Relationship Between Different Risk Factor Patterns and Follow-Up Outcomes in Patients With ST-Segment Elevation Myocardial Infarction - Chen, Si; Che, Qianzi; Zheng, Qiwen; Zhang, Yan; Jia, Jia; Wu, Yiqun; Huo, Yong; Chen, Dafang (Frontiers in Cardiovascular Medicine, 2021)
Objective Few studies focused on the combined influence of the presence of multiple risk factors on follow-up outcomes in AMI patients. Our study aimed at identifying risk factor patterns that may be associated with 1-year survival in male patients with ST-segment elevation myocardial infarction (STEMI). Methods Data was from the China STEMI Care Project Phase 2(CSCAP-2) and collected between 2015 and 2018, and 15675 male STEMI patients were enrolled in this study. Risk factor patterns were characterized using latent class analysis (LCA) based on seven risk factors. The association between risk factor patterns and follow-up outcomes, including the incidence of major adverse cardiovascular and cerebrovascular events(MACCE) and all-cause death, was investigated by carrying out Cox proportional hazard regression analysis. Results We obtained four risk factor patterns, namely “young and middle-aged with low levels of multimorbidity”, “middle-aged with overweight”, “middle-aged and elderly with normal weight” and “elderly with high multimorbidity”. The four patterns had significant differences in event-free survival (P<0.001). Compared with the patient of “young and middle-aged with low levels of multimorbidity”, the incidence risk of MACCE and all-cause death increased in patients of “middle-aged with overweight” pattern (All-cause death: HR=1.70, 95%CI:1.29~2.23; MACCE: HR=1.49, 95%CI:1.29~1.72), “middle-aged and elderly with normal weight” pattern (All-cause death: HR=3.04, 95%CI: 2.33~3.98; MACCE: HR=1.82, 95%CI: 1.56~2.12), and “elderly with high multimorbidity” pattern (All-cause death: HR=5.78, 95%CI: 4.49~7.42; MACCE: HR=2.67, 95%CI: 2.31~3.10). Conclusions By adopting a Latent Class Analysis Approach, STEMI patients can be divided into four risk factor patterns with significantly different prognosis. The data is useful for the improvement of community health management in each specific subgroup of patients, which indicates a particular risk factor pattern.