Predictive Factors of Length of Stay in Intensive Care Unit after Coronary Artery Bypass Graft Surgery based on Machine Learning Methods.

Abstract

Introduction: Coronary artery bypass grafting (CABG) surgery requires an extended length of stay (LOS) in the intensive care unit (ICU). This study aimed to predict the factors affecting LOS in the ICU after CABG surgery using machine learning methods.

Methods: In this study, after extracting factors affecting the LOS of patients in the ICU after CABG surgery from the literature and confirming these factors by experts, the medical records of 605 patients at Farshchian Specialized Heart Hospital were reviewed between April 20 and August 9, 2024. Four machine learning models were trained and tested to predict the most desired factors, and finally, the performance of the models was evaluated based on the relevant criteria.

Results: The most important predictors of the LOS of CABG patients in the ICU were the length of intubation, body mass index (BMI), age, duration of surgery, and the number of postoperative transfusions of packed cells. The Random Forest model also performed best in predicting the effective factors (Mean square Error = 1.64, Mean absolute error = 0.93, and R² = 0.28).

Conclusion: The insights gained from the mashine learning model highlight the significance of demographic and clinical variables in predicting LOS in ICU. By understanding these predictors, healthcare professionals can better identify patients at higher risk for prolonged ICU stays.