基于胸痛数据库构建急诊急性高危胸痛患者急性心肌梗死的无创诊断模型及临床意义

The noninvasive diagnosis models and its clinical significance of acute myocardial infarction in emergency patients with high-risk chest pain established based on chest pain database

目的基于胸痛数据库构建急诊急性高危胸痛患者急性心肌梗死(AMI)的无创诊断模型,并分析其临床意义。方法回顾性选取2020年1月至2022年10月济宁医学院附属医院收治的467例急性高危胸痛患者,根据是否发生AMI分为AMI组(317例)、非AMI组(150例),比较两组临床资料,采用Lasso回归和Logistic回归分析发生AMI的危险因素,采用R语言建立诊断模型,采用一致性指数(C-index)评价模型的预测能力,采用校准曲线和决策分析曲线(DCA)对所建立模型进行外部验证和评价。结果单因素分析结果显示,AMI组冠心病患者比例、呼吸频率、肌红蛋白、肌酸激酶同工酶-MB(CK-MB)、心肌肌钙蛋白I(cTnI)、D-二聚体、N末端B型利钠肽前体、C反应蛋白、纤维蛋白原、乳酸、ST段抬高患者比例、室壁运动异常患者比例高于非AMI组[51.10%(162/317)比21.33%(32/150)、(19.25±2.44)次/min比(16.30±2.15)次/min、(270.03±26.59)μg/L比(71.44±19.85)μg/L、(30.51±8.22)μg/L比(3.22±0.88)μg/L、(4.51±1.38)μg/L比(0.04±0.01)μg/L、(1.69±0.51)mg/L比(0.32±0.09)mg/L、(2085.66±561.24)ng/L比(964.39±257.40)ng/L、(13.98±4.52)mg/L比(7.11±2.26)mg/L、(4.07±0.83)g/L比(2.95±0.78)g/L、(2.20±0.49)mmol/L比(1.36±0.35)mmol/L、80.76%(256/317)比16.67%(25/150)、95.27%(302/317)比17.33%(26/150)],血小板计数、活化部分凝血活酶时间、凝血酶原时间、左室射血分数低于非AMI组[(168.97±29.66)×10^(9)/L比(230.58±30.57)×10^(9)/L、(30.25±4.59)s比(33.59±4.16)s、(11.82±0.74)s比(13.25±1.02)s、(47.25±5.33)%比(58.49±5.07)%],差异均有统计学意义(P<0.05)。Lasso回归分析选出7个预测变量,分别为冠心病、肌红蛋白、CK-MB、cTnI、D-二聚体、ST段抬高、室壁运动异常;多因素Logistic回归分析结果显示,冠心病、肌红蛋白、CK-MB、cTnI、D-二聚体、ST段抬高、室壁运动异常是发生AMI的独立危险因素(P<0.05),经Hosmer-Lemeshow拟合优度检验显示拟合良好(χ^(2)=2.56,df=9,P=0.860);运用R语言绘制AMI的无创诊断模型,其C-index为0.945,提示预测能力良好;校准曲线分析显示,诊断模型校准度为0.918,提示模型与实际观测结果有较好的一致性;绘制DCA显示诊断AMI的列线图模型具有明显的正向净收益,临床效用良好。结论冠心病、肌红蛋白、CK-MB、cTnI、D-二聚体、ST段抬高、室壁运动异常可作为急诊急性高危胸痛患者AMI的无创诊断标志物,基于以上因素所构建的诊断模型预测性能良好。

Objective:To explore the noninvasive diagnosis model and its clinical significance of acute myocardial infarction(AMI)in emergency patients with high-risk chest pain established based on chest pain database.Methods:A total of 467 patients with acute high-risk chest pain admitted to the Affiliated Hospital of Jining Medical University from January 2020 to October 2022 were selected.The patients were divided into AMI group(317 cases)and non-AMI group(150 cases)according to the occurrence of AMI.The clinical data of the two groups were compared,and Lasso regression and Logistic regression were used to analyze the related risk factors of AMI.R language was used to establish a diagnostic model,and concordance index(C-index)was used to evaluate the predictive ability of the model.Calibration curve and decision analysis curve(DCA)were used to verify and evaluate the established model externally.Results:The results of the univariate analysis showed that the proportion of patients with coronary heart disease,respiratory rate,myoglobin,creatine kinase isoenzyme-MB(CK-MB),cardiac troponin I(cTnI),D-dimer,N-terminal pro-brain natriuretic peptide,C-reactive protein,fibrinogen,lactic acid,ST-segment elevation and abnormal ventricular wall movement in the AMI group were higher than those in the non-AMI group:51.10%(162/317)vs.21.33%(32/150),(19.25±2.44)times/min vs.(16.30±2.15)times/min,(270.03±26.59)μg/L vs.(71.44±19.85)μg/L,(30.51±8.22)μg/L vs.(3.22±0.88)μg/L,(4.51±1.38)μg/L vs.(0.04±0.01)μg/L,(1.69±0.51)mg/L vs.(0.32±0.09)mg/L,(2085.66±561.24)ng/L vs.(964.39±257.40)ng/L,(13.98±4.52)mg/L vs.(7.11±2.26)mg/L,(4.07±0.83)g/L vs.(2.95±0.78)g/L,(2.20±0.49)mmol/L vs.(1.36±0.35)mmol/L,80.76%(256/317)vs.16.67%(25/150),95.27%(302/317)vs.17.33%(26/150);the platelet count,activited partial thomboplastin time,prothombin time and left ventricular ejection fractionin in the AMI group were lower than those in the non-AMI group:(168.97±29.66)×10^(9)/L vs.(230.58±30.57)×10^(9)/L,(30.25±4.59)s vs.(33.59±4.16)s,(11.82±0.74)s vs.(13.25±1.02)s,(47.25±5.33)%vs.(58.49±5.07)%,there were statistical differences(P<0.05).Using 17 variables with P<0.05 in univariate analysis as independent variables,Lasso regression analysis selected 7 predictive variables as coronary heart disease,myoglobin,CK-MB,cTnI,D-dimer,ST segment elevation and abnormal ventricular wall movement.Multivariate Logistic regression analysis showed that coronary heart disease,myoglobin,CK-MB,cTnI,D-dimer,ST-segment elevation and abnormal ventricular wall movement were the related risk factors of AMI(P<0.05).Hosmer-Lemeshow goodness of fit test showed that the fit was good(χ^(2)=2.56,df=9,P=0.860);R language was used to draw the non-invasive diagnosis model of AMI,and the C-index was 0.945,indicated good predictive ability.Calibration curve analysis showed that the calibration degrees of the model establishment population and the external verification population were 0.918 and 0.924,respectively,indicated that the model was in good agreement with the actual observation results.The DCA curve showed that the column graph model for diagnosing AMI had significant positive net benefit and good clinical utility.Conclusions:Coronary heart disease,myoglobin,CK-MB,cTnI,D-dimer,ST-segment elevation and abnormal ventricular wall movement can be used as non-invasive diagnostic markers for AMI in patients with acute high-risk chest pain in emergency department.The prediction performance of the diagnostic model based on the above factors is good.