Identification of Clinical Phenotypes in Type 2 Myocardial Infarction: Insights Into Characteristics, Prognosis, and Management Strategies

Background: Type 2 myocardial infarction (T2MI) accounts for a substantial share of acute coronary syndromes but remains challenging to diagnose and manage due to its varied presentations and underlying profiles. This study aims to identify key differences and distinct clinical phenotypes in a large T2MI population. Methods: All consecutive patients with non-ST-segment-elevation myocardial infarction undergoing coronary angiography with a confirmed T2MI diagnosis between January 1, 2017, and March 31, 2023, were analyzed. Precipitating factors of supply-demand mismatch were identified, and coronary burden was assessed using the Gensini score. Latent class analysis was used to identify clinical phenotypes, and multivariable analyses were performed to determine prognostic predictors. A composite of major adverse cardiovascular events was assessed during follow-up, along with additional outcomes including cardiovascular death and nonfatal type 2 reinfarction. Results: Among 774 patients with T2MI, latent class analysis identified 2 phenotypes. Phenotype 1 (31.5%) was younger with a higher prevalence of nonatherosclerotic coronary causes and unknown pathogeneses. Phenotype 2 (68.5%) exhibited greater comorbidity and a higher atherosclerotic burden, reflected by elevated Gensini scores (median, 11 versus 1.5; P<0.001). Over a median follow-up of 53 months, major adverse cardiovascular events occurred in 49.1% of patients, with a higher rate in phenotype 2 (60.8% versus 23.8%, P<0.001). Predictors of major adverse cardiovascular events included peak cardiac troponin levels for phenotype 1 and age, known cardiovascular disease, chronic obstructive pulmonary disease, peak cardiac troponin levels, and Gensini score for phenotype 2. Conclusions: This study identified 2 clinical phenotypes in T2MI, highlighting differences in characteristics, precipitating factors, outcomes, and prognostic predictors, emphasizing the potential for phenotype-driven approaches in diagnosis and management.