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Deep learned representations of the resting 12-lead electrocardiogram to predict at peak exercise.
Khurshid, Shaan; Churchill, Timothy W; Diamant, Nathaniel; Di Achille, Paolo; Reeder, Christopher; Singh, Pulkit; Friedman, Samuel F; Wasfy, Meagan M; Alba, George A; Maron, Bradley A; Systrom, David M; Wertheim, Bradley M; Ellinor, Patrick T; Ho, Jennifer E; Baggish, Aaron L; Batra, Puneet; Lubitz, Steven A; Guseh, J Sawalla.
Affiliation
  • Khurshid S; Cardiovascular Research Center, Massachusetts General Hospital, 185 Cambridge Street Suite 3201, Boston, MA 02114, USA.
  • Churchill TW; Demoulas Center for Cardiac Arrhythmias, Division of Cardiology, Massachusetts General Hospital, 55 Fruit Street, GRB 109, Boston, MA 02114, USA.
  • Diamant N; Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, 415 Main Street, Cambridge, MA 02142, USA.
  • Di Achille P; Cardiovascular Research Center, Massachusetts General Hospital, 185 Cambridge Street Suite 3201, Boston, MA 02114, USA.
  • Reeder C; Cardiovascular Performance Program, Division of Cardiology, Mass General Sports Medicine, Massachusetts General Hospital, 55 Fruit Street, GRB 109, Boston, MA 02114, USA.
  • Singh P; Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA 02142, USA.
  • Friedman SF; Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA 02142, USA.
  • Wasfy MM; Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA 02142, USA.
  • Alba GA; Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA 02142, USA.
  • Maron BA; Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA 02142, USA.
  • Systrom DM; Cardiovascular Research Center, Massachusetts General Hospital, 185 Cambridge Street Suite 3201, Boston, MA 02114, USA.
  • Wertheim BM; Cardiovascular Performance Program, Division of Cardiology, Mass General Sports Medicine, Massachusetts General Hospital, 55 Fruit Street, GRB 109, Boston, MA 02114, USA.
  • Ellinor PT; Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, MA 02114, USA.
  • Ho JE; Division of Cardiovascular Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA.
  • Baggish AL; Department of Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA.
  • Batra P; University of Maryland, Institute for Health Computing, Bethesda, MD, USA.
  • Lubitz SA; Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA.
  • Guseh JS; Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA.
Eur J Prev Cardiol ; 31(2): 252-262, 2024 Jan 25.
Article de En | MEDLINE | ID: mdl-37798122
Researchers here present data describing a method of estimating exercise capacity from the resting electrocardiogram. Electrocardiogram estimation of exercise capacity was accurate and was found to predict the onset of the wide range of cardiovascular diseases including heart attacks, heart failure, arrhythmia, and death.This approach offers the ability to estimate exercise capacity without dedicated exercise testing and may enable efficient risk stratification of cardiac patients at scale.
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Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Sujet principal: Électrocardiographie / Défaillance cardiaque Type d'étude: Prognostic_studies / Risk_factors_studies Limites: Adult / Female / Humans / Male / Middle aged Langue: En Journal: Eur J Prev Cardiol Année: 2024 Type de document: Article Pays d'affiliation: États-Unis d'Amérique Pays de publication: Royaume-Uni
Texte intégral
Ajouter à My VHL
Imprimer
XML
PubMed Links
Recherche sur Google

Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Sujet principal: Électrocardiographie / Défaillance cardiaque Type d'étude: Prognostic_studies / Risk_factors_studies Limites: Adult / Female / Humans / Male / Middle aged Langue: En Journal: Eur J Prev Cardiol Année: 2024 Type de document: Article Pays d'affiliation: États-Unis d'Amérique Pays de publication: Royaume-Uni