Machine Learning-Based Clustering Using a 12-Lead Electrocardiogram in Patients With a Implantable Cardioverter Defibrillator to Identify Future Ventricular Arrhythmia.

Journal: Circulation journal : official journal of the Japanese Circulation Society
PMID: 39358305

Abstract

BACKGROUND: Implantable cardioverter defibrillators (ICDs) reduce mortality associated with ventricular arrhythmia in high-risk patients with cardiovascular disease. Machine learning (ML) approaches are promising tools in arrhythmia research; however, their application in predicting ventricular arrhythmias in patients with ICDs remains unexplored. We aimed to predict and stratify ventricular arrhythmias requiring ICD therapy using 12-lead electrocardiograms (ECGs) in patients with an ICD.

Authors

  • Ryo Tateishi
    Department of Cardiology, Yokohama Minami Kyosai Hospital.
  • Masato Shimizu
    Department of Cardiology, Yokohama Minami Kyosai Hospital, Yokohama, Japan.
  • Makoto Suzuki
    Department of Pathology, Shizuoka General Hospital, Shizuoka 420-8527, Japan.
  • Eiko Sakai
    Department of Cardiology, Yokohama Minami Kyosai Hospital.
  • Atsuya Shimizu
    Department of Cardiology, Yokohama Minami Kyosai Hospital.
  • Hiroshi Shimada
    Department of Cardiology, Yokohama Minami Kyosai Hospital.
  • Nobutaka Katoh
    Department of Cardiology, Yokohama Minami Kyosai Hospital.
  • Mitsuhiro Nishizaki
    Department of Cardiology, Odawara Cardiovascular Hospital.
  • Tetsuo Sasano
    Department of Cardiovascular Medicine, Tokyo Medical and Dental University, Tokyo, Japan. Electronic address: sasano.cvm@tmd.ac.jp.

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