Serum Potassium Monitoring Using AI-Enabled Smartwatch Electrocardiograms.

Journal: JACC. Clinical electrophysiology
PMID: 39387744

Abstract

BACKGROUND: Hyperkalemia, characterized by elevated serum potassium levels, heightens the risk of sudden cardiac death, particularly increasing risk for individuals with chronic kidney disease and end-stage renal disease (ESRD). Traditional laboratory test monitoring is resource-heavy, invasive, and unable to provide continuous tracking. Wearable technologies like smartwatches with electrocardiogram (ECG) capabilities are emerging as valuable tools for remote monitoring, potentially allowing for personalized monitoring with artificial intelligence (AI)-ECG interpretation.

Authors

  • I-Min Chiu
    Department of Emergency Medicine, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan.
  • Po-Jung Wu
    Division of Nephrology, Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan.
  • Huan Zhang
    Department of Plant Protection, Zhejiang University, 866 Yuhangtang Road, 5 Hangzhou 310058, China.
  • J Weston Hughes
    Department of Computer Science, Stanford University, Palo Alto, CA 94025.
  • Albert J Rogers
    Cardiovascular Institute and Division of Cardiovascular Medicine, Stanford University, Stanford, CA, USA.
  • Laleh Jalilian
    Department of Anesthesiology and Perioperative Medicine, University of California-Los Angeles, Los Angeles, California, USA.
  • Marco Perez
    Division of Cardiology, Stanford University, Palo Alto, CA, USA.
  • Chun-Hung Richard Lin
    Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan.
  • Chien-Te Lee
    Division of Nephrology, Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital and Kaohsiung Municipal Feng-Shan Hospital, Kaohsiung, Taiwan.
  • James Zou
    Department of Biomedical Data Science, Stanford University, Stanford, California.
  • David Ouyang
    Division of Artificial Intelligence, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA.

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