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Detection of the Fingerprint of the Electrophysiological Abnormalities that Increase Vulnerability to Life-Threatening Ventricular Arrhythmias

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Abstract

Reduction of sudden death requires accurate identification of patients at risk for ventricular tachycardia (VT) and effective therapies. The Multicenter Unsustained Tachycardia Trial and Multicenter Automatic Defibrillator Implantation Trials demonstrate that the implantable cardioverter defibrillator impacts favorably on the incidence of VT in patients with myocardial infarction, underscoring the need to detect the electrophysiologic abnormalities required for the development of VT. Methods used for this purpose include: Holter monitoring, ejection fraction, signal-averaged ECG, heart rate variability, T-wave alternans, baroreflex sensitivity, and programmed stimulation. Performance of each method alone has demonstrated high-negative but low-positive predictive values. Recent studies confirm that their use in combination augments performance.

A second approach for improving performance has been to reexamine how well each method detects the electrophysiological derangements that lead to VT. Our recent work has focused on the signal-averaged ECG. Judging from transmural maps of ventricular activation during VT and sinus rhythm obtained from patients, late potentials fail to detect completely signals from myocardium responsible for VT. To obviate this limitation we developed an approach based on inferred epicardial potentials in the frequency domain from 190-surface ECGs using individualized heart-torso models. Torso geometry and electrode positions are measured with a 3-armed digitizer. The location of cardiac structures is determined using echocardiography. The pericardial surface is approximated by a sphere that encloses the heart. Epicardial potentials are inferred using the boundary element method with zero-order Tikhonov regularization and the Composite Residual Smoothing Operator over the QRS complex. Studies are underway to determine if analysis of bioelectrical signals enveloping arrhythmogenic tissue improves identification of patients vulnerable to VT.

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  1. Cardiovascular Division, Department of Medicine, School of Medicine and Department of Electrical Engineering, School of Engineering, Washington University, St. Louis, MO, USA

    Michael E. Cain, R. Martin Arthur & Jason W. Trobaugh

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  1. Michael E. Cain
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  2. R. Martin Arthur
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  3. Jason W. Trobaugh
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Correspondence to Michael E. Cain.

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Cain, M.E., Arthur, R.M. & Trobaugh, J.W. Detection of the Fingerprint of the Electrophysiological Abnormalities that Increase Vulnerability to Life-Threatening Ventricular Arrhythmias. J Interv Card Electrophysiol 9, 103–118 (2003). https://doi.org/10.1023/A:1026259702892

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