超高危动脉粥样硬化性心血管疾病患者随访1年内再发心血管事件的影响因素及其风险预测列线图模型构建  

Influencing Factors of Recurrent Cardiovascular Events in Patients with Ultra High-Risk Atherosclerotic Cardiovascular Disease within One Year of Follow-up and Construction of Nomogram Model for Predicting Its Risk

作　　者：谢煌烈 周谷林 韩鹏宇 曾敏 雷鑫 张珂 姚耿圳 郑朝阳 XIE Huanglie;ZHOU Gulin;HAN Pengyu;ZENG Min;LEI Xin;ZHANG Ke;YAO Gengzhen;ZHENG Chaoyang(Department of Cardiology,the Second Affiliated Hospital of Guangzhou University of Chinese Medicine,Guangzhou 510120,China;The Second Clinical Medical College of Guangzhou University of Chinese Medicine,Guangzhou 510006,China)

机构地区：[1]广州中医药大学第二附属医院心血管科,广东省广州市510120 [2]广州中医药大学第二临床医学院,广东省广州市510006

出　　处：《实用心脑肺血管病杂志》2025年第3期51-56,共6页Practical Journal of Cardiac Cerebral Pneumal and Vascular Disease

基　　金：省部共建中医湿证国家重点实验室2021年度开放课题(SZ2021KF07);广州市科技局市院联合资助项目(2023A03J0745);2021年度广东省中医药局中医药科研项目(20211195);2022年度广东省中医院中医药科学技术研究专项(YN2022MS08,YN2022QN28)。

摘　　要：目的探讨超高危动脉粥样硬化性心血管疾病(ASCVD)患者随访1年内再发心血管事件的影响因素,并构建其风险预测列线图模型。方法选取2017年1月—2022年9月广州中医药大学第二附属医院心血管科收治的超高危ASCVD患者318例作为研究对象,根据随访1年内心血管事件发生情况将患者分为A组254例(未再发心血管事件)与B组64例(再发心血管事件)。收集患者临床资料,采用多因素Logistic回归分析探讨超高危ASCVD患者随访1年内再发心血管事件的影响因素,基于多因素Logistic回归分析结果构建超高危ASCVD患者随访1年内再发心血管事件风险预测列线图模型。采用ROC曲线分析该列线图模型对超高危ASCVD患者随访1年内再发心血管事件的区分度;采用Bootstrap法重复抽样1000次对该列线图模型进行内部验证,计算一致性指数(CI);采用Hosmer-Lemeshow拟合优度检验评价该列线图模型的拟合程度;绘制决策曲线以评价该列线图模型的临床适用性。结果两组年龄、BMI≥28 kg/m^(2)者占比、有吸烟史者占比、多血管病变发生率、糖尿病发生率、慢性肾脏病(3/4期)发生率、血脂达标率、超敏肌钙蛋白T(hs-TnT)、乳酸脱氢酶(LDH)、血红蛋白(Hb)、eGFR、TC、LDL-C、nonHDL-C比较,差异有统计学意义(P<0.05)。多因素Logistic回归分析结果显示,年龄[OR=1.056,95%CI(1.028~1.085)]、BMI[OR=2.455,95%CI(1.212~4.973)]、吸烟史[OR=3.707,95%CI(1.929~7.123)]、LDL-C[OR=1.394,95%CI(1.072~1.812)]是超高危ASCVD患者随访1年内再发心血管事件的独立影响因素(P<0.05)。基于多因素Logistic回归分析结果构建超高危ASCVD患者随访1年内再发心血管事件的风险预测列线图模型,ROC曲线分析结果显示,该列线图模型预测超高危ASCVD患者随访1年内再发心血管事件的AUC为0.777[95%CI(0.716~0.837)]。采用Bootstrap法重复抽样1000次以对该列线图模型进行内部验证,结果显示,该列线图模型的Objective To discuss the influencing factors of recurrent cardiovascular events in patients with ultra high-risk atherosclerotic cardiovascular disease(ASCVD)within one year of follow-up and construct the nomogram model for predicting its risk.Methods A total of 318 patients with ultra high-risk ASCVD in the Department of Cardiovascular,the Second Affiliated Hospital of Guangzhou University of Chinese Medicine from January 2017 to September 2022 were selected as study objects,and patients were divided into the group A with 254 cases(without recurrent cardiovascular events)and the group B with 64 cases(with recurrent cardiovascular events).Clinical data of patients were collected.Multivariate Logistic regression analysis was used to explore the influencing factors of recurrent cardiovascular events in patients with ultra high-risk ASCVD within one year of follow-up.Nomogram model for predicting recurrent cardiovascular events in patients with ultra high-risk ASCVD within one year of follow-up was constructed based on the results of multivariate Logistic regression analysis.ROC curve was used the discrimination of the nomogram model for predicting recurrent cardiovascular events in patients with ultra high-risk ASCVD within one year of follow-up.Bootstrap method was used to repeatedly sample 1000 times to verify the internal validation of nomogram model,and the consistency index(CI)was calculated.Hosmer-Lemeshow goodness of fit test was used to evaluate the fitting degree of the nomogram model.Decision curve was drawn to evaluate the clinical applicability of the nomogram model.Results There were significant differences of age,proportion of people with BMI≥28 kg/m^(2),proportion of people with smoking history,incidence of polyangiopathy,incidence of diabetes mellitus,incidence of chronic kidney disease(stage 3/4),standard rate of blood lipid,high-sensitivity troponin T(hs-TnT),lactate dehydrogenase(LDH),hemoglobin(Hb),eGFR,TC,LDL-C,nonHDL-C between the two groups(P<0.05).Multivariate Logistic regression analysis

关 键 词：心血管疾病 动脉粥样硬化性心血管疾病 超高危 心血管事件 影响因素分析 列线图 

分 类 号：R54[医药卫生—心血管疾病]