Literature DB >> 33751074

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

Patrick M Boyle1,2,3, Alexander R Ochs1, Rheeda L Ali4, Nikhil Paliwal4, Natalia A Trayanova4,5.   

Abstract

AIMS: Computationally guided persistent atrial fibrillation (PsAF) ablation has emerged as an alternative to conventional treatment planning. To make this approach scalable, computational cost and the time required to conduct simulations must be minimized while maintaining predictive accuracy. Here, we assess the sensitivity of the process to finite-element mesh resolution. We also compare methods for pacing site distribution used to evaluate inducibility arrhythmia sustained by re-entrant drivers (RDs). METHODS AND
RESULTS: Simulations were conducted in low- and high-resolution models (average edge lengths: 400/350 µm) reconstructed from PsAF patients' late gadolinium enhancement magnetic resonance imaging scans. Pacing was simulated from 80 sites to assess RD inducibility. When pacing from the same site led to different outcomes in low-/high-resolution models, we characterized divergence dynamics by analysing dissimilarity index over time. Pacing site selection schemes prioritizing even spatial distribution and proximity to fibrotic tissue were evaluated. There were no RD sites observed in low-resolution models but not high-resolution models, or vice versa. Dissimilarity index analysis suggested that differences in simulation outcome arising from differences in discretization were the result of isolated conduction block incidents in one model but not the other; this never led to RD sites unique to one mesh resolution. Pacing site selection based on fibrosis proximity led to the best observed trade-off between number of stimulation locations and predictive accuracy.
CONCLUSION: Simulations conducted in meshes with 400 µm average edge length and ∼40 pacing sites proximal to fibrosis are sufficient to reveal the most comprehensive possible list of RD sites, given feasibility constraints. Published on behalf of the European Society of Cardiology. All rights reserved.
© The Author(s) 2021. For permissions, please email: journals.permissions@oup.com.

Entities:  

Keywords:  Atrial fibrillation; Convergence analysis; Fibrosis; Patient-specific computational modelling; Reentry

Mesh:

Substances:

Year:  2021        PMID: 33751074      PMCID: PMC7943367          DOI: 10.1093/europace/euaa385

Source DB:  PubMed          Journal:  Europace        ISSN: 1099-5129            Impact factor:   5.214


  27 in total

1.  Verification of cardiac tissue electrophysiology simulators using an N-version benchmark.

Authors:  Steven A Niederer; Eric Kerfoot; Alan P Benson; Miguel O Bernabeu; Olivier Bernus; Chris Bradley; Elizabeth M Cherry; Richard Clayton; Flavio H Fenton; Alan Garny; Elvio Heidenreich; Sander Land; Mary Maleckar; Pras Pathmanathan; Gernot Plank; José F Rodríguez; Ishani Roy; Frank B Sachse; Gunnar Seemann; Ola Skavhaug; Nic P Smith
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2011-11-13       Impact factor: 4.226

2.  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

Review 3.  The clinical profile and pathophysiology of atrial fibrillation: relationships among clinical features, epidemiology, and mechanisms.

Authors:  Jason Andrade; Paul Khairy; Dobromir Dobrev; Stanley Nattel
Journal:  Circ Res       Date:  2014-04-25       Impact factor: 17.367

Review 4.  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

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

Authors:  Rheeda L Ali; Joe B Hakim; Patrick M Boyle; Sohail Zahid; Bhradeev Sivasambu; Joseph E Marine; Hugh Calkins; Natalia A Trayanova; David D Spragg
Journal:  Cardiovasc Res       Date:  2019-10-01       Impact factor: 10.787

7.  Patient-specific simulations predict efficacy of ablation of interatrial connections for treatment of persistent atrial fibrillation.

Authors:  Caroline H Roney; Steven E Williams; Hubert Cochet; Rahul K Mukherjee; Louisa O'Neill; Iain Sim; John Whitaker; Orod Razeghi; George J Klein; Edward J Vigmond; Mark O'Neill; Steven A Niederer
Journal:  Europace       Date:  2018-11-01       Impact factor: 5.214

8.  Constructing a Human Atrial Fibre Atlas.

Authors:  Caroline H Roney; Rokas Bendikas; Farhad Pashakhanloo; Cesare Corrado; Edward J Vigmond; Elliot R McVeigh; Natalia A Trayanova; Steven A Niederer
Journal:  Ann Biomed Eng       Date:  2020-05-26       Impact factor: 3.934

9.  Epicardial Fibrosis Explains Increased Endo-Epicardial Dissociation and Epicardial Breakthroughs in Human Atrial Fibrillation.

Authors:  Ali Gharaviri; Elham Bidar; Mark Potse; Stef Zeemering; Sander Verheule; Simone Pezzuto; Rolf Krause; Jos G Maessen; Angelo Auricchio; Ulrich Schotten
Journal:  Front Physiol       Date:  2020-02-21       Impact factor: 4.566

10.  In silico investigation of the mechanisms underlying atrial fibrillation due to impaired Pitx2.

Authors:  Jieyun Bai; Andy Lo; Patrick A Gladding; Martin K Stiles; Vadim V Fedorov; Jichao Zhao
Journal:  PLoS Comput Biol       Date:  2020-02-25       Impact factor: 4.475

View more
  2 in total

1.  Detection of focal source and arrhythmogenic substrate from body surface potentials to guide atrial fibrillation ablation.

Authors:  Yingjing Feng; Caroline H Roney; Jason D Bayer; Steven A Niederer; Mélèze Hocini; Edward J Vigmond
Journal:  PLoS Comput Biol       Date:  2022-03-21       Impact factor: 4.475

2.  Fast Characterization of Inducible Regions of Atrial Fibrillation Models With Multi-Fidelity Gaussian Process Classification.

Authors:  Lia Gander; Simone Pezzuto; Ali Gharaviri; Rolf Krause; Paris Perdikaris; Francisco Sahli Costabal
Journal:  Front Physiol       Date:  2022-03-07       Impact factor: 4.566

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.