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

2019 ◽  
Vol 115 (12) ◽  
pp. 1757-1765 ◽  
Author(s):  
Rheeda L Ali ◽  
Joe B Hakim ◽  
Patrick M Boyle ◽  
Sohail Zahid ◽  
Bhradeev Sivasambu ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Atrial Fibrillation ◽  
Spatial Distribution ◽  
Magnetic Resonance ◽  
Left Atrial ◽  
Computational Models ◽  
Resonance Imaging ◽  
Successful Ablation ◽  
Magnetic Resonance Imaging Mri ◽  
Af Recurrence

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.

P613Cardiac magnetic resonance imaging derived left ventricular mechanical function in patients with atrial fibrillation and left atrial low voltage zones

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
S Ulbrich ◽  
R S Schoenbauer ◽  
B Kirstein ◽  
J Tomala ◽  
Y Huo ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Atrial Fibrillation ◽  
Magnetic Resonance ◽  
Stroke Volume ◽  
Left Atrial ◽  
Low Voltage ◽  
Left Ventricular ◽  
Left Ventricular Stroke Volume ◽  
Resonance Imaging ◽  
Ventricular Filling

Abstract Background The relation of left atrial low voltage zones (LVZ) to left ventricular function in patients undergoing pulmonary vein isolation (PVI) is not known. Objective To explore the relationship of left atrial low voltage zones (LVZ) on left ventricular function in patients with atrial fibrillation. Methods From June to Nov. 2018, 107 (mean age 67y, 70 men, 73 persistent AF) consecutive patients with symptomatic AF underwent a PVI with LVZ mapping. Before PVI the left ventricular ejection fraction (EF) and stroke volume (SV) were measured by cardiac magnetic resonance imaging (CMR). From feature-tracking of CMR-cine images left ventricular global, systolic and diastolic longitudinal strains (GLS), circumferential strains (GCS) and radial strains (GRS) were calculated. Results Of 59 patients CMR scanning in sinus rhythm was performed, LVZ were present in 24 patients. LVEF was significantly lower in patients with left atrial LVZ (62±9% vs. 55±15%) (p=0,03). Left ventricular stroke volume was significantly decreased by the extent of LVZ (94±23 vs. 72±21ml), (p=0,03). The left ventricular diastolic strains during ventricular filling (caused by atrial contraction) of GLS (r=−0,52), GCS (r=−0,65) and GRS (r=−0,65) were highly signifcantly correlated to the occurence and extent of LVZ (each p<0,001 respectively). The only systolic ventricular strain was GLS, which decreased (r=−0,3, p=0,03) by the occurance of atrial low voltage. Conclusion The active, atrial part of diastolic left ventricular filling properties is impaired by the occurrence and extent of left atrial LVZ. In patients with left atrial LVZ the left ventricular stroke volume and ejection fraction is decreased already in sinus rhythm. It seems possible that atrial mechanical dysfunction and presence of atrial low voltage maybe predicted by LV diastolic strain analysis.

scholarly journals Extent and spatial distribution of left atrial arrhythmogenic sites, late gadolinium enhancement at magnetic resonance imaging, and low-voltage areas in patients with persistent atrial fibrillation: comparison of imaging vs. electrical parameters of fibrosis and arrhythmogenesis

EP Europace ◽  
2019 ◽  
Vol 21 (10) ◽  
pp. 1484-1493 ◽  
Author(s):  
Juan Chen ◽  
Thomas Arentz ◽  
Hubert Cochet ◽  
Björn Müller-Edenborn ◽  
Steven Kim ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Atrial Fibrillation ◽  
Magnetic Resonance ◽  
Left Atrial ◽  
Low Voltage ◽  
Spatial Relationship ◽  
Delayed Enhancement ◽  
Resonance Imaging ◽  
Spatio Temporal ◽  
Temporal Dispersion

Abstract Aims Atrial fibrosis contributes to arrhythmogenesis in atrial fibrillation and can be detected by MRI or electrophysiological mapping. The current study compares the spatial correlation between delayed enhancement (DE) areas to low-voltage areas (LVAs) and to arrhythmogenic areas with spatio-temporal dispersion (ST-Disp) or continuous activity (CA) in atrial fibrillation (AF). Methods and results Sixteen patients with persistent AF (nine long-standing) underwent DE-magnetic resonance imaging (1.25 mm × 1.25 mm × 2.5 mm) prior to pulmonary vein isolation. Left atrial (LA) voltage mapping was acquired in AF and the regional activation patterns of 7680 AF wavelets were analysed. Sites with ST-Disp or CA were characterized (voltage, duration) and their spatial relationship to DE areas and LVAs <0.5 mV was assessed. Delayed enhancement areas and LVAs covered 55% and 24% (P < 0.01) of total LA surface, respectively. Delayed enhancement area was present at 61% of LVAs, whereas low voltage was present at 28% of DE areas. Most DE areas (72%) overlapped with atrial high-voltage areas (>0.5 mV). Spatio-temporal dispersion and CA more frequently co-localized with LVAs than with DE areas (78% vs. 63%, P = 0.02). Regional bipolar voltage of ST-Disp vs. CA was 0.64 ± 0.47 mV vs. 0.58 ± 0.51 mV. All 28 ST-Disp and 56 CA areas contained electrograms with prolonged duration (115 ± 14 ms) displaying low voltage (0.34 ± 0.11 mV). Conclusion A small portion of DE areas and LVAs harbour the arrhythmogenic areas displaying ST-Disp or CA. Most arrhythmogenic activities co-localized with LVAs, while there was less co-localization with DE areas. There is an important mismatch between DE areas and LVAs which needs to be considered when used as target for catheter ablation.

scholarly journals New Magnetic Resonance Imaging-Based Method for Defining the Extent of Left Atrial Wall Injury After the Ablation of Atrial Fibrillation

2008 ◽  
Vol 52 (15) ◽  
pp. 1263-1271 ◽  
Author(s):  
Christopher J. McGann ◽  
Eugene G. Kholmovski ◽  
Robert S. Oakes ◽  
Joshua J.E. Blauer ◽  
Marcos Daccarett ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Atrial Fibrillation ◽  
Magnetic Resonance ◽  
Left Atrial ◽  
Resonance Imaging ◽  
Atrial Wall ◽  
Left Atrial Wall ◽  
Wall Injury

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

2020 ◽  
Vol 13 (7) ◽  
Author(s):  
Julie K. Shade ◽  
Rheeda L. Ali ◽  
Dante Basile ◽  
Dan Popescu ◽  
Tauseef Akhtar ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Machine Learning ◽  
Atrial Fibrillation ◽  
Magnetic Resonance ◽  
Computational Modeling ◽  
Pulmonary Vein ◽  
Pulmonary Vein Isolation ◽  
Area Under The Curve ◽  
Resonance Imaging ◽  
Af Recurrence

Background: Pulmonary vein isolation (PVI) is an effective treatment strategy for patients with atrial fibrillation (AF), but many experience AF recurrence and require repeat ablation procedures. The goal of this study was to develop and evaluate a methodology that combines machine learning (ML) and personalized computational modeling to predict, before PVI, which patients are most likely to experience AF recurrence after PVI. Methods: This single-center retrospective proof-of-concept study included 32 patients with documented paroxysmal AF who underwent PVI and had preprocedural late gadolinium enhanced magnetic resonance imaging. For each patient, a personalized computational model of the left atrium simulated AF induction via rapid pacing. Features were derived from pre-PVI late gadolinium enhanced magnetic resonance images and from results of simulations of AF induction. The most predictive features were used as input to a quadratic discriminant analysis ML classifier, which was trained, optimized, and evaluated with 10-fold nested cross-validation to predict the probability of AF recurrence post-PVI. Results: In our cohort, the ML classifier predicted probability of AF recurrence with an average validation sensitivity and specificity of 82% and 89%, respectively, and a validation area under the curve of 0.82. Dissecting the relative contributions of simulations of AF induction and raw images to the predictive capability of the ML classifier, we found that when only features from simulations of AF induction were used to train the ML classifier, its performance remained similar (validation area under the curve, 0.81). However, when only features extracted from raw images were used for training, the validation area under the curve significantly decreased (0.47). Conclusions: ML and personalized computational modeling can be used together to accurately predict, using only pre-PVI late gadolinium enhanced magnetic resonance imaging scans as input, whether a patient is likely to experience AF recurrence following PVI, even when the patient cohort is small.

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