Literature DB >> 30195579

Measuring defibrillator surface potentials: The validation of a predictive defibrillation computer model.

Jess Tate1, Jeroen Stinstra2, Thomas Pilcher3, Ahrash Poursaid2, Matthew A Jolley4, Elizabeth Saarel3, John Triedman5, Rob S MacLeod2.   

Abstract

Implantable cardioverter defibrillators (ICDs) are commonly used to reduce the risk in patients with life-threatening arrhythmias, however, clinicians have little systematic guidance to place the device, especially in cases of unusual anatomy. We have previously developed a computational model that evaluates the efficacy of a delivered shock as a clinical and research aid to guide ICD placement on a patient specific basis. We report here on progress to validate this model with measured ICD surface potential maps from patients undergoing ICD implantation and testing for defibrillation threshold (DFT). We obtained body surface potential maps of the defibrillation pulses by adapting a limited lead selection and potential estimation algorithm to deal with the limited space for recording electrodes. Comparison of the simulated and measured potential maps of the defibrillation shock yielded similar patterns, a typical correlation greater than 0.9, and a relative error less than 15%. Comparison of defibrillation thresholds also showed accurate prediction of the simulations. The high agreement of the potential maps and DFTs suggests that the predictive simulation generates realistic potential values and can accurately predict DFTs in patients. These validation results pave the way for use of this model in optimization studies prior to device implantation.
Copyright © 2018 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Body surface mapping; Defibrillation; Defibrillation modeling; Defibrillation threshold; Limited lead selection; Patient-specific modeling

Mesh:

Year:  2018        PMID: 30195579      PMCID: PMC6221557          DOI: 10.1016/j.compbiomed.2018.08.025

Source DB:  PubMed          Journal:  Comput Biol Med        ISSN: 0010-4825            Impact factor:   4.589


  38 in total

Review 1.  Virtual electrode-induced positive and negative graded responses: new insights into fibrillation induction and defibrillation.

Authors:  Natalia A Trayanova; Richard A Gray; David W Bourn; James C Eason
Journal:  J Cardiovasc Electrophysiol       Date:  2003-07

2.  Long-term comparison of the implantable cardioverter defibrillator versus amiodarone: eleven-year follow-up of a subset of patients in the Canadian Implantable Defibrillator Study (CIDS).

Authors:  Fayez Bokhari; David Newman; Mary Greene; Victoria Korley; Iqwal Mangat; Paul Dorian
Journal:  Circulation       Date:  2004-07-06       Impact factor: 29.690

3.  High-energy defibrillation impairs myocyte contractility and intracellular calcium dynamics.

Authors:  Giuseppe Ristagno; Tong Wang; Wanchun Tang; Shijie Sun; Carlos Castillo; Max Harry Weil
Journal:  Crit Care Med       Date:  2008-11       Impact factor: 7.598

4.  Nontransvenous implantable cardioverter defibrillator systems: not just for small pediatric patients.

Authors:  John D Kugler; Christopher C Erickson
Journal:  J Cardiovasc Electrophysiol       Date:  2006-01

Review 5.  Current concepts for selecting the location, size and shape of defibrillation electrodes.

Authors:  R E Ideker; P D Wolf; C Alferness; W Krassowska; W M Smith
Journal:  Pacing Clin Electrophysiol       Date:  1991-02       Impact factor: 1.976

6.  Predicting cardiothoracic voltages during high energy shocks: methodology and comparison of experimental to finite element model data.

Authors:  D B Jorgenson; P H Schimpf; I Shen; G Johnson; G H Bardy; D R Haynor; Y Kim
Journal:  IEEE Trans Biomed Eng       Date:  1995-06       Impact factor: 4.538

7.  Wearable cardioverter-defibrillator use in patients perceived to be at high risk early post-myocardial infarction.

Authors:  Andrew E Epstein; William T Abraham; Nicole R Bianco; Karl B Kern; Michael Mirro; Sunil V Rao; Edward K Rhee; Scott D Solomon; Steven J Szymkiewicz
Journal:  J Am Coll Cardiol       Date:  2013-07-31       Impact factor: 24.094

8.  Body-surface maps of heart potentials: tentative localization of pre-excited areas in forty-two Wolff-Parkinson-White patients.

Authors:  L De Ambroggi; B Taccardi; E Macchi
Journal:  Circulation       Date:  1976-08       Impact factor: 29.690

9.  Implications of implantable cardioverter defibrillator therapy in congenital heart disease and pediatrics.

Authors:  Mark E Alexander; Frank Cecchin; Edward P Walsh; John K Triedman; Laura M Bevilacqua; Charles I Berul
Journal:  J Cardiovasc Electrophysiol       Date:  2004-01

Review 10.  The subcutaneous defibrillator: a review of the literature.

Authors:  Sally Aziz; Angel R Leon; Mikhael F El-Chami
Journal:  J Am Coll Cardiol       Date:  2014-02-12       Impact factor: 24.094

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  3 in total

1.  Validating defibrillation simulation in a human-shaped phantom.

Authors:  Jess D Tate; Thomas A Pilcher; Kedar K Aras; Brett M Burton; Rob S MacLeod
Journal:  Heart Rhythm       Date:  2019-11-23       Impact factor: 6.343

2.  Novel experimental model for studying the spatiotemporal electrical signature of acute myocardial ischemia: a translational platform.

Authors:  Brian Zenger; Wilson W Good; Jake A Bergquist; Brett M Burton; Jess D Tate; Leo Berkenbile; Vikas Sharma; Rob S MacLeod
Journal:  Physiol Meas       Date:  2020-02-05       Impact factor: 2.833

3.  Computational Model for Therapy Optimization of Wearable Cardioverter Defibrillator: Shockable Rhythm Detection and Optimal Electrotherapy.

Authors:  Oishee Mazumder; Rohan Banerjee; Dibyendu Roy; Ayan Mukherjee; Avik Ghose; Sundeep Khandelwal; Aniruddha Sinha
Journal:  Front Physiol       Date:  2021-12-10       Impact factor: 4.566

  3 in total

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