Unsupervised clustering identifies distinct phenotypes in acute myocardial infarction: insights from the FAST-MI 2015 registry.

BACKGROUND: Current classification of Acute Myocardial Infarction (AMI) into ST-elevation (STEMI) and non-ST-elevation (NSTEMI) myocardial infarction does not fully reflect the clinical heterogeneity of patients. OBJECTIVES: To identify clinically meaningful phenotypes of AMI patients using an unsupervised clustering approach and assess their associations with management strategies and long-term outcomes. METHODS: Hierarchical agglomerative clustering using Ward's method was performed in 4,947 patients from the French FAST-MI 2015 nationwide registry. Clustering was based on baseline clinical, demographic, and laboratory features. Between-cluster differences in baseline characteristics, management, in-hospital complications, and 1-year mortality were analyzed. RESULTS: Four phenotypic clusters were identified. Cluster 1 (n = 1,488) gathered older patients, predominantly men, with typical coronary risk factors, intermediate risk profile, and balanced STEMI/NSTEMI presentations. Cluster 2 (n = 1,305) gathered older polymorbid patients, 45% were women, 60% with NSTEMI presentation, with limited use of invasive strategies (71%), and a high 1-year mortality. Cluster 3 (n = 815) gathered young (mean age 44 years), predominantly male patients, with a high prevalence of smoking (73%) and few comorbidities, 62% STEMI, high rates of revascularization, and excellent prognosis. Cluster 4 (n = 1,339) was close to Cluster 3 with a more metabolic profile with higher rates of obesity, diabetes, and dyslipidemia, also with favorable outcomes. In-hospital complications were more frequent in Clusters 1 and 2. One-year mortality was lowest in Clusters 3 and 4 (1.5 and 2.3%), intermediate in Cluster 1 (6.0%), and highest in Cluster 2 (15.7%). CONCLUSION: Unsupervised clustering identified four clinically relevant AMI phenotypes with distinct characteristics, management patterns, and prognoses. This data-driven classification highlights the diversity of AMI presentations and may offer complementary insights for patient profiling and risk stratification.