. SHAP analysis identified CRP, D-dimer, and hypoproteinemia as the most influential predictors. CONCLUSIONS: Machine learning models effectively predict emergency delivery in early-onset PE using clinically interpretable features. Integration into obstetric practice may enhance risk stratification, though prospective validation is warranted.

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