Data-driven point-of-care risk model in patients with acute myocardial infarction and cardiogenic shock

Background Prognosis models based on stepwise regression methods show modest performance in patients with cardiogenic shock (CS). Automated variable selection allows data-driven risk evaluation by recognizing distinct patterns in data. We sought to evaluate an automated variable selection method (least absolute shrinkage and selection operator, LASSO) for predicting 30-day mortality in patients with acute myocardial infarction and CS (AMICS)...