Publications by authors named "Caroline H Roney"

33 Publications

Computational modeling identifies embolic stroke of undetermined source patients with potential arrhythmic substrate.

Elife 2021 May 4;10. Epub 2021 May 4.

Bioengineering, University of Washington, Seattle, United States.

Cardiac magnetic resonance imaging (MRI) has revealed fibrosis in embolic stroke of undetermined source (ESUS) patients comparable to levels seen in atrial fibrillation (AFib). We used computational modeling to understand the absence of arrhythmia in ESUS despite the presence of putatively pro-arrhythmic fibrosis. MRI-based atrial models were reconstructed for 45 ESUS and 45 AFib patients. The fibrotic substrate's arrhythmogenic capacity in each patient was assessed computationally. Reentrant drivers were induced in 24/45 (53%) ESUS and 22/45 (49%) AFib models. Inducible models had more fibrosis (16.7±5.45%) than non-inducible models (11.07±3.61%; P<0.0001); however, inducible subsets of ESUS and AFib models had similar fibrosis levels (P=0.90), meaning the of fibrosis in ESUS and AFib are indistinguishable. This suggests some ESUS patients have latent pre-clinical fibrotic substrate that could be a future source of arrhythmogenicity. Thus, our work prompts the hypothesis that ESUS patients with fibrotic atria are spared from AFib due to an absence of arrhythmia triggers.
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http://dx.doi.org/10.7554/eLife.64213DOI Listing
May 2021

OpenEP: A Cross-Platform Electroanatomic Mapping Data Format and Analysis Platform for Electrophysiology Research.

Front Physiol 2021 26;12:646023. Epub 2021 Feb 26.

Imperial College London, London, United Kingdom.

Background: Electroanatomic mapping systems are used to support electrophysiology research. Data exported from these systems is stored in proprietary formats which are challenging to access and storage-space inefficient. No previous work has made available an open-source platform for parsing and interrogating this data in a standardized format. We therefore sought to develop a standardized, open-source data structure and associated computer code to store electroanatomic mapping data in a space-efficient and easily accessible manner.

Methods: A data structure was defined capturing the available anatomic and electrical data. OpenEP, implemented in MATLAB, was developed to parse and interrogate this data. Functions are provided for analysis of chamber geometry, activation mapping, conduction velocity mapping, voltage mapping, ablation sites, and electrograms as well as visualization and input/output functions. Performance benchmarking for data import and storage was performed. Data import and analysis validation was performed for chamber geometry, activation mapping, voltage mapping and ablation representation. Finally, systematic analysis of electrophysiology literature was performed to determine the suitability of OpenEP for contemporary electrophysiology research.

Results: The average time to parse clinical datasets was 400 ± 162 s per patient. OpenEP data was two orders of magnitude smaller than compressed clinical data (OpenEP: 20.5 ± 8.7 Mb, vs clinical: 1.46 ± 0.77 Gb). OpenEP-derived geometry metrics were correlated with the same clinical metrics (Area: = 0.7726, < 0.0001; Volume: = 0.5179, < 0.0001). Investigating the cause of systematic bias in these correlations revealed OpenEP to outperform the clinical platform in recovering accurate values. Both activation and voltage mapping data created with OpenEP were correlated with clinical values (mean voltage = 0.8708, < 0.001; local activation time = 0.8892, < 0.0001). OpenEP provides the processing necessary for 87 of 92 qualitatively assessed analysis techniques (95%) and 119 of 136 quantitatively assessed analysis techniques (88%) in a contemporary cohort of mapping studies.

Conclusions: We present the OpenEP framework for evaluating electroanatomic mapping data. OpenEP provides the core functionality necessary to conduct electroanatomic mapping research. We demonstrate that OpenEP is both space-efficient and accurately representative of the original data. We show that OpenEP captures the majority of data required for contemporary electroanatomic mapping-based electrophysiology research and propose a roadmap for future development.
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http://dx.doi.org/10.3389/fphys.2021.646023DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7952326PMC
February 2021

Fully Automatic Atrial Fibrosis Assessment Using a Multilabel Convolutional Neural Network.

Circ Cardiovasc Imaging 2020 12 15;13(12):e011512. Epub 2020 Dec 15.

Biomedical Engineering and Imaging Sciences, King's College London, United Kingdom (O.R., I.S., C.H.R., R.K., H.C., J.W., L.O., R.M., M.O., S.E.W., S.N.).

Background: Pathological atrial fibrosis is a major contributor to sustained atrial fibrillation. Currently, late gadolinium enhancement (LGE) scans provide the only noninvasive estimate of atrial fibrosis. However, widespread adoption of atrial LGE has been hindered partly by nonstandardized image processing techniques, which can be operator and algorithm dependent. Minimal validation and limited access to transparent software platforms have also exacerbated the problem. This study aims to estimate atrial fibrosis from cardiac magnetic resonance scans using a reproducible operator-independent fully automatic open-source end-to-end pipeline.

Methods: A multilabel convolutional neural network was designed to accurately delineate atrial structures including the blood pool, pulmonary veins, and mitral valve. The output from the network removed the operator dependent steps in a reproducible pipeline and allowed for automated estimation of atrial fibrosis from LGE-cardiac magnetic resonance scans. The pipeline results were compared against manual fibrosis burdens, calculated using published thresholds: image intensity ratio 0.97, image intensity ratio 1.61, and mean blood pool signal +3.3 SD.

Results: We validated our methods on a large 3-dimensional LGE-cardiac magnetic resonance data set from 207 labeled scans. Automatic atrial segmentation achieved a 91% Dice score, compared with the mutual agreement of 85% in Dice seen in the interobserver analysis of operators. Intraclass correlation coefficients of the automatic pipeline with manually generated results were excellent and better than or equal to interobserver correlations for all 3 thresholds: 0.94 versus 0.88, 0.99 versus 0.99, 0.99 versus 0.96 for image intensity ratio 0.97, image intensity ratio 1.61, and +3.3 SD thresholds, respectively. Automatic analysis required 3 minutes per case on a standard workstation. The network and the analysis software are publicly available.

Conclusions: Our pipeline provides a fully automatic estimation of fibrosis burden from LGE-cardiac magnetic resonance scans that is comparable to manual analysis. This removes one key source of variability in the measurement of atrial fibrosis.
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http://dx.doi.org/10.1161/CIRCIMAGING.120.011512DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7771635PMC
December 2020

Left Atrial Enhancement Correlates With Myocardial Conduction Velocity in Patients With Persistent Atrial Fibrillation.

Front Physiol 2020 12;11:570203. Epub 2020 Nov 12.

ElectroCardioMaths Programme of The Imperial Centre for Cardiac Engineering, Imperial College London, London, United Kingdom.

Background: Conduction velocity (CV) heterogeneity and myocardial fibrosis both promote re-entry, but the relationship between fibrosis as determined by left atrial (LA) late-gadolinium enhanced cardiac magnetic resonance imaging (LGE-CMRI) and CV remains uncertain.

Objective: Although average CV has been shown to correlate with regional LGE-CMRI in patients with persistent AF, we test the hypothesis that a localized relationship exists to underpin LGE-CMRI as a minimally invasive tool to map myocardial conduction properties for risk stratification and treatment guidance.

Method: 3D LA electroanatomic maps during LA pacing were acquired from eight patients with persistent AF following electrical cardioversion. Local CVs were computed using triads of concurrently acquired electrograms and were co-registered to allow correlation with LA wall intensities obtained from LGE-CMRI, quantified using normalized intensity (NI) and image intensity ratio (IIR). Association was evaluated using multilevel linear regression.

Results: An association between CV and LGE-CMRI intensity was observed at scales comparable to the size of a mapping electrode: -0.11 m/s per unit increase in NI ( < 0.001) and -0.96 m/s per unit increase in IIR ( < 0.001). The magnitude of this change decreased with larger measurement area. Reproducibility of the association was observed with NI, but not with IIR.

Conclusion: At clinically relevant spatial scales, comparable to area of a mapping catheter electrode, LGE-CMRI correlates with CV. Measurement scale is important in accurately quantifying the association of CV and LGE-CMRI intensity. Importantly, NI, but not IIR, accounts for changes in the dynamic range of CMRI and enables quantitative reproducibility of the association.
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http://dx.doi.org/10.3389/fphys.2020.570203DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7693630PMC
November 2020

Comparison of Left Atrial Ablation Techniques That Target the Anatomical, Structural, and Electrical Substrates of Atrial Fibrillation.

Front Physiol 2020 16;11:1145. Epub 2020 Sep 16.

School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.

Catheter ablation therapy for persistent atrial fibrillation (AF) typically includes pulmonary vein isolation (PVI) and may include additional ablation lesions that target patient-specific anatomical, electrical, or structural features. Clinical centers employ different ablation strategies, which use imaging data together with electroanatomic mapping data, depending on data availability. The aim of this study was to compare ablation techniques across a virtual cohort of AF patients. We constructed 20 paroxysmal and 30 persistent AF patient-specific left atrial (LA) bilayer models incorporating fibrotic remodeling from late-gadolinium enhancement (LGE) MRI scans. AF was simulated and post-processed using phase mapping to determine electrical driver locations over 15 s. Six different ablation approaches were tested: (i) PVI alone, modeled as wide-area encirclement of the pulmonary veins; PVI together with: (ii) roof and inferior lines to model posterior wall box isolation; (iii) isolating the largest fibrotic area (identified by LGE-MRI); (iv) isolating all fibrotic areas; (v) isolating the largest driver hotspot region [identified as high simulated phase singularity (PS) density]; and (vi) isolating all driver hotspot regions. Ablation efficacy was assessed to predict optimal ablation therapies for individual patients. We subsequently trained a random forest classifier to predict ablation response using (a) imaging metrics alone, (b) imaging and electrical metrics, or (c) imaging, electrical, and ablation lesion metrics. The optimal ablation approach resulting in termination, or if not possible atrial tachycardia (AT), varied among the virtual patient cohort: (i) 20% PVI alone, (ii) 6% box ablation, (iii) 2% largest fibrosis area, (iv) 4% all fibrosis areas, (v) 2% largest driver hotspot, and (vi) 46% all driver hotspots. Around 20% of cases remained in AF for all ablation strategies. The addition of patient-specific and ablation pattern specific lesion metrics to the trained random forest classifier improved predictive capability from an accuracy of 0.73 to 0.83. The trained classifier results demonstrate that the surface areas of pre-ablation driver regions and of fibrotic tissue not isolated by the proposed ablation strategy are both important for predicting ablation outcome. Overall, our study demonstrates the need to select the optimal ablation strategy for each patient. It suggests that both patient-specific fibrosis properties and driver locations are important for planning ablation approaches, and the distribution of lesions is important for predicting an acute response.
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http://dx.doi.org/10.3389/fphys.2020.572874DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7526475PMC
September 2020

Using cardiac ionic cell models to interpret clinical data.

Wiley Interdiscip Rev Syst Biol Med 2020 Oct 7:e1508. Epub 2020 Oct 7.

King's College London, London, UK.

For over 100 years cardiac electrophysiology has been measured in the clinic. The electrical signals that can be measured span from noninvasive ECG and body surface potentials measurements through to detailed invasive measurements of local tissue electrophysiology. These electrophysiological measurements form a crucial component of patient diagnosis and monitoring; however, it remains challenging to quantitatively link changes in clinical electrophysiology measurements to biophysical cellular function. Multi-scale biophysical computational models represent one solution to this problem. These models provide a formal framework for linking cellular function through to emergent whole organ function and routine clinical diagnostic signals. In this review, we describe recent work on the use of computational models to interpret clinical electrophysiology signals. We review the simulation of human cardiac myocyte electrophysiology in the atria and the ventricles and how these models are being used to link organ scale function to patient disease mechanisms and therapy response in patients receiving implanted defibrillators, \cardiac resynchronisation therapy or suffering from atrial fibrillation and ventricular tachycardia. There is a growing use of multi-scale biophysical models to interpret clinical data. This allows cardiologists to link clinical observations with cellular mechanisms to better understand cardiopathophysiology and identify novel treatment strategies. This article is categorized under: Cardiovascular Diseases > Computational Models Cardiovascular Diseases > Biomedical Engineering Cardiovascular Diseases > Molecular and Cellular Physiology.
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http://dx.doi.org/10.1002/wsbm.1508DOI Listing
October 2020

Challenges Associated with Interpreting Mechanisms of AF.

Arrhythm Electrophysiol Rev 2020 Feb;8(4):273-284

Imperial Centre for Cardiac Engineering, Imperial College London, London, UK.

Determining optimal treatment strategies for complex arrhythmogenesis in AF is confounded by the lack of consensus regarding the mechanisms causing AF. Studies report different mechanisms for AF, ranging from hierarchical drivers to anarchical multiple activation wavelets. Differences in the assessment of AF mechanisms are likely due to AF being recorded across diverse models using different investigational tools, spatial scales and clinical populations. The authors review different AF mechanisms, including anatomical and functional re-entry, hierarchical drivers and anarchical multiple wavelets. They then describe different cardiac mapping techniques and analysis tools, including activation mapping, phase mapping and fibrosis identification. They explain and review different data challenges, including differences between recording devices in spatial and temporal resolutions, spatial coverage and recording surface, and report clinical outcomes using different data modalities. They suggest future research directions for investigating the mechanisms underlying human AF.
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http://dx.doi.org/10.15420/aer.2019.08DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7358959PMC
February 2020

Constructing a Human Atrial Fibre Atlas.

Ann Biomed Eng 2021 Jan 26;49(1):233-250. Epub 2020 May 26.

School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.

Atrial anisotropy affects electrical propagation patterns, anchor locations of atrial reentrant drivers, and atrial mechanics. However, patient-specific atrial fibre fields and anisotropy measurements are not currently available, and consequently assigning fibre fields to atrial models is challenging. We aimed to construct an atrial fibre atlas from a high-resolution DTMRI dataset that optimally reproduces electrophysiology simulation predictions corresponding to patient-specific fibre fields, and to develop a methodology for automatically assigning fibres to patient-specific anatomies. We extended an atrial coordinate system to map the pulmonary veins, vena cava and appendages to standardised positions in the coordinate system corresponding to the average location across the anatomies. We then expressed each fibre field in this atrial coordinate system and calculated an average fibre field. To assess the effects of fibre field on patient-specific modelling predictions, we calculated paced activation time maps and electrical driver locations during AF. In total, 756 activation time maps were calculated (7 anatomies with 9 fibre maps and 2 pacing locations, for the endocardial, epicardial and bilayer surface models of the LA and RA). Patient-specific fibre fields had a relatively small effect on average paced activation maps (range of mean local activation time difference for LA fields: 2.67-3.60 ms, and for RA fields: 2.29-3.44 ms), but had a larger effect on maximum LAT differences (range for LA 12.7-16.6%; range for RA 11.9-15.0%). A total of 126 phase singularity density maps were calculated (7 anatomies with 9 fibre maps for the LA and RA bilayer models). The fibre field corresponding to anatomy 1 had the highest median PS density map correlation coefficient for LA bilayer simulations (0.44 compared to the other correlations, ranging from 0.14 to 0.39), while the average fibre field had the highest correlation for the RA bilayer simulations (0.61 compared to the other correlations, ranging from 0.37 to 0.56). For sinus rhythm simulations, average activation time is robust to fibre field direction; however, maximum differences can still be significant. Patient specific fibres are more important for arrhythmia simulations, particularly in the left atrium. We propose using the fibre field corresponding to DTMRI dataset 1 for LA simulations, and the average fibre field for RA simulations as these optimally predicted arrhythmia properties.
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http://dx.doi.org/10.1007/s10439-020-02525-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7773625PMC
January 2021

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

Philos Trans A Math Phys Eng Sci 2020 Jun 25;378(2173):20190345. Epub 2020 May 25.

School of Mathematics and Statistics, University of Sheffield, Sheffield, UK.

In patients with atrial fibrillation, local activation time (LAT) maps are routinely used for characterizing patient pathophysiology. The gradient of LAT maps can be used to calculate conduction velocity (CV), which directly relates to material conductivity and may provide an important measure of atrial substrate properties. Including uncertainty in CV calculations would help with interpreting the reliability of these measurements. Here, we build upon a recent insight into reduced-rank Gaussian processes (GPs) to perform probabilistic interpolation of uncertain LAT directly on human atrial manifolds. Our Gaussian process manifold interpolation (GPMI) method accounts for the topology of the atrium, and allows for calculation of statistics for predicted CV. We demonstrate our method on two clinical cases, and perform validation against a simulated ground truth. CV uncertainty depends on data density, wave propagation direction and CV magnitude. GPMI is suitable for probabilistic interpolation of other uncertain quantities on non-Euclidean manifolds. This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'.
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http://dx.doi.org/10.1098/rsta.2019.0345DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7287339PMC
June 2020

Ventricular fibrillation mechanism and global fibrillatory organization are determined by gap junction coupling and fibrosis pattern.

Cardiovasc Res 2021 Mar;117(4):1078-1090

National Heart & Lung Institute, Imperial College London, 4th Floor, ICTEM Building, 72 Du Cane Road, London W12 0NN, UK.

Aims: Conflicting data exist supporting differing mechanisms for sustaining ventricular fibrillation (VF), ranging from disorganized multiple-wavelet activation to organized rotational activities (RAs). Abnormal gap junction (GJ) coupling and fibrosis are important in initiation and maintenance of VF. We investigated whether differing ventricular fibrosis patterns and the degree of GJ coupling affected the underlying VF mechanism.

Methods And Results: Optical mapping of 65 Langendorff-perfused rat hearts was performed to study VF mechanisms in control hearts with acute GJ modulation, and separately in three differing chronic ventricular fibrosis models; compact fibrosis (CF), diffuse fibrosis (DiF), and patchy fibrosis (PF). VF dynamics were quantified with phase mapping and frequency dominance index (FDI) analysis, a power ratio of the highest amplitude dominant frequency in the cardiac frequency spectrum. Enhanced GJ coupling with rotigaptide (n = 10) progressively organized fibrillation in a concentration-dependent manner; increasing FDI (0 nM: 0.53 ± 0.04, 80 nM: 0.78 ± 0.03, P < 0.001), increasing RA-sustained VF time (0 nM: 44 ± 6%, 80 nM: 94 ± 2%, P < 0.001), and stabilized RAs (maximum rotations for an RA; 0 nM: 5.4 ± 0.5, 80 nM: 48.2 ± 12.3, P < 0.001). GJ uncoupling with carbenoxolone progressively disorganized VF; the FDI decreased (0 µM: 0.60 ± 0.05, 50 µM: 0.17 ± 0.03, P < 0.001) and RA-sustained VF time decreased (0 µM: 61 ± 9%, 50 µM: 3 ± 2%, P < 0.001). In CF, VF activity was disorganized and the RA-sustained VF time was the lowest (CF: 27 ± 7% vs. PF: 75 ± 5%, P < 0.001). Global fibrillatory organization measured by FDI was highest in PF (PF: 0.67 ± 0.05 vs. CF: 0.33 ± 0.03, P < 0.001). PF harboured the longest duration and most spatially stable RAs (patchy: 1411 ± 266 ms vs. compact: 354 ± 38 ms, P < 0.001). DiF (n = 11) exhibited an intermediately organized VF pattern, sustained by a combination of multiple-wavelets and short-lived RAs.

Conclusion: The degree of GJ coupling and pattern of fibrosis influences the mechanism sustaining VF. There is a continuous spectrum of organization in VF, ranging between globally organized fibrillation sustained by stable RAs and disorganized, possibly multiple-wavelet driven fibrillation with no RAs.
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http://dx.doi.org/10.1093/cvr/cvaa141DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7983010PMC
March 2021

Simulating ventricular systolic motion in a four-chamber heart model with spatially varying robin boundary conditions to model the effect of the pericardium.

J Biomech 2020 03 21;101:109645. Epub 2020 Jan 21.

Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.

The pericardium affects cardiac motion by limiting epicardial displacement normal to the surface. In computational studies, it is important for the model to replicate realistic motion, as this affects the physiological fidelity of the model. Previous computational studies showed that accounting for the effect of the pericardium allows for a more realistic motion simulation. In this study, we describe the mechanism through which the pericardium causes improved cardiac motion. We simulated electrical activation and contraction of the ventricles on a four-chamber heart in the presence and absence of the effect of the pericardium. We simulated the mechanical constraints imposed by the pericardium by applying normal Robin boundary conditions on the ventricular epicardium. We defined a regional scaling of normal springs stiffness based on image-derived motion from CT images. The presence of the pericardium reduced the error between simulated and image-derived end-systolic configurations from 12.8±4.1 mm to 5.7±2.5 mm. First, the pericardium prevents the ventricles from spherising during isovolumic contraction, reducing the outward motion of the free walls normal to the surface and the upwards motion of the apex. Second, by restricting the inward motion of the free and apical walls of the ventricles the pericardium increases atrioventricular plane displacement by four folds during ejection. Our results provide a mechanistic explanation of the importance of the pericardium in physiological simulations of electromechanical cardiac function.
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http://dx.doi.org/10.1016/j.jbiomech.2020.109645DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7677892PMC
March 2020

Standardised Framework for Quantitative Analysis of Fibrillation Dynamics.

Sci Rep 2019 11 13;9(1):16671. Epub 2019 Nov 13.

National Heart and Lung Institute, Hammersmith Campus, Imperial College London, 72 Du Cane Rd, London, W120UQ, UK.

The analysis of complex mechanisms underlying ventricular fibrillation (VF) and atrial fibrillation (AF) requires sophisticated tools for studying spatio-temporal action potential (AP) propagation dynamics. However, fibrillation analysis tools are often custom-made or proprietary, and vary between research groups. With no optimal standardised framework for analysis, results from different studies have led to disparate findings. Given the technical gap, here we present a comprehensive framework and set of principles for quantifying properties of wavefront dynamics in phase-processed data recorded during myocardial fibrillation with potentiometric dyes. Phase transformation of the fibrillatory data is particularly useful for identifying self-perpetuating spiral waves or rotational drivers (RDs) rotating around a phase singularity (PS). RDs have been implicated in sustaining fibrillation, and thus accurate localisation and quantification of RDs is crucial for understanding specific fibrillatory mechanisms. In this work, we assess how variation of analysis parameters and thresholds in the tracking of PSs and quantification of RDs could result in different interpretations of the underlying fibrillation mechanism. These techniques have been described and applied to experimental AF and VF data, and AF simulations, and examples are provided from each of these data sets to demonstrate the range of fibrillatory behaviours and adaptability of these tools. The presented methodologies are available as an open source software and offer an off-the-shelf research toolkit for quantifying and analysing fibrillatory mechanisms.
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http://dx.doi.org/10.1038/s41598-019-52976-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6853901PMC
November 2019

Acetylcholine Delays Atrial Activation to Facilitate Atrial Fibrillation.

Front Physiol 2019 4;10:1105. Epub 2019 Sep 4.

Department of Cardiology, Academic Medical Center, Amsterdam, Netherlands.

Background: Acetylcholine (ACh) shortens action potential duration (APD) in human atria. APD shortening facilitates atrial fibrillation (AF) by reducing the wavelength for reentry. However, the influence of ACh on electrical conduction in human atria and its contribution to AF are unclear, particularly when combined with impaired conduction from interstitial fibrosis.

Objective: To investigate the effect of ACh on human atrial conduction and its role in AF with computational, experimental, and clinical approaches.

Methods: S1S2 pacing (S1 = 600 ms and S2 = variable cycle lengths) was applied to the following human AF computer models: a left atrial appendage (LAA) myocyte to quantify the effects of ACh on APD, maximum upstroke velocity (V ), and resting membrane potential (RMP); a monolayer of LAA myocytes to quantify the effects of ACh on conduction; and 3) an intact left atrium (LA) to determine the effects of ACh on arrhythmogenicity. Heterogeneous ACh and interstitial fibrosis were applied to the monolayer and LA models. To corroborate the simulations, APD and RMP from isolated human atrial myocytes were recorded before and after 0.1 μM ACh. At the tissue level, LAAs from AF patients were optically mapped using Di-4-ANEPPS. The difference in total activation time (AT) was determined between AT initially recorded with S1 pacing, and AT recorded during subsequent S1 pacing without ( = 6) or with ( = 7) 100 μM ACh.

Results: In LAA myocyte simulations, S1 pacing with 0.1 μM ACh shortened APD by 41 ms, hyperpolarized RMP by 7 mV, and increased V by 27 mV/ms. In human atrial myocytes, 0.1 μM ACh shortened APD by 48 ms, hyperpolarized RMP by 3 mV, and increased V by 6 mV/ms. In LAA monolayer simulations, S1 pacing with ACh hyperpolarized RMP to delay total AT by 32 ms without and 35 ms with fibrosis. This led to unidirectional conduction block and sustained reentry in fibrotic LA with heterogeneous ACh during S2 pacing. In AF patient LAAs, S1 pacing with ACh increased total AT from 39.3 ± 26 ms to 71.4 ± 31.2 ms ( = 0.036) compared to no change without ACh (56.7 ± 29.3 ms to 50.0 ± 21.9 ms, = 0.140).

Conclusion: In fibrotic atria with heterogeneous parasympathetic activation, ACh facilitates AF by shortening APD and slowing conduction to promote unidirectional conduction block and reentry.
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http://dx.doi.org/10.3389/fphys.2019.01105DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6737394PMC
September 2019

Preventing recurrence through analysing recurrence.

J Cardiovasc Electrophysiol 2019 11 2;30(11):2239-2241. Epub 2019 Oct 2.

Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.

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http://dx.doi.org/10.1111/jce.14167DOI Listing
November 2019

Left atrial effective conducting size predicts atrial fibrillation vulnerability in persistent but not paroxysmal atrial fibrillation.

J Cardiovasc Electrophysiol 2019 09 18;30(9):1416-1427. Epub 2019 Jun 18.

Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom.

Background: The multiple wavelets and functional re-entry hypotheses are mechanistic theories to explain atrial fibrillation (AF). If valid, a chamber's ability to support AF should depend upon the left atrial size, conduction velocity (CV), and refractoriness. Measurement of these parameters could provide a new therapeutic target for AF. We investigated the relationship between left atrial effective conducting size (LA ), a function of area, CV and refractoriness, and AF vulnerability in patients undergoing AF ablation.

Methods And Results: Activation mapping was performed in patients with paroxysmal (n = 21) and persistent AF (n = 18) undergoing pulmonary vein isolation. Parameters used for calculating LA were: (a) left atrial body area (A); (b) effective refractory period (ERP); and (c) total activation time (T). Global CV was estimated as . Effective atrial conducting size was calculated as . Post ablation, AF inducibility testing was performed. The critical LA required for multiple wavelet termination was determined from computational modeling. LA was greater in patients with persistent vs paroxysmal AF (4.4 ± 2.0 cm vs 3.2 ± 1.4 cm; P = .049). AF was inducible in 14/39 patients. LA was greater in AF-inducible patients (4.4 ± 1.8 cm vs 3.3 ± 1.7 cm; P = .035, respectively). The difference in LA between inducible and noninducible patients was significant in patients with persistent (P = .0046) but not paroxysmal AF (P = .6359). Computational modeling confirmed that LA  > 4 cm was required for continuation of AF.

Conclusions: LA measured post ablation was associated with AF inducibility in patients with persistent, but not paroxysmal AF. These data support a role for this method in electrical substrate assessment in AF patients.
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http://dx.doi.org/10.1111/jce.13990DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6746623PMC
September 2019

Universal atrial coordinates applied to visualisation, registration and construction of patient specific meshes.

Med Image Anal 2019 07 17;55:65-75. Epub 2019 Apr 17.

Institute of Electrophysiology and Heart Modeling (IHU Liryc), Foundation Bordeaux University, Pessac-Bordeaux, France; IMB Bordeaux Institute of Mathematics, University of Bordeaux, 351 cours de la Libération, Talence 33405, France.

Integrating spatial information about atrial physiology and anatomy in a single patient from multimodal datasets, as well as generalizing these data across patients, requires a common coordinate system. In the atria, this is challenging due to the complexity and variability of the anatomy. We aimed to develop and validate a Universal Atrial Coordinate (UAC) system for the following applications: combination and assessment of multimodal data; comparison of spatial data across patients; 2D visualization; and construction of patient specific geometries to test mechanistic hypotheses. Left and right atrial LGE-MRI data were segmented and meshed. Two coordinates were calculated for each atrium by solving Laplace's equation, with boundary conditions assigned using five landmark points. The coordinate system was used to map spatial information between atrial meshes, including scalar fields measured using different mapping modalities, and atrial anatomic structures and fibre directions from a reference geometry. Average error in point transfer from a source mesh to a destination mesh and back again was less than 0.1 mm for the left atrium and 0.02 mm for the right atrium. Patient specific meshes were constructed using the coordinate system and phase singularity density maps from arrhythmia simulations were visualised in 2D. In conclusion, we have developed a universal atrial coordinate system allowing automatic registration of imaging and electroanatomic mapping data, 2D visualisation, and patient specific model creation.
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http://dx.doi.org/10.1016/j.media.2019.04.004DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6543067PMC
July 2019

Probabilistic Interpolation of Uncertain Local Activation Times on Human Atrial Manifolds.

IEEE Trans Biomed Eng 2020 01 9;67(1):99-109. Epub 2019 Apr 9.

Objective: Local activation time (LAT) mapping of the atria is important for targeted treatment of atrial arrhythmias, but current methods do not interpolate on the atrial manifold and neglect uncertainties associated with LAT observations. In this paper, we describe novel methods to, first, quantify uncertainties in LAT arising from bipolar electrogram analysis and assignment of electrode recordings to the anatomical mesh, second, interpolate uncertain LAT measurements directly on left atrial manifolds to obtain complete probabilistic activation maps, and finally, interpolate LAT jointly across both the manifold and different S1-S2 pacing protocols.

Methods: A modified center of mass approach was used to process bipolar electrograms, yielding a LAT estimate and error distribution from the electrogram morphology. An error distribution for assigning measurements to the anatomical mesh was estimated. Probabilistic LAT maps were produced by interpolating on a left atrial manifold using Gaussian Markov random fields, taking into account observation errors and characterizing LAT predictions by their mean and standard deviation. This approach was extended to interpolate across S1-S2 pacing protocols.

Results: We evaluated our approach using recordings from three patients undergoing atrial ablation. Cross-validation showed consistent and accurate prediction of LAT observations both at different locations on the left atrium and for different S1-S2 intervals.

Significance: Interpolation of scalar and vector fields across anatomical structures from point measurements is a challenging problem in biomedical engineering, compounded by uncertainties in measurements and meshes. New methods and approaches are required, and in this paper, we have demonstrated an effective method for probabilistic interpolation of uncertain LAT.
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http://dx.doi.org/10.1109/TBME.2019.2908486DOI Listing
January 2020

Determinants of new wavefront locations in cholinergic atrial fibrillation.

Europace 2018 Nov;20(suppl_3):iii3-iii15

LIRYC Electrophysiology and Heart Modeling Institute, Bordeaux Fondation, Avenue du Haut-Lévèque, Pessac, France.

Aims: Atrial fibrillation (AF) wavefront dynamics are complex and difficult to interpret, contributing to uncertainty about the mechanisms that maintain AF. We aimed to investigate the interplay between rotors, wavelets, and focal sources during fibrillation.

Methods And Results: Arrhythmia wavefront dynamics were analysed for four optically mapped canine cholinergic AF preparations. A bilayer computer model was tuned to experimental preparations, and varied to have (i) fibrosis in both layers or the epicardium only, (ii) different spatial acetylcholine distributions, (iii) different intrinsic action potential duration between layers, and (iv) varied interlayer connectivity. Phase singularities (PSs) were identified and tracked over time to identify rotational drivers. New focal wavefronts were identified using phase contours. Phase singularity density and new wavefront locations were calculated during AF. There was a single dominant mechanism for sustaining AF in each of the preparations, either a rotational driver or repetitive new focal wavefronts. High-density PS sites existed preferentially around the pulmonary vein junctions. Three of the four preparations exhibited stable preferential sites of new wavefronts. Computational simulations predict that only a small number of connections are functionally important in sustaining AF, with new wavefront locations determined by the interplay between fibrosis distribution, acetylcholine concentration, and heterogeneity in repolarization within layers.

Conclusion: We were able to identify preferential sites of new wavefront initiation and rotational activity, in order to determine the mechanisms sustaining AF. Electrical measurements should be interpreted differently according to whether they are endocardial or epicardial recordings.
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http://dx.doi.org/10.1093/europace/euy235DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6251188PMC
November 2018

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

Europace 2018 Nov;20(suppl_3):iii55-iii68

School of Biomedical Engineering & Imaging Sciences, King's College London, St. Thomas' Hospital, Westminster Bridge Road, UK.

Aims: Treatments for persistent atrial fibrillation (AF) offer limited efficacy. One potential strategy aims to return the right atrium (RA) to sinus rhythm (SR) by ablating interatrial connections (IAC) to isolate the atria, but there is limited clinical data to evaluate this ablation approach. We aimed to use simulation to evaluate and predict patient-specific suitability for ablation of IAC to treat AF.

Methods And Results: Persistent AF was simulated in 12 patient-specific geometries, incorporating electrophysiological heterogeneity and fibres, with IAC at Bachmann's bundle, the coronary sinus, and fossa ovalis. Simulations were performed to test the effect of left atrial (LA)-to-RA frequency gradient and fibrotic remodelling on IAC ablation efficacy. During AF, we simulated ablation of one, two, or all three IAC, with or without pulmonary vein isolation and determined if this altered or terminated the arrhythmia. For models without structural remodelling, ablating all IAC terminated RA arrhythmia in 83% of cases. Models with the LA-to-RA frequency gradient removed had an increased success rate (100% success). Ablation of IACs is less effective in cases with fibrotic remodelling (interstitial fibrosis 50% success rate; combination remodelling 67%). Mean number of phase singularities in the RA was higher pre-ablation for IAC failure (success 0.6 ± 0.8 vs. failure 3.2 ± 2.5, P < 0.001).

Conclusion: This simulation study predicts that IAC ablation is effective in returning the RA to SR for many cases. Patient-specific modelling approaches have the potential to stratify patients prior to ablation by predicting if drivers are located in the LA or RA. We present a platform for predicting efficacy and informing patient selection for speculative treatments.
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http://dx.doi.org/10.1093/europace/euy232DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6251187PMC
November 2018

A technique for measuring anisotropy in atrial conduction to estimate conduction velocity and atrial fibre direction.

Comput Biol Med 2019 01 1;104:278-290. Epub 2018 Nov 1.

School of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom.

Background: Cardiac conduction properties exhibit large variability, and affect patient-specific arrhythmia mechanisms. However, it is challenging to clinically measure conduction velocity (CV), anisotropy and fibre direction. Our aim is to develop a technique to estimate conduction anisotropy and fibre direction from clinically available electrical recordings.

Methods: We developed and validated automated algorithms for estimating cardiac CV anisotropy, from any distribution of recording locations on the atrial surface. The first algorithm is for elliptical wavefront fitting to a single activation map (method 1), which works well close to the pacing location, but decreases in accuracy further from the pacing location (due to spatial heterogeneity in the conductivity and fibre fields). As such, we developed a second methodology for measuring local conduction anisotropy, using data from two or three activation maps (method 2: ellipse fitting to wavefront propagation velocity vectors from multiple activation maps).

Results: Ellipse fitting to CV vectors from two activation maps (method 2) leads to an improved estimation of longitudinal and transverse CV compared to method 1, but fibre direction estimation is still relatively poor. Using three activation maps with method 2 provides accurate estimation, with approximately 70% of atrial fibres estimated within 20. We applied the technique to clinical activation maps to demonstrate the presence of heterogeneous conduction anisotropy, and then tested the effects of this conduction anisotropy on predicted arrhythmia dynamics using computational simulation.

Conclusions: We have developed novel algorithms for calculating CV and measuring the direction dependency of atrial activation to estimate atrial fibre direction, without the need for specialised pacing protocols, using clinically available electrical recordings.
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http://dx.doi.org/10.1016/j.compbiomed.2018.10.019DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6506689PMC
January 2019

Wavelength and Fibrosis Affect Phase Singularity Locations During Atrial Fibrillation.

Front Physiol 2018 10;9:1207. Epub 2018 Sep 10.

IMB, UMR 5251, University of Bordeaux, Pessac, France.

The mechanisms underlying atrial fibrillation (AF), the most common sustained cardiac rhythm disturbance, remain elusive. Atrial fibrosis plays an important role in the development of AF and rotor dynamics. Both electrical wavelength (WL) and the degree of atrial fibrosis change as AF progresses. However, their combined effect on rotor core location remains unknown. The aim of this study was to analyze the effects of WL change on rotor core location in both fibrotic and non-fibrotic atria. Three patient specific fibrosis distributions (total fibrosis content: 16.6, 22.8, and 19.2%) obtained from clinical imaging data of persistent AF patients were incorporated in a bilayer atrial computational model. Fibrotic effects were modeled as myocyte-fibroblast coupling + conductivity remodeling; structural remodeling; ionic current changes + conductivity remodeling; and combinations of these methods. To change WL, action potential duration (APD) was varied from 120 to 240ms, representing the range of clinically observed AF cycle length, by modifying the inward rectifier potassium current () conductance between 80 and 140% of the original value. Phase singularities (PSs) were computed to identify rotor core locations. Our results show that conductance variation resulted in a decrease of APD and WL across the atria. For large WL in the absence of fibrosis, PSs anchored to regions with high APD gradient at the center of the left atrium (LA) anterior wall and near the junctions of the inferior pulmonary veins (PVs) with the LA. Decreasing the WL induced more PSs, whose distribution became less clustered. With fibrosis, PS locations depended on the fibrosis distribution and the fibrosis implementation method. The proportion of PSs in fibrotic areas and along the borders varied with both WL and fibrosis modeling method: for patient one, this was 4.2-14.9% as varied for the structural remodeling representation, but 12.3-88.4% using the combination of structural remodeling with myocyte-fibroblast coupling. The degree and distribution of fibrosis and the choice of implementation technique had a larger effect on PS locations than the WL variation. Thus, distinguishing the fibrotic mechanisms present in a patient is important for interpreting clinical fibrosis maps to create personalized models.
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http://dx.doi.org/10.3389/fphys.2018.01207DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6139329PMC
September 2018

Analytical approaches for myocardial fibrillation signals.

Comput Biol Med 2018 11 17;102:315-326. Epub 2018 Jul 17.

ElectroCardioMaths, Imperial Centre for Cardiac Engineering, National Heart & Lung Institute, Imperial College London, United Kingdom. Electronic address:

Atrial and ventricular fibrillation are complex arrhythmias, and their underlying mechanisms remain widely debated and incompletely understood. This is partly because the electrical signals recorded during myocardial fibrillation are themselves complex and difficult to interpret with simple analytical tools. There are currently a number of analytical approaches to handle fibrillation data. Some of these techniques focus on mapping putative drivers of myocardial fibrillation, such as dominant frequency, organizational index, Shannon entropy and phase mapping. Other techniques focus on mapping the underlying myocardial substrate sustaining fibrillation, such as voltage mapping and complex fractionated electrogram mapping. In this review, we discuss these techniques, their application and their limitations, with reference to our experimental and clinical data. We also describe novel tools including a new algorithm to map microreentrant circuits sustaining fibrillation.
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http://dx.doi.org/10.1016/j.compbiomed.2018.07.008DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6215772PMC
November 2018

Unraveling the Underlying Arrhythmia Mechanism in Persistent Atrial Fibrillation: Results From the STARLIGHT Study.

Circ Arrhythm Electrophysiol 2018 06;11(6):e005897

Department of Cardiology, Guy's and St Thomas' Hospital, London, United Kingdom (J.B., C.A.R., M.J.W., J.G.).

Background: The mechanisms that initiate and sustain persistent atrial fibrillation are not well characterized. Ablation results remain significantly worse than in paroxysmal atrial fibrillation in which the mechanism is better understood and subsequent targeted therapy has been developed. The aim of this study was to characterize and quantify patterns of activation during atrial fibrillation using contact mapping.

Methods: Patients with persistent atrial fibrillation (n=14; mean age, 61±8 years; ejection fraction, 59±10%) underwent simultaneous biatrial contact mapping with 64 electrode catheters. The atrial electrograms were transformed into phase, and subsequent spatiotemporal mapping was performed to identify phase singularities (PSs).

Results: PSs were located in both atria, but we observed more PSs in the left atrium compared with the right atrium (779±302, 552±235; =0.015). Although some PSs of duration sufficient to complete >1 rotation were detected, the maximum PS duration was only 1150 ms, and the vast majority (97%) of PSs persisted for too short a period to complete a full rotation. Although in selected patients there was evidence of PS local clustering, overall, PSs were distributed globally throughout both chambers with no clear anatomic predisposition. In a subset of patients (n=7), analysis was repeated using an alternative established atrial PS mapping technique, which confirmed our initial findings.

Conclusions: No sustained rotors or localized drivers were detected, and instead, the mechanism of arrhythmia maintenance was consistent with the multiple wavelet hypothesis, with passive activation of short-lived rotational activity.

Clinical Trial Registration: URL: https://www.clinicaltrials.gov. Unique identifier: NCT01765075.
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http://dx.doi.org/10.1161/CIRCEP.117.005897DOI Listing
June 2018

Variability in pulmonary vein electrophysiology and fibrosis determines arrhythmia susceptibility and dynamics.

PLoS Comput Biol 2018 05 24;14(5):e1006166. Epub 2018 May 24.

IHU Liryc, Electrophysiology and Heart Modeling Institute, foundation Bordeaux Université, F-33600 Pessac- Bordeaux, France.

Success rates for catheter ablation of persistent atrial fibrillation patients are currently low; however, there is a subset of patients for whom electrical isolation of the pulmonary veins alone is a successful treatment strategy. It is difficult to identify these patients because there are a multitude of factors affecting arrhythmia susceptibility and maintenance, and the individual contributions of these factors are difficult to determine clinically. We hypothesised that the combination of pulmonary vein (PV) electrophysiology and atrial body fibrosis determine driver location and effectiveness of pulmonary vein isolation (PVI). We used bilayer biatrial computer models based on patient geometries to investigate the effects of PV properties and atrial fibrosis on arrhythmia inducibility, maintenance mechanisms, and the outcome of PVI. Short PV action potential duration (APD) increased arrhythmia susceptibility, while longer PV APD was found to be protective. Arrhythmia inducibility increased with slower conduction velocity (CV) at the LA/PV junction, but not for cases with homogeneous CV changes or slower CV at the distal PV. Phase singularity (PS) density in the PV region for cases with PV fibrosis was increased. Arrhythmia dynamics depend on both PV properties and fibrosis distribution, varying from meandering rotors to PV reentry (in cases with baseline or long APD), to stable rotors at regions of high fibrosis density. Measurement of fibrosis and PV properties may indicate patient specific susceptibility to AF initiation and maintenance. PV PS density before PVI was higher for cases in which AF terminated or converted to a macroreentry; thus, high PV PS density may indicate likelihood of PVI success.
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http://dx.doi.org/10.1371/journal.pcbi.1006166DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5997352PMC
May 2018

Spatial Resolution Requirements for Accurate Identification of Drivers of Atrial Fibrillation.

Circ Arrhythm Electrophysiol 2017 May;10(5):e004899

From the ElectroCardioMaths Programme (C.H.R., C.D.C., N.A.Q., P.B.L., P.K., N.S.P., F.S.N.), and the Department of Bioengineering (J.H.T.), Imperial College London, United Kingdom; IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Pessac-Bordeaux, France (J.D.B., E.J.V.); and Université de Bordeaux, IMB, UMR 5251, Talence, France (J.D.B., E.J.V.).

Background: Recent studies have demonstrated conflicting mechanisms underlying atrial fibrillation (AF), with the spatial resolution of data often cited as a potential reason for the disagreement. The purpose of this study was to investigate whether the variation in spatial resolution of mapping may lead to misinterpretation of the underlying mechanism in persistent AF.

Methods And Results: Simulations of rotors and focal sources were performed to estimate the minimum number of recording points required to correctly identify the underlying AF mechanism. The effects of different data types (action potentials and unipolar or bipolar electrograms) and rotor stability on resolution requirements were investigated. We also determined the ability of clinically used endocardial catheters to identify AF mechanisms using clinically recorded and simulated data. The spatial resolution required for correct identification of rotors and focal sources is a linear function of spatial wavelength (the distance between wavefronts) of the arrhythmia. Rotor localization errors are larger for electrogram data than for action potential data. Stationary rotors are more reliably identified compared with meandering trajectories, for any given spatial resolution. All clinical high-resolution multipolar catheters are of sufficient resolution to accurately detect and track rotors when placed over the rotor core although the low-resolution basket catheter is prone to false detections and may incorrectly identify rotors that are not present.

Conclusions: The spatial resolution of AF data can significantly affect the interpretation of the underlying AF mechanism. Therefore, the interpretation of human AF data must be taken in the context of the spatial resolution of the recordings.
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http://dx.doi.org/10.1161/CIRCEP.116.004899DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5434962PMC
May 2017

Modelling methodology of atrial fibrosis affects rotor dynamics and electrograms.

Europace 2016 Dec;18(suppl 4):iv146-iv155

IHU Liryc, Electrophysiology and Heart Modeling Institute, foundation Bordeaux Université, F-33600 Pessac- Bordeaux, France.

Aims: Catheter ablation is an effective technique for terminating atrial arrhythmia. However, given a high atrial fibrillation (AF) recurrence rate, optimal ablation strategies have yet to be defined. Computer modelling can be a powerful aid but modelling of fibrosis, a major factor associated with AF, is an open question. Several groups have proposed methodologies based on imaging data, but no comparison to determine which methodology best corroborates clinically observed reentrant behaviour has been performed. We examined several methodologies to determine the best method for capturing fibrillation dynamics.

Methods And Results: Patient late gadolinium-enhanced magnetic resonance imaging data were transferred onto a bilayer atrial computer model and used to assign fibrosis distributions. Fibrosis was modelled as conduction disturbances (lower conductivity, edge splitting, or percolation), transforming growth factor-β1 ionic channel effects, myocyte-fibroblast coupling, and combinations of the preceding. Reentry was induced through pulmonary vein ectopy and the ensuing rotor dynamics characterized. Non-invasive electrocardiographic imaging data of the patients in AF was used for comparison. Electrograms were computed and the fractionation durations measured over the surface. Edge splitting produced more phase singularities from wavebreaks than the other representations. The number of phase singularities seen with percolation was closer to the clinical values. Addition of fibroblast coupling had an organizing effect on rotor dynamics. Simple tissue conductivity changes with ionic changes localized rotors over fibrosis which was not observed with clinical data.

Conclusion: The specific representation of fibrosis has a large effect on rotor dynamics and needs to be carefully considered for patient specific modelling.
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http://dx.doi.org/10.1093/europace/euw365DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6279153PMC
December 2016

Rotor Tracking Using Phase of Electrograms Recorded During Atrial Fibrillation.

Ann Biomed Eng 2017 04 5;45(4):910-923. Epub 2016 Dec 5.

National Heart and Lung Institute, Imperial College London, 4th floor Imperial Centre for Translational and Experimental Medicine, Hammersmith Campus, Du Cane Road, London, W12 0NN, UK.

Extracellular electrograms recorded during atrial fibrillation (AF) are challenging to interpret due to the inherent beat-to-beat variability in amplitude and duration. Phase mapping represents these voltage signals in terms of relative position within the cycle, and has been widely applied to action potential and unipolar electrogram data of myocardial fibrillation. To date, however, it has not been applied to bipolar recordings, which are commonly acquired clinically. The purpose of this study is to present a novel algorithm for calculating phase from both unipolar and bipolar electrograms recorded during AF. A sequence of signal filters and processing steps are used to calculate phase from simulated, experimental, and clinical, unipolar and bipolar electrograms. The algorithm is validated against action potential phase using simulated data (trajectory centre error <0.8 mm); between experimental multi-electrode array unipolar and bipolar phase; and for wavefront identification in clinical atrial tachycardia. For clinical AF, similar rotational content (R  = 0.79) and propagation maps (median correlation 0.73) were measured using either unipolar or bipolar recordings. The algorithm is robust, uses standard signal processing techniques, and accurately quantifies AF wavefronts and sources. Identifying critical sources, such as rotors, in AF, may allow for more accurate targeting of ablation therapy and improved patient outcomes.
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http://dx.doi.org/10.1007/s10439-016-1766-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5362653PMC
April 2017

Novel Radiofrequency Ablation Strategies for Terminating Atrial Fibrillation in the Left Atrium: A Simulation Study.

Front Physiol 2016 12;7:108. Epub 2016 Apr 12.

Electrophysiology and Heart Modeling Institute (LIRYC), Bordeaux University FoundationPessac, France; Institute of Mathematics of Bordeaux (IMB), University of BordeauxTalence, France.

Pulmonary vein isolation (PVI) with radiofrequency ablation (RFA) is the cornerstone of atrial fibrillation (AF) therapy, but few strategies exist for when it fails. To guide RFA, phase singularity (PS) mapping locates reentrant electrical waves (rotors) that perpetuate AF. The goal of this study was to test existing and develop new RFA strategies for terminating rotors identified with PS mapping. It is unsafe to test experimental RFA strategies in patients, so they were evaluated in silico using a bilayer computer model of the human atria with persistent AF (pAF) electrical (ionic) and structural (fibrosis) remodeling. pAF was initiated by rapidly pacing the right (RSPV) and left (LSPV) superior pulmonary veins during sinus rhythm, and rotor dynamics quantified by PS analysis. Three RFA strategies were studied: (i) PVI, roof, and mitral lines; (ii) circles, perforated circles, lines, and crosses 0.5-1.5 cm in diameter/length administered near rotor locations/pathways identified by PS mapping; and (iii) 4-8 lines streamlining the sequence of electrical activation during sinus rhythm. As in pAF patients, 2 ± 1 rotors with cycle length 185 ± 4 ms and short PS duration 452 ± 401 ms perpetuated simulated pAF. Spatially, PS density had weak to moderate positive correlations with fibrosis density (RSPV: r = 0.38, p = 0.35, LSPV: r = 0.77, p = 0.02). RFA PVI, mitral, and roof lines failed to terminate pAF, but RFA perforated circles and lines 1.5 cm in diameter/length terminated meandering rotors from RSPV pacing when placed at locations with high PS density. Similarly, RFA circles, perforated circles, and crosses 1.5 cm in diameter/length terminated stationary rotors from LSPV pacing. The most effective strategy for terminating pAF was to streamline the sequence of activation during sinus rhythm with >4 RFA lines. These results demonstrate that co-localizing 1.5 cm RFA lesions with locations of high PS density is a promising strategy for terminating pAF rotors. For patients immune to PVI, roof, mitral, and PS guided RFA strategies, streamlining patient-specific activation sequences during sinus rhythm is a robust but challenging alternative.
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http://dx.doi.org/10.3389/fphys.2016.00108DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4828663PMC
May 2016

A technique for visualising three-dimensional left atrial cardiac activation data in two dimensions with minimal distance distortion.

Annu Int Conf IEEE Eng Med Biol Soc 2015 ;2015:7296-9

Electro-anatomic mapping and medical imaging systems, used during clinical procedures for treatment of atrial arrhythmias, frequently record and display measurements on an anatomical surface of the left atrium. As such, obtaining a complete picture of activation necessitates simultaneous views from multiple angles. In addition, post-processing of three-dimensional surface data is challenging, since algorithms are typically applicable to planar or volumetric data. We applied a surface flattening methodology to medical imaging data and electro-anatomic mapping data to generate a two-dimensional representation that best preserves distances, since the calculation of many clinically relevant metrics, including conduction velocity and rotor trajectory identification require an accurate representation of distance. Distance distortions were small and improved upon exclusion of the pulmonary veins. The technique is demonstrated using maps of local activation time, based on clinical data, and plotting rotor-core trajectories, using simulated data.
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http://dx.doi.org/10.1109/EMBC.2015.7320076DOI Listing
September 2016