Risk factors analysis and construction of risk prediction model for unplanned readmission in patients with acute myocardial infarction / 中国实用护理杂志

ABSTRACT

Objective:To explore the risk factors of unplanned readmission in patients with acute myocardial infarction, and to construct a risk prediction model.Methods:This study used cross-sectional survey method. A total of 270 acute myocardial infarction patients admitted from Tianjin Union Medical Cencer from March 2020 to March 2021 were evaluated in a cardiology department. We used the electronic medical record system to collect the patients′ data. Patients were divided into two groups according to the occurrence of readmission within 1 year or not. Logistic regression analysis was performed to identify risk factors and formulated prediction model.Results:Totally 81 patients (30%) were readmitted. Binary Logistic regression model showed that the independent influencing factors of unplanned readmission in acute myocardial infarction patients included smoking ( X1), hypertension ( X2), marital status ( X3), hospitalization days ( X4), percutaneous coronary intervention ( X5), and heart failure ( X6). Area under ROC curve was 0.840, the maximum value of the Youden index was 0.560, and the sensitivity was 85.2%, the specificity was 70.8%, and the cutoff value was 0.377. Prediction model expression of unplanned readmission risk in patients with acute myocardial infarction was Logit(p/1-p)=-4.012+1.172 X1+1.104 X2+0.992 X3+0.118 X4+1.191 X5+1.093 X6. Conclusions:The risk prediction model of unplanned readmission in patients with acute myocardial infarction established in this article was with a good predictive effect, and it could be used in early identification of those patients with high-risk in unplanned readmission. At the same time, combined with the risk factors of depression, targeted intervention measures can be formulated.