An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction.

Journal: Lancet (London, England)
PMID: 31378392

Abstract

BACKGROUND: Atrial fibrillation is frequently asymptomatic and thus underdetected but is associated with stroke, heart failure, and death. Existing screening methods require prolonged monitoring and are limited by cost and low yield. We aimed to develop a rapid, inexpensive, point-of-care means of identifying patients with atrial fibrillation using machine learning.

Authors

  • Zachi I Attia
    Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota, USA.
  • Peter A Noseworthy
    Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota.
  • Francisco Lopez-Jimenez
    Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA.
  • Samuel J Asirvatham
    Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.
  • Abhishek J Deshmukh
    Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota.
  • Bernard J Gersh
    Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
  • Rickey E Carter
    Department of Health Sciences Research, Mayo Clinic, Jacksonville, Florida.
  • Xiaoxi Yao
    Department of Health Sciences Research, Division of Health Care Policy and Research, Mayo Clinic, Rochester, Minnesota.
  • Alejandro A Rabinstein
    Department of Neurology, Mayo Clinic, Rochester, MN, USA.
  • Brad J Erickson
    Department of Radiology, Mayo Clinic, Rochester, MN, USA.
  • Suraj Kapa
    Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
  • Paul A Friedman
    Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota, USA.

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