Predicting the risk of postoperative gastrointestinal bleeding in patients with type A aortic dissection based on an interpretable machine learning model

Lin LiXing YangSiyu Che^*Wenxian WuMeixia GuoHuanhuan LiXueyan Wang

• Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Tongji Shanxi Hospital, Taiyuan, China

Background Gastrointestinal bleeding (GIB) is a common complication following Type A aortic dissection (TAAD) surgery, significantly impacting prognosis and increasing mortality risk. This study developed and validated a predictive model based on machine learning (ML) algorithms to enable early and precise assessment of postoperative GIB risk in TAAD patients.Medical records of patients who underwent TAAD surgery at Shanxi Bethune Hospital from January 2019 to September 2024 were retrospectively collected. Predictors were screened using LASSO regression, and four ML algorithms-Random Forest (RF), K-nearest neighbor (KNN), Support Vector Machines (SVM), and Decision Tree (DT)-were employed to construct models for predicting postoperative GIB risk. The dataset was divided into training and validation sets in a 7:3 ratio. Predictive performance was evaluated and compared using Receiver Operating Characteristic (ROC) curves and DeLong tests. Calibration curves and decision curve analysis (DCA) were used to assess model calibration and clinical utility. The SHapley Additive exPlanation (SHAP) algorithm was applied for interpretability analysis. This study adhered to the "Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis + Artificial Intelligence (TRIPOD+AI) guidelines." Results A total of 525 TAAD patients were included, with 63 (12%) developing GIB. Nine predictors were selected via LASSO regression for model construction. The RF model outperformed the SVM, KNN, and DT models in predicting postoperative GIB, with areas under the ROC curve (AUC) of 0.933, 0.892, 0.902, and 0.768, respectively, showing statistically significant differences (DeLong test, P < 0.05). Calibration curves and DCA further confirmed the RF model's excellent calibration and clinical utility. SHAP analysis identified the three most influential clinical features on the RF model's output: duration of mechanical ventilation (MV), Time to aortic occlusion, and red blood cell (RBC) transfusion. Conclusions The machine learning-based predictive model effectively assesses postoperative GIB risk in TAAD patients, aiding healthcare providers in early identification of risk factors and implementation of targeted preventive strategies.