Literature DB >> 30773268

Velocity characteristics of atrial fibrillation sources determined by electrographic flow mapping before and after catheter ablation.

Barbara Bellmann1, Marit Zettwitz2, Tina Lin3, Peter Ruppersberg4, Selma Guttmann2, Verena Tscholl2, Patrick Nagel2, Mattias Roser2, Ulf Landmesser2, Andreas Rillig5.   

Abstract

BACKGROUND: Electrographic-Flow-(EGF)-Mapping is a novel method to identify Atrial Fibrillation (AF) drivers. Sources of excitation during AF can be characterized and monitored.
OBJECTIVE: The aim of this study was to evaluate the correlation between velocity of EGF around a respective AF source and its spatial variability (SV) and stability (SST).
METHODS: 25 patients with AF were included in this study (persistent: n = 24, long-standing persistent: n = 1; mean age 70 ± 8.3 years, male: n = 17). Focal impulse and Rotor-Mapping (FIRM) was performed in addition to pulmonary vein isolation. One-minute epochs of unipolar electrograms recorded via a 64-pole basket catheter in both atria were re-analyzed with EGF-Mapping. SST was calculated as the percentage of time in which a source was detected.
RESULTS: AF sources identified with EGF-Mapping show a wide range of SV during 1 min covering between 0.12% and 38% of the recorded basket-catheter surface. The 12 atria where the sources showed highest temporal stability (TS; between 34% and 97% of 1 min recorded) and those 12 with the lowest TS (between 11 and 20%) differed significantly in their velocities (17.8 el/s vs 12.2 el/s; p < 0.01). In 11 atria ablation caused an average decrease of TS by 47% and of velocity by 27% while SV more than doubled.
CONCLUSION: Less stable AF-sources with high spatial variability showed reduced excitation propagation velocity while stable AF sources displayed a high average velocity in their vicinity. Importantly, catheter ablation reduced stability of sources and velocity suggesting a role of these parameters in guidance of ablation. CONDENSED ABSTRACT: Electrographic Flow (EGF)-Mapping is a novel method to identify Atrial Fibrillation (AF) drivers based on modeling of an electrical potential surface and subsequent flow analysis. Sources of excitation during AF can be characterized and monitored. The aim of this study was to evaluate the correlation between velocity of EGF around a respective AF source and its spatial variability and stability. Less stable AF sources with high spatial variability showed reduced excitation propagation velocity while very stable AF sources displayed a high average velocity in their vicinity. Catheter ablation reduced stability of sources and velocity. Crown
Copyright © 2019. Published by Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Atrial fibrillation; Catheter ablation; Electrographic flow mapping; Focal impulse and rotor modulation; Propagation velocity; Rotor; Spatial variability

Mesh:

Year:  2019        PMID: 30773268     DOI: 10.1016/j.ijcard.2019.02.006

Source DB:  PubMed          Journal:  Int J Cardiol        ISSN: 0167-5273            Impact factor:   4.164


  9 in total

1.  Machine Learning to Classify Intracardiac Electrical Patterns During Atrial Fibrillation: Machine Learning of Atrial Fibrillation.

Authors:  Mahmood I Alhusseini; Firas Abuzaid; Albert J Rogers; Junaid A B Zaman; Tina Baykaner; Paul Clopton; Peter Bailis; Matei Zaharia; Paul J Wang; Wouter-Jan Rappel; Sanjiv M Narayan
Journal:  Circ Arrhythm Electrophysiol       Date:  2020-07-06

Review 2.  Electrographic flow mapping for atrial fibrillation: theoretical basis and preliminary observations.

Authors:  David E Haines; Melissa H Kong; Peter Ruppersberg; Philip Haeusser; Boaz Avitall; Tamas Szili Torok; Atul Verma
Journal:  J Interv Card Electrophysiol       Date:  2022-08-15       Impact factor: 1.759

3.  Stochastic termination of spiral wave dynamics in cardiac tissue.

Authors:  Wouter-Jan Rappel; David E Krummen; Tina Baykaner; Junaid Zaman; Alan Donsky; Vijay Swarup; John M Miller; Sanjiv M Narayan
Journal:  Front Netw Physiol       Date:  2022-01-26

4.  Gaussian process manifold interpolation for probabilistic atrial activation maps and uncertain conduction velocity.

Authors:  Sam Coveney; Cesare Corrado; Caroline H Roney; Daniel O'Hare; Steven E Williams; Mark D O'Neill; Steven A Niederer; Richard H Clayton; Jeremy E Oakley; Richard D Wilkinson
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2020-05-25       Impact factor: 4.226

5.  Three dimensional reconstruction to visualize atrial fibrillation activation patterns on curved atrial geometry.

Authors:  Ricardo Abad; Orvil Collart; Prasanth Ganesan; A J Rogers; Mahmood I Alhusseini; Miguel Rodrigo; Sanjiv M Narayan; Wouter-Jan Rappel
Journal:  PLoS One       Date:  2021-04-09       Impact factor: 3.240

6.  Left atrial anatomical variations correlate with atrial fibrillation sources near the left atrial ridge.

Authors:  Rita B Gagyi; Nándor Szegedi; Judit Simon; Sip Wijchers; Rohit Bhagwandien; Melissa H Kong; Peter Ruppersberg; Tamas Szili-Torok
Journal:  Front Cardiovasc Med       Date:  2022-09-28

Review 7.  Catheter Ablation of Atrial Fibrillation: State of the Art and Future Perspectives.

Authors:  Laura Rottner; Barbara Bellmann; Tina Lin; Bruno Reissmann; Tobias Tönnis; Ruben Schleberger; Moritz Nies; Christiane Jungen; Leon Dinshaw; Niklas Klatt; Jannis Dickow; Paula Münkler; Christian Meyer; Andreas Metzner; Andreas Rillig
Journal:  Cardiol Ther       Date:  2020-01-02

Review 8.  Toward Mechanism-Directed Electrophenotype-Based Treatments for Atrial Fibrillation.

Authors:  Fu Siong Ng; Balvinder S Handa; Xinyang Li; Nicholas S Peters
Journal:  Front Physiol       Date:  2020-08-28       Impact factor: 4.566

9.  Functional electrographic flow patterns in patients with persistent atrial fibrillation predict outcome of catheter ablation.

Authors:  Tamas Szili-Torok; Zsuzsanna Kis; Rohit Bhagwandien; Sip Wijchers; Sing-Chien Yap; Mark Hoogendijk; Nadege Dumas; Philip Haeusser; Tamas Geczy; Melissa H Kong; Peter Ruppersberg
Journal:  J Cardiovasc Electrophysiol       Date:  2021-06-07
  9 in total

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