Abstract
Acute Type A aortic dissection (ATAAD) is characterized by acute onset and rapid progression, with aortic rupture due to dissection extension being the primary lethal mechanism. Timely identification of high-risk patients is critical for prioritizing surgical intervention to reduce rupture incidence. This study aimed to develop and validate an interpretable machine learning model to predict aortic rupture in ATAAD patients, thereby improving risk classification and supporting clinical decisions. Medical records of ATAAD patients from Xiamen Cardiovascular Hospital (January 2019-October 2024) were retrospectively analyzed. Predictors were screened via statistical significance (p < 0.05) using seven machine learning algorithms, with the Salp Swarm Optimization Algorithm (SSA) optimizing hyperparameters for Random Forest and XGBoost models. To address class imbalance (47 rupture cases, 6.1%), SMOTE was implemented for data augmentation. Model performance was evaluated by accuracy, F1-score, precision, ROC-AUC, sensitivity, and specificity, supplemented by interpretability analyses through feature importance ranking and SHAP. Among 774 included ATAAD patients, the SSA-optimized Random Forest model achieved optimal performance (test dataset: 97.41% accuracy, 0.980 ROC-AUC, 81.82% F1-score). Key predictors included estimated glomerular filtration rate (eGFR), hypotension at admission, and white blood cell count. This work provides a quantitative tool for emergency care prioritization, with SSA enhancing model precision for high-risk patient identification, though multicenter studies are needed to validate generalizability.