Literature DB >> 26976038

Percolation as a mechanism to explain atrial fractionated electrograms and reentry in a fibrosis model based on imaging data.

Edward Vigmond1, Ali Pashaei2, Sana Amraoui3, Hubert Cochet4, Michel Hassaguerre4.   

Abstract

BACKGROUND: Complex fractionated atrial electrograms (CFAEs) have long been associated with proarrhythmic alterations in atrial structure or electrophysiology. Structural alterations disrupt and slow smoothly propagating wavefronts, leading to wavebreaks and electrogram (EGM) fractionation, but the exact nature and characteristics for arrhythmia remain unknown. Clinically, in atrial fibrillation (AF) patients, increases in frequency, whether by pacing or fibrillation, increase EGM fractionation and duration, and reentry can occur in relation with the conduction disturbance. Recently, percolation has been proposed as an arrhythmogenic mechanism, but its role in AF has not been investigated.
OBJECTIVE: We sought to determine if percolation can explain reentry formation and EGM behavior observed in AF patients.
METHODS: Computer models of fibrotic tissue with different densities were generated based on late gadolinium-enhanced magnetic resonance images, using pixel intensity as a fibrosis probability to avoid an arbitrary binary threshold. Clinical pacing protocols were followed to induce AF, and EGMs were computed.
RESULTS: Reentry could be elicited, with a biphasic behavior dependent on fibrotic density. CFAEs were recorded above fibrotic regions, and consistent with clinical data, EGM duration and fractionation increased with more rapid pacing.
CONCLUSION: These findings confirm percolation as a potential mechanism to explain AF in humans and give new insights into dynamics underlying conduction distortions and fractionated signals in excitable media, which correlate well with the experimental findings in fibrotic regions. The greater understanding of the different patterns of conduction changes and related EGMs could lead to more individualized and effective approaches to AF ablation therapy.
Copyright © 2016 Heart Rhythm Society. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Atrial fibrillation; Atrial fibrosis; Cardiac electrophysiology; Computer modeling; DE-MRI; Fractionated electrogram; Percolation

Mesh:

Year:  2016        PMID: 26976038     DOI: 10.1016/j.hrthm.2016.03.019

Source DB:  PubMed          Journal:  Heart Rhythm        ISSN: 1547-5271            Impact factor:   6.343


  44 in total

1.  How does fibrosis promote atrial fibrillation persistence: in silico findings, clinical observations, and experimental data.

Authors:  Stanley Nattel
Journal:  Cardiovasc Res       Date:  2016-04-30       Impact factor: 10.787

2.  Modeling dynamics in diseased cardiac tissue: Impact of model choice.

Authors:  Tanmay A Gokhale; Eli Medvescek; Craig S Henriquez
Journal:  Chaos       Date:  2017-09       Impact factor: 3.642

Review 3.  Towards personalized computational modelling of the fibrotic substrate for atrial arrhythmia.

Authors:  Patrick M Boyle; Sohail Zahid; Natalia A Trayanova
Journal:  Europace       Date:  2016-12       Impact factor: 5.214

4.  Modelling methodology of atrial fibrosis affects rotor dynamics and electrograms.

Authors:  Caroline H Roney; Jason D Bayer; Sohail Zahid; Marianna Meo; Patrick M J Boyle; Natalia A Trayanova; Michel Haïssaguerre; Rémi Dubois; Hubert Cochet; Edward J Vigmond
Journal:  Europace       Date:  2016-12       Impact factor: 5.214

Review 5.  Addressing challenges of quantitative methodologies and event interpretation in the study of atrial fibrillation.

Authors:  Edward J Ciaccio; Elaine Y Wan; Deepak S Saluja; U Rajendra Acharya; Nicholas S Peters; Hasan Garan
Journal:  Comput Methods Programs Biomed       Date:  2019-06-15       Impact factor: 5.428

Review 6.  The role of personalized atrial modeling in understanding atrial fibrillation mechanisms and improving treatment.

Authors:  Konstantinos N Aronis; Rheeda Ali; Natalia A Trayanova
Journal:  Int J Cardiol       Date:  2019-01-31       Impact factor: 4.164

7.  Identifying Potential Re-Entrant Circuit Locations From Atrial Fibre Maps.

Authors:  Max Falkenberg; David Hickey; Louie Terrill; Alberto Ciacci; Nicholas S Peters; Kim Christensen
Journal:  Comput Cardiol (2010)       Date:  2019-11-08

8.  Personalized Imaging and Modeling Strategies for Arrhythmia Prevention and Therapy.

Authors:  Natalia A Trayanova; Patrick M Boyle; Plamen P Nikolov
Journal:  Curr Opin Biomed Eng       Date:  2018-03

Review 9.  Computational models in cardiology.

Authors:  Steven A Niederer; Joost Lumens; Natalia A Trayanova
Journal:  Nat Rev Cardiol       Date:  2019-02       Impact factor: 32.419

10.  Coronary Sinus Electrograms May Predict New-onset Atrial Fibrillation After Typical Atrial Flutter Radiofrequency Ablation (CSE-AF).

Authors:  Usama Boles; Enes Elvin Gul; Andres Enriquez; Neasa Starr; Sohaib Haseeb; Hoshiar Abdollah; Christopher Simpson; Adrian Baranchuk; Damian Redfearn; Kevin Michael; Wilma Hopman; Benedict Glover
Journal:  J Atr Fibrillation       Date:  2018-06-30
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