联合预测因子对急性心肌梗死患者院内并发1型心肾综合征风险的预测价值  

Establishment and validation of a new predictive equation with multiple risk factors for the development of cardiorenal syndrome type 1 in patients with acute myocardial infarction

作　　者：贺彩红 刘金武 朱照贺 潘宏伟[1] 郑昭芬[1] 何晋[1] 刘征宇[1] 张宇[1] 王长录[1] 荣晶晶 唐毅 张青海 He Caihong;Liu Jinwu;Zhu Zhaohe;Pan Hongwei;Zheng Zhaofen;He Jin;Liu Zhengyu;Zhang Yu;Wang Changlu;Rong Jingjing;Tang Yi;Zhang Qinghai(Department of Cardiology,Hunan Provincial People's Hospital,First Affiliated Hospital of Hunan Normal University,Changsha 410000,China;Department of Cardiology,Luohe Central Hospital,Luohe 462000,China)

机构地区：[1]湖南省人民医院湖南师范大学附属第一医院心内科,长沙410000 [2]漯河市中心医院心内科,462000

出　　处：《中华心血管病杂志》2021年第8期802-808,共7页Chinese Journal of Cardiology

摘　　要：目的探讨急性心肌梗死(AMI)患者院内并发1型心肾综合征(CRS1)的危险因素,同时联合相关危险因素构建联合预测因子并评价其的预测价值。方法该研究为病例对照研究。连续纳入2017年1月至2018年12月在湖南省人民医院住院的AMI患者,根据住院期间是否并发CRS1分为CRS1组和无CRS1组。通过电子病历系统收集入选患者的临床资料。通过最邻近匹配法对CRS1组和无CRS1组患者进行1∶1匹配,logistic回归模型计算评分值。将单因素分析中差异有统计学意义的因素纳入多因素logistic回归模型,分析AMI患者并发CRS1的危险因素,通过logistic的模型方程进行转换构建联合预测因子,根据受试者工作特征(ROC)曲线确定联合预测因子的曲线下面积(AUC)值及最佳截断值。采用Python 3.8软件对建模样本进行十折交叉验证。结果该研究共纳入942例AMI患者,其中并发CRS1者113例(CRS1组),未发生CRS1者829例(无CRS1组)。将CRS1组和无CRS1组患者进行1∶1匹配后,最终匹配成功的患者两组各99例。倾向性匹配后两组患者的基线年龄、性别、心率、平均动脉压、有糖尿病史者占比、有高血压病史者占比、ST段抬高型心肌梗死者占比、心肌缺血时间以及血管紧张素转换酶抑制剂/血管紧张素Ⅱ受体阻滞剂及β受体阻滞剂的应用情况差异均无统计学意义(P均>0.05)。CRS1组患者对比剂用量与无CRS1组比较差异无统计学意义(P=0.266),两组患者心肌肌钙蛋白I(cTnI)峰值、N末端B型利钠肽原(NT-proBNP)、白细胞计数、基础估算的肾小球滤过率(eGFR)、白蛋白及血红蛋白水平差异均有统计学意义(P均<0.01)。多因素logistic回归模型分析结果显示,基线eGFR降低以及NT-proBNP、cTnI峰值和白细胞计数升高是AMI患者并发CRS1的独立危险因素(P均<0.05)。通过对logistic的模型方程进行转换得到联合预测因子的计算公式,即L_(联合)=0.031×cTnI+0.0002×NT-proBNP-Objective To investigate the independent risk factors of cardiorenal syndrome type 1(CRS1)in patients with acute myocardial infarction(AMI)and to build a predictive equation for the development of CRS1 in these patients.Method Consecutive inpatients with AMI,who hospitalized from January 2017 to December 2018 in the Hunan Provincial People′s Hospital,were enrolled in this case-control study.Patients were divided into CRS1 group and non-CRS1 group according to the presence or absence of CRS1.The clinical data were collected through the electronic medical record system of Hunan Provincial People′s Hospital.The matching process was conducted with a minimum-distance scoring method and a 1∶1 match between the CRS1 group and the no-CRS1 group,the propensity score was calculated through the logistic regression model.Factors with statistically significant differences in univariate analysis were included in the multivariate logistic regression model to analyze the risk factors of AMI patients with CRS1,then the independent risk factors were used to establish a predicting equation for CRS1 by logistic regression function for model building.Area under the curve(AUC)value and the best cut-off value of the combined predictors was determined according to the ROC curve.Python 3.8 software was used to perform 10-fold cross-validation on modeling samples.Results A total of 942 patients were included,there were 113 cases in CRS1 group and 829 cases in non-CRS1 group.Ultimately,99 CRS1 patients were successfully matched to 99 non-CRS1 patient using 1∶1 matching.After propensity score matching,the baseline age and sex along with heart rate,mean arterial pressure,percentage of people with a history of diabetes,hypertension,ST-segment elevation myocardial infarction,myocardial ischemia time,angiotensin converting enzyme inhibitors or angiotensin Ⅱ receptor blockers use,and β receptor blocker use were similar between the two groups(all P>0.05).The contrast agent dosage was also similar between the two groups(P=0.266).The pea

关 键 词：心肌梗死 预测 心肾综合征 1型 

分 类 号：R542.22[医药卫生—心血管疾病]