Literature DB >> 23798492

Placement of implantable cardioverter-defibrillators in paediatric and congenital heart defect patients: a pipeline for model generation and simulation prediction of optimal configurations.

Lukas J Rantner1, Fijoy Vadakkumpadan, Philip J Spevak, Jane E Crosson, Natalia A Trayanova.   

Abstract

There is currently no reliable way of predicting the optimal implantable cardioverter-defibrillator (ICD) placement in paediatric and congenital heart defect (CHD) patients. This study aimed to: (1) develop a new image processing pipeline for constructing patient-specific heart-torso models from clinical magnetic resonance images (MRIs); (2) use the pipeline to determine the optimal ICD configuration in a paediatric tricuspid valve atresia patient; (3) establish whether the widely used criterion of shock-induced extracellular potential (Φe) gradients ≥5 V cm(-1) in ≥95% of ventricular volume predicts defibrillation success. A biophysically detailed heart-torso model was generated from patient MRIs. Because transvenous access was impossible, three subcutaneous and three epicardial lead placement sites were identified along with five ICD scan locations. Ventricular fibrillation was induced, and defibrillation shocks were applied from 11 ICD configurations to determine defibrillation thresholds (DFTs). Two configurations with epicardial leads resulted in the lowest DFTs overall and were thus considered optimal. Three configurations shared the lowest DFT among subcutaneous lead ICDs. The Φe gradient criterion was an inadequate predictor of defibrillation success, as defibrillation failed in numerous instances even when 100% of the myocardium experienced such gradients. In conclusion, we have developed a new image processing pipeline and applied it to a CHD patient to construct the first active heart-torso model from clinical MRIs.

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Year:  2013        PMID: 23798492      PMCID: PMC3779119          DOI: 10.1113/jphysiol.2013.255109

Source DB:  PubMed          Journal:  J Physiol        ISSN: 0022-3751            Impact factor:   5.182


  56 in total

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Review 2.  Computed tomography--an increasing source of radiation exposure.

Authors:  David J Brenner; Eric J Hall
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3.  Shocks burden and increased mortality in implantable cardioverter-defibrillator patients.

Authors:  Gail K Larsen; John Evans; William E Lambert; Yiyi Chen; Merritt H Raitt
Journal:  Heart Rhythm       Date:  2011-08-02       Impact factor: 6.343

4.  Transmural and apicobasal gradients in repolarization contribute to T-wave genesis in human surface ECG.

Authors:  Jun-Ichi Okada; Takumi Washio; Akiko Maehara; Shin-Ichi Momomura; Seiryo Sugiura; Toshiaki Hisada
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5.  A generalized activating function for predicting virtual electrodes in cardiac tissue.

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6.  System survival of nontransvenous implantable cardioverter-defibrillators compared to transvenous implantable cardioverter-defibrillators in pediatric and congenital heart disease patients.

Authors:  Andrew E Radbill; John K Triedman; Charles I Berul; Francis Fynn-Thompson; Joseph Atallah; Mark E Alexander; Edward P Walsh; Frank Cecchin
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7.  A computer modeling tool for comparing novel ICD electrode orientations in children and adults.

Authors:  Matthew Jolley; Jeroen Stinstra; Steve Pieper; Rob Macleod; Dana H Brooks; Frank Cecchin; John K Triedman
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8.  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
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Review 9.  Do clinically relevant transthoracic defibrillation energies cause myocardial damage and dysfunction?

Authors:  Gregory P Walcott; Cheryl R Killingsworth; Raymond E Ideker
Journal:  Resuscitation       Date:  2003-10       Impact factor: 5.262

10.  Arrhythmogenesis in the heart: Multiscale modeling of the effects of defibrillation shocks and the role of electrophysiological heterogeneity.

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  20 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

Review 2.  Computational modeling of cardiac optogenetics: Methodology overview & review of findings from simulations.

Authors:  Patrick M Boyle; Thomas V Karathanos; Emilia Entcheva; Natalia A Trayanova
Journal:  Comput Biol Med       Date:  2015-05-07       Impact factor: 4.589

3.  Pacing and Defibrillators in Complex Congenital Heart Disease.

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Journal:  Arrhythm Electrophysiol Rev       Date:  2016-05

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

Authors:  Jess Tate; Jeroen Stinstra; Thomas Pilcher; Ahrash Poursaid; Matthew A Jolley; Elizabeth Saarel; John Triedman; Rob S MacLeod
Journal:  Comput Biol Med       Date:  2018-08-29       Impact factor: 4.589

5.  Arrhythmia: 100 years on from George Ralph Mines.

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Journal:  J Physiol       Date:  2013-09-01       Impact factor: 5.182

6.  Methodology for image-based reconstruction of ventricular geometry for patient-specific modeling of cardiac electrophysiology.

Authors:  A Prakosa; P Malamas; S Zhang; F Pashakhanloo; H Arevalo; D A Herzka; A Lardo; H Halperin; E McVeigh; N Trayanova; F Vadakkumpadan
Journal:  Prog Biophys Mol Biol       Date:  2014-08-19       Impact factor: 3.667

Review 7.  How computer simulations of the human heart can improve anti-arrhythmia therapy.

Authors:  Natalia A Trayanova; Kelly C Chang
Journal:  J Physiol       Date:  2016-01-18       Impact factor: 5.182

Review 8.  New insights into defibrillation of the heart from realistic simulation studies.

Authors:  Natalia A Trayanova; Lukas J Rantner
Journal:  Europace       Date:  2014-05       Impact factor: 5.214

9.  Multi-scale Modeling of the Cardiovascular System: Disease Development, Progression, and Clinical Intervention.

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10.  Virtual 3D heart models to aid pacemaker implantation in children.

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Journal:  Future Cardiol       Date:  2014-01
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