Electrocardiography-Based Artificial Intelligence Algorithm Aids in Prediction of Long-term Mortality After Cardiac Surgery.

Journal: Mayo Clinic proceedings
PMID: 34863396

Abstract

OBJECTIVE: To assess whether an electrocardiography-based artificial intelligence (AI) algorithm developed to detect severe ventricular dysfunction (left ventricular ejection fraction [LVEF] of 35% or below) independently predicts long-term mortality after cardiac surgery among patients without severe ventricular dysfunction (LVEF>35%).

Authors

  • Abdulah A Mahayni
    Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN.
  • Zachi I Attia
    Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota, USA.
  • Jose R Medina-Inojosa
    Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN.
  • Mohamed F A Elsisy
    Department of Cardiovascular Surgery, Mayo Clinic, Rochester, MN.
  • Peter A Noseworthy
    Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota.
  • Francisco Lopez-Jimenez
    Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA.
  • Suraj Kapa
    Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
  • Samuel J Asirvatham
    Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.
  • Paul A Friedman
    Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota, USA.
  • Juan A Crestenallo
    Department of Cardiovascular Surgery, Mayo Clinic, Rochester, MN.
  • Mohamad Alkhouli
    Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA.

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