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
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.
Mots clés
Heart failure, biomarkers, collagen, echocardiogram, spironolactone
Référence
Eur J Heart Fail. 2023 04 16;: