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.

Fiche publication

Date publication

avril 2023

Journal

European journal of heart failure

Auteurs

Membres identifiés du Cancéropôle Est :
Pr ROSSIGNOL Patrick

Tous les auteurs :
Kobayashi M, Huttin O, Ferreira JP, Duarte K, González A, Heymans S, Verdonschot JAJ, Rocca HB, Pellicori P, Clark AL, Petutschnigg J, Edelmann F, Cleland JG, Rossignol P, Zannad F, Girerd N,

Lien Pubmed

https://www.ncbi.nlm.nih.gov/pubmed/37062878

Résumé

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.