Unsupervised machine learning to identify subphenotypes among cardiac intensive care unit patients with heart failure.

Journal: ESC heart failure
PMID: 39160644

Abstract

AIMS: Hospitalized patients with heart failure (HF) are a heterogeneous population, with multiple phenotypes proposed. Prior studies have not examined the biological phenotypes of critically ill patients with HF admitted to the contemporary cardiac intensive care unit (CICU). We aimed to leverage unsupervised machine learning to identify previously unknown HF phenotypes in a large and diverse cohort of patients with HF admitted to the CICU.

Authors

  • Jacob C Jentzer
    Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA.
  • Yogesh N V Reddy
    Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN (F.H.V., Y.N.V.R., Z.I.A., P.A.F., P.A.N., F.L.-J., S.K., B.A.B.).
  • Sabri Soussi
  • Ruben Crespo-Diaz
    Mayo Clinic, Department of Cardiovascular Diseases, Rochester, MN.
  • Parag C Patel
    Department of Cardiovascular Medicine, Mayo Clinic Florida, Jacksonville, Florida, USA.
  • Patrick R Lawler
    Divisions of Cardiology and Clinical Epidemiology, Jewish General Hospital/McGill University, Montreal, Quebec, Canada.
  • Alexandre Mebazaa
    Université Paris Cité, MASCOT Inserm Unit, 45 Rue des Saints-Pères, 75006 Paris, France.
  • Shannon M Dunlay
    Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA.

Keywords

External Resources

Popular Topics
Recent Journals