Abstract

To explore the establishment of nomogram prediction model for the risk of no-reflow after percutaneous coronary intervention in patients with acute myocardial infarction and to evaluate the discrimination and accuracy of the model. 327 patients with acute myocardial infarction who underwent emergency percutaneous coronary intervention in our hospital from January 2019 to March 2020 were selected as the research objects. According to whether the patients had no-reflow after percutaneous coronary intervention, they were divided into reflow group and no-reflow group. The risk factors of no-reflow were screened by single factor and multi factor logistic regression model and the nomogram prediction model of no-reflow risk was established based on the risk factors. The area under receiver operating characteristic curve was used to test the prediction effect of the model. Logistic regression model was used for multivariate analysis. The results showed that the number of coronary artery lesion, ischemic time, neutrophil percentage, white blood cell count, thrombus grade and vasospasm grade were independent risk factors affecting no-reflow (p<0.05). According to the risk factors affecting no-reflow screened by multivariate logistic regression, a nomographic model for predicting the risk of no-reflow was established by using R software (R 3.6.3) regression modeling strategies package. The area under receiver operating characteristic curve was 0.860 with the maximum of Youden index as the best critical value of the prediction model. The calibration curve of nomogram was drawn. The calibration curve was a straight line with slope close to 1. Hosmer-Lemeshow goodness of fit test results showed that,χ²=10.278, p=0.246. Based on the risk factors affecting no-reflow after percutaneous coronary intervention in patients with acute myocardial infarction, including the number of coronary artery lesions, ischemic time, neutrophil percentage, white blood cell count, thrombus grade and vasospasm grade, this study established a nomogram prediction model. The model has good discrimination and consistency and can provide certain guidance for the prediction and preventive intervention of the risk of no-reflow after percutaneous coronary intervention.