Abstract
The length of hospital stay (LOS) is crucial for assessing medical service quality. This study aimed to develop machine learning models for predicting risk factors of prolonged LOS in patients with aortic dissection (AD). The data of 516 AD patients were obtained from the hospital's medical system, with 111 patients in the prolonged LOS (> 30 days) group based on three quarters of the LOS in the entire cohort. Given the screened variables and prediction models, the XGBoost model demonstrated superior predictive performance in identifying prolonged LOS, due to the highest area under the receiver operating characteristic curve, sensitivity, and F1-score in both subsets. The SHapley Additive exPlanation analysis indicated that high density lipoprotein cholesterol, alanine transaminase, systolic blood pressure, percentage of lymphocyte, and operation time were the top five risk factors associated with prolonged LOS. These findings have a guiding value for the clinical management of patients with AD.