Literature DB >> 31447108

Characterizing Conduction Channels in Postinfarction Patients Using a Personalized Virtual Heart.

Dongdong Deng1, Adityo Prakosa2, Julie Shade2, Plamen Nikolov2, Natalia A Trayanova3.   

Abstract

Patients with myocardial infarction have an abundance of conduction channels (CC); however, only a small subset of these CCs sustain ventricular tachycardia (VT). Identifying these critical CCs (CCCs) in the clinic so that they can be targeted by ablation remains a significant challenge. The objective of this study is to use a personalized virtual-heart approach to conduct a three-dimensional (3D) assessment of CCCs sustaining VTs of different morphologies in these patients, to investigate their 3D structural features, and to determine the optimal ablation strategy for each VT. To achieve these goals, ventricular models were constructed from contrast enhanced magnetic resonance imagings of six postinfarction patients. Rapid pacing induced VTs in each model. CCCs that sustained different VT morphologies were identified. CCCs' 3D structure and type and the resulting rotational electrical activity were examined. Ablation was performed at the optimal part of each CCC, aiming to terminate each VT with a minimal lesion size. Predicted ablation locations were compared to clinical. Analyzing the simulation results, we found that the observed VTs in each patient model were sustained by a limited number (2.7 ± 1.2) of CCCs. Further, we identified three types of CCCs sustaining VTs: I-type and T-type channels, with all channel branches bounded by scar, and functional reentry channels, which were fully or partially bounded by conduction block surfaces. The different types of CCCs accounted for 43.8, 18.8, and 37.4% of all CCCs, respectively. The mean narrowest width of CCCs or a branch of CCC was 9.7 ± 3.6 mm. Ablation of the narrowest part of each CCC was sufficient to terminate VT. Our results demonstrate that a personalized virtual-heart approach can determine the possible VT morphologies in each patient and identify the CCCs that sustain reentry. The approach can aid clinicians in identifying accurately the optimal VT ablation targets in postinfarction patients.
Copyright © 2019 Biophysical Society. Published by Elsevier Inc. All rights reserved.

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Year:  2019        PMID: 31447108      PMCID: PMC6990147          DOI: 10.1016/j.bpj.2019.07.024

Source DB:  PubMed          Journal:  Biophys J        ISSN: 0006-3495            Impact factor:   4.033


  34 in total

1.  A novel rule-based algorithm for assigning myocardial fiber orientation to computational heart models.

Authors:  J D Bayer; R C Blake; G Plank; N A Trayanova
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2.  Model of reentrant ventricular tachycardia based on infarct border zone geometry predicts reentrant circuit features as determined by activation mapping.

Authors:  Edward J Ciaccio; Hiroshi Ashikaga; Riyaz A Kaba; Daniel Cervantes; Bruce Hopenfeld; Andrew L Wit; Nicholas S Peters; Elliot R McVeigh; Hasan Garan; James Coromilas
Journal:  Heart Rhythm       Date:  2007-05-04       Impact factor: 6.343

3.  Patient-derived models link re-entrant driver localization in atrial fibrillation to fibrosis spatial pattern.

Authors:  Sohail Zahid; Hubert Cochet; Patrick M Boyle; Erica L Schwarz; Kaitlyn N Whyte; Edward J Vigmond; Rémi Dubois; Mélèze Hocini; Michel Haïssaguerre; Pierre Jaïs; Natalia A Trayanova
Journal:  Cardiovasc Res       Date:  2016-04-07       Impact factor: 10.787

4.  Feasibility of using patient-specific models and the "minimum cut" algorithm to predict optimal ablation targets for left atrial flutter.

Authors:  Sohail Zahid; Kaitlyn N Whyte; Erica L Schwarz; Robert C Blake; Patrick M Boyle; Jonathan Chrispin; Adityo Prakosa; Esra G Ipek; Farhad Pashakhanloo; Henry R Halperin; Hugh Calkins; Ronald D Berger; Saman Nazarian; Natalia A Trayanova
Journal:  Heart Rhythm       Date:  2016-04-19       Impact factor: 6.343

5.  Infarct-Related Ventricular Tachycardia: Redefining the Electrophysiological Substrate of the Isthmus During Sinus Rhythm.

Authors:  Elad Anter; Andre G Kleber; Markus Rottmann; Eran Leshem; Michael Barkagan; Cory M Tschabrunn; Fernando M Contreras-Valdes; Alfred E Buxton
Journal:  JACC Clin Electrophysiol       Date:  2018-06-27

6.  Nonsurgical transthoracic epicardial catheter ablation to treat recurrent ventricular tachycardia occurring late after myocardial infarction.

Authors:  E Sosa; M Scanavacca; A d'Avila; F Oliveira; J A Ramires
Journal:  J Am Coll Cardiol       Date:  2000-05       Impact factor: 24.094

7.  3D delayed-enhanced magnetic resonance sequences improve conducting channel delineation prior to ventricular tachycardia ablation.

Authors:  David Andreu; Jose T Ortiz-Pérez; Juan Fernández-Armenta; Esther Guiu; Juan Acosta; Susanna Prat-González; Teresa M De Caralt; Rosario J Perea; César Garrido; Lluis Mont; Josep Brugada; Antonio Berruezo
Journal:  Europace       Date:  2015-01-23       Impact factor: 5.214

8.  A swine model of infarct-related reentrant ventricular tachycardia: Electroanatomic, magnetic resonance, and histopathological characterization.

Authors:  Cory M Tschabrunn; Sébastien Roujol; Reza Nezafat; Beverly Faulkner-Jones; Alfred E Buxton; Mark E Josephson; Elad Anter
Journal:  Heart Rhythm       Date:  2015-07-28       Impact factor: 6.343

9.  Feasibility of image-based simulation to estimate ablation target in human ventricular arrhythmia.

Authors:  Hiroshi Ashikaga; Hermenegild Arevalo; Fijoy Vadakkumpadan; Robert C Blake; Jason D Bayer; Saman Nazarian; M Muz Zviman; Harikrishna Tandri; Ronald D Berger; Hugh Calkins; Daniel A Herzka; Natalia A Trayanova; Henry R Halperin
Journal:  Heart Rhythm       Date:  2013-04-19       Impact factor: 6.343

10.  Sensitivity of Ablation Targets Prediction to Electrophysiological Parameter Variability in Image-Based Computational Models of Ventricular Tachycardia in Post-infarction Patients.

Authors:  Dongdong Deng; Adityo Prakosa; Julie Shade; Plamen Nikolov; Natalia A Trayanova
Journal:  Front Physiol       Date:  2019-05-24       Impact factor: 4.566

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  6 in total

Review 1.  How personalized heart modeling can help treatment of lethal arrhythmias: A focus on ventricular tachycardia ablation strategies in post-infarction patients.

Authors:  Natalia A Trayanova; Ashish N Doshi; Adityo Prakosa
Journal:  Wiley Interdiscip Rev Syst Biol Med       Date:  2020-01-09

2.  Preliminary Study: Learning the Impact of Simulation Time on Reentry Location and Morphology Induced by Personalized Cardiac Modeling.

Authors:  Lv Tong; Caiming Zhao; Zhenyin Fu; Ruiqing Dong; Zhenghong Wu; Zefeng Wang; Nan Zhang; Xinlu Wang; Boyang Cao; Yutong Sun; Dingchang Zheng; Ling Xia; Dongdong Deng
Journal:  Front Physiol       Date:  2021-12-24       Impact factor: 4.566

3.  The Heart by Numbers.

Authors:  Kenneth S Campbell; Daniel A Beard; Zhilin Qu
Journal:  Biophys J       Date:  2019-11-29       Impact factor: 4.033

4.  Impact of augmented-reality improvement in ablation catheter navigation as assessed by virtual-heart simulations of ventricular tachycardia ablation.

Authors:  Adityo Prakosa; Michael K Southworth; Jennifer N Avari Silva; Jonathan R Silva; Natalia A Trayanova
Journal:  Comput Biol Med       Date:  2021-04-02       Impact factor: 6.698

5.  Simulated late gadolinium enhanced cardiac magnetic resonance imaging dataset from mechanical XCAT phantom including a myocardial infarct.

Authors:  Evianne Kruithof; Sina Amirrajab; Kevin D Lau; Marcel Breeuwer
Journal:  Data Brief       Date:  2021-12-08

Review 6.  How to use pace mapping for ventricular tachycardia ablation in postinfarct patients.

Authors:  Charles Guenancia; Gregory Supple; Jean-Marc Sellal; Isabelle Magnin-Poull; Karim Benali; Nefissa Hammache; Mathieu Echivard; Francis Marchlinski; Christian de Chillou
Journal:  J Cardiovasc Electrophysiol       Date:  2022-07-03       Impact factor: 2.942

  6 in total

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