Literature DB >> 24763468

Mathematical approaches to understanding and imaging atrial fibrillation: significance for mechanisms and management.

Natalia A Trayanova1.   

Abstract

Atrial fibrillation (AF) is the most common sustained arrhythmia in humans. The mechanisms that govern AF initiation and persistence are highly complex, of dynamic nature, and involve interactions across multiple temporal and spatial scales in the atria. This article aims to review the mathematical modeling and computer simulation approaches to understanding AF mechanisms and aiding in its management. Various atrial modeling approaches are presented, with descriptions of the methodological basis and advancements in both lower-dimensional and realistic geometry models. A review of the most significant mechanistic insights made by atrial simulations is provided. The article showcases the contributions that atrial modeling and simulation have made not only to our understanding of the pathophysiology of atrial arrhythmias, but also to the development of AF management approaches. A summary of the future developments envisioned for the field of atrial simulation and modeling is also presented. The review contends that computational models of the atria assembled with data from clinical imaging modalities that incorporate electrophysiological and structural remodeling could become a first line of screening for new AF therapies and approaches, new diagnostic developments, and new methods for arrhythmia prevention.

Entities:  

Keywords:  arrhythmias, cardiac; atrial fibrillation; atrial remodeling; computer simulation

Mesh:

Year:  2014        PMID: 24763468      PMCID: PMC4043630          DOI: 10.1161/CIRCRESAHA.114.302240

Source DB:  PubMed          Journal:  Circ Res        ISSN: 0009-7330            Impact factor:   17.367


  135 in total

1.  Time course and mechanisms of endo-epicardial electrical dissociation during atrial fibrillation in the goat.

Authors:  Jens Eckstein; Bart Maesen; Dominik Linz; Stef Zeemering; Arne van Hunnik; Sander Verheule; Maurits Allessie; Ulrich Schotten
Journal:  Cardiovasc Res       Date:  2010-10-26       Impact factor: 10.787

2.  Electrotonic myofibroblast-to-myocyte coupling increases propensity to reentrant arrhythmias in two-dimensional cardiac monolayers.

Authors:  Sharon Zlochiver; Viviana Muñoz; Karen L Vikstrom; Steven M Taffet; Omer Berenfeld; José Jalife
Journal:  Biophys J       Date:  2008-07-25       Impact factor: 4.033

3.  Ablation of multiwavelet re-entry guided by circuit-density and distribution: maximizing the probability of circuit annihilation.

Authors:  Richard T Carrick; Bryce Benson; Nicole Habel; Oliver R J Bates; Jason H T Bates; Peter S Spector
Journal:  Circ Arrhythm Electrophysiol       Date:  2013-09-15

4.  Electrotonic coupling between human atrial myocytes and fibroblasts alters myocyte excitability and repolarization.

Authors:  Mary M Maleckar; Joseph L Greenstein; Wayne R Giles; Natalia A Trayanova
Journal:  Biophys J       Date:  2009-10-21       Impact factor: 4.033

5.  Optimizing local capture of atrial fibrillation by rapid pacing: study of the influence of tissue dynamics.

Authors:  Laurent Uldry; Nathalie Virag; Vincent Jacquemet; Jean-Marc Vesin; Lukas Kappenberger
Journal:  Ann Biomed Eng       Date:  2010-07-09       Impact factor: 3.934

6.  Action potential morphology heterogeneity in the atrium and its effect on atrial reentry: a two-dimensional and quasi-three-dimensional study.

Authors:  Samuel R Kuo; Natalia A Trayanova
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2006-06-15       Impact factor: 4.226

Review 7.  Pacing therapy for prevention of atrial fibrillation.

Authors:  Kenneth A Ellenbogen
Journal:  Heart Rhythm       Date:  2006-12-15       Impact factor: 6.343

8.  Pulmonary vein reentry--properties and size matter: insights from a computational analysis.

Authors:  Elizabeth M Cherry; Joachim R Ehrlich; Stanley Nattel; Flavio H Fenton
Journal:  Heart Rhythm       Date:  2007-08-24       Impact factor: 6.343

9.  Prevalence, age distribution, and gender of patients with atrial fibrillation. Analysis and implications.

Authors:  W M Feinberg; J L Blackshear; A Laupacis; R Kronmal; R G Hart
Journal:  Arch Intern Med       Date:  1995-03-13

10.  Subcellular calcium dynamics in a whole-cell model of an atrial myocyte.

Authors:  Rüdiger Thul; Stephen Coombes; H Llewelyn Roderick; Martin D Bootman
Journal:  Proc Natl Acad Sci U S A       Date:  2012-01-23       Impact factor: 11.205

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  47 in total

Review 1.  Lessons from computer simulations of ablation of atrial fibrillation.

Authors:  Vincent Jacquemet
Journal:  J Physiol       Date:  2016-03-04       Impact factor: 5.182

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.  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

4.  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

5.  Paroxysmal atrial fibrillation during intracoronary acetylcholine provocation test.

Authors:  Yuichi Saito; Hideki Kitahara; Toshihiro Shoji; Satoshi Tokimasa; Takashi Nakayama; Kazumasa Sugimoto; Yoshihide Fujimoto; Yoshio Kobayashi
Journal:  Heart Vessels       Date:  2016-12-22       Impact factor: 2.037

Review 6.  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 7.  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

Review 8.  Atrial fibrillation driver mechanisms: Insight from the isolated human heart.

Authors:  Thomas A Csepe; Brian J Hansen; Vadim V Fedorov
Journal:  Trends Cardiovasc Med       Date:  2016-05-24       Impact factor: 6.677

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

Authors:  Yanhang Zhang; Victor H Barocas; Scott A Berceli; Colleen E Clancy; David M Eckmann; Marc Garbey; Ghassan S Kassab; Donna R Lochner; Andrew D McCulloch; Roger Tran-Son-Tay; Natalia A Trayanova
Journal:  Ann Biomed Eng       Date:  2016-05-02       Impact factor: 3.934

Review 10.  Anti-arrhythmic strategies for atrial fibrillation: The role of computational modeling in discovery, development, and optimization.

Authors:  Eleonora Grandi; Mary M Maleckar
Journal:  Pharmacol Ther       Date:  2016-09-06       Impact factor: 12.310

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