Machine learning-derived cycle length variability metrics predict spontaneously terminating ventricular tachycardia in implantable cardioverter defibrillator recipients.

Journal: European heart journal. Digital health
Published Date: Oct 24, 2023

Abstract

AIMS: Implantable cardioverter defibrillator (ICD) therapies have been associated with increased mortality and should be minimized when safe to do so. We hypothesized that machine learning-derived ventricular tachycardia (VT) cycle length (CL) variability metrics could be used to discriminate between sustained and spontaneously terminating VT.

Authors

  • Arunashis Sau
    National Heart and Lung Institute, Hammersmith Campus, Imperial College London, 72 Du Cane Road, W12 0HS, London, UK.
  • Amar Ahmed
    National Heart and Lung Institute, Hammersmith Campus, Imperial College London, 72 Du Cane Road, W12 0HS, London, UK.
  • Jun Yu Chen
    National Heart and Lung Institute, Hammersmith Campus, Imperial College London, 72 Du Cane Road, W12 0HS, London, UK.
  • Libor Pastika
    National Heart and Lung Institute, Hammersmith Campus, Imperial College London, 72 Du Cane Road, W12 0HS, London, UK.
  • Ian Wright
    Department of Cardiology, Hammersmith Hospital, Imperial College Healthcare NHS Trust, 72 Du Cane Road, W12 0HS, London, UK.
  • Xinyang Li
    National Heart and Lung Institute, Hammersmith Campus, Imperial College London, 72 Du Cane Road, W12 0HS, London, UK.
  • Balvinder Handa
    National Heart and Lung Institute, Hammersmith Campus, Imperial College London, 72 Du Cane Road, W12 0HS, London, UK.
  • Norman Qureshi
    National Heart and Lung Institute, Hammersmith Campus, Imperial College London, 72 Du Cane Road, W12 0HS, London, UK.
  • Michael Koa-Wing
    National Heart and Lung Institute, Hammersmith Campus, Imperial College London, 72 Du Cane Road, W12 0HS, London, UK.
  • Daniel Keene
    National Heart and Lung Institute, Hammersmith Campus, Imperial College London, 72 Du Cane Road, W12 0HS, London, UK.
  • Louisa Malcolme-Lawes
    National Heart and Lung Institute, Hammersmith Campus, Imperial College London, 72 Du Cane Road, W12 0HS, London, UK.
  • Amanda Varnava
    National Heart and Lung Institute, Hammersmith Campus, Imperial College London, 72 Du Cane Road, W12 0HS, London, UK.
  • Nicholas W F Linton
    National Heart and Lung Institute, Hammersmith Campus, Imperial College London, 72 Du Cane Road, W12 0HS, London, UK.
  • Phang Boon Lim
    National Heart and Lung Institute, Hammersmith Campus, Imperial College London, 72 Du Cane Road, W12 0HS, London, UK.
  • David Lefroy
    National Heart and Lung Institute, Hammersmith Campus, Imperial College London, 72 Du Cane Road, W12 0HS, London, UK.
  • Prapa Kanagaratnam
    National Heart and Lung Institute, Hammersmith Campus, Imperial College London, 72 Du Cane Road, W12 0HS, London, UK.
  • Nicholas S Peters
    National Heart and Lung Institute, Hammersmith Campus, Imperial College London, 72 Du Cane Road, W12 0HS, London, UK.
  • Zachary Whinnett
    National Heart and Lung Institute, Hammersmith Campus, Imperial College London, 72 Du Cane Road, W12 0HS, London, UK.
  • Fu Siong Ng
    National Heart and Lung Institute, Hammersmith Campus, Imperial College London, 72 Du Cane Road, W12 0HS, London, UK.

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