Development and external validation of machine learning models for predicting hysterectomy in patients with postpartum hemorrhage

Development and external validation of machine learning models for predicting hysterectomy in patients with postpartum hemorrhage. Download PDF Abstract Postpartum hemorrhage (PPH) is the leading cause of preventable maternal mortality worldwide. Emergency postpartum hysterectomy represents the ultimate life-saving intervention for refractory PPH but results in irreversible infertility and severe long-term maternal complications.

Current clinical risk assessment relies primarily on empirical judgment and conventional scoring tools, lacking precise, interpretable, and externally validated models for predicting PPH-related hysterectomy. This study aimed to develop and validate a solid machine learning (ML) model with explainable artificial intelligence (XAI) for individualized preoperative risk stratification of postpartum hysterectomy among PPH patients. This retrospective observational study was conducted based on the Medical Information Mart for Intensive Care IV (MIMIC-IV v3.1) database.

A total of 1669 eligible PPH patients were enrolled and randomly divided into a training cohort (n = 1169) and an internal validation cohort (n = 500) at a 7:3 stratified ratio. Four rigorous algorithms (LASSO, Boruta, random forest, and XGBoost) were integrated to screen core predictive variables. Eight ML models were constructed and optimized using 10-fold cross-validation.

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