@article{10.37349/ec.2026.1012111,
abstract = {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.},
author = {Fiagbey, Emmanuel Delali Kofi and Wang, Jiawen and Saad, Salama Habibu and Feng, Tianling and Kipanga, Zikomo Gaudence and Nyame, Daniel Kofi and Ndoli, Edwin Sospeter and Iweh, Etima Jeremiah and Yao, Weifeng and Hong, Liang and Zou, Jianjun},
doi = {10.37349/ec.2026.1012111},
journal = {Exploration of Cardiology},
elocation-id = {1012111},
title = {Prediction of low cardiac output syndrome in patients following non-isolated coronary artery bypass grafting surgery using machine learning},
url = {https://www.explorationpub.com/Journals/ec/Article/1012111},
volume = {4},
year = {2026}
}