オフポンプ冠動脈バイパス術における出血リスク予測モデルの開発と検証：多施設共同後ろ向きコホート研究

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.

This study aims to develop and validate a novel perioperative bleeding prediction model tailored for OPCABG patients.

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.

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.

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.