Development and internal validation of a machine learning prediction model for postoperative hemorrhage following modified radical mastectomy for breast cancer

Background: Postoperative hemorrhage is a clinically meaningful complication of modified radical mastectomy (MRM), but existing risk-prediction models rely on logistic regression, rarely provide interpretable individualized risk attribution, and have not been specifically developed or internally validated for MRM. We aimed to develop and internally validate machine learning models for predicting clinically significant postoperative hemorrhage after MRM.

Methods: We retrospectively analyzed consecutive patients who underwent MRM at the Affiliated Hospital of Xuzhou Medical University between January 2022 and October 2024. Patients operated between January 2022 and December 2023 constituted the development cohort; patients operated between January and October 2024 constituted a strictly held-out temporal validation cohort. The primary endpoint was clinically significant hemorrhage, defined as unplanned reoperation for bleeding, transfusion for bleeding, or postoperative wound drainage exceeding 500 mL within any 24-hour period, ascertained over a 30-day postoperative follow-up window. Five algorithms—logistic regression (LR), decision tree (DT), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost)—were trained on least absolute shrinkage and selection operator (LASSO)–selected predictors and evaluated by area under the receiver operating characteristic curve (AUC), calibration metrics, decision curve analysis, and 1,000-iteration optimism-corrected bootstrap. Shapley additive explanations (SHAP) were used for model interpretation.

Results: A total of 1,842 patients were analyzed, of whom 74 developed clinically significant postoperative hemorrhage. The XGBoost model achieved the best performance (temporal validation AUC 0.827, 95% confidence interval (CI): 0.734–0.920; sensitivity 0.737 and specificity 0.814 at the Youden-derived threshold; Brier score 0.036), outperforming RF (AUC 0.819), SVM (0.794), LR (0.783), and DT (0.738). The XGBoost model demonstrated good calibration and the highest decision-curve net benefit across threshold probabilities of 5–30%. SHAP analysis identified tumor size, age, operation time, neoadjuvant chemotherapy, lymph node metastasis, pathological stage, and preoperative anticoagulant use as the seven leading predictors.

Conclusions: An XGBoost-based machine learning model predicted clinically significant postoperative hemorrhage after MRM and provided interpretable individualized risk estimates that may support early perioperative risk stratification and individualized hemostatic management. As a single-center study with internal (temporal) validation only and modest sensitivity, these findings should be regarded as exploratory, and multicenter external validation is required before clinical use.