Relationship Between Different Risk Factor Patterns and Follow-Up Outcomes in Patients With ST-Segment Elevation Myocardial Infarction

Objective: Few studies have been concerned with the combined influences of the presence of multiple risk factors on follow-up outcomes in AMI patients. Our study aimed to identify risk factor patterns that may be associated with 1-year survival in male patients with ST-segment elevation myocardial infarction (STEMI). Methods: Data were from the China STEMI Care Project Phase 2 (CSCAP-2) collected between 2015 and 2018. A total of 15,675 male STEMI patients were enrolled in this study. Risk factor patterns were characterized using latent class analysis (LCA) according to seven risk factors. Associations between risk factor patterns and follow-up outcomes, including the incidence of major adverse cardiovascular and cerebrovascular events (MACCE) and all-cause death, were investigated by Cox proportional hazard regression analysis. Results: We obtained four risk factor patterns as "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." Four patterns had significant differences in event-free survival (P < 0.001). As compared with the patients of "young and middle-aged with low levels of multimorbidity" pattern, the risk of incidence of MACCE and all-cause death were increased in patients of "middle-aged with overweight" pattern (All-cause death: HR = 1.70, 95% CI:1.29 similar to 2.23; MACCE: HR = 1.49, 95% CI:1.29 similar to 1.72), "middle-aged and elderly with normal weight" pattern (All-cause death: HR = 3.04, 95% CI: 2.33 similar to 3.98; MACCE: HR = 1.82, 95% CI: 1.56 similar to 2.12), and "elderly with high multimorbidity" pattern (All-cause death: HR = 5.78, 95% CI: 4.49 similar to 7.42; MACCE: HR = 2.67, 95% CI: 2.31 similar to 3.10). Conclusions: By adopting a Latent Class Analysis Approach, STEMI patients can be characterized 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.