A machine learning derived echocardiographic algorithm identifies people at risk of heart failure with distinct cardiac structure, function, and response to spironolactone: findings from the HOMAGE trial

International audience; Background: An echocardiographic algorithm derived by machine learning (e'VM) characterizes preclinical individuals with different cardiac structure and function, biomarkers, and long-term risk of heart failure (HF). Our aim was the external validation of the e'VM algorithm and to explore whether it may identify subgroups who benefit from spironolactone.Methods: The HOMAGE (Heart OMics in Aging) trial enrolled participants at high risk of developing HF randomly assigned to spironolactone or placebo over 9 months. The e'VM algorithm was applied to 416 participants (mean age 74±7years, 25% women) with available echocardiographic variables (i.e., e' mean, left ventricular [LV] end-diastolic volume and mass indexed by body surface area [LVMi]). The effects of spironolactone on changes in echocardiographic and biomarker variables were assessed across e'VM phenotypes.Results: A majority (>80%) had either "diastolic changes (D)", or "diastolic changes with structural remodeling (D/S)" phenotype. D/S phenotype had the highest LVMi, left atrial volume, E/e', natriuretic peptide and troponin levels (all p0.10; Pinteraction