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Development and validation of a bleeding risk model for off-pump coronary artery bypass grafting: a multi-center
Zi Wang1, Runhua Ma2, Qiming Wang3
1Department of Pharmacy, Zhongshan Hospital, Fudan University, Shanghai, China.
Insights
A new model, CABG Bleeding Risk of 10 Variables (CABG-BR10), accurately predicts bleeding risk in off-pump coronary artery bypass grafting (OPCABG) patients. This tool offers improved accuracy and clinical insights for better patient management.
Area of Science:
- Cardiovascular Surgery
- Medical Informatics
- Predictive Analytics
Background:
- Perioperative bleeding is a significant complication in coronary artery bypass grafting (CABG).
- Existing bleeding risk models often lack specificity for off-pump CABG (OPCABG) patients, necessitating tailored approaches.
Purpose of the Study:
- To develop and validate a novel perioperative bleeding prediction model specifically for OPCABG patients.
- To identify key predictors of bleeding risk in the OPCABG population.
Main Methods:
- A retrospective, multi-center cohort study involving internal and external validation.
- Development and comparison of fourteen machine learning models, including Random Forest, Gradient Boosting, and Support Vector Machines.
- Utilized SHapley Additive exPlanations (SHAP) for model interpretation and feature importance analysis.
Main Results:
- The developed CABG Bleeding Risk of 10 Variables (CABG-BR10) model, built with Random Forest, demonstrated strong predictive performance (AUC ROC: 0.90 internal, 0.87 external).
- Identified ten key predictors of bleeding risk, including antiplatelet drug discontinuation, NT-proBNP, and coagulation parameters.
- An online tool was created for practical clinical application of the CABG-BR10 model.
Conclusions:
- The CABG-BR10 model provides accurate and interpretable prediction of perioperative bleeding risk in OPCABG patients.
- This novel model surpasses traditional scoring systems in specificity and clinical utility for OPCABG.
- The findings facilitate enhanced risk stratification and clinical decision-making in OPCABG procedures.
Background:
Perioperative bleeding is a major challenge in coronary artery bypass grafting (CABG). Existing bleeding risk models often lack specificity for off-pump CABG (OPCABG) patients.
Objective:
This study aims to develop and validate a novel perioperative bleeding prediction model tailored for OPCABG patients.
Methods:
This retrospective, multi-center cohort study was conducted using both internal and external validation cohorts. Fourteen different models, including Binary Logistic Regression, Random Forest, Decision Tree, Extra Trees, Adaptive Boosting, Extreme Gradient Boosting, Categorical Boosting, Gradient Boosting, Naive Bayes, Artificial Neural Network, Light Gradient Boosting Machine, K-nearest Neighbors, Support Vector Machine, and LogitBoost, were applied for model development. SHapley Additive exPlanations (SHAP) were used to interpret feature importance and the model's outputs.
Results:
The final model, CABG Bleeding Risk of 10 Variables (CABG-BR10), was built using Random Rorest. This model identified 10 key variables: antiplatelet drug discontinuation, N-terminal pro B-type natriuretic peptide, activated partial thromboplastin time, hemoglobin, urea, cardiac troponin T, estimated glomerular filtration rate, total bilirubin, fibrinogen, and international normalized ratio. In the internal and external validation cohorts, the model demonstrated solid performance with Receiver Operating Characteristic - Area Under the Curve values of 0.90 and 0.87, and Precision-Recall - Area Under the Curve values of 0.70 and 0.67, respectively. SHAP analysis identified key predictors of bleeding risk, and an online tool was developed to facilitate bleeding risk assessment.
Conclusion:
The CABG-BR10 model accurately predicts perioperative bleeding risk in OPCABG patients, outperforming traditional scoring systems and providing interpretable, clinically relevant insights into bleeding risk factors.
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