兰州大学学报（医学版）2026，Vol.52Issue(2)：30-38,9.DOI:10.13885/j.issn.2097-681X.M20251161
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
Development of a machine learning-based model to predict one-year all-cause mortality in elderly patients with chronic heart failure
摘要
Abstract
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.关键词
老年/慢性心力衰竭/机器学习/全因死亡/预测模型/极端梯度提升算法/左室射血分数Key words
elderly/chronic heart failure/machine learning/all-cause mortality/predictive model/extreme gradient boosting algorithm/left ventricular ejection fraction分类
医药卫生引用本文复制引用
陈东升,周泓屹,姜帆..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[J].兰州大学学报（医学版）,2026,52(2):30-38,9.基金项目
首都全科医学与社区卫生研究专项资助项目(2023-2Y-014) （2023-2Y-014）
