Machine Learning Model for Risk Prediction of Prolonged Intensive Care Unit in Patients Receiving Intra-aortic Balloon Pump Therapy during Coronary Artery Bypass Graft Surgery

Abstract

This study aimed to construct machine learning models and predict prolonged intensive care units (ICU) stay in patients receiving perioperative intra-aortic balloon pump (IABP) therapy during cardiac surgery. 236 patients were divided into the normal (≤ 14 days) and prolonged (> 14 days) ICU groups based on the 75th percentile of ICU duration across the entire cohort. Seven machine learning models were trained and validated. The Shapley Additive explanations (SHAP) method was employed to illustrate the effects of the features. 94 patients (39.83%) experienced prolonged ICU stay. The XGBoost model outperformed other models in predictive performance, as evidenced by its highest area under the receiver operating characteristic curve (training: 0.92; validation: 0.73). The SHAP analysis identified tracheotomy, albumin, Sv1, and cardiac troponin T as the top four risk variables. The XGBoost model predicted risk variables for prolonged ICU stay in patients, possibly contributing to improving perioperative management and reducing ICU duration.

Keywords: CABG; IABP; Machine learning; Prolonged ICU stay; Risk factors.