Machine Learning Identifies Clinically Distinct Phenotypes in Patients With Aortic Regurgitation.

Journal: Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography
PMID: 39566668

Abstract

BACKGROUND: Aortic regurgitation (AR) is a prevalent valve disease with a long latent period before symptoms appear. Recent data has suggested the role of novel markers of myocardial overload in assessing onset of decompensation.

Authors

  • Brototo Deb
    Department of Medicine, MedStar Georgetown University - Washington Hospital Center, Washington DC, USA.
  • Christopher G Scott
    Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA.
  • Hector I Michelena
    From Divisions of Cardiovascular Surgery (R.M.S., A.T., H.M.B., R.C.D., J.A.D.), Anesthesiology (W.M.), Cardiovascular Diseases (R.A.N., H.I.M., M.E.-S.), and Biomedical Statistics and Informatics (Z.L.), Mayo Clinic, Rochester, MN.
  • Sorin V Pislaru
    Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA.
  • Vuyisile T Nkomo
    Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota.
  • Garvan C Kane
    Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA.
  • Juan A Crestanello
    Department of Cardiovascular Surgery (A.A., J.A.C.), Mayo Clinic, Rochester, MN.
  • Patricia A Pellikka
    Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA.
  • Vidhu Anand
    Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota; Department of Cardiovascular Medicine, Mayo Clinic Health System, Eau Claire, Wisconsin. Electronic address: anand.vidhu@mayo.edu.

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