Prediction of low cardiac output syndrome in patients following non-isolated coronary artery bypass grafting surgery using machine learning

Aim: Low cardiac output syndrome (LCOS) may be improvable; hence, timely detection and intervention are essential. However, no model has been established for the prediction of LCOS onset post non-isolated coronary artery bypass grafting (CABG) surgery. Therefore, this study aimed to develop a machine-learning-based model to predict LCOS after non-isolated CABG. Methods: A total of 378 patients who underwent non-isolated CABG at Nanjing First Hospital, China, were retrospectively assessed. Five algorithms [L2 regularized logistic regression (LR), random forest (RF) classifier, extreme gradient boosting (XGB), light gradient boosting machine (LGBM), and support vector machine (SVM)] were employed. Model performance and clinical utility were evaluated using area under the curve (AUC), 10-fold cross-validation, and decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) were used to assess the model’s interpretability. A web calculator was developed. Results: XGB showed superior performance and calibration (AUC: 0.933, 95% CI: 0.903–0.962; Brier score of 0.107), with excellent specificity (0.865), accuracy (0.860), and precision (0.753). In testing, XGB maintained excellent discrimination (AUC: 0.868, 95% CI: 0.799–0.936), best specificity (0.785), accuracy (0.781), and precision (0.614). DCA confirmed clinical usefulness. SHAP analysis identified the ejection fraction, left ventricular end-systolic diameter, and lactate levels as the most influential predictors. The web calculator is accessible via https://lcos-cabg-xgb-model.streamlit.app/ Conclusions: The developed web-based XGB model effectively predicts LCOS after non-isolated CABG, aiding early risk stratification and detection.