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Trayanova NA, Lyon A, Shade J, Heijman J. Computational modeling of cardiac electrophysiology and arrhythmogenesis: toward clinical translation. Physiol Rev 2024; 104:1265-1333. [PMID: 38153307 DOI: 10.1152/physrev.00017.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 12/19/2023] [Accepted: 12/21/2023] [Indexed: 12/29/2023] Open
Abstract
The complexity of cardiac electrophysiology, involving dynamic changes in numerous components across multiple spatial (from ion channel to organ) and temporal (from milliseconds to days) scales, makes an intuitive or empirical analysis of cardiac arrhythmogenesis challenging. Multiscale mechanistic computational models of cardiac electrophysiology provide precise control over individual parameters, and their reproducibility enables a thorough assessment of arrhythmia mechanisms. This review provides a comprehensive analysis of models of cardiac electrophysiology and arrhythmias, from the single cell to the organ level, and how they can be leveraged to better understand rhythm disorders in cardiac disease and to improve heart patient care. Key issues related to model development based on experimental data are discussed, and major families of human cardiomyocyte models and their applications are highlighted. An overview of organ-level computational modeling of cardiac electrophysiology and its clinical applications in personalized arrhythmia risk assessment and patient-specific therapy of atrial and ventricular arrhythmias is provided. The advancements presented here highlight how patient-specific computational models of the heart reconstructed from patient data have achieved success in predicting risk of sudden cardiac death and guiding optimal treatments of heart rhythm disorders. Finally, an outlook toward potential future advances, including the combination of mechanistic modeling and machine learning/artificial intelligence, is provided. As the field of cardiology is embarking on a journey toward precision medicine, personalized modeling of the heart is expected to become a key technology to guide pharmaceutical therapy, deployment of devices, and surgical interventions.
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Affiliation(s)
- Natalia A Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland, United States
| | - Aurore Lyon
- Department of Biomedical Engineering, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
- Division of Heart and Lungs, Department of Medical Physiology, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Julie Shade
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland, United States
| | - Jordi Heijman
- Department of Cardiology, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
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Meng S, Al-Kaisey AM, Parameswaran R, Sunderland N, Budgett DM, Kistler PM, Smaill BH, Kalman JM. Pulmonary Veins Function as Echo Chambers in Persistent Atrial Fibrillation: Circuitous Re-Entry Generates Outgoing Wavefronts. JACC Clin Electrophysiol 2024; 10:1313-1325. [PMID: 38752958 DOI: 10.1016/j.jacep.2024.03.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Revised: 02/20/2024] [Accepted: 03/20/2024] [Indexed: 07/26/2024]
Abstract
BACKGROUND Although the substrate in persistent atrial fibrillation (PeAF) is not limited to the pulmonary veins (PVs), PV isolation (PVI) remains the cornerstone ablation strategy. OBJECTIVES The aim of this study was to describe the mechanism of outgoing wavefronts (WFs) originating in the PV sleeves during PeAF. METHODS Eleven patients presenting for first-time PeAF ablation were recruited (mean age 63.1 ± 10.9 years, 91% men). A 64-electrode catheter (Constellation; 38 mm) was positioned within the PV under fluoroscopic guidance. An inverse mapping technique was used to reconstruct unipolar atrial electrograms on the PV surface, and the resulting phase maps were used to identify incoming and outgoing WFs at the PV junction and to classify focal and re-entrant activity within the PV sleeves. RESULTS During PeAF, the PVs gave rise to outgoing WFs with a frequency of 3.7 s-1 (Q1-Q3: 3.4-5.4 s-1) compared with 3.6 s-1 (Q1-Q3: 2.8-4.2 s-1) for incoming WFs. Circuitous macroscopic re-entry was the dominant mechanism driving outgoing WFs (frequency of re-entry 2.7 s-1 [Q1-Q3: 2.0-3.3 s-1] compared with focal activity 1.4 s-1 [Q1-Q3: 1.1-1.5 s-1]; P < 0.006). This was initiated by incoming WFs in 80% of cases. Consecutive focal activation from the same location was infrequent (10.0% ± 6.6%, n = 10). Rotors ≥360° were never observed. The median ratio (R) of outgoing to incoming WF frequency was 1.14 (Q1-Q3: 0.84-1.75), with R > 1 in 6 of 11 PVs. CONCLUSIONS Electric activity generated by PV sleeves during PeAF is due mainly to circuitous re-entry initiated by incoming waves, frequently with R > 1. That is, the PVs act less as drivers of atrial fibrillation than as "echo chambers" that sustain and amplify fibrillatory activity.
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Affiliation(s)
- Shu Meng
- Auckland Bioengineering Institute, University of Auckland, Auckland New Zealand
| | - Ahmed M Al-Kaisey
- Department of Medicine, University of Melbourne, Melbourne, Australia; Department of Cardiology, Royal Melbourne Hospital, Melbourne, Australia
| | - Ramanathan Parameswaran
- Department of Medicine, University of Melbourne, Melbourne, Australia; Department of Cardiology, Royal Melbourne Hospital, Melbourne, Australia
| | | | - David M Budgett
- Auckland Bioengineering Institute, University of Auckland, Auckland New Zealand
| | - Peter M Kistler
- Department of Medicine, University of Melbourne, Melbourne, Australia; Heart Centre, Alfred Hospital, Melbourne, Australia; Baker IDI Heart & Diabetes Institute, Melbourne, Australia
| | - Bruce H Smaill
- Auckland Bioengineering Institute, University of Auckland, Auckland New Zealand
| | - Jonathan M Kalman
- Department of Medicine, University of Melbourne, Melbourne, Australia; Department of Cardiology, Royal Melbourne Hospital, Melbourne, Australia; Baker IDI Heart & Diabetes Institute, Melbourne, Australia.
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Colman MA, Varela M, MacLeod RS, Hancox JC, Aslanidi OV. Interactions between calcium-induced arrhythmia triggers and the electrophysiological-anatomical substrate underlying the induction of atrial fibrillation. J Physiol 2024; 602:835-853. [PMID: 38372694 DOI: 10.1113/jp285740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 01/17/2024] [Indexed: 02/20/2024] Open
Abstract
Atrial fibrillation (AF) is the most common cardiac arrhythmia and is sustained by spontaneous focal excitations and re-entry. Spontaneous electrical firing in the pulmonary vein (PV) sleeves is implicated in AF generation. The aim of this simulation study was to identify the mechanisms determining the localisation of AF triggers in the PVs and their contribution to the genesis of AF. A novel biophysical model of the canine atria was used that integrates stochastic, spontaneous subcellular Ca2+ release events (SCRE) with regional electrophysiological heterogeneity in ionic properties and a detailed three-dimensional model of atrial anatomy, microarchitecture and patchy fibrosis. Simulations highlighted the importance of the smaller inward rectifier potassium current (IK1 ) in PV cells compared to the surrounding atria, which enabled SCRE more readily to result in delayed-afterdepolarisations that induced triggered activity. There was a leftward shift in the dependence of the probability of triggered activity on sarcoplasmic reticulum Ca2+ load. This feature was accentuated in 3D tissue compared to single cells (Δ half-maximal [Ca2+ ]SR = 58 μM vs. 22 μM). In 3D atria incorporating electrical heterogeneity, excitations preferentially emerged from the PV region. These triggered focal excitations resulted in transient re-entry in the left atrium. Addition of fibrotic patches promoted localised emergence of focal excitations and wavebreaks that had a more substantial impact on generating AF-like patterns than the PVs. Thus, a reduced IK1 , less negative resting membrane potential, and fibrosis-induced changes of the electrotonic load all contribute to the emergence of complex excitation patterns from spontaneous focal triggers. KEY POINTS: Focal excitations in the atria are most commonly associated with the pulmonary veins, but the mechanisms for this localisation are yet to be elucidated. We applied a multi-scale computational modelling approach to elucidate the mechanisms underlying such localisations. Myocytes in the pulmonary vein region of the atria have a less negative resting membrane potential and reduced time-independent potassium current; we demonstrate that both of these factors promote triggered activity in single cells and tissues. The less negative resting membrane potential also contributes to heterogeneous inactivation of the fast sodium current, which can enable re-entrant-like excitation patterns to emerge without traditional conduction block.
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Affiliation(s)
- Michael A Colman
- School of Biomedical Sciences, Faculty of Biological Sciences, University of Leeds, Leeds, UK
| | - Marta Varela
- National Heart & Lung Institute, Faculty of Medicine, Imperial College London, London, UK
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Rob S MacLeod
- The Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA
| | - Jules C Hancox
- School of Physiology, Pharmacology & Neuroscience, Faculty of Life Sciences, University of Bristol, Bristol, UK
| | - Oleg V Aslanidi
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
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Igarashi W, Takagi D, Okada D, Kobayashi D, Oka M, Io T, Ishii K, Ono K, Yamamoto H, Okamoto Y. Bioinformatic Identification of Potential RNA Alterations on the Atrial Fibrillation Remodeling from Human Pulmonary Veins. Int J Mol Sci 2023; 24:10501. [PMID: 37445678 DOI: 10.3390/ijms241310501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Revised: 05/16/2023] [Accepted: 06/20/2023] [Indexed: 07/15/2023] Open
Abstract
Atrial fibrillation (AF) is the most frequent persistent arrhythmia. Many genes have been reported as a genetic background for AF. However, most transcriptome analyses of AF are limited to the atrial samples and have not been evaluated by multiple cardiac regions. In this study, we analyzed the expression levels of protein-coding and long noncoding RNAs (lncRNAs) in six cardiac regions by RNA-seq. Samples were donated from six subjects with or without persistent AF for left atria, left atrial appendages, right atria, sinoatrial nodes, left ventricles, right ventricles, and pulmonary veins (PVs), and additional four right atrial appendages samples were collected from patients undergoing mitral valve replacement. In total, 23 AF samples were compared to 23 non-AF samples. Surprisingly, the most influenced heart region in gene expression by AF was the PV, not the atria. The ion channel-related gene set was significantly enriched upon analysis of these significant genes. In addition, some significant genes are cancer-related lncRNAs in PV in AF. A co-expression network analysis could detect the functional gene clusters. In particular, the cancer-related lncRNA, such as SAMMSON and FOXCUT, belong to the gene network with the cancer-related transcription factor FOXC1. Thus, they may also play an aggravating role in the pathogenesis of AF, similar to carcinogenesis. In the least, this study suggests that (1) RNA alteration is most intense in PVs and (2) post-transcriptional gene regulation by lncRNA may contribute to the progression of AF. Through the screening analysis across the six cardiac regions, the possibility that the PV region can play a role other than paroxysmal triggering in the pathogenesis of AF was demonstrated for the first time. Future research with an increase in the number of PV samples will lead to a novel understanding of the pathophysiology of AF.
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Affiliation(s)
- Wataru Igarashi
- Department of Cardiovascular Surgery, Akita University Graduate School of Medicine, 1-1-1 Hondo, Akita 010-8543, Japan
| | - Daichi Takagi
- Department of Cardiovascular Surgery, Akita University Graduate School of Medicine, 1-1-1 Hondo, Akita 010-8543, Japan
| | - Daigo Okada
- Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, Shogoinkawahara-cho, Kyoto 606-8507, Japan
| | - Daiki Kobayashi
- Department of Cell Physiology, Akita University Graduate School of Medicine, 1-1-1 Hondo, Akita 010-8543, Japan
| | - Miho Oka
- Research Department, Ono Pharmaceutical Co., Ltd., Kyutaromachi, Osaka 541-0056, Japan
| | - Toshiro Io
- Research Department, Ono Pharmaceutical Co., Ltd., Kyutaromachi, Osaka 541-0056, Japan
| | - Kuniaki Ishii
- Department of Pharmacology, Faculty of Medicine, Yamagata University, Iida-Nishi, Yamagata 990-9585, Japan
| | - Kyoichi Ono
- Department of Cell Physiology, Akita University Graduate School of Medicine, 1-1-1 Hondo, Akita 010-8543, Japan
| | - Hiroshi Yamamoto
- Department of Cardiovascular Surgery, Akita University Graduate School of Medicine, 1-1-1 Hondo, Akita 010-8543, Japan
| | - Yosuke Okamoto
- Department of Cell Physiology, Akita University Graduate School of Medicine, 1-1-1 Hondo, Akita 010-8543, Japan
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Ogbomo-Harmitt S, Muffoletto M, Zeidan A, Qureshi A, King AP, Aslanidi O. Exploring interpretability in deep learning prediction of successful ablation therapy for atrial fibrillation. Front Physiol 2023; 14:1054401. [PMID: 36998987 PMCID: PMC10043207 DOI: 10.3389/fphys.2023.1054401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 02/28/2023] [Indexed: 03/16/2023] Open
Abstract
Background: Radiofrequency catheter ablation (RFCA) therapy is the first-line treatment for atrial fibrillation (AF), the most common type of cardiac arrhythmia globally. However, the procedure currently has low success rates in dealing with persistent AF, with a reoccurrence rate of ∼50% post-ablation. Therefore, deep learning (DL) has increasingly been applied to improve RFCA treatment for AF. However, for a clinician to trust the prediction of a DL model, its decision process needs to be interpretable and have biomedical relevance.Aim: This study explores interpretability in DL prediction of successful RFCA therapy for AF and evaluates if pro-arrhythmogenic regions in the left atrium (LA) were used in its decision process.Methods: AF and its termination by RFCA have been simulated in MRI-derived 2D LA tissue models with segmented fibrotic regions (n = 187). Three ablation strategies were applied for each LA model: pulmonary vein isolation (PVI), fibrosis-based ablation (FIBRO) and a rotor-based ablation (ROTOR). The DL model was trained to predict the success of each RFCA strategy for each LA model. Three feature attribution (FA) map methods were then used to investigate interpretability of the DL model: GradCAM, Occlusions and LIME.Results: The developed DL model had an AUC (area under the receiver operating characteristic curve) of 0.78 ± 0.04 for predicting the success of the PVI strategy, 0.92 ± 0.02 for FIBRO and 0.77 ± 0.02 for ROTOR. GradCAM had the highest percentage of informative regions in the FA maps (62% for FIBRO and 71% for ROTOR) that coincided with the successful RFCA lesions known from the 2D LA simulations, but unseen by the DL model. Moreover, GradCAM had the smallest coincidence of informative regions of the FA maps with non-arrhythmogenic regions (25% for FIBRO and 27% for ROTOR).Conclusion: The most informative regions of the FA maps coincided with pro-arrhythmogenic regions, suggesting that the DL model leveraged structural features of MRI images to identify such regions and make its prediction. In the future, this technique could provide a clinician with a trustworthy decision support tool.
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Adenosine and Adenosine Receptors: Advances in Atrial Fibrillation. Biomedicines 2022; 10:biomedicines10112963. [PMID: 36428533 PMCID: PMC9687155 DOI: 10.3390/biomedicines10112963] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 11/07/2022] [Accepted: 11/14/2022] [Indexed: 11/19/2022] Open
Abstract
Atrial fibrillation (AF) is the most common arrhythmia in the world. Because the key to developing innovative therapies that limit the onset and the progression of AF is to fully understand the underlying molecular mechanisms of AF, the aim of the present narrative review is to report the most recent advances in the potential role of the adenosinergic system in the pathophysiology of AF. After a comprehensive approach describing adenosinergic system signaling and the mechanisms of the initiation and maintenance of AF, we address the interactions of the adenosinergic system's signaling with AF. Indeed, adenosine release can activate four G-coupled membrane receptors, named A1, A2A, A2B and A3. Activation of the A2A receptors can promote the occurrence of delayed depolarization, while activation of the A1 receptors can shorten the action potential's duration and induce the resting membrane's potential hyperpolarization, which promote pulmonary vein firing, stabilize the AF rotors and allow for functional reentry. Moreover, the A2B receptors have been associated with atrial fibrosis homeostasis. Finally, the adenosinergic system can modulate the autonomous nervous system and is associated with AF risk factors. A question remains regarding adenosine release and the adenosine receptors' activation and whether this would be a cause or consequence of AF.
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Morris MF, Carlson C, Bhagat A. Role of advanced imaging with cardiac computed tomography and MRI in atrial and ventricular ablation. Curr Opin Cardiol 2022; 37:431-438. [PMID: 35880445 DOI: 10.1097/hco.0000000000000986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
PURPOSE OF REVIEW Increasing evidence supports the use of advanced imaging with cardiac computed tomography (CCT) and cardiac magnetic resonance (CMR) in the work-up of patients with arrythmias being considered for ablation. RECENT FINDINGS Advances in imaging technology and postprocessing are facilitating the use of advanced imaging before, during and after ablation in patients with both atrial and ventricular arrhythmias.In atrial arrythmias, quantitative assessment of left atrial wall thickness on CCT and quantification of late gadolinium enhancement (LGE) on CMR identify patients more likely to develop recurrent atrial arrythmias following ablation. In addition, in patients with recurrent arrythmia post ablation, LGE CMR can potentially identify targets for repeat ablation.In ventricular arrythmias, qualitative assessment of LGE can aide in determining the optimal ablation approach and predicts likelihood of ventricular arrythmias inducibility. Quantitative assessment of LGE can identify conduction channels that can be targeted for ablation. On CCT, quantitative assessment of left ventricular wall thickness can demonstrate myocardial ridges associated with re-entrant circuits for ablation. SUMMARY This review focuses on the utility of CCT and CMR in identifying key anatomical components and arrhythmogenic substrate contributing to both atrial and ventricular arrhythmias in patients being considered for ablation. Advanced imaging has the potential to improve procedural outcomes, decrease complications and shorten procedural time.
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Affiliation(s)
| | - Chelsea Carlson
- Department of Medicine, Banner University Medical Center Phoenix, Phoenix, Arizona, USA
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8
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Falkenberg M, Coleman JA, Dobson S, Hickey DJ, Terrill L, Ciacci A, Thomas B, Sau A, Ng FS, Zhao J, Peters NS, Christensen K. Identifying locations susceptible to micro-anatomical reentry using a spatial network representation of atrial fibre maps. PLoS One 2022; 17:e0267166. [PMID: 35737662 PMCID: PMC9223322 DOI: 10.1371/journal.pone.0267166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 04/03/2022] [Indexed: 11/18/2022] Open
Abstract
Micro-anatomical reentry has been identified as a potential driver of atrial fibrillation (AF). In this paper, we introduce a novel computational method which aims to identify which atrial regions are most susceptible to micro-reentry. The approach, which considers the structural basis for micro-reentry only, is based on the premise that the accumulation of electrically insulating interstitial fibrosis can be modelled by simulating percolation-like phenomena on spatial networks. Our results suggest that at high coupling, where micro-reentry is rare, the micro-reentrant substrate is highly clustered in areas where the atrial walls are thin and have convex wall morphology, likely facilitating localised treatment via ablation. However, as transverse connections between fibres are removed, mimicking the accumulation of interstitial fibrosis, the substrate becomes less spatially clustered, and the bias to forming in thin, convex regions of the atria is reduced, possibly restricting the efficacy of localised ablation. Comparing our algorithm on image-based models with and without atrial fibre structure, we find that strong longitudinal fibre coupling can suppress the micro-reentrant substrate, whereas regions with disordered fibre orientations have an enhanced risk of micro-reentry. With further development, these methods may be useful for modelling the temporal development of the fibrotic substrate on an individualised basis.
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Affiliation(s)
- Max Falkenberg
- Centre for Complexity Science, Imperial College London, London, United Kingdom
- Department of Physics, Imperial College London, London, United Kingdom
- ElectroCardioMaths Programme, Imperial Centre for Cardiac Engineering, National Heart & Lung Institute, Imperial College London, London, United Kingdom
| | - James A. Coleman
- Department of Physics, Imperial College London, London, United Kingdom
| | - Sam Dobson
- Department of Physics, Imperial College London, London, United Kingdom
| | - David J. Hickey
- Department of Physics, Imperial College London, London, United Kingdom
| | - Louie Terrill
- Department of Physics, Imperial College London, London, United Kingdom
| | - Alberto Ciacci
- Centre for Complexity Science, Imperial College London, London, United Kingdom
- Department of Physics, Imperial College London, London, United Kingdom
- ElectroCardioMaths Programme, Imperial Centre for Cardiac Engineering, National Heart & Lung Institute, Imperial College London, London, United Kingdom
| | - Belvin Thomas
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Arunashis Sau
- ElectroCardioMaths Programme, Imperial Centre for Cardiac Engineering, National Heart & Lung Institute, Imperial College London, London, United Kingdom
| | - Fu Siong Ng
- ElectroCardioMaths Programme, Imperial Centre for Cardiac Engineering, National Heart & Lung Institute, Imperial College London, London, United Kingdom
| | - Jichao Zhao
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Nicholas S. Peters
- ElectroCardioMaths Programme, Imperial Centre for Cardiac Engineering, National Heart & Lung Institute, Imperial College London, London, United Kingdom
| | - Kim Christensen
- Centre for Complexity Science, Imperial College London, London, United Kingdom
- Department of Physics, Imperial College London, London, United Kingdom
- ElectroCardioMaths Programme, Imperial Centre for Cardiac Engineering, National Heart & Lung Institute, Imperial College London, London, United Kingdom
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Jæger KH, Edwards AG, Giles WR, Tveito A. Arrhythmogenic influence of mutations in a myocyte-based computational model of the pulmonary vein sleeve. Sci Rep 2022; 12:7040. [PMID: 35487957 PMCID: PMC9054808 DOI: 10.1038/s41598-022-11110-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 04/12/2022] [Indexed: 11/09/2022] Open
Abstract
In the heart, electrophysiological dysregulation arises from defects at many biological levels (from point mutations in ion channel proteins to gross structural abnormalities). These defects disrupt the normal pattern of electrical activation, producing ectopic activity and reentrant arrhythmia. To interrogate mechanisms that link these primary biological defects to macroscopic electrophysiologic dysregulation most prior computational studies have utilized either (i) detailed models of myocyte ion channel dynamics at limited spatial scales, or (ii) homogenized models of action potential conduction that reproduce arrhythmic activity at tissue and organ levels. Here we apply our recent model (EMI), which integrates electrical activation and propagation across these scales, to study human atrial arrhythmias originating in the pulmonary vein (PV) sleeves. These small structures initiate most supraventricular arrhythmias and include pronounced myocyte-to-myocyte heterogeneities in ion channel expression and intercellular coupling. To test EMI's cell-based architecture in this physiological context we asked whether ion channel mutations known to underlie atrial fibrillation are capable of initiating arrhythmogenic behavior via increased excitability or reentry in a schematic PV sleeve geometry. Our results illustrate that EMI's improved spatial resolution can directly interrogate how electrophysiological changes at the individual myocyte level manifest in tissue and as arrhythmia in the PV sleeve.
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Affiliation(s)
| | | | - Wayne R Giles
- Simula Research Laboratory, Oslo, Norway.,Department of Physiology and Pharmacology, Cumming School of Medicine, University of Calgary, Calgary, Canada
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Feng Y, Roney CH, Bayer JD, Niederer SA, Hocini M, Vigmond EJ. Detection of focal source and arrhythmogenic substrate from body surface potentials to guide atrial fibrillation ablation. PLoS Comput Biol 2022; 18:e1009893. [PMID: 35312675 PMCID: PMC8970486 DOI: 10.1371/journal.pcbi.1009893] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 03/31/2022] [Accepted: 02/02/2022] [Indexed: 11/18/2022] Open
Abstract
Focal sources (FS) are believed to be important triggers and a perpetuation mechanism for paroxysmal atrial fibrillation (AF). Detecting FS and determining AF sustainability in atrial tissue can help guide ablation targeting. We hypothesized that sustained rotors during FS-driven episodes indicate an arrhythmogenic substrate for sustained AF, and that non-invasive electrical recordings, like electrocardiograms (ECGs) or body surface potential maps (BSPMs), could be used to detect FS and AF sustainability. Computer simulations were performed on five bi-atrial geometries. FS were induced by pacing at cycle lengths of 120-270 ms from 32 atrial sites and four pulmonary veins. Self-sustained reentrant activities were also initiated around the same 32 atrial sites with inexcitable cores of radii of 0, 0.5 and 1 cm. FS fired for two seconds and then AF inducibility was tested by whether activation was sustained for another second. ECGs and BSPMs were simulated. Equivalent atrial sources were extracted using second-order blind source separation, and their cycle length, periodicity and contribution, were used as features for random forest classifiers. Longer rotor duration during FS-driven episodes indicates higher AF inducibility (area under ROC curve = 0.83). Our method had accuracy of 90.6±1.0% and 90.6±0.6% in detecting FS presence, and 93.1±0.6% and 94.2±1.2% in identifying AF sustainability, and 80.0±6.6% and 61.0±5.2% in determining the atrium of the focal site, from BSPMs and ECGs of five atria. The detection of FS presence and AF sustainability were insensitive to vest placement (±9.6%). On pre-operative BSPMs of 52 paroxysmal AF patients, patients classified with initiator-type FS on a single atrium resulted in improved two-to-three-year AF-free likelihoods (p-value < 0.01, logrank tests). Detection of FS and arrhythmogenic substrate can be performed from ECGs and BSPMs, enabling non-invasive mapping towards mechanism-targeted AF treatment, and malignant ectopic beat detection with likely AF progression.
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Affiliation(s)
- Yingjing Feng
- IHU Liryc, Electrophysiology and Heart Modeling Institute, fondation Bordeaux Université, Pessac-Bordeaux, France
- Univ. Bordeaux, IMB, UMR 5251, Talence, France
| | - Caroline H. Roney
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Jason D. Bayer
- IHU Liryc, Electrophysiology and Heart Modeling Institute, fondation Bordeaux Université, Pessac-Bordeaux, France
- Univ. Bordeaux, IMB, UMR 5251, Talence, France
| | - Steven A. Niederer
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Mélèze Hocini
- IHU Liryc, Electrophysiology and Heart Modeling Institute, fondation Bordeaux Université, Pessac-Bordeaux, France
- Bordeaux University Hospital (CHU), Electrophysiology and Ablation Unit, Pessac, France
| | - Edward J. Vigmond
- IHU Liryc, Electrophysiology and Heart Modeling Institute, fondation Bordeaux Université, Pessac-Bordeaux, France
- Univ. Bordeaux, IMB, UMR 5251, Talence, France
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11
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Roney CH, Sim I, Yu J, Beach M, Mehta A, Alonso Solis-Lemus J, Kotadia I, Whitaker J, Corrado C, Razeghi O, Vigmond E, Narayan SM, O’Neill M, Williams SE, Niederer SA. Predicting Atrial Fibrillation Recurrence by Combining Population Data and Virtual Cohorts of Patient-Specific Left Atrial Models. Circ Arrhythm Electrophysiol 2022; 15:e010253. [PMID: 35089057 PMCID: PMC8845531 DOI: 10.1161/circep.121.010253] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 01/03/2022] [Indexed: 01/14/2023]
Abstract
BACKGROUND Current ablation therapy for atrial fibrillation is suboptimal, and long-term response is challenging to predict. Clinical trials identify bedside properties that provide only modest prediction of long-term response in populations, while patient-specific models in small cohorts primarily explain acute response to ablation. We aimed to predict long-term atrial fibrillation recurrence after ablation in large cohorts, by using machine learning to complement biophysical simulations by encoding more interindividual variability. METHODS Patient-specific models were constructed for 100 atrial fibrillation patients (43 paroxysmal, 41 persistent, and 16 long-standing persistent), undergoing first ablation. Patients were followed for 1 year using ambulatory ECG monitoring. Each patient-specific biophysical model combined differing fibrosis patterns, fiber orientation maps, electrical properties, and ablation patterns to capture uncertainty in atrial properties and to test the ability of the tissue to sustain fibrillation. These simulation stress tests of different model variants were postprocessed to calculate atrial fibrillation simulation metrics. Machine learning classifiers were trained to predict atrial fibrillation recurrence using features from the patient history, imaging, and atrial fibrillation simulation metrics. RESULTS We performed 1100 atrial fibrillation ablation simulations across 100 patient-specific models. Models based on simulation stress tests alone showed a maximum accuracy of 0.63 for predicting long-term fibrillation recurrence. Classifiers trained to history, imaging, and simulation stress tests (average 10-fold cross-validation area under the curve, 0.85±0.09; recall, 0.80±0.13; precision, 0.74±0.13) outperformed those trained to history and imaging (area under the curve, 0.66±0.17) or history alone (area under the curve, 0.61±0.14). CONCLUSION A novel computational pipeline accurately predicted long-term atrial fibrillation recurrence in individual patients by combining outcome data with patient-specific acute simulation response. This technique could help to personalize selection for atrial fibrillation ablation.
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Affiliation(s)
- Caroline H. Roney
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (C.H.R., I.S., J.Y., M.B., A.M., J.A.S.-L., I.K., J.W., C.C., O.R., M.O., S.E.W., S.A.N.)
- School of Engineering and Materials Science, Queen Mary University of London, United Kingdom (C.H.R.)
| | - Iain Sim
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (C.H.R., I.S., J.Y., M.B., A.M., J.A.S.-L., I.K., J.W., C.C., O.R., M.O., S.E.W., S.A.N.)
| | - Jin Yu
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (C.H.R., I.S., J.Y., M.B., A.M., J.A.S.-L., I.K., J.W., C.C., O.R., M.O., S.E.W., S.A.N.)
| | - Marianne Beach
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (C.H.R., I.S., J.Y., M.B., A.M., J.A.S.-L., I.K., J.W., C.C., O.R., M.O., S.E.W., S.A.N.)
| | - Arihant Mehta
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (C.H.R., I.S., J.Y., M.B., A.M., J.A.S.-L., I.K., J.W., C.C., O.R., M.O., S.E.W., S.A.N.)
| | - Jose Alonso Solis-Lemus
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (C.H.R., I.S., J.Y., M.B., A.M., J.A.S.-L., I.K., J.W., C.C., O.R., M.O., S.E.W., S.A.N.)
| | - Irum Kotadia
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (C.H.R., I.S., J.Y., M.B., A.M., J.A.S.-L., I.K., J.W., C.C., O.R., M.O., S.E.W., S.A.N.)
| | - John Whitaker
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (C.H.R., I.S., J.Y., M.B., A.M., J.A.S.-L., I.K., J.W., C.C., O.R., M.O., S.E.W., S.A.N.)
- The Department of Internal Medicine, Cardiovascular Division, Brigham and Women’s Hospital, Boston, MA (J.W.)
| | - Cesare Corrado
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (C.H.R., I.S., J.Y., M.B., A.M., J.A.S.-L., I.K., J.W., C.C., O.R., M.O., S.E.W., S.A.N.)
| | - Orod Razeghi
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (C.H.R., I.S., J.Y., M.B., A.M., J.A.S.-L., I.K., J.W., C.C., O.R., M.O., S.E.W., S.A.N.)
| | - Edward Vigmond
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, France (E.V.)
- Univ. Bordeaux, IMB, UMR 5251, F-33400 Talence, France (E.V.)
| | - Sanjiv M. Narayan
- Department of Medicine and Cardiovascular Institute, Stanford University, Palo Alto, CA (S.M.N.)
| | - Mark O’Neill
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (C.H.R., I.S., J.Y., M.B., A.M., J.A.S.-L., I.K., J.W., C.C., O.R., M.O., S.E.W., S.A.N.)
| | - Steven E. Williams
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (C.H.R., I.S., J.Y., M.B., A.M., J.A.S.-L., I.K., J.W., C.C., O.R., M.O., S.E.W., S.A.N.)
- Centre for Cardiovascular Science, College of Medicine and Veterinary Medicine, University of Edinburgh (S.E.W.)
| | - Steven A. Niederer
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (C.H.R., I.S., J.Y., M.B., A.M., J.A.S.-L., I.K., J.W., C.C., O.R., M.O., S.E.W., S.A.N.)
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12
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Kawai S, Mukai Y, Inoue S, Yakabe D, Nagaoka K, Sakamoto K, Takase S, Chishaki A, Tsutsui H. Location and coupling interval of an ectopic excitation determine the initiation of atrial fibrillation from the pulmonary veins. J Cardiovasc Electrophysiol 2022; 33:629-637. [PMID: 35048463 PMCID: PMC9303182 DOI: 10.1111/jce.15371] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 09/21/2021] [Accepted: 11/18/2021] [Indexed: 12/03/2022]
Abstract
Introduction Ectopic beats originating from the pulmonary vein (PV) trigger atrial fibrillation (AF). The purpose of this study was to clarify the electrophysiological determinant of AF initiation from the PVs. Methods Pacing studies were performed with a single extra stimulus mimicking an ectopic beat in the left superior PVs (LSPVs) in 62 patients undergoing AF ablation. Inducibility of AF, effective refractory period (ERP), and conduction properties within the PVs were analyzed. Results A single extra stimulus in LSPV induced AF in 20 patients (32% of all patients) at the mean coupling interval (CI) of 172 ms. A CI‐dependent anisotropic conduction at the AF onset was visualized in a three‐dimensional mapping. Onset of AF was site‐specific with reproducibility in each individual. Mean ERP in LSPV in the AF‐inducible group was shorter than that in the AF‐noninducible group (182 ± 55 vs. 254 ± 51 ms, p < .0001). LSPV ERP dispersion was greater in the AF‐inducible group than in the AF‐noninducible group (45 ± 28 vs. 27 ± 19 ms, p < .01). Circumferential intra‐PV conduction time (IPVCT) exhibited decremental properties in response to shortening of CI and the prolongation of IPVCT in the AF‐inducible site was greater than that in the AF‐noninducible site (p < .05) in each individual. Conclusions Location and CI of an ectopic excitation ultimately determine the initiation of AF from the PVs. ERP dispersion and circumferential conduction delay may lead to anisotropic conduction and reentry within the PVs that initiate AF.
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Affiliation(s)
- Shunsuke Kawai
- Department of Cardiovascular Medicine Japanese Red Cross Fukuoka Hospital Fukuoka Japan
- Department of Cardiovascular Medicine Kyushu University Graduate School of Medical Sciences Fukuoka Japan
| | - Yasushi Mukai
- Department of Cardiovascular Medicine Japanese Red Cross Fukuoka Hospital Fukuoka Japan
- Department of Cardiovascular Medicine Kyushu University Graduate School of Medical Sciences Fukuoka Japan
| | - Shujiro Inoue
- Department of Cardiovascular Medicine Kyushu University Graduate School of Medical Sciences Fukuoka Japan
| | - Daisuke Yakabe
- Department of Cardiovascular Medicine Kyushu University Graduate School of Medical Sciences Fukuoka Japan
| | - Kazuhiro Nagaoka
- Department of Cardiovascular Medicine Kyushu University Graduate School of Medical Sciences Fukuoka Japan
| | - Kazuo Sakamoto
- Department of Cardiovascular Medicine Kyushu University Graduate School of Medical Sciences Fukuoka Japan
| | - Susumu Takase
- Department of Cardiovascular Medicine Kyushu University Graduate School of Medical Sciences Fukuoka Japan
| | - Akiko Chishaki
- Department of Health Sciences Fukuoka Dental College Fukuoka Japan
| | - Hiroyuki Tsutsui
- Department of Cardiovascular Medicine Kyushu University Graduate School of Medical Sciences Fukuoka Japan
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13
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Kumagai K. The electrophysiological characteristics of the initiation of atrial fibrillation from the pulmonary veins. J Cardiovasc Electrophysiol 2022; 33:638-640. [PMID: 35044024 DOI: 10.1111/jce.15376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 01/16/2022] [Indexed: 11/28/2022]
Affiliation(s)
- Koji Kumagai
- Department of Cardiovascular Medicine, Tohoku Medical and Pharmaceutical University, Miyagi, Japan
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14
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Roney CH, Child N, Porter B, Sim I, Whitaker J, Clayton RH, Laughner JI, Shuros A, Neuzil P, Williams SE, Razavi RS, O'Neill M, Rinaldi CA, Taggart P, Wright M, Gill JS, Niederer SA. Time-Averaged Wavefront Analysis Demonstrates Preferential Pathways of Atrial Fibrillation, Predicting Pulmonary Vein Isolation Acute Response. Front Physiol 2021; 12:707189. [PMID: 34646149 PMCID: PMC8503618 DOI: 10.3389/fphys.2021.707189] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2021] [Accepted: 08/24/2021] [Indexed: 11/13/2022] Open
Abstract
Electrical activation during atrial fibrillation (AF) appears chaotic and disorganised, which impedes characterisation of the underlying substrate and treatment planning. While globally chaotic, there may be local preferential activation pathways that represent potential ablation targets. This study aimed to identify preferential activation pathways during AF and predict the acute ablation response when these are targeted by pulmonary vein isolation (PVI). In patients with persistent AF (n = 14), simultaneous biatrial contact mapping with basket catheters was performed pre-ablation and following each ablation strategy (PVI, roof, and mitral lines). Unipolar wavefront activation directions were averaged over 10 s to identify preferential activation pathways. Clinical cases were classified as responders or non-responders to PVI during the procedure. Clinical data were augmented with a virtual cohort of 100 models. In AF pre-ablation, pathways originated from the pulmonary vein (PV) antra in PVI responders (7/7) but not in PVI non-responders (6/6). We proposed a novel index that measured activation waves from the PV antra into the atrial body. This index was significantly higher in PVI responders than non-responders (clinical: 16.3 vs. 3.7%, p = 0.04; simulated: 21.1 vs. 14.1%, p = 0.02). Overall, this novel technique and proof of concept study demonstrated that preferential activation pathways exist during AF. Targeting patient-specific activation pathways that flowed from the PV antra to the left atrial body using PVI resulted in AF termination during the procedure. These PV activation flow pathways may correspond to the presence of drivers in the PV regions.
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Affiliation(s)
- Caroline H. Roney
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Nicholas Child
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Bradley Porter
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Iain Sim
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - John Whitaker
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Richard H. Clayton
- INSIGNEO Institute for In Silico Medicine and Department of Computer Science, University of Sheffield, Sheffield, United Kingdom
| | | | - Allan Shuros
- Boston Scientific Corp, St. Paul, MN, United States
| | - Petr Neuzil
- Department of Cardiology, Na Holmolce Hospital, Prague, Czechia
| | - Steven E. Williams
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Reza S. Razavi
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Mark O'Neill
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | | | - Peter Taggart
- Institute of Cardiovascular Science, University College London, London, United Kingdom
| | - Matt Wright
- Department of Cardiology, Guy's and St Thomas' Hospital, London, United Kingdom
| | - Jaswinder S. Gill
- Department of Cardiology, Guy's and St Thomas' Hospital, London, United Kingdom
| | - Steven A. Niederer
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
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15
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Unger LA, Azzolin L, Nothstein M, Sánchez J, Luik A, Seemann G, Yeshwant S, Oesterlein T, Dössel O, Schmitt C, Spector P, Loewe A. Cycle length statistics during human atrial fibrillation reveal refractory properties of the underlying substrate: a combined in silico and clinical test of concept study. Europace 2021; 23:i133-i142. [PMID: 33751084 DOI: 10.1093/europace/euaa404] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 12/04/2020] [Indexed: 11/12/2022] Open
Abstract
AIMS The treatment of atrial fibrillation beyond pulmonary vein isolation has remained an unsolved challenge. Targeting regions identified by different substrate mapping approaches for ablation resulted in ambiguous outcomes. With the effective refractory period being a fundamental prerequisite for the maintenance of fibrillatory conduction, this study aims at estimating the effective refractory period with clinically available measurements. METHODS AND RESULTS A set of 240 simulations in a spherical model of the left atrium with varying model initialization, combination of cellular refractory properties, and size of a region of lowered effective refractory period was implemented to analyse the capabilities and limitations of cycle length mapping. The minimum observed cycle length and the 25% quantile were compared to the underlying effective refractory period. The density of phase singularities was used as a measure for the complexity of the excitation pattern. Finally, we employed the method in a clinical test of concept including five patients. Areas of lowered effective refractory period could be distinguished from their surroundings in simulated scenarios with successfully induced multi-wavelet re-entry. Larger areas and higher gradients in effective refractory period as well as complex activation patterns favour the method. The 25% quantile of cycle lengths in patients with persistent atrial fibrillation was found to range from 85 to 190 ms. CONCLUSION Cycle length mapping is capable of highlighting regions of pathologic refractory properties. In combination with complementary substrate mapping approaches, the method fosters confidence to enhance the treatment of atrial fibrillation beyond pulmonary vein isolation particularly in patients with complex activation patterns.
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Affiliation(s)
- Laura A Unger
- Department of Electrical Engineering and Information Technology, Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, 76131 Karlsruhe, Germany
| | - Luca Azzolin
- Department of Electrical Engineering and Information Technology, Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, 76131 Karlsruhe, Germany
| | - Mark Nothstein
- Department of Electrical Engineering and Information Technology, Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, 76131 Karlsruhe, Germany
| | - Jorge Sánchez
- Department of Electrical Engineering and Information Technology, Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, 76131 Karlsruhe, Germany
| | - Armin Luik
- Medizinische Klinik IV, Städtisches Klinikum Karlsruhe, Karlsruhe 76133, Germany
| | - Gunnar Seemann
- Institute for Experimental Cardiovascular Medicine, University Heart Center Freiburg Bad Krozingen, Medical Center, University of Freiburg, Freiburg 79110, Germany.,Faculty of Medicine, University of Freiburg, Freiburg 79110, Germany
| | - Srinath Yeshwant
- Division of Cardiovascular Medicine, College of Medicine, University of Vermont, Burlington, Colchester, VT 05446, USA
| | - Tobias Oesterlein
- Department of Electrical Engineering and Information Technology, Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, 76131 Karlsruhe, Germany
| | - Olaf Dössel
- Department of Electrical Engineering and Information Technology, Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, 76131 Karlsruhe, Germany
| | - Claus Schmitt
- Medizinische Klinik IV, Städtisches Klinikum Karlsruhe, Karlsruhe 76133, Germany
| | - Peter Spector
- Division of Cardiovascular Medicine, College of Medicine, University of Vermont, Burlington, Colchester, VT 05446, USA
| | - Axel Loewe
- Department of Electrical Engineering and Information Technology, Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, 76131 Karlsruhe, Germany
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16
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Sánchez J, Luongo G, Nothstein M, Unger LA, Saiz J, Trenor B, Luik A, Dössel O, Loewe A. Using Machine Learning to Characterize Atrial Fibrotic Substrate From Intracardiac Signals With a Hybrid in silico and in vivo Dataset. Front Physiol 2021; 12:699291. [PMID: 34290623 PMCID: PMC8287829 DOI: 10.3389/fphys.2021.699291] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 06/08/2021] [Indexed: 11/15/2022] Open
Abstract
In patients with atrial fibrillation, intracardiac electrogram signal amplitude is known to decrease with increased structural tissue remodeling, referred to as fibrosis. In addition to the isolation of the pulmonary veins, fibrotic sites are considered a suitable target for catheter ablation. However, it remains an open challenge to find fibrotic areas and to differentiate their density and transmurality. This study aims to identify the volume fraction and transmurality of fibrosis in the atrial substrate. Simulated cardiac electrograms, combined with a generalized model of clinical noise, reproduce clinically measured signals. Our hybrid dataset approach combines in silico and clinical electrograms to train a decision tree classifier to characterize the fibrotic atrial substrate. This approach captures different in vivo dynamics of the electrical propagation reflected on healthy electrogram morphology and synergistically combines it with synthetic fibrotic electrograms from in silico experiments. The machine learning algorithm was tested on five patients and compared against clinical voltage maps as a proof of concept, distinguishing non-fibrotic from fibrotic tissue and characterizing the patient's fibrotic tissue in terms of density and transmurality. The proposed approach can be used to overcome a single voltage cut-off value to identify fibrotic tissue and guide ablation targeting fibrotic areas.
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Affiliation(s)
- Jorge Sánchez
- Institute of Biomedical Engineering, Karlsruhe Institute for Technology, Karlsruhe, Germany
- Centro de Investigación e Innovación en Bioingeniería (Ci2B), Universitàt Politècnica de València, Valencia, Spain
| | - Giorgio Luongo
- Institute of Biomedical Engineering, Karlsruhe Institute for Technology, Karlsruhe, Germany
| | - Mark Nothstein
- Institute of Biomedical Engineering, Karlsruhe Institute for Technology, Karlsruhe, Germany
| | - Laura A. Unger
- Institute of Biomedical Engineering, Karlsruhe Institute for Technology, Karlsruhe, Germany
| | - Javier Saiz
- Centro de Investigación e Innovación en Bioingeniería (Ci2B), Universitàt Politècnica de València, Valencia, Spain
| | - Beatriz Trenor
- Centro de Investigación e Innovación en Bioingeniería (Ci2B), Universitàt Politècnica de València, Valencia, Spain
| | - Armin Luik
- Medizinische Klinik IV, Städtisches Klinikum Karlsruhe, Karlsruhe, Germany
| | - Olaf Dössel
- Institute of Biomedical Engineering, Karlsruhe Institute for Technology, Karlsruhe, Germany
| | - Axel Loewe
- Institute of Biomedical Engineering, Karlsruhe Institute for Technology, Karlsruhe, Germany
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17
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Azzolin L, Schuler S, Dössel O, Loewe A. A Reproducible Protocol to Assess Arrhythmia Vulnerability in silico: Pacing at the End of the Effective Refractory Period. Front Physiol 2021; 12:656411. [PMID: 33868025 PMCID: PMC8047415 DOI: 10.3389/fphys.2021.656411] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 03/10/2021] [Indexed: 01/31/2023] Open
Abstract
In both clinical and computational studies, different pacing protocols are used to induce arrhythmia and non-inducibility is often considered as the endpoint of treatment. The need for a standardized methodology is urgent since the choice of the protocol used to induce arrhythmia could lead to contrasting results, e.g., in assessing atrial fibrillation (AF) vulnerabilty. Therefore, we propose a novel method-pacing at the end of the effective refractory period (PEERP)-and compare it to state-of-the-art protocols, such as phase singularity distribution (PSD) and rapid pacing (RP) in a computational study. All methods were tested by pacing from evenly distributed endocardial points at 1 cm inter-point distance in two bi-atrial geometries. Seven different atrial models were implemented: five cases without specific AF-induced remodeling but with decreasing global conduction velocity and two persistent AF cases with an increasing amount of fibrosis resembling different substrate remodeling stages. Compared with PSD and RP, PEERP induced a larger variety of arrhythmia complexity requiring, on average, only 2.7 extra-stimuli and 3 s of simulation time to initiate reentry. Moreover, PEERP and PSD were the protocols which unveiled a larger number of areas vulnerable to sustain stable long living reentries compared to RP. Finally, PEERP can foster standardization and reproducibility, since, in contrast to the other protocols, it is a parameter-free method. Furthermore, we discuss its clinical applicability. We conclude that the choice of the inducing protocol has an influence on both initiation and maintenance of AF and we propose and provide PEERP as a reproducible method to assess arrhythmia vulnerability.
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Affiliation(s)
- Luca Azzolin
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
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18
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Corral-Acero J, Margara F, Marciniak M, Rodero C, Loncaric F, Feng Y, Gilbert A, Fernandes JF, Bukhari HA, Wajdan A, Martinez MV, Santos MS, Shamohammdi M, Luo H, Westphal P, Leeson P, DiAchille P, Gurev V, Mayr M, Geris L, Pathmanathan P, Morrison T, Cornelussen R, Prinzen F, Delhaas T, Doltra A, Sitges M, Vigmond EJ, Zacur E, Grau V, Rodriguez B, Remme EW, Niederer S, Mortier P, McLeod K, Potse M, Pueyo E, Bueno-Orovio A, Lamata P. The 'Digital Twin' to enable the vision of precision cardiology. Eur Heart J 2020; 41:4556-4564. [PMID: 32128588 PMCID: PMC7774470 DOI: 10.1093/eurheartj/ehaa159] [Citation(s) in RCA: 206] [Impact Index Per Article: 51.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Revised: 11/29/2019] [Accepted: 02/24/2020] [Indexed: 12/26/2022] Open
Abstract
Providing therapies tailored to each patient is the vision of precision medicine, enabled by the increasing ability to capture extensive data about individual patients. In this position paper, we argue that the second enabling pillar towards this vision is the increasing power of computers and algorithms to learn, reason, and build the 'digital twin' of a patient. Computational models are boosting the capacity to draw diagnosis and prognosis, and future treatments will be tailored not only to current health status and data, but also to an accurate projection of the pathways to restore health by model predictions. The early steps of the digital twin in the area of cardiovascular medicine are reviewed in this article, together with a discussion of the challenges and opportunities ahead. We emphasize the synergies between mechanistic and statistical models in accelerating cardiovascular research and enabling the vision of precision medicine.
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Affiliation(s)
| | - Francesca Margara
- Department of Computer Science, British Heart Foundation Centre of Research Excellence, University of Oxford, Oxford, UK
| | - Maciej Marciniak
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, UK
| | - Cristobal Rodero
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, UK
| | - Filip Loncaric
- Institut Clínic Cardiovascular, Hospital Clínic, Universitat de Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Yingjing Feng
- IHU Liryc, Electrophysiology and Heart Modeling Institute, fondation Bordeaux Université, Pessac-Bordeaux F-33600, France
- IMB, UMR 5251, University of Bordeaux, Talence F-33400, France
| | | | - Joao F Fernandes
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, UK
| | - Hassaan A Bukhari
- IMB, UMR 5251, University of Bordeaux, Talence F-33400, France
- Aragón Institute of Engineering Research, Universidad de Zaragoza, IIS Aragón, Zaragoza, Spain
| | - Ali Wajdan
- The Intervention Centre, Oslo University Hospital, Rikshospitalet, Oslo, Norway
| | | | | | - Mehrdad Shamohammdi
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
| | - Hongxing Luo
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
| | - Philip Westphal
- Medtronic PLC, Bakken Research Center, Maastricht, the Netherlands
| | - Paul Leeson
- Radcliffe Department of Medicine, Division of Cardiovascular Medicine, Oxford Cardiovascular Clinical Research Facility, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Paolo DiAchille
- Healthcare and Life Sciences Research, IBM T.J. Watson Research Center, Yorktown Heights, NY, USA
| | - Viatcheslav Gurev
- Healthcare and Life Sciences Research, IBM T.J. Watson Research Center, Yorktown Heights, NY, USA
| | - Manuel Mayr
- King’s British Heart Foundation Centre, King’s College London, London, UK
| | - Liesbet Geris
- Virtual Physiological Human Institute, Leuven, Belgium
| | - Pras Pathmanathan
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, USA
| | - Tina Morrison
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, USA
| | | | - Frits Prinzen
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
| | - Tammo Delhaas
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
| | - Ada Doltra
- Institut Clínic Cardiovascular, Hospital Clínic, Universitat de Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Marta Sitges
- Institut Clínic Cardiovascular, Hospital Clínic, Universitat de Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- CIBERCV, Instituto de Salud Carlos III, (CB16/11/00354), CERCA Programme/Generalitat de, Catalunya, Spain
| | - Edward J Vigmond
- IHU Liryc, Electrophysiology and Heart Modeling Institute, fondation Bordeaux Université, Pessac-Bordeaux F-33600, France
- IMB, UMR 5251, University of Bordeaux, Talence F-33400, France
| | - Ernesto Zacur
- Department of Engineering Science, University of Oxford, Oxford, UK
| | - Vicente Grau
- Department of Engineering Science, University of Oxford, Oxford, UK
| | - Blanca Rodriguez
- Department of Computer Science, British Heart Foundation Centre of Research Excellence, University of Oxford, Oxford, UK
| | - Espen W Remme
- The Intervention Centre, Oslo University Hospital, Rikshospitalet, Oslo, Norway
| | - Steven Niederer
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, UK
| | | | | | - Mark Potse
- IHU Liryc, Electrophysiology and Heart Modeling Institute, fondation Bordeaux Université, Pessac-Bordeaux F-33600, France
- IMB, UMR 5251, University of Bordeaux, Talence F-33400, France
- Inria Bordeaux Sud-Ouest, CARMEN team, Talence F-33400, France
| | - Esther Pueyo
- Aragón Institute of Engineering Research, Universidad de Zaragoza, IIS Aragón, Zaragoza, Spain
- CIBER in Bioengineering, Biomaterials and Nanomedicine (CIBER‐BBN), Madrid, Spain
| | - Alfonso Bueno-Orovio
- Department of Computer Science, British Heart Foundation Centre of Research Excellence, University of Oxford, Oxford, UK
| | - Pablo Lamata
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, UK
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19
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Bifulco SF, Akoum N, Boyle PM. Translational applications of computational modelling for patients with cardiac arrhythmias. Heart 2020; 107:heartjnl-2020-316854. [PMID: 33303478 PMCID: PMC10896425 DOI: 10.1136/heartjnl-2020-316854] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Revised: 11/13/2020] [Accepted: 11/19/2020] [Indexed: 11/04/2022] Open
Abstract
Cardiac arrhythmia is associated with high morbidity, and its underlying mechanisms are poorly understood. Computational modelling and simulation approaches have the potential to improve standard-of-care therapy for these disorders, offering deeper understanding of complex disease processes and sophisticated translational tools for planning clinical procedures. This review provides a clinician-friendly summary of recent advancements in computational cardiology. Organ-scale models automatically generated from clinical-grade imaging data are used to custom tailor our understanding of arrhythmia drivers, estimate future arrhythmogenic risk and personalise treatment plans. Recent mechanistic insights derived from atrial and ventricular arrhythmia simulations are highlighted, and the potential avenues to patient care (eg, by revealing new antiarrhythmic drug targets) are covered. Computational approaches geared towards improving outcomes in resynchronisation therapy have used simulations to elucidate optimal patient selection and lead location. Technology to personalise catheter ablation procedures are also covered, specifically preliminary outcomes form early-stage or pilot clinical studies. To conclude, future developments in computational cardiology are discussed, including improving the representation of patient-specific fibre orientations and fibrotic remodelling characterisation and how these might improve understanding of arrhythmia mechanisms and provide transformative tools for patient-specific therapy.
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Affiliation(s)
- Savannah F Bifulco
- Department of Bioengineering, University of Washington, Seattle, Washington, USA
| | - Nazem Akoum
- Department of Cardiology, University of Washington, Seattle, Washington, USA
| | - Patrick M Boyle
- Department of Bioengineering, University of Washington, Seattle, Washington, USA
- Center for Cardiovascular Biology, University of Washington, Seattle, WA, USA
- Institute for Stem Cell & Regenerative Medicine, University of Washington, Seattle, WA, USA
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20
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Balaban G, Costa CM, Porter B, Halliday B, Rinaldi CA, Prasad S, Plank G, Ismail TF, Bishop MJ. 3D Electrophysiological Modeling of Interstitial Fibrosis Networks and Their Role in Ventricular Arrhythmias in Non-Ischemic Cardiomyopathy. IEEE Trans Biomed Eng 2020; 67:3125-3133. [PMID: 32275581 PMCID: PMC7116885 DOI: 10.1109/tbme.2020.2976924] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
OBJECTIVE Interstitial fibrosis is a pathological expansion of the heart's inter-cellular collagen matrix. It is a potential complication of nonischemic cardiomyopathy (NICM), a class of diseases involving electrical and or mechanical dysfunction of cardiac tissue not caused by atherosclerosis. Patients with NICM and interstitial fibrosis often suffer from life threatening arrhythmias, which we aim to simulate in this study. METHODS Our methodology builds on an efficient discrete finite element (DFE) method which allows for the representation of fibrosis as infinitesimal splits in a mesh. We update the DFE method with a local connectivity analysis which creates a consistent topology in the fibrosis network. This is particularly important in nonischemic disease due to the potential presence of large and contiguous fibrotic regions and therefore potentially complex fibrosis networks. RESULTS In experiments with an image-based model, we demonstrate that our methodology is able to simulate reentrant electrical events associated with cardiac arrhythmias. These reentries depended crucially upon sufficient fibrosis density, which was marked by conduction slowing at high pacing rates. We also created a 2D test-case which demonstrated that fibrosis topologies can modulate transient conduction block, and thereby reentrant activations. CONCLUSION Ventricular arrhythmias due to interstitial fibrosis in NICM can be efficiently simulated using our methods in medical image based geometries. Furthermore, fibrosis topology modulates transient conduction block, and should be accounted for in electrophysiological simulations with interstitial fibrosis. SIGNIFICANCE Our study provides methodology which has the potential to predict arrhythmias and to optimize treatments non-invasively for nonischemic cardiomyopathies.
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21
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Varró A, Tomek J, Nagy N, Virág L, Passini E, Rodriguez B, Baczkó I. Cardiac transmembrane ion channels and action potentials: cellular physiology and arrhythmogenic behavior. Physiol Rev 2020; 101:1083-1176. [PMID: 33118864 DOI: 10.1152/physrev.00024.2019] [Citation(s) in RCA: 86] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Cardiac arrhythmias are among the leading causes of mortality. They often arise from alterations in the electrophysiological properties of cardiac cells and their underlying ionic mechanisms. It is therefore critical to further unravel the pathophysiology of the ionic basis of human cardiac electrophysiology in health and disease. In the first part of this review, current knowledge on the differences in ion channel expression and properties of the ionic processes that determine the morphology and properties of cardiac action potentials and calcium dynamics from cardiomyocytes in different regions of the heart are described. Then the cellular mechanisms promoting arrhythmias in congenital or acquired conditions of ion channel function (electrical remodeling) are discussed. The focus is on human-relevant findings obtained with clinical, experimental, and computational studies, given that interspecies differences make the extrapolation from animal experiments to human clinical settings difficult. Deepening the understanding of the diverse pathophysiology of human cellular electrophysiology will help in developing novel and effective antiarrhythmic strategies for specific subpopulations and disease conditions.
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Affiliation(s)
- András Varró
- Department of Pharmacology and Pharmacotherapy, Faculty of Medicine, University of Szeged, Szeged, Hungary.,MTA-SZTE Cardiovascular Pharmacology Research Group, Hungarian Academy of Sciences, Szeged, Hungary
| | - Jakub Tomek
- Department of Computer Science, British Heart Foundation Centre of Research Excellence, University of Oxford, Oxford, United Kingdom
| | - Norbert Nagy
- Department of Pharmacology and Pharmacotherapy, Faculty of Medicine, University of Szeged, Szeged, Hungary.,MTA-SZTE Cardiovascular Pharmacology Research Group, Hungarian Academy of Sciences, Szeged, Hungary
| | - László Virág
- Department of Pharmacology and Pharmacotherapy, Faculty of Medicine, University of Szeged, Szeged, Hungary
| | - Elisa Passini
- Department of Computer Science, British Heart Foundation Centre of Research Excellence, University of Oxford, Oxford, United Kingdom
| | - Blanca Rodriguez
- Department of Computer Science, British Heart Foundation Centre of Research Excellence, University of Oxford, Oxford, United Kingdom
| | - István Baczkó
- Department of Pharmacology and Pharmacotherapy, Faculty of Medicine, University of Szeged, Szeged, Hungary
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22
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Blok M, Boukens BJ. Mechanisms of Arrhythmias in the Brugada Syndrome. Int J Mol Sci 2020; 21:ijms21197051. [PMID: 32992720 PMCID: PMC7582368 DOI: 10.3390/ijms21197051] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 09/15/2020] [Accepted: 09/21/2020] [Indexed: 12/13/2022] Open
Abstract
Arrhythmias in Brugada syndrome patients originate in the right ventricular outflow tract (RVOT). Over the past few decades, the characterization of the unique anatomy and electrophysiology of the RVOT has revealed the arrhythmogenic nature of this region. However, the mechanisms that drive arrhythmias in Brugada syndrome patients remain debated as well as the exact site of their occurrence in the RVOT. Identifying the site of origin and mechanism of Brugada syndrome would greatly benefit the development of mechanism-driven treatment strategies.
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Affiliation(s)
- Michiel Blok
- Department of Medical Biology, Amsterdam University Medical Center, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands
- Department of Experimental Cardiology, Amsterdam University Medical Center, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands
| | - Bastiaan J. Boukens
- Department of Medical Biology, Amsterdam University Medical Center, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands
- Department of Experimental Cardiology, Amsterdam University Medical Center, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands
- Correspondence: ; Tel.: +31-(0)20-566-4659
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23
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Strocchi M, Augustin CM, Gsell MAF, Karabelas E, Neic A, Gillette K, Razeghi O, Prassl AJ, Vigmond EJ, Behar JM, Gould J, Sidhu B, Rinaldi CA, Bishop MJ, Plank G, Niederer SA. A publicly available virtual cohort of four-chamber heart meshes for cardiac electro-mechanics simulations. PLoS One 2020; 15:e0235145. [PMID: 32589679 PMCID: PMC7319311 DOI: 10.1371/journal.pone.0235145] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Accepted: 06/09/2020] [Indexed: 12/12/2022] Open
Abstract
Computational models of the heart are increasingly being used in the development of devices, patient diagnosis and therapy guidance. While software techniques have been developed for simulating single hearts, there remain significant challenges in simulating cohorts of virtual hearts from multiple patients. To facilitate the development of new simulation and model analysis techniques by groups without direct access to medical data, image analysis techniques and meshing tools, we have created the first publicly available virtual cohort of twenty-four four-chamber hearts. Our cohort was built from heart failure patients, age 67±14 years. We segmented four-chamber heart geometries from end-diastolic (ED) CT images and generated linear tetrahedral meshes with an average edge length of 1.1±0.2mm. Ventricular fibres were added in the ventricles with a rule-based method with an orientation of -60° and 80° at the epicardium and endocardium, respectively. We additionally refined the meshes to an average edge length of 0.39±0.10mm to show that all given meshes can be resampled to achieve an arbitrary desired resolution. We ran simulations for ventricular electrical activation and free mechanical contraction on all 1.1mm-resolution meshes to ensure that our meshes are suitable for electro-mechanical simulations. Simulations for electrical activation resulted in a total activation time of 149±16ms. Free mechanical contractions gave an average left ventricular (LV) and right ventricular (RV) ejection fraction (EF) of 35±1% and 30±2%, respectively, and a LV and RV stroke volume (SV) of 95±28mL and 65±11mL, respectively. By making the cohort publicly available, we hope to facilitate large cohort computational studies and to promote the development of cardiac computational electro-mechanics for clinical applications.
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Affiliation(s)
- Marina Strocchi
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, City of London, United Kingdom
| | | | | | - Elias Karabelas
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, City of London, United Kingdom
| | | | - Karli Gillette
- Institute of Biophysics, Medical University of Graz, Graz, Steiermark, Austria
| | - Orod Razeghi
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, City of London, United Kingdom
| | - Anton J. Prassl
- Institute of Biophysics, Medical University of Graz, Graz, Steiermark, Austria
| | - Edward J. Vigmond
- IHU Liryc, Electrophysiology and Heart Modeling Institute, fondation Bordeaux Université, F-33600 Pessac- Bordeaux, France
- University of Bordeaux, IMB, UMR 5251, F-33400 Talence, France
| | - Jonathan M. Behar
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, City of London, United Kingdom
- Guy’s and St Thomas’ NHS Foundation Trust, London, City of London, United Kingdom
| | - Justin Gould
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, City of London, United Kingdom
- Guy’s and St Thomas’ NHS Foundation Trust, London, City of London, United Kingdom
| | - Baldeep Sidhu
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, City of London, United Kingdom
- Guy’s and St Thomas’ NHS Foundation Trust, London, City of London, United Kingdom
| | - Christopher A. Rinaldi
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, City of London, United Kingdom
- Guy’s and St Thomas’ NHS Foundation Trust, London, City of London, United Kingdom
| | - Martin J. Bishop
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, City of London, United Kingdom
| | - Gernot Plank
- Institute of Biophysics, Medical University of Graz, Graz, Steiermark, Austria
| | - Steven A. Niederer
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, City of London, United Kingdom
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24
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Abstract
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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25
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Abstract
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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Affiliation(s)
- Caroline H Roney
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.,Imperial Centre for Cardiac Engineering, Imperial College London, London, UK
| | - Andrew L Wit
- Imperial Centre for Cardiac Engineering, Imperial College London, London, UK.,Department of Pharmacology, Columbia University College of Physicians and Surgeons, New York, NY, US
| | - Nicholas S Peters
- Imperial Centre for Cardiac Engineering, Imperial College London, London, UK.,National Heart and Lung Institute, Imperial College London, London, UK
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26
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Strocchi M, Gsell MAF, Augustin CM, Razeghi O, Roney CH, Prassl AJ, Vigmond EJ, Behar JM, Gould JS, Rinaldi CA, Bishop MJ, Plank G, Niederer SA. 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; 101:109645. [PMID: 32014305 PMCID: PMC7677892 DOI: 10.1016/j.jbiomech.2020.109645] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Revised: 01/15/2020] [Accepted: 01/15/2020] [Indexed: 12/11/2022]
Abstract
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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Affiliation(s)
- Marina Strocchi
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
| | | | | | - Orod Razeghi
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Caroline H Roney
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Anton J Prassl
- Department of Biophysics, Medical University of Graz, Graz, Austria
| | - Edward J Vigmond
- University of Bordeaux, Talence, France; LIRYC Electrophysiology and Heart Modeling Institute, Campus Xavier Arnozan, Pessac, France
| | - Jonathan M Behar
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK; Cardiology Department, Guys and St Thomas' NHS Foundation Trust, London, UK
| | - Justin S Gould
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK; Cardiology Department, Guys and St Thomas' NHS Foundation Trust, London, UK
| | - Christopher A Rinaldi
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK; Cardiology Department, Guys and St Thomas' NHS Foundation Trust, London, UK
| | - Martin J Bishop
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Gernot Plank
- Department of Biophysics, Medical University of Graz, Graz, Austria
| | - Steven A Niederer
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
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27
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Roney CH, Williams SE, Cochet H, Mukherjee RK, O'Neill L, Sim I, Whitaker J, Razeghi O, Klein GJ, Vigmond EJ, O'Neill M, Niederer SA. Patient-specific simulations predict efficacy of ablation of interatrial connections for treatment of persistent atrial fibrillation. Europace 2019; 20:iii55-iii68. [PMID: 30476055 PMCID: PMC6251187 DOI: 10.1093/europace/euy232] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2018] [Accepted: 10/12/2018] [Indexed: 11/23/2022] Open
Abstract
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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Affiliation(s)
- Caroline H Roney
- School of Biomedical Engineering & Imaging Sciences, King's College London, St. Thomas' Hospital, Westminster Bridge Road, UK
| | - Steven E Williams
- School of Biomedical Engineering & Imaging Sciences, King's College London, St. Thomas' Hospital, Westminster Bridge Road, UK
| | - Hubert Cochet
- LIRYC Electrophysiology and Heart Modeling Institute, Bordeaux Fondation, Avenue du Haut-Lévèque, Pessac, France
| | - Rahul K Mukherjee
- School of Biomedical Engineering & Imaging Sciences, King's College London, St. Thomas' Hospital, Westminster Bridge Road, UK
| | - Louisa O'Neill
- School of Biomedical Engineering & Imaging Sciences, King's College London, St. Thomas' Hospital, Westminster Bridge Road, UK
| | - Iain Sim
- School of Biomedical Engineering & Imaging Sciences, King's College London, St. Thomas' Hospital, Westminster Bridge Road, UK
| | - John Whitaker
- School of Biomedical Engineering & Imaging Sciences, King's College London, St. Thomas' Hospital, Westminster Bridge Road, UK
| | - Orod Razeghi
- School of Biomedical Engineering & Imaging Sciences, King's College London, St. Thomas' Hospital, Westminster Bridge Road, UK
| | | | - Edward J Vigmond
- LIRYC Electrophysiology and Heart Modeling Institute, Bordeaux Fondation, Avenue du Haut-Lévèque, Pessac, France.,IMB, Univ. Bordeaux, Talence, France
| | - Mark O'Neill
- School of Biomedical Engineering & Imaging Sciences, King's College London, St. Thomas' Hospital, Westminster Bridge Road, UK
| | - Steven A Niederer
- School of Biomedical Engineering & Imaging Sciences, King's College London, St. Thomas' Hospital, Westminster Bridge Road, UK
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28
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Saliani A, Tsikhanovich A, Jacquemet V. Visualization of interpolated atrial fiber orientation using evenly-spaced streamlines. Comput Biol Med 2019; 111:103349. [DOI: 10.1016/j.compbiomed.2019.103349] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2019] [Revised: 06/04/2019] [Accepted: 07/02/2019] [Indexed: 11/15/2022]
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29
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Sánchez J, Gomez JF, Martinez-Mateu L, Romero L, Saiz J, Trenor B. Heterogeneous Effects of Fibroblast-Myocyte Coupling in Different Regions of the Human Atria Under Conditions of Atrial Fibrillation. Front Physiol 2019; 10:847. [PMID: 31333496 PMCID: PMC6620707 DOI: 10.3389/fphys.2019.00847] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2018] [Accepted: 06/19/2019] [Indexed: 12/19/2022] Open
Abstract
Background: Atrial fibrillation (AF), the most common cardiac arrhythmia, is characterized by alteration of the action potential (AP) propagation. Under persistent AF, myocytes undergo electrophysiological and structural remodeling, which involves fibroblast proliferation and differentiation, modifying the substrate for AP propagation. The aim of this study was to analyze the effects on the AP of fibroblast-myocyte coupling during AF and its propagation in different regions of the atria. Methods: Isolated myocytes were coupled to different numbers of fibroblasts using the established AP models and tissue simulations were performed by randomly distributing fibroblasts. Fibroblast formulations were updated to match recent experimental data. Major ion current conductances of the myocyte model were modified to simulate AP heterogeneity in four different atrial regions (right atrium posterior wall, crista terminalis, left atrium posterior wall, and pulmonary vein) according to experimental and computational studies. Results: The results of the coupled myocyte-fibroblast simulations suggest that a more depolarized membrane potential and higher fibroblast membrane capacitance have a greater impact on AP duration and myocyte maximum depolarization velocity. The number of coupled fibroblasts and the stimulation frequency are determining factors in altering myocyte AP. Strand simulations show that conduction velocity tends to homogenize in all regions, while the left atrium is more likely to be affected by fibroblast and AP propagation block is more likely to occur. The pulmonary vein is the most affected region, even at low fibroblast densities. In 2D sheets with randomly placed fibroblasts, wavebreaks are observed in the low density (10%) central fibrotic zone and when fibroblast density increases (40%) propagation in the fibrotic region is practically blocked. At densities of 10 and 20% the width of the vulnerable window increases with respect to control but is decreased at 40%. Conclusion: Myocyte-fibroblast coupling characteristics heterogeneously affect AP propagation and features in the different atrial zones, and myocytes from the left atria are more sensitive to fibroblast coupling.
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Affiliation(s)
- Jorge Sánchez
- Centre for Research and Innovation in Bioengineering, Universitat Politècnica de València, Valencia, Spain
| | - Juan F Gomez
- Centre for Research and Innovation in Bioengineering, Universitat Politècnica de València, Valencia, Spain
| | - Laura Martinez-Mateu
- Centre for Research and Innovation in Bioengineering, Universitat Politècnica de València, Valencia, Spain
| | - Lucia Romero
- Centre for Research and Innovation in Bioengineering, Universitat Politècnica de València, Valencia, Spain
| | - Javier Saiz
- Centre for Research and Innovation in Bioengineering, Universitat Politècnica de València, Valencia, Spain
| | - Beatriz Trenor
- Centre for Research and Innovation in Bioengineering, Universitat Politècnica de València, Valencia, Spain
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30
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Aronis KN, Ali RL, Liang JA, Zhou S, Trayanova NA. Understanding AF Mechanisms Through Computational Modelling and Simulations. Arrhythm Electrophysiol Rev 2019; 8:210-219. [PMID: 31463059 PMCID: PMC6702471 DOI: 10.15420/aer.2019.28.2] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Accepted: 06/17/2019] [Indexed: 12/21/2022] Open
Abstract
AF is a progressive disease of the atria, involving complex mechanisms related to its initiation, maintenance and progression. Computational modelling provides a framework for integration of experimental and clinical findings, and has emerged as an essential part of mechanistic research in AF. The authors summarise recent advancements in development of multi-scale AF models and focus on the mechanistic links between alternations in atrial structure and electrophysiology with AF. Key AF mechanisms that have been explored using atrial modelling are pulmonary vein ectopy; atrial fibrosis and fibrosis distribution; atrial wall thickness heterogeneity; atrial adipose tissue infiltration; development of repolarisation alternans; cardiac ion channel mutations; and atrial stretch with mechano-electrical feedback. They review modelling approaches that capture variability at the cohort level and provide cohort-specific mechanistic insights. The authors conclude with a summary of future perspectives, as envisioned for the contributions of atrial modelling in the mechanistic understanding of AF.
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Affiliation(s)
- Konstantinos N Aronis
- Department of Biomedical Engineering and the Institute for Computational Medicine, Johns Hopkins UniversityBaltimore, MD, US
- Division of Cardiology, Johns Hopkins HospitalBaltimore, MD, US
| | - Rheeda L Ali
- Department of Biomedical Engineering and the Institute for Computational Medicine, Johns Hopkins UniversityBaltimore, MD, US
| | - Jialiu A Liang
- Department of Biomedical Engineering and the Institute for Computational Medicine, Johns Hopkins UniversityBaltimore, MD, US
| | - Shijie Zhou
- Department of Biomedical Engineering and the Institute for Computational Medicine, Johns Hopkins UniversityBaltimore, MD, US
| | - Natalia A Trayanova
- Department of Biomedical Engineering and the Institute for Computational Medicine, Johns Hopkins UniversityBaltimore, MD, US
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Deng D, Prakosa A, Shade J, Nikolov P, Trayanova NA. Sensitivity of Ablation Targets Prediction to Electrophysiological Parameter Variability in Image-Based Computational Models of Ventricular Tachycardia in Post-infarction Patients. Front Physiol 2019; 10:628. [PMID: 31178758 PMCID: PMC6543853 DOI: 10.3389/fphys.2019.00628] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Accepted: 05/03/2019] [Indexed: 12/18/2022] Open
Abstract
Ventricular tachycardia (VT), which could lead to sudden cardiac death, occurs frequently in patients with myocardial infarction. Computational modeling has emerged as a powerful platform for the non-invasive investigation of lethal heart rhythm disorders in post-infarction patients and for guiding patient VT ablation. However, it remains unclear how VT dynamics and predicted ablation targets are influenced by inter-patient variability in action potential duration (APD) and conduction velocity (CV). The goal of this study was to systematically assess the effect of changes in the electrophysiological parameters on the induced VTs and predicted ablation targets in personalized models of post-infarction hearts. Simulations were conducted in 5 patient-specific left ventricular models reconstructed from late gadolinium-enhanced magnetic resonance imaging scans. We comprehensively characterized all possible pre-ablation and post-ablation VTs in simulations conducted with either an “average human VT”-based electrophysiological representation (i.e., EPavg) or with ±10% APD or CV (i.e., EPvar); additional simulations were also executed in some models for an extended range of these parameters. The results showed that: (1) a subset of reentries (76.2–100%, depending on EP parameter set) conducted with ±10% APD/CV was observed in approximately the same locations as reentries observed in EPavg cases; (2) emergent VTs could be induced sometimes after ablation in EPavg models, and these emergent VTs often corresponded to the pre-ablation reentries in simulations with EPvar parameter sets. These findings demonstrate that the VT ablation target uncertainty in patient-specific ventricular models with an average representation of VT-remodeled electrophysiology is relatively low and the ablation targets stable, as the localization of the induced VTs was primarily driven by the remodeled structural substrate. Thus, personalized ventricular modeling with an average representation of infarct-remodeled electrophysiology may uncover most targets for VT ablation.
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Affiliation(s)
- Dongdong Deng
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States.,School of Biomedical Engineering, Dalian University of Technology, Dalian, China
| | - Adityo Prakosa
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Julie Shade
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Plamen Nikolov
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Natalia A Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
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Aronis KN, Ali R, Trayanova NA. The role of personalized atrial modeling in understanding atrial fibrillation mechanisms and improving treatment. Int J Cardiol 2019; 287:139-147. [PMID: 30755334 DOI: 10.1016/j.ijcard.2019.01.096] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/28/2018] [Revised: 01/24/2019] [Accepted: 01/28/2019] [Indexed: 12/13/2022]
Abstract
Atrial fibrillation is the most common arrhythmia in humans and is associated with high morbidity, mortality and health-related expenses. Computational approaches have been increasingly utilized in atrial electrophysiology. In this review we summarize the recent advancements in atrial fibrillation modeling at the organ scale. Multi-scale atrial models now incorporate high level detail of atrial anatomy, tissue ultrastructure and fibrosis distribution. We provide the state-of-the art methodologies in developing personalized atrial fibrillation models with realistic geometry and tissue properties. We then focus on the use of multi-scale atrial models to gain mechanistic insights in AF. Simulations using atrial models have provided important insight in the mechanisms underlying AF, showing the importance of the atrial fibrotic substrate and altered atrial electrophysiology in initiation and maintenance of AF. Last, we summarize the translational evidence that supports incorporation of computational modeling in clinical practice for development of personalized treatment strategies for patients with AF. In early-stages clinical studies, AF models successfully identify patients where pulmonary vein isolation alone is not adequate for treatment of AF and suggest novel targets for ablation. We conclude with a summary of the future developments envisioned for the field of atrial computational electrophysiology.
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Affiliation(s)
- Konstantinos N Aronis
- Department of Biomedical Engineering and the Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA; Division of Cardiology, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Rheeda Ali
- Department of Biomedical Engineering and the Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Natalia A Trayanova
- Department of Biomedical Engineering and the Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA.
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Roney CH, Whitaker J, Sim I, O'Neill L, Mukherjee RK, Razeghi O, Vigmond EJ, Wright M, O'Neill MD, Williams SE, Niederer SA. A technique for measuring anisotropy in atrial conduction to estimate conduction velocity and atrial fibre direction. Comput Biol Med 2019; 104:278-290. [PMID: 30415767 PMCID: PMC6506689 DOI: 10.1016/j.compbiomed.2018.10.019] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2018] [Revised: 10/17/2018] [Accepted: 10/17/2018] [Indexed: 01/04/2023]
Abstract
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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Affiliation(s)
- Caroline H Roney
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom.
| | - John Whitaker
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom
| | - Iain Sim
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom
| | - Louisa O'Neill
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom
| | - Rahul K Mukherjee
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom
| | - Orod Razeghi
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom
| | - Edward J Vigmond
- LIRYC Electrophysiology and Heart Modeling Institute, Campus Xavier Arnozan, Avenue du Haut Lévêque, 33600, Pessac, France; Univ. Bordeaux, IMB, UMR 5251, F-33400, Talence, France
| | - Matthew Wright
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom
| | - Mark D O'Neill
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom
| | - Steven E Williams
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom
| | - Steven A Niederer
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom
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Saha M, Roney CH, Bayer JD, Meo M, Cochet H, Dubois R, Vigmond EJ. Wavelength and Fibrosis Affect Phase Singularity Locations During Atrial Fibrillation. Front Physiol 2018; 9:1207. [PMID: 30246796 PMCID: PMC6139329 DOI: 10.3389/fphys.2018.01207] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2018] [Accepted: 08/10/2018] [Indexed: 01/06/2023] Open
Abstract
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 (IK1) conductance between 80 and 140% of the original value. Phase singularities (PSs) were computed to identify rotor core locations. Our results show that IK1 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 IK1 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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Affiliation(s)
- Mirabeau Saha
- IMB, UMR 5251, University of Bordeaux, Pessac, France
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux University, Pessac, France
| | - Caroline H. Roney
- Department of Biomedical Engineering, King's College London, London, United Kingdom
| | - Jason D. Bayer
- IMB, UMR 5251, University of Bordeaux, Pessac, France
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux University, Pessac, France
| | - Marianna Meo
- IMB, UMR 5251, University of Bordeaux, Pessac, France
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux University, Pessac, France
| | - Hubert Cochet
- IMB, UMR 5251, University of Bordeaux, Pessac, France
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux University, Pessac, France
| | - Remi Dubois
- IMB, UMR 5251, University of Bordeaux, Pessac, France
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux University, Pessac, France
| | - Edward J. Vigmond
- IMB, UMR 5251, University of Bordeaux, Pessac, France
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux University, Pessac, France
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