Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram.

Journal: JAMA cardiology
Published Date: May 1, 2019

Abstract

IMPORTANCE: For patients with chronic kidney disease (CKD), hyperkalemia is common, associated with fatal arrhythmias, and often asymptomatic, while guideline-directed monitoring of serum potassium is underused. A deep-learning model that enables noninvasive hyperkalemia screening from the electrocardiogram (ECG) may improve detection of this life-threatening condition.

Authors

  • Conner D Galloway
    AliveCor Inc, Mountain View, California.
  • Alexander V Valys
    AliveCor Inc, Mountain View, California.
  • Jacqueline B Shreibati
    AliveCor Inc, Mountain View, California.
  • Daniel L Treiman
    AliveCor Inc, Mountain View, California.
  • Frank L Petterson
    AliveCor Inc, Mountain View, California.
  • Vivek P Gundotra
    AliveCor Inc, Mountain View, California.
  • David E Albert
    AliveCor Inc, Mountain View, California.
  • Zachi I Attia
    Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota, USA.
  • Rickey E Carter
    Department of Health Sciences Research, Mayo Clinic, Jacksonville, Florida.
  • Samuel J Asirvatham
    Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.
  • Michael J Ackerman
    Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.
  • Peter A Noseworthy
    Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota.
  • John J Dillon
    Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota.
  • Paul A Friedman
    Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota, USA.

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