摘要
目的 分析急性心肌梗死(AMI)患者发生心力衰竭(HF)的影响因素,并构建风险预测模型。方法 纳入1 061例AMI患者,分为模型构建的训练集(786例)和模型验证的测试集(275例)。利用Lasso回归和多因素Logistic回归构建AMI患者发生HF的预测模型,并绘制列线图。采用受试者工作特征(ROC)曲线和校准曲线评价模型的区分度和校准度。结果 利用Lasso回归和多因素Logistic回归筛选出年龄、心率(HR)、ST段偏移、N端脑利钠肽前体(NT-proBNP)、同型半胱氨酸(Hcy)、纤维蛋白原(Fib)、左心室射血分数(LVEF)共7个变量建立模型。多因素Logistic回归构建预测模型的回归方程为Logit(P)=0.718×ST段偏移+0.042×年龄+0.037×HR+0.000 294×NT-proBNP+0.040×Hcy+0.220×Fib-5.617×LVEF-5.781。预测模型训练集的ROC曲线下面积(AUC)为0.846(95%CI:0.817~0.875),敏感度为78.50%,特异度为76.60%。校准曲线显示训练集患者HF的发生率与实际发生率基本相符。利用测试集对模型进行外部验证,AUC为0.848(95%CI:0.801~0.896),敏感度76.40%,特异度78.00%。结论 AMI患者发生HF与ST段偏移、年龄、入院HR、NT-proBNP、Hcy、Fib、LVEF有关,利用以上变量构建的预测模型具有较高的预测效能,有助于早期识别此类患者。
Objective To analyze the factors affecting of heart failure(HF)in patients with acute myocardial infarction(AMI),and use the selected indicators to construct a risk prediction model.Methods A total of 1061 AMI patients were included,and they were divided into the model-constructed training set(786 cases)and the test set(275 cases).Lasso regression and multiple Logistic regression were used to build a predictive model for HF occurrence in AMI patients,and a Nomogram diagram was drawn.Receiver operating characteristic(ROC)curve and calibration curve were used to evaluate the discrimination and calibration of the model.Results Lasso regression and multiple Logistic regression were used to select 7 variables to establish the model,including age,heart rate(HR),ST segment deviation,N-telencephalic natriuretic peptide precursor(NT-proBNP),homocysteine(Hcy),fibrinogen(Fib)and left ventricular ejection fraction(LVEF).The regression equation for constructing predictive model by multivariate Logistic regression was Logit(P)=0.718×ST segment deviation+0.042×age+0.037×HR+0.000 294×NT-proBNP+0.040×Hcy+0.220×Fib-5.617×LVEF-5.781. The area under ROC curve of the training set was 0.846 (95%CI: 0.817-0.875), the sensitivity was 78.50% and the specificity was 76.60%. The calibration curve showed that the incidence of HF in the training set was basically consistent with the actual incidence. The test set was used to verify the model externally. The area under ROC curve was 0.848 (95%CI: 0.801-0.896), the sensitivity was 76.40% and the specificity was 78.00%. Conclusion The occurrence of HF in AMI patients is related to ST segment deviation, age, HR, NT-proBNP, Hcy, Fib and LVEF. The predictive model based on the above variables has high predictive efficacy and is helpful for early identification of such patients.
作者
马萌雪
马萍
徐清斌
张世昌
MA Mengxue;MA Ping;XU Qingbin;ZHANG Shichang(Department of Geriatrics and Special Needs,Cardiovascular and Cerebrovascular Disease,the General Hospital of Ningxia Medical University,Yinchuan 750000,China;Department of Cardiovascular,Cardiovascular and Cerebrovascular Disease,the General Hospital of Ningxia Medical University,Yinchuan 750000,China)
出处
《天津医药》
CAS
北大核心
2023年第11期1221-1226,共6页
Tianjin Medical Journal
关键词
心肌梗死
心力衰竭
危险因素
预测
列线图
敏感性与特异性
myocardial infarction
heart failure
risk factors
predict
nomograms
sensitivity and specificity
