Substrate Spatial Complexity Analysis for the Prediction of Ventricular Arrhythmias in Patients With Ischemic Cardiomyopathy.

Journal: Circulation. Arrhythmia and electrophysiology
Published Date: Apr 1, 2020

Abstract

BACKGROUND: Transition zones between healthy myocardium and scar form a spatially complex substrate that may give rise to reentrant ventricular arrhythmias (VAs). We sought to assess the utility of a novel machine learning approach for quantifying 3-dimensional spatial complexity of grayscale patterns on late gadolinium enhanced cardiac magnetic resonance images to predict VAs in patients with ischemic cardiomyopathy.

Authors

  • David R Okada
    Division of Cardiology, Department of Medicine (D.R.O., J.C., S.J., K.C.W.).
  • Jason Miller
    Department of Applied Mathematics (J.A., M.M.).
  • Jonathan Chrispin
    Division of Cardiology, Department of Medicine (D.R.O., J.C., S.J., K.C.W.).
  • Adityo Prakosa
    Department of Biomedical Engineering (A.P., N.T.).
  • Natalia Trayanova
    Department of Biomedical Engineering (A.P., N.T.).
  • Steven Jones
    Division of Cardiology, Department of Medicine (D.R.O., J.C., S.J., K.C.W.).
  • Mauro Maggioni
    Alliance for Cardiovascular Diagnostic and Treatment Innovation (ADVANCE), Johns Hopkins University, Baltimore, Maryland.
  • Katherine C Wu
    Division of Cardiology, Johns Hopkins University Department of Medicine, Baltimore, Maryland, USA.

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