Construction and validation of a risk prediction model for delayed medical treatment in patients with acute myocardial infarction
10.3969/j.issn.1673-9701.2025.10.004
- VernacularTitle:急性心肌梗死患者就医延迟风险预测模型的构建与验证
- Author:
Yuanfeng XU
1
;
Chenglin ZHANG
;
Xuemei LI
;
Xiaoyan LI
;
Wu CHEN
Author Information
1. 盐城市第三人民医院心内科,江苏盐城 224001
- Publication Type:Journal Article
- Keywords:
Acute myocardial infarction;
Delayed medical treatment;
Predictive model
- From:
China Modern Doctor
2025;63(10):16-19,33
- CountryChina
- Language:Chinese
-
Abstract:
Objective To construct a risk prediction model for delayed medical treatment in patients with acute myocardial infarction(AMI),and to evaluate the predictive performance of the model.Methods Convenience sampling method was used to select 219 patients with AMI who were hospitalized in Yancheng Third People's Hospital from March 2023 to May 2024 as investigation objects.The patients with AMI were divided into delayed group(n=106)and undelayed group(n=1 13)with the 6-hour interval.Logistic regression analysis was used to establish a risk prediction model for AMI patients with delayed medical treatment.Hosmer-Lemeshow test and receiver operating characteristic curve were used to evaluate the goodness of fit and prediction ability of the model.Results Binary Logistic regression analysis showed that age,unknown heart disease at the time of onset,first chest pain and low score of brief health literacy screen were all risk factors for delayed medical treatment in AMI patients(P＜0.05).The model predicted that the area under the curve of AMI patients with delayed hospitalization was 0.771,the Youden index was 0.562,the optimal cutoff value was 0.514,and the sensitivity and specificity were 77.3％and 86.5％,respectively.Conclusion The constructed risk prediction model can effectively predict and screen the high-risk groups of AMI patients with delayed medical treatment,reduce the risk of AMI patients with delayed medical treatment,and provide a scientific basis for taking reasonable intervention measures to shorten the time of medical treatment for AMI patients.
