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
Abstract
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 p<0.05). Spironolactone significantly reduced E/e' and b-type natriuretic peptide (BNP) levels in D/S phenotype (p<0.01), but not in other phenotypes (p>0.10; Pinteraction <0.05 for both). These interactions were not observed when considering guideline-recommended echocardiographic structural and functional abnormalities. The magnitude of effects of spironolactone on LVMi, left atrial volume and a type I collagen marker was numerically higher in D/S phenotype than D phenotype but the interaction test did not reach significance.
Conclusions: In the HOMAGE trial, the e'VM algorithm identified echocardiographic phenotypes with distinct responses to spironolactone as assessed by changes in E/e' and BNP.
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