Machine learning-based model development for predicting risk factors of prolonged intra-aortic balloon pump therapy in patients with coronary artery bypass grafting

Abstract

Abstract Machine learning algorithms are frequently used to clinical risk prediction. Our study was designed to predict risk factors of prolonged intra-aortic balloon pump (IABP) use in patients with coronary artery bypass grafting (CABG) through developing machine learning-based models. Patients who received perioperative IABP therapy were divided into two groups based on their length of IABP implantation longer than the 75th percentile for the whole cohort: normal (≤ 10 days) and prolonged (> 10 days) groups. Seven machine learning-based models were created and evaluated, and then the Shapley Additive exPlanations (SHAP) method was employed to further illustrate the influence of the features on model. In our study, a total of 143 patients were included, comprising 56 cases (38.16%) in the prolonged group. The logistic regression model was considered the final prediction model according to its most excellent performance. Furthermore, feature important analysis identified left ventricular end-systolic or diastolic diameter, preoperative IABP use, diabetes, and cardiac troponin T as the top five risk variables for prolonged IABP implantation in patients. The SHAP analysis further explained the features attributed to the model. Machine learning models were successfully developed and used to predict risk variables of prolonged IABP implantation in patients with CABG. This may help early identification for prolonged IABP use and initiate clinical interventions.

Keywords

• Machine learning
• Prolonged IABP
• Risk factors
• Prediction