Construction of risk prediction model for predicting death or readmission in acute heart failure patients during vulnerable phase based on machine learning
10.16016/j.2097-0927.202312147
- VernacularTitle:基于机器学习构建急性心力衰竭患者易损期死亡或再入院风险预测模型
- Author:
Jing ZENG
1
;
Xiaolong HE
;
Huajuan HU
;
Xiaoyu LUO
;
Zhinian GUO
;
Yunlong CHEN
;
Min WANG
;
Jiang WANG
Author Information
1. 400037 重庆,陆军军医大学(第三军医大学)第二附属医院心血管内科,全军心血管病研究所
- Keywords:
acute heart failure;
vulnerable phase;
machine learning;
prediction model;
Shapley additive explanation algorithms
- From:
Journal of Army Medical University
2024;46(7):738-745
- CountryChina
- Language:Chinese
-
Abstract:
Objective To construct risk prediction models of death or readmission in patients with acute heart failure(AHF)during the vulnerable phase based on machine learning algorithms and screen the optimal model.Methods A total of 651 AHF patients with admitted to Department of Cardiology of the Second Affiliated Hospital of Army Medical University from October 2019 to July 2021 were included.The clinical data consisting of admission vital signs,comorbidities and laboratory results were collected from electronic medical records.The composite endpoint was defined as all-cause death or readmission for worsening heart failure within 3 months after discharge.The patients were divided into a training set(521 patients)and a test set(130 patients)in a ratio of 8:2 through the simple random sampling.Six machine learning models were developed,including logistic regression(LR),random forest(RF),decision tree(DT),light gradient boosting machine(LGBM),extreme gradient boosting(XGBoost)and neural networks(NN).Receiver operating characteristic(ROC)curve and decision curve analysis(DCA)were used to evaluate the predictive performance and clinical benefit of the models.Shapley additive explanation(SHAP)was used to explain and evaluate the effect of different clinical characteristics on the models.Results A total of 651 AHF patients were included,of whom 203 patients(31.2%)died or were readmitted during the vulnerable phase.ROC curve analysis showed that the AUC values of the LR,RF,DT,LGBM,XGBoost and NN model were 0.707,0.756,0.616,0.677,0.768 and 0.681,respectively.The XGBoost model had the highest AUC value.DCA showed that the XGBoost model exhibited greater clinical net benefit compared with other models,with the best predictive performance.SHAP algorithm analysis showed that the clinical features that had the greatest impact on the output of the model were serum uric acid,D-dimer,mean arterial pressure,B-type natriuretic peptide,left atrial diameter,body mass index,and New York Heart Association(NYHA)classification.Conclusion The XGBoost model has the best predictive performance in predicting the risk of death or readmission of AHF patients during the vulnerable phase.
