基于机器学习建立老年慢性心力衰竭患者1年全因死亡预测模型OA

Objective To develop a machine learning(ML)-based model for predicting 1-year all-cause mor-tality in elderly patients with chronic heart failure(CHF).Methods A ML-based model was built for predict-ing 1-year all-cause mortality in elderly patients with CHF.Results The 1-year all-cause mortality rate was 12.28%in the training set and 10.83%in the validation set.LASSO regression and multivariate logistic regres-sion(LR)identified body mass index(BMI)and left ventricular ejection fraction(LVEF)as independent pro-tective factors,while New York Heart Association(NYHA)class Ⅳ,elevated C-reactive protein(CRP),D-di-mer(D-D),and N-terminal pro-brain natriuretic peptide(NT-proBNP)were independent risk factors(P<0.05).Receiver operator characteristic(ROC)curve showed that the extreme gradient boosting(XGBoost)model had the highest area under the curve(AUC)in both training and validation sets(0.897 and 0.864,respective-ly),outperforming LR(AUC=0.860,0.822),decision tree(DT)(AUC=0.767,0.761),random forest(RF)(AUC=0.875,0.818),and support vector machine(SVM)(AUC=0.859,0.788).The calibration curve in the validation set indicated that the XGBoost model closely aligned predicted and observed probabilities.DCA showed clinical bene-fit when predicted probability exceeded 0.10.Conclusion BMI,NYHA class,LVEF,CRP,and NT-proBNP were independent predictors of one-year all-cause mortality in elderly CHF patients.The XGBoost model based on these variables demonstrated superior predictive performance.