Abstract 17213: Machine Learning-Based Prediction of Type A Aortic Dissection

Abstract

Background: Existing risk predictors of aortic dissection have certain limitations. We hypothesized that machine learning models trained on clinical, demographic, and anthropometric features can further improve the prediction of patient outcomes. Objective: This study aims to develop a machine learning model that predicts type A aortic dissection and can help clinical decision making. Methods: This cohort study used the Yale Aortic Institute database. The models incorporated variables spanning demographic, anthropometric, medical history, radiological, and laboratory domains. The models were trained and validated using stratified 10-fold cross-validation. Hyperparameters for each algorithm were tuned through grid-search on the training folds. The models were trained to optimize the area under the receiver operator characteristic curve (AUROC) and were assessed in a held-out test set. Results: A total of 2,109 patients were analyzed in our study. Among them, 271 were diagnosed with type A aortic dissection. The models demonstrated strong performance on the held-out test set. Specifically, the extreme gradient boosting decision tree model achieved an AUROC of 0.821, while the random forest model achieved an AUROC of 0.820. Importantly, these models outperformed the prediction of type A aortic dissection when based solely on the ascending aorta diameter, which had an AUROC of 0.549. Besides the ascending aorta diameter, the key predictors were age, weight, height, family history, smoking, bicuspid aortic valve, and hypertension. Conclusion: We developed a machine learning model that provides an individualized prediction of the development of type A aortic dissection. This approach provides an accessible, efficient, and remote tool to identify high-risk patients.

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