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Introduction <p>Bleeding is a serious complication in cardiac surgery, especially among patients receiving combined anticoagulant and antiplatelet therapy. Current prediction models rarely include those undergoing both valve surgery and coronary artery bypass grafting (CABG) or account for postoperative factors and dual antithrombotic therapy, limiting their clinical utility.</p> Aim <p>This study aimed to develop and validate machine learning models for the individualized prediction of bleeding events within three months after discharge in patients receiving warfarin plus aspirin following combined CABG and valve surgery.</p> Method <p>Data from 499 adult patients who underwent cardiac surgery and received combined anticoagulant and antiplatelet therapy between June 2019 and December 2023 were retrospectively analyzed. Patients were randomly assigned to training (70%) and internal validation (30%) cohorts. Eleven key bleeding predictors were selected using the least absolute shrinkage and selection operator (LASSO) method. Seven machine learning algorithms, logistic regression, decision tree, random forest (RF), support vector machine, eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM) and k-nearest neighbors, were subsequently trained using these predictors. The model performance was evaluated based on the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), F1 score, and Brier score. External validation was performed in a temporally independent cohort of 93 patients. Model interpretability was assessed through Shapley Additive Explanations (SHAP), which were used to visualize both global and individual risk contributions.</p> Results <p>Eleven clinical variables, including anemia, diabetes, heart failure, atrial fibrillation, age, postoperative drainage, previous bleeding, stroke, body mass index, estimated glomerular filtration rates and intraoperative bleeding, were identified as predictors. Among the models, the RF algorithm demonstrated the best performance (internal validation: AUC = 0.85, accuracy = 0.80, sensitivity = 0.75, specificity = 0.82, PPV = 0.64, NPV = 0.89 and F1-score = 0.69). In the external validation, the RF model maintained strong performance (AUC = 0.82, accuracy = 0.73, sensitivity = 0.76, specificity = 0.72, PPV = 0.44, NPV = 0.92 and F1-score = 0.56). Decision curve analysis confirmed the clinical utility of the model, and SHAP visualizations provided a transparent interpretation of individualized bleeding risks.</p> Conclusion <p>This study demonstrates the feasibility and clinical utility of ML-based prediction of short-term bleeding risk in cardiac surgery patients on dual antithrombotic therapy. The RF model enables individualized risk assessment, with SHAP interpretation supporting clinical applicability.</p>

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Development of an interpretable machine learning model to predict short-term bleeding risk in patients receiving dual antithrombotic therapy following cardiac surgery

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月24日

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  • Haolong Han,
  • Jifan Zhang,
  • Xia Wang,
  • Weihong Ge,
  • Jason Z Qu

摘要

Introduction

Bleeding is a serious complication in cardiac surgery, especially among patients receiving combined anticoagulant and antiplatelet therapy. Current prediction models rarely include those undergoing both valve surgery and coronary artery bypass grafting (CABG) or account for postoperative factors and dual antithrombotic therapy, limiting their clinical utility.

Aim

This study aimed to develop and validate machine learning models for the individualized prediction of bleeding events within three months after discharge in patients receiving warfarin plus aspirin following combined CABG and valve surgery.

Method

Data from 499 adult patients who underwent cardiac surgery and received combined anticoagulant and antiplatelet therapy between June 2019 and December 2023 were retrospectively analyzed. Patients were randomly assigned to training (70%) and internal validation (30%) cohorts. Eleven key bleeding predictors were selected using the least absolute shrinkage and selection operator (LASSO) method. Seven machine learning algorithms, logistic regression, decision tree, random forest (RF), support vector machine, eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM) and k-nearest neighbors, were subsequently trained using these predictors. The model performance was evaluated based on the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), F1 score, and Brier score. External validation was performed in a temporally independent cohort of 93 patients. Model interpretability was assessed through Shapley Additive Explanations (SHAP), which were used to visualize both global and individual risk contributions.

Results

Eleven clinical variables, including anemia, diabetes, heart failure, atrial fibrillation, age, postoperative drainage, previous bleeding, stroke, body mass index, estimated glomerular filtration rates and intraoperative bleeding, were identified as predictors. Among the models, the RF algorithm demonstrated the best performance (internal validation: AUC = 0.85, accuracy = 0.80, sensitivity = 0.75, specificity = 0.82, PPV = 0.64, NPV = 0.89 and F1-score = 0.69). In the external validation, the RF model maintained strong performance (AUC = 0.82, accuracy = 0.73, sensitivity = 0.76, specificity = 0.72, PPV = 0.44, NPV = 0.92 and F1-score = 0.56). Decision curve analysis confirmed the clinical utility of the model, and SHAP visualizations provided a transparent interpretation of individualized bleeding risks.

Conclusion

This study demonstrates the feasibility and clinical utility of ML-based prediction of short-term bleeding risk in cardiac surgery patients on dual antithrombotic therapy. The RF model enables individualized risk assessment, with SHAP interpretation supporting clinical applicability.