Construction and validation of explainable machine learning models to predict in-hospital mortality for patients with acute type A aortic dissection surgery.

Objective: To construct and validate a risk prediction model of in-hospital mortality using machine learning (ML) algorithm in a retrospective cohort of acute type A aortic dissection (ATAAD) patients undergoing surgical treatment. Methods: Patients with ATAAD undergoing surgical treatment between January 2014 and December 2022 were enrolled to predict in-hospital mortality. To address class imbalance and overfitting, we developed a robust Random Forest (RF)-based classification framework using a nested stratified 5-fold cross-validation (NCV). This was a single-center, retrospective study with internal validation only; no external validation was performed. Performance was evaluated via ROC-AUC, Precision-Recall Area Under the Curve (PR-AUC), sensitivity, brier score and calibration metrics, with Shapley Additive exPlanations (SHAP) utilized for feature interpretation. Results: A total of 639 ATAAD patients were included in the analytical cohort, with an in-hospital mortality rate of 5.6% (36/639). The calibrated full RF model (50 preoperative clinical variables) achieved an ROC-AUC of 0.666, PR-AUC of 0.145, brier score of 0.051, and calibration slope of 0.836, with a sensitivity of 0.694 at an optimized threshold. A parsimonious 15-feature model maintained robust performance (ROC-AUC: 0.752, PR-AUC: 0.207, brier score: 0.050 and calibration slope: 0.934). SHAP analysis identified Creatine Kinase-MB, Myoglobin, and Fibrinogen Concentration as the top mortality predictors. Conclusion: We developed and internally validated an explainable RF model to predict in-hospital mortality after ATAAD surgery. Given the low positive predictive value and high negative predictive value, the model is best regarded as a promising preliminary rule-out/triage tool that requires multicenter external validation before clinical use.