Literature DB >> 30977811

Arrhythmogenic propensity of the fibrotic substrate after atrial fibrillation ablation: a longitudinal study using magnetic resonance imaging-based atrial models.

Rheeda L Ali1, Joe B Hakim1, Patrick M Boyle1,2,3, Sohail Zahid2, Bhradeev Sivasambu4, Joseph E Marine4, Hugh Calkins4, Natalia A Trayanova1,2,5, David D Spragg4.   

Abstract

AIMS: Inadequate modification of the atrial fibrotic substrate necessary to sustain re-entrant drivers (RDs) may explain atrial fibrillation (AF) recurrence following failed pulmonary vein isolation (PVI). Personalized computational models of the fibrotic atrial substrate derived from late gadolinium enhanced (LGE)-magnetic resonance imaging (MRI) can be used to non-invasively determine the presence of RDs. The objective of this study is to assess the changes of the arrhythmogenic propensity of the fibrotic substrate after PVI. METHODS AND
RESULTS: Pre- and post-ablation individualized left atrial models were constructed from 12 AF patients who underwent pre- and post-PVI LGE-MRI, in six of whom PVI failed. Pre-ablation AF sustained by RDs was induced in 10 models. RDs in the post-ablation models were classified as either preserved or emergent. Pre-ablation models derived from patients for whom the procedure failed exhibited a higher number of RDs and larger areas defined as promoting RD formation when compared with atrial models from patients who had successful ablation, 2.6 ± 0.9 vs. 1.8 ± 0.2 and 18.9 ± 1.6% vs. 13.8 ± 1.5%, respectively. In cases of successful ablation, PVI eliminated completely the RDs sustaining AF. Preserved RDs unaffected by ablation were documented only in post-ablation models of patients who experienced recurrent AF (2/5 models); all of these models had also one or more emergent RDs at locations distinct from those of pre-ablation RDs. Emergent RDs occurred in regions that had the same characteristics of the fibrosis spatial distribution (entropy and density) as regions that harboured RDs in pre-ablation models.
CONCLUSION: Recurrent AF after PVI in the fibrotic atria may be attributable to both preserved RDs that sustain AF pre- and post-ablation, and the emergence of new RDs following ablation. The same levels of fibrosis entropy and density underlie the pro-RD propensity in both pre- and post-ablation substrates. Published on behalf of the European Society of Cardiology. All rights reserved.
© The Author(s) 2019. For permissions, please email: journals.permissions@oup.com.

Entities:  

Keywords:  Atrial fibrillation; Cardiac MRI; Drivers; Fibrosis; Modelling

Mesh:

Year:  2019        PMID: 30977811      PMCID: PMC6755354          DOI: 10.1093/cvr/cvz083

Source DB:  PubMed          Journal:  Cardiovasc Res        ISSN: 0008-6363            Impact factor:   10.787


  48 in total

1.  User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability.

Authors:  Paul A Yushkevich; Joseph Piven; Heather Cody Hazlett; Rachel Gimpel Smith; Sean Ho; James C Gee; Guido Gerig
Journal:  Neuroimage       Date:  2006-03-20       Impact factor: 6.556

2.  Circumferential radiofrequency ablation of pulmonary vein ostia: A new anatomic approach for curing atrial fibrillation.

Authors:  C Pappone; S Rosanio; G Oreto; M Tocchi; F Gugliotta; G Vicedomini; A Salvati; C Dicandia; P Mazzone; V Santinelli; S Gulletta; S Chierchia
Journal:  Circulation       Date:  2000-11-21       Impact factor: 29.690

3.  Automatically generated, anatomically accurate meshes for cardiac electrophysiology problems.

Authors:  Anton J Prassl; Ferdinand Kickinger; Helmut Ahammer; Vicente Grau; Jürgen E Schneider; Ernst Hofer; Edward J Vigmond; Natalia A Trayanova; Gernot Plank
Journal:  IEEE Trans Biomed Eng       Date:  2009-02-06       Impact factor: 4.538

Review 4.  Atrial fibrosis: mechanisms and clinical relevance in atrial fibrillation.

Authors:  Brett Burstein; Stanley Nattel
Journal:  J Am Coll Cardiol       Date:  2008-02-26       Impact factor: 24.094

5.  Increased vulnerability to atrial fibrillation in transgenic mice with selective atrial fibrosis caused by overexpression of TGF-beta1.

Authors:  Sander Verheule; Toshiaki Sato; Thomas Everett; Steven K Engle; Dan Otten; Michael Rubart-von der Lohe; Hisako O Nakajima; Hidehiro Nakajima; Loren J Field; Jeffrey E Olgin
Journal:  Circ Res       Date:  2004-04-29       Impact factor: 17.367

Review 6.  From mitochondrial ion channels to arrhythmias in the heart: computational techniques to bridge the spatio-temporal scales.

Authors:  Gernot Plank; Lufang Zhou; Joseph L Greenstein; Sonia Cortassa; Raimond L Winslow; Brian O'Rourke; Natalia A Trayanova
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2008-09-28       Impact factor: 4.226

7.  Spatial distribution of fibrosis governs fibrillation wave dynamics in the posterior left atrium during heart failure.

Authors:  Kazuhiko Tanaka; Sharon Zlochiver; Karen L Vikstrom; Masatoshi Yamazaki; Javier Moreno; Matthew Klos; Alexey V Zaitsev; Ravi Vaidyanathan; David S Auerbach; Steve Landas; Gérard Guiraudon; José Jalife; Omer Berenfeld; Jérôme Kalifa
Journal:  Circ Res       Date:  2007-08-17       Impact factor: 17.367

Review 8.  Solvers for the cardiac bidomain equations.

Authors:  E J Vigmond; R Weber dos Santos; A J Prassl; M Deo; G Plank
Journal:  Prog Biophys Mol Biol       Date:  2007-08-11       Impact factor: 3.667

9.  Fibrosis in left atrial tissue of patients with atrial fibrillation with and without underlying mitral valve disease.

Authors:  A Boldt; U Wetzel; J Lauschke; J Weigl; J Gummert; G Hindricks; H Kottkamp; S Dhein
Journal:  Heart       Date:  2004-04       Impact factor: 5.994

10.  Structural correlate of atrial fibrillation in human patients.

Authors:  Sawa Kostin; Gabi Klein; Zoltan Szalay; Stefan Hein; Erwin P Bauer; Jutta Schaper
Journal:  Cardiovasc Res       Date:  2002-05       Impact factor: 10.787

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  17 in total

1.  Computational models of the atrial fibrillation substrate: can they explain post-ablation recurrences and help to prevent them.

Authors:  Stanley Nattel
Journal:  Cardiovasc Res       Date:  2019-10-01       Impact factor: 10.787

Review 2.  An audit of uncertainty in multi-scale cardiac electrophysiology models.

Authors:  Richard H Clayton; Yasser Aboelkassem; Chris D Cantwell; Cesare Corrado; Tammo Delhaas; Wouter Huberts; Chon Lok Lei; Haibo Ni; Alexander V Panfilov; Caroline Roney; Rodrigo Weber Dos Santos
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2020-05-25       Impact factor: 4.226

Review 3.  Artificial Intelligence and Machine Learning in Arrhythmias and Cardiac Electrophysiology.

Authors:  Albert K Feeny; Mina K Chung; Anant Madabhushi; Zachi I Attia; Maja Cikes; Marjan Firouznia; Paul A Friedman; Matthew M Kalscheur; Suraj Kapa; Sanjiv M Narayan; Peter A Noseworthy; Rod S Passman; Marco V Perez; Nicholas S Peters; Jonathan P Piccini; Khaldoun G Tarakji; Suma A Thomas; Natalia A Trayanova; Mintu P Turakhia; Paul J Wang
Journal:  Circ Arrhythm Electrophysiol       Date:  2020-07-06

Review 4.  Current progress of computational modeling for guiding clinical atrial fibrillation ablation.

Authors:  Zhenghong Wu; Yunlong Liu; Lv Tong; Diandian Dong; Dongdong Deng; Ling Xia
Journal:  J Zhejiang Univ Sci B       Date:  2021-10-15       Impact factor: 3.066

5.  Preprocedure Application of Machine Learning and Mechanistic Simulations Predicts Likelihood of Paroxysmal Atrial Fibrillation Recurrence Following Pulmonary Vein Isolation.

Authors:  Julie K Shade; Rheeda L Ali; Dante Basile; Dan Popescu; Tauseef Akhtar; Joseph E Marine; David D Spragg; Hugh Calkins; Natalia A Trayanova
Journal:  Circ Arrhythm Electrophysiol       Date:  2020-06-14

6.  Characterizing the arrhythmogenic substrate in personalized models of atrial fibrillation: sensitivity to mesh resolution and pacing protocol in AF models.

Authors:  Patrick M Boyle; Alexander R Ochs; Rheeda L Ali; Nikhil Paliwal; Natalia A Trayanova
Journal:  Europace       Date:  2021-03-04       Impact factor: 5.214

Review 7.  Comprehensive evaluation of electrophysiological and 3D structural features of human atrial myocardium with insights on atrial fibrillation maintenance mechanisms.

Authors:  Aleksei V Mikhailov; Anuradha Kalyanasundaram; Ning Li; Shane S Scott; Esthela J Artiga; Megan M Subr; Jichao Zhao; Brian J Hansen; John D Hummel; Vadim V Fedorov
Journal:  J Mol Cell Cardiol       Date:  2020-10-29       Impact factor: 5.000

Review 8.  Pro-Arrhythmic Signaling of Thyroid Hormones and Its Relevance in Subclinical Hyperthyroidism.

Authors:  Narcis Tribulova; Lin Hai Kurahara; Peter Hlivak; Katsuya Hirano; Barbara Szeiffova Bacova
Journal:  Int J Mol Sci       Date:  2020-04-19       Impact factor: 5.923

9.  Creation and application of virtual patient cohorts of heart models.

Authors:  S A Niederer; Y Aboelkassem; C D Cantwell; C Corrado; S Coveney; E M Cherry; T Delhaas; F H Fenton; A V Panfilov; P Pathmanathan; G Plank; M Riabiz; C H Roney; R W Dos Santos; L Wang
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2020-05-25       Impact factor: 4.226

10.  Competitive Drivers of Atrial Fibrillation: The Interplay Between Focal Drivers and Multiwavelet Reentry.

Authors:  Richard T Carrick; Bryce E Benson; Oliver R J Bates; Peter S Spector
Journal:  Front Physiol       Date:  2021-03-16       Impact factor: 4.566

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