ArticleJournal of cardiothoracic surgery2025
Machine learning in predicting preoperative intra-aortic balloon pump use in patients undergoing coronary artery bypass grafting.
Article in Journal of cardiothoracic surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundIntra-aortic balloon pump (IABP) implantation in the perioperative period of cardiac surgery is an auxiliary treatment for cardiogenic shock. However, there is a lack of effective prediction models for preoperative IABP implantation.
objectivesThis study was designed to build machine learning algorithm-based models for early predicting risk factors of preoperative IABP implantation in patients who underwent coronary artery bypass grafting (CABG) surgery.
methodsPatients undergoing CABG were retrospectively enrolled from the hospital between January 2015 and March 2024 and divided into the preoperative and non-preoperative (including intraoperative and postoperative) IABP implantation groups. After feature selection by the cross-validation least absolute shrinkage and selection operator (LassoCV) algorithm, machine learning models were developed. The final model was considered according to its discrimination, including area under the receiver operating characteristic curve (AUC) and kolmogorov-smirnov (KS) plot.
resultsThe preoperative IABP group enrolled 95 (40.3%) patients. The Gaussian Naïve Bayes (GNB) model achieved the most excellent prediction ability based on its highest AUC of 0.76 (0.69-0.82) in the training set, 0.72 (0.49-0.94) in the validation set, and good KS plot and identified the top six features. The SHapley Additive exPlanations force analysis further illustrated visualized individualized prediction of preoperative IABP implantation.
conclusionOur study suggests that the GNB model achieved superior performance compared to others in predicting preoperative IABP implantation in patients undergoing CABG surgery. This may contribute to risk-prediction and decision-making in clinical practice.
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