Risk factors analysis and construction of risk prediction model for unplanned readmission in patients with acute myocardial infarction
10.3760/cma.j.cn211501-20210907-02547
- VernacularTitle:急性心肌梗死患者非计划性再入院危险因素分析及风险预测模型构建
- Author:
Yuqing WANG
1
;
Zimeng LI
;
Hongwen MA
Author Information
1. 天津市人民医院心内科，天津 300000
- Keywords:
Acute myocardial infarction;
Risk factors;
Risk prediction model;
Unplanned readmission
- From:
Chinese Journal of Practical Nursing
2022;38(11):817-822
- CountryChina
- Language:Chinese
-
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.
