Prediction model for the risk of postoperative death in patients with acute type A aortic dissection
10.3760/cma.j.cn112434-20230423-00095
- VernacularTitle:急性A型主动脉夹层患者术后死亡风险的预测模型
- Author:
Peiquan LI
1
;
Shaopeng ZHANG
;
Yunpeng BAI
;
Tongyun CHEN
;
Feng ZHAO
;
Nan JIANG
;
Qingliang CHEN
Author Information
1. 天津医科大学研究生院，天津 300070
- Keywords:
Acute type A aortic dissection;
Machine learning;
Prediction model;
Death
- From:
Chinese Journal of Thoracic and Cardiovascular Surgery
2024;40(2):72-78
- CountryChina
- Language:Chinese
-
Abstract:
Objective:Using different machine learning methods to construct and screen the best prediction model for predicting the risk of death within 30 days after surgery in patients with acute type A aortic dissection.Methods:Five hundred and twenty-one patients with acute type A aortic dissection who underwent surgery between 2015 and 2022 were included, after collecting their perioperative date and screening them, 329 patients were retained. two different groups of predictor variables were generated by using Lasso regression and principal component analysis, after that, logistic regression, support vector machine algorithm, random forest algorithm, gradient boosting algorithm, and super learning algorithm were used to develop prediction models for the risk of death within 30 days after surgery. Finally, we compare the models and select the best one. Results:The AUC values for all models rangrd from 0.791-0.959. The model using Lasso regression to determine the predictor variables and built by the super learning algorithm had the best prediction with an AUC value of 0.959. Conclusion:The super learning algorithm better than other algorithms in predicting death within 30 days after acute type A aortic dissection.
