Construction and Validation of Risk Prediction Model for Reinfarction or Recurrent Myocardial Infarction After Percutaneous Coronary Intervention
Objective To construct a risk prediction model for reinfarction or recurrent myocardial infarction(MI)after percutaneous coronary intervention(PCI),to validate and evaluate the predictive performance of the model,and to provide a prediction and evaluation tool for clinical medical staff to predict and evaluate.Methods A stratified sampling method was used to select 1359 patients who underwent PCI in 3 hospitals in Guizhou province as the modeling group.They were followed up for 1 year,and were divided into the event group(185 cases)and the non-event group(1174 cases)according to whether reinfarction or recurrence occurred.A case-control study was conducted.Logistic regression was used to construct a risk prediction model for reinfarction or recurrent myocardial infarction,and the predictive abil-ity was evaluated by the area under the receiver operating characteristic curve(AUC).A total of 395 patients were pro-spectively collected to form the validation group to validate the model.Results Family history of coronary heart disease and diabetes,high-sensitivity troponin I＞0.342 μg/L,low density lipoprotein cholesterol≥ 3.37 mmol/L,and lack of exercise after operation were the independent risk factors for reinfarction or recurrent myocardial infarction.ROC curve area was 0.80 and AUC＞0.70.The maximum Youden index was 0.48.Sensitivity was 80％,and specificity was 68.40％.The ROC curve area of the validation group was 0.76,with the sensitivity as 77.90％,and specificity as 65.70％.Conclu-sions The risk prediction model constructed has good fitness,high accuracy,and good predictive ability.It can provide reference for clinical medical staff to identify high-risk patients with myocardial infarction early.
percutaneous coronary interventionmyocardial infarction and reinfarctionrecurrent myocar-dial infarctionrisk prediction model
NETL
NSTL
万方数据
维普
