Zhang Jing, Xiong Wuyu, Yang Jiajuan, Sang Ye, Zhen Huiling, Tan Caiwei, Huang Cuiyuan, She Jin, Liu Li, Li Wenqiang, Wang Wei, Zhang Songlin, Yang Jian
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.
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. 2025 Apr;169(4):1191-1200.e3. doi: 10.1016/j.jtcvs.2024.09.019. Epub 2024 Sep 18.
The 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.
We 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.
The 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.
The 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.
本研究的目的是开发并验证一种可解释的机器学习模型，以预测A型主动脉夹层患者的1年死亡率，改善风险分类并辅助临床决策。
我们纳入了289例A型主动脉夹层患者，将他们分为训练队列（202例患者）和验证队列（87例患者）。采用具有10倍交叉验证的最小绝对收缩和选择算子方法确定了8个与1年死亡率相关的关键因素。使用准确率、F1分数、布里尔分数、曲线下面积和精确召回曲线下面积评估树袋模型的性能，并通过决策曲线评估校准和临床效用。夏普利值分析确定了最具影响力的预测因素。
树袋模型表现优于其他模型，布里尔分数为0.128，曲线下面积为0.91。关键风险因素包括年龄较大和白细胞计数升高，而较高的收缩压、淋巴细胞、二氧化碳结合力、嗜酸性粒细胞、β受体阻滞剂使用和手术干预具有保护作用。开发了一个基于网络的应用程序TAAD一年预后风险评估网络（网址为https://taad-1year-mortality-predictor.streamlit.app/）供临床使用。该平台可根据确定的预测因素预测A型主动脉夹层患者的1年死亡率，便于临床决策和患者管理。
树袋机器学习模型能有效预测A型主动脉夹层患者的1年死亡率，对风险进行分层。提高生存率的关键因素包括手术干预、β受体阻滞剂给药以及收缩压、淋巴细胞、二氧化碳结合力、嗜酸性粒细胞和白细胞水平的管理，为改善患者预后提供了一个有价值的工具。
