实用临床医药杂志2026，Vol.30Issue(6)：103-110,8.DOI:10.7619/jcmp.20253447
基于机器学习构建和验证慢性心力衰竭恶化风险预测模型
Construction and validation of risk prediction models for chronic heart failure deterioration based on machine learning
摘要
Abstract
Objective To construct six risk prediction models for heart failure deterioration in patients with chronic heart failure based on machine learning algorithms and conduct a comparative a-nalysis of their predictive performance.Methods A retrospective collection of 608 CHF patients in Hefei Second People's Hospital from January 2019 to December 2023 was conducted as the study sub-jects,and they were randomly divided into modeling group(n=486)and internal validation group(n=122)in a 4∶1 ratio.Additionally,400 chronic heart failure patients in the First Affiliated Hos-pital of Bengbu Medical University in the same period were collected as external validation group.LASSO regression analysis was used to screen key variables for heart failure deterioration in chronic heartfailure patients for multivariate analysis.Based on the independent risk factors for heart failure deterioration in chronic heart failure patients,six risk prediction models were constructed using ma-chine learning algorithms,and their performance was validated.Results A total of 1 008 chronic heart failure patients were included in this study,among whom 294 experienced heart failure deterioration,with an incidence rate of 29.17％.LASSO regression analysis identified 13 key variables.Multivariate analysis based on these 13 key variables revealed that atrial fibrillation,left ventricular ejection fraction,N-terminalpro-brain natriuretic peptide,uricacid,creatinine,and the emotional domain were inde-pendent risk factors for heart failure deterioration in chronic heart failure patients(P＜0.05).Logistic,decision tree,neural network,support vector machine,random forest,and XGBoost mod-els were constructed based on these six independent risk factors.Validation showed that the area un-der the curve(AUC)of all six prediction models was larger than 0.8,with the Logistic and XGBoost models demonstrating the best predictive performance.Conclusion This study construc-ted six risk prediction models for heart failure deterioration in chronic heart failure patients based on machine learning algorithms,all of which exhibite good predictive performance.However,consider-ing clinical applicability and convenience,the Logistic model may have higher clinical application value and can provide a reference for the early identification of heart failure deterioration and the for-mulation of prevention and treatment plans.关键词
心力衰竭/疾病恶化/机器学习/预测模型/风险评估/列线图/情绪障碍/模型验证Key words
heart failure/disease deterioration/machine learning/prediction model/risk as-sessment/nomogram/emotional disorder/model validation分类
医药卫生引用本文复制引用
王静,方宁,黄容..基于机器学习构建和验证慢性心力衰竭恶化风险预测模型[J].实用临床医药杂志,2026,30(6):103-110,8.基金项目
2024年度安徽省中医药学会中医药科研项目(2024ZYYXH044) （2024ZYYXH044）
2022年度蚌埠医学院科研课题(2022byzd206) （2022byzd206）
