Development and Validation of An Interpretable Machine Learning-Based Prediction Model of Postpartum Hemorrhage in Placenta Previa Following Cesarean Section: A Multicenter Study.

Abstract

The objective of this study is to predict the occurrence of postpartum hemorrhage in women with placenta previa based on machine learning. This retrospective study enrolled 845 singleton pregnant patients with placenta previa from two hospitals. They were allocated into a training cohort (n = 403), a testing cohort (n = 174), and the external validation cohort (n = 268). Univariate and multivariate regression analyses were employed to select clinical variables (p < 0.05), which were subsequently utilized to develop 11 machine learning prediction models. The area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA), accuracy (ACC), sensitivity (SEN), and specificity (SPE) were used to evaluate the performance of the models. Besides, SHapley Additive exPlanations (SHAP) was used to interpret the role and effectiveness of variables in the predictive model. Three machine learning models with the best predictive performance were combined into a Prediction Ensemble Classifier through voting. The Gradient Boosting Machine demonstrated the best predictive performance. In the validation cohort, AUC of the Gradient Boosting Machine model is 0.810(95% CI 0.754-0.865), ACC was 0.765(95% CI 0.716-0.813), SEN was 0.613(95% CI 0.513-0.723), while these values of the Prediction Ensemble Classifier were 0.813(0.756-0.871), 0.806(0.757-0.854), and 0.480(0.375-0.597), respectively. The importance of SHAP variables in the model, ranked from high to low, is as follows: d-dimer, ultrasound diagnosis of placenta accreta spectrum, neutrophils, prothrombin time, and platelets. The Gradient Boosting Machine model demonstrated excellent performance in predicting postpartum hemorrhage in cases of placenta previa. Furthermore, SHAP analysis enabled interpretation of the variables in the model.