Aggruppamento fenotipico dell'infarto miocardico complicato da arresto cardiaco fuori dall'ospedale utilizzando l'apprendimento automatico non supervisionato.

BACKGROUND: Patients with myocardial infarction (MI) complicated by out-of-hospital cardiac arrest (OHCA) represent a heterogeneous population with variable outcomes. Data-driven approaches may help uncover clinically meaningful subgroups to improve risk stratification and guide management. METHODS: We applied an unsupervised machine learning analysis using k-means clustering to a prospective cohort of 478 patients admitted after OHCA related to MI. Candidate variables included demographics, cardiovascular risk factors, cardiac arrest characteristics, admission laboratory data, hemodynamic parameters, and coronary angiography findings. In-hospital outcomes included all-cause mortality, blee