• Jing Zhang, Wuyu Xiong, Jiajuan Yang, Ye Sang, Huiling Zhen, Caiwei Tan, Cuiyuan Huang, Jin She, Li Liu, Wenqiang Li, Wei Wang, Songlin Zhang, and Jian Yang.
• Department of Cardiology, The First College of Clinical Medical Science, China Three Gorges University & Yichang Central People's Hospital, Yichang, China; Central Laboratory, The First College of Clinical Medical Science, China Three Gorges University & Yichang Central People's Hospital, Yichang, China; Hubei Key Laboratory of Ischemic Cardiovascular Disease, Yichang, China; Hubei Provincial Clinical Research Center for Ischemic Cardiovascular Disease, Yichang, China.
• J. Thorac. Cardiovasc. Surg. 2024 Sep 18.

ObjectiveThe study objective was to develop and validate an interpretable machine learning model to predict 1-year mortality in patients with type A aortic dissection, improving risk classification and aiding clinical decision-making.MethodsWe enrolled 289 patients with type A aortic dissection, dividing them into a training cohort (202 patients) and a validation cohort (87 patients). The Least Absolute Shrinkage and Selection Operator method with 10-fold cross-validation identified 8 key factors related to 1-year mortality. The Treebag model's performance was assessed using accuracy, F1-Score, Brier score, area under the curve, and area under the precision-recall curve with calibration and clinical utility evaluated through decision curves. Shapley Additive Explanations analysis determined the most influential predictors.ResultsThe Treebag model outperformed others, achieving a Brier score of 0.128 and an area under the curve of 0.91. Key risk factors included older age and elevated white blood cell count, whereas higher systolic blood pressure, lymphocyte, carbon dioxide combining power, eosinophil, β-receptor blocker use, and surgical intervention were protective. A web-based application, TAAD One-Year Prognostic Risk Assessment Web, was developed for clinical use (available at https://taad-1year-mortality-predictor.streamlit.app/). This platform allows for the prediction of 1-year mortality in patients with type A aortic dissection based on the identified predictive factors, facilitating clinical decision-making and patient management.ConclusionsThe Treebag machine learning model effectively predicts 1-year mortality in patients with type A aortic dissection, stratifying risk profiles. Key factors for enhancing survival include surgical intervention, β-blocker administration, and management of systolic blood pressure, lymphocyte, carbon dioxide combining power, eosinophil, and white blood cell levels, offering a valuable tool for improving patient outcomes.Copyright © 2024 The American Association for Thoracic Surgery. Published by Elsevier Inc. All rights reserved.

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