Prediction of major adverse cardiovascular events after acute type A aortic dissection combined with coronary malperfusion by machine learning-based interpretable models
10.3760/cma.j.cn112434-20241230-00334
- VernacularTitle:基于机器学习的可解释模型在急性A型主动脉夹层合并冠状动脉灌注不良术后主要不良心血管事件的预测研究
- Author:
Hao ZHANG
1
;
Bo JIA
;
Zuo ZHANG
;
Huanyu QIAO
;
Bo YANG
;
Jing YANG
;
Feilong HEI
;
Xiaotong HOU
;
Junming ZHU
;
Yongmin LIU
Author Information
1. 首都医科大学附属北京安贞医院体外循环与机械循环辅助科，北京 100029
- Publication Type:Journal Article
- Keywords:
Acute aortic dissection;
MACEs;
Myocardial protection;
Prediction model;
Machine learning
- From:
Chinese Journal of Thoracic and Cardiovascular Surgery
2025;41(3):129-135
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore and model risk factors in patients with major adverse cardiovascular events (MACEs) after acute type A aortic dissection (ATAAD), and to develop and validate a personalized machine learning model to assess risk factors and predict MACEs in these patients.Methods:Clinical data of patients who attended Beijing Anzhen Hospital and underwent surgical treatment for ATAAD from January 2018 to October 2022 were retrospectively analyzed. Using MACEs as the endpoint, 70% of these patients were randomly divided into the training set and the remaining 30% into the validation set. LASSO regression was applied to explore key clinical variables in the training set. The optimal predictive model was selected from nine machine learning algorithms based on area under the curve. And Shapley Additive explanations was used to elucidate the predictive model. Results:Of the 481 patients included in this study, 135 (35.6%) patients experienced an endpoint event. By combining the results of the training and validation sets, when assessing the validity of the single model with the highest predictive accuracy for the outcome, it was shown that the logistic model (0.774, 95% CI: 0.717-0.830) was the most effective in the combined effect and had a high model accuracy (0.743, 95% CI: 0.720-0.766). According to the results of the LASSO, the factors most associated with postoperative MACEs were history of cerebrovascular disease, coronary artery involvement, shock status on admission to the operating room, FDP, PLT, CPB, ascending aortic clamping, and age. Conclusion:In this study, nine machine learning models were developed to predict the occurrence of postoperative MACEs in patients with acute type A aortic dissection. The logistic model performed significantly better compared to other algorithms. Our study successfully predicted postoperative MACES and identified the factors most associated with MACEs.
