实用心脑肺血管病杂志2026，Vol.34Issue(9)：51-56,6.DOI:10.12114/j.issn.1008-5971.2026.00.200
慢性心力衰竭患者发生主要不良心血管事件的风险预测列线图模型构建及重要性矩阵分析
Construction of Risk Prediction Nomogram Model of Major Adverse Cardiovascular Events in Patients with Chronic Heart Failure and the Importance Matrix Analysis
摘要
Abstract
Objective To construct a risk prediction nomogram model of major adverse cardiovascular events(MACE)in patients with chronic heart failure(CHF),and conduct an importance matrix analysis of the related factors.Methods A total of 305 patients with CHF admitted to the Department of Cardiovascular Medicine of Fuyang Fifth People's Hospital from January 2021 to January 2025 were selected as the study subjects.The clinical data of patients were collected.After 12 months of follow-up,the patients were divided into the MACE group and the non-MACE group according to the occurrence of MACE.Multivariate Logistic regression analysis was used to explore the influencing factors of MACE in CHF patients.R 4.0.3 software and rms package were used to construct the risk prediction nomogram model of MACE in CHF patients.Bootstrap method(repeated sampling 1 000 times)was used for internal verification,and the consistency index was calculated.The Hosmer-Lemeshow goodness-of-fit test was used to analyze the fitting degree of the model.The importance matrix analysis was used to analyze the priority of clinical intervention of related factors.Results During 12 months of follow-up,72 patients(23.61%)experienced MACE.The results of multivariate Logistic regression analysis showed that age,obesity status,number of combined basic diseases,disease type,NYHA grading,Hospital Anxiety and Depression Scale(HADS)score,and NT-proBNP were independent influencing factors of MACE in CHF patients(P<0.05).A risk prediction nomogram model of MACE in CHF patients was constructed based on the above influencing factors.The Bootstrap method results showed that the consistency index of the model was 0.900[95%CI(0.858-0.942)].The results of the Hosmer-Lemeshow goodness-of-fit test showed that the fitting degree of the model was good(P>0.05).The results of the importance matrix analysis showed that the influence weight and the difficulty of improvement of obesity were moderate,so it was classified as a key research area;the influence weights of NT-proBNP,HADS score,and NYHA grading were larger,and the difficulty of improvement of them was lower,so they were classified as a priority improvement area;the influence weights of disease type,number of combined basic diseases,and age were smaller,and the difficulty of improvement of them was higher,so they were classified as a candidate improvement area.Conclusion The incidence of MACE in CHF patients in this study is 23.61%.The risk prediction nomogram model of MACE in CHF patients constructed by age,obesity status,number of combined basic diseases,disease type,NYHA grading,HADS score and NT-proBNP has good discrimination and calibration,among them obesity is a long-term special comprehensive management index,NT-proBNP,HADS score and NYHA grading are clinical first-level intervention targets,disease type,number of combined basic diseases and age are long-term basic control indicators.关键词
心力衰竭/主要不良心血管事件/影响因素分析/列线图Key words
Heart failure/Major adverse cardiovascular events/Root cause analysis/Nomogram分类
医药卫生引用本文复制引用
邵琳,李巍巍,张晶晶,刘晓晴,徐丽..慢性心力衰竭患者发生主要不良心血管事件的风险预测列线图模型构建及重要性矩阵分析[J].实用心脑肺血管病杂志,2026,34(9):51-56,6.基金项目
2024年度安徽省转化医学研究院科研基金项目(2024zh-04) （2024zh-04）
