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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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2
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Okenov A, Nezlobinsky T, Zeppenfeld K, Vandersickel N, Panfilov AV. Computer based method for identification of fibrotic scars from electrograms and local activation times on the epi- and endocardial surfaces of the ventricles. PLoS One 2024; 19:e0300978. [PMID: 38625849 PMCID: PMC11020530 DOI: 10.1371/journal.pone.0300978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 03/07/2024] [Indexed: 04/18/2024] Open
Abstract
Cardiac fibrosis stands as one of the most critical conditions leading to lethal cardiac arrhythmias. Identifying the precise location of cardiac fibrosis is crucial for planning clinical interventions in patients with various forms of ventricular and atrial arrhythmias. As fibrosis impedes and alters the path of electrical waves, detecting fibrosis in the heart can be achieved through analyzing electrical signals recorded from its surface. In current clinical practices, it has become feasible to record electrical activity from both the endocardial and epicardial surfaces of the heart. This paper presents a computational method for reconstructing 3D fibrosis using unipolar electrograms obtained from both surfaces of the ventricles. The proposed method calculates the percentage of fibrosis in various ventricular segments by analyzing the local activation times and peak-to-peak amplitudes of the electrograms. Initially, the method was tested using simulated data representing idealized fibrosis in a heart segment; subsequently, it was validated in the left ventricle with fibrosis obtained from a patient with nonischemic cardiomyopathy. The method successfully determined the location and extent of fibrosis in 204 segments of the left ventricle model with an average error of 0.0±4.3% (N = 204). Moreover, the method effectively detected fibrotic scars in the mid-myocardial region, a region known to present challenges in accurate detection using electrogram amplitude as the primary criterion.
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Affiliation(s)
- Arstanbek Okenov
- Department of Physics and Astronomy, Ghent University, Gent, Belgium
| | - Timur Nezlobinsky
- Department of Physics and Astronomy, Ghent University, Gent, Belgium
| | - Katja Zeppenfeld
- Department of Cardiology, Leiden University Medical Centre, Leiden, The Netherlands
| | - Nele Vandersickel
- Department of Physics and Astronomy, Ghent University, Gent, Belgium
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3
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Roney CH, Solis Lemus JA, Lopez Barrera C, Zolotarev A, Ulgen O, Kerfoot E, Bevis L, Misghina S, Vidal Horrach C, Jaffery OA, Ehnesh M, Rodero C, Dharmaprani D, Ríos-Muñoz GR, Ganesan A, Good WW, Neic A, Plank G, Hopman LHGA, Götte MJW, Honarbakhsh S, Narayan SM, Vigmond E, Niederer S. Constructing bilayer and volumetric atrial models at scale. Interface Focus 2023; 13:20230038. [PMID: 38106921 PMCID: PMC10722212 DOI: 10.1098/rsfs.2023.0038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 11/15/2023] [Indexed: 12/19/2023] Open
Abstract
To enable large in silico trials and personalized model predictions on clinical timescales, it is imperative that models can be constructed quickly and reproducibly. First, we aimed to overcome the challenges of constructing cardiac models at scale through developing a robust, open-source pipeline for bilayer and volumetric atrial models. Second, we aimed to investigate the effects of fibres, fibrosis and model representation on fibrillatory dynamics. To construct bilayer and volumetric models, we extended our previously developed coordinate system to incorporate transmurality, atrial regions and fibres (rule-based or data driven diffusion tensor magnetic resonance imaging (MRI)). We created a cohort of 1000 biatrial bilayer and volumetric models derived from computed tomography (CT) data, as well as models from MRI, and electroanatomical mapping. Fibrillatory dynamics diverged between bilayer and volumetric simulations across the CT cohort (correlation coefficient for phase singularity maps: left atrial (LA) 0.27 ± 0.19, right atrial (RA) 0.41 ± 0.14). Adding fibrotic remodelling stabilized re-entries and reduced the impact of model type (LA: 0.52 ± 0.20, RA: 0.36 ± 0.18). The choice of fibre field has a small effect on paced activation data (less than 12 ms), but a larger effect on fibrillatory dynamics. Overall, we developed an open-source user-friendly pipeline for generating atrial models from imaging or electroanatomical mapping data enabling in silico clinical trials at scale (https://github.com/pcmlab/atrialmtk).
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Affiliation(s)
- Caroline H. Roney
- School of Engineering and Materials Science, Queen Mary University of London, London, UK
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK
| | - Jose Alonso Solis Lemus
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Carlos Lopez Barrera
- School of Engineering and Materials Science, Queen Mary University of London, London, UK
- Center for Research in Advanced Materials S.C (CIMAV), Chihuahua, Mexico
| | - Alexander Zolotarev
- School of Engineering and Materials Science, Queen Mary University of London, London, UK
| | - Onur Ulgen
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK
| | - Eric Kerfoot
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK
| | - Laura Bevis
- School of Engineering and Materials Science, Queen Mary University of London, London, UK
| | - Semhar Misghina
- School of Engineering and Materials Science, Queen Mary University of London, London, UK
| | - Caterina Vidal Horrach
- School of Engineering and Materials Science, Queen Mary University of London, London, UK
| | - Ovais A. Jaffery
- School of Engineering and Materials Science, Queen Mary University of London, London, UK
| | - Mahmoud Ehnesh
- School of Engineering and Materials Science, Queen Mary University of London, London, UK
| | - Cristobal Rodero
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Dhani Dharmaprani
- College of Medicine and Public Health, Flinders University, Adelaide, Australia
| | - Gonzalo R. Ríos-Muñoz
- Bioengineering Department, Universidad Carlos III de Madrid, Madrid 28911, Spain
- Department of Cardiology, Gregorio Marañón Health Research Institute (IiSGM), Hospital General Universitario Gregorio Marañón, Madrid 28007, Spain
- Center for Biomedical Research in Cardiovascular Disease Network (CIBERCV), Madrid 28029, Spain
| | - Anand Ganesan
- College of Medicine and Public Health, Flinders University, Adelaide, Australia
| | | | | | - Gernot Plank
- Gottfried Schatz Research Center-Biophysics, Medical University of Graz, Graz, Austria
- BioTechMed-Graz, Graz, Austria
| | | | | | - Shohreh Honarbakhsh
- Electrophysiology Department, Barts Heart Centre, Barts Health NHS Trust, London, UK
| | - Sanjiv M. Narayan
- Department of Medicine and Cardiovascular Institute, Stanford University, Palo Alto, CA, USA
| | - Edward Vigmond
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Bordeaux, France
- IMB, UMR 5251, University Bordeaux, Talence 33400, France
| | - Steven Niederer
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
- Turing Research and Innovation Cluster in Digital Twins (TRIC: DT), The Alan Turing Institute, London, UK
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Corrado C, Roney CH, Razeghi O, Lemus JAS, Coveney S, Sim I, Williams SE, O'Neill MD, Wilkinson RD, Clayton RH, Niederer SA. Quantifying the impact of shape uncertainty on predicted arrhythmias. Comput Biol Med 2023; 153:106528. [PMID: 36634600 DOI: 10.1016/j.compbiomed.2022.106528] [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: 09/09/2022] [Revised: 12/15/2022] [Accepted: 12/31/2022] [Indexed: 01/11/2023]
Abstract
BACKGROUND Personalised computer models are increasingly used to diagnose cardiac arrhythmias and tailor treatment. Patient-specific models of the left atrium are often derived from pre-procedural imaging of anatomy and fibrosis. These images contain noise that can affect simulation predictions. There are few computationally tractable methods for propagating uncertainties from images to clinical predictions. METHOD We describe the left atrium anatomy using our Bayesian shape model that captures anatomical uncertainty in medical images and has been validated on 63 independent clinical images. This algorithm describes the left atrium anatomy using Nmodes=15 principal components, capturing 95% of the shape variance and calculated from 70 clinical cardiac magnetic resonance (CMR) images. Latent variables encode shape uncertainty: we evaluate their posterior distribution for each new anatomy. We assume a normally distributed prior. We use the unscented transform to sample from the posterior shape distribution. For each sample, we assign the local material properties of the tissue using the projection of late gadolinium enhancement CMR (LGE-CMR) onto the anatomy to estimate local fibrosis. To test which activation patterns an atrium can sustain, we perform an arrhythmia simulation for each sample. We consider 34 possible outcomes (31 macro-re-entries, functional re-entry, atrial fibrillation, and non-sustained arrhythmia). For each sample, we determine the outcome by comparing pre- and post-ablation activation patterns following a cross-field stimulus. RESULTS We create patient-specific atrial electrophysiology models of ten patients. We validate the mean and standard deviation maps from the unscented transform with the same statistics obtained with 12,000 Monte Carlo (ground truth) samples. We found discrepancies <3% and <2% for the mean and standard deviation for fibrosis burden and activation time, respectively. For each patient case, we then compare the predicted outcome from a model built on the clinical data (deterministic approach) with the probability distribution obtained from the simulated samples. We found that the deterministic approach did not predict the most likely outcome in 80% of the cases. Finally, we estimate the influence of each source of uncertainty independently. Fixing the anatomy to the posterior mean and maintaining uncertainty in fibrosis reduced the prediction of self-terminating arrhythmias from ≃14% to ≃7%. Keeping the fibrosis fixed to the sample mean while retaining uncertainty in shape decreased the prediction of substrate-driven arrhythmias from ≃33% to ≃18% and increased the prediction of macro-re-entries from ≃54% to ≃68%. CONCLUSIONS We presented a novel method for propagating shape uncertainty in atrial models through to uncertainty in numerical simulations. The algorithm takes advantage of the unscented transform to compute the output distribution of the outcomes. We validated the unscented transform as a viable sampling strategy to deal with anatomy uncertainty. We then showed that the prediction computed with a deterministic model does not always coincide with the most likely outcome. Finally, we found that shape uncertainty affects the predictions of macro-re-entries, while fibrosis uncertainty affects the predictions of functional re-entries.
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Affiliation(s)
- Cesare Corrado
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom.
| | - Caroline H Roney
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom; School of Engineering and Materials Science, Queen Mary University of London, London, United Kingdom
| | - Orod Razeghi
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom; UCL Centre for Advanced Research Computing, London, United Kingdom
| | - Josè Alonso Solís Lemus
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom
| | - Sam Coveney
- Insigneo Institute for in-silico Medicine and Department of Computer Science, University of Sheffield, Sheffield, United Kingdom
| | - Iain Sim
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom
| | - Steven E Williams
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom
| | - Mark D O'Neill
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom
| | - Richard D Wilkinson
- School of Mathematical Sciences, University of Nottingham, Nottingham, United Kingdom
| | - Richard H Clayton
- Insigneo Institute for in-silico Medicine and Department of Computer Science, University of Sheffield, Sheffield, United Kingdom
| | - Steven A Niederer
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom
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Yamamoto C, Trayanova NA. Atrial fibrillation: Insights from animal models, computational modeling, and clinical studies. EBioMedicine 2022; 85:104310. [PMID: 36309006 PMCID: PMC9619190 DOI: 10.1016/j.ebiom.2022.104310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 09/25/2022] [Accepted: 10/04/2022] [Indexed: 11/11/2022] Open
Abstract
Atrial fibrillation (AF) is the most common human arrhythmia, affecting millions of patients worldwide. A combination of risk factors and comorbidities results in complex atrial remodeling, which increases AF vulnerability and persistence. Insights from animal models, clinical studies, and computational modeling have advanced the understanding of the mechanisms and pathophysiology of AF. Areas of heterogeneous pathological remodeling, as well as altered electrophysiological properties, serve as a substrate for AF drivers and spontaneous activations. The complex and individualized presentation of this arrhythmia suggests that mechanisms-based personalized approaches will likely be needed to overcome current challenges in AF management. In this paper, we review the insights on the mechanisms of AF obtained from animal models and clinical studies and how computational models integrate this knowledge to advance AF clinical management. We also assess the challenges that need to be overcome to implement these mechanistic models in clinical practice.
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Affiliation(s)
- Carolyna Yamamoto
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Natalia A. Trayanova
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA,Alliance for Cardiovascular Diagnostic and Treatment Innovation (ADVANCE), Johns Hopkins University, Baltimore, MD, USA,Corresponding author. Johns Hopkins, Johns Hopkins University, United States.
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6
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DG-Mapping: a novel software package for the analysis of any type of reentry and focal activation of simulated, experimental or clinical data of cardiac arrhythmia. Med Biol Eng Comput 2022; 60:1929-1945. [DOI: 10.1007/s11517-022-02550-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 02/13/2022] [Indexed: 01/24/2023]
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A Review on Atrial Fibrillation (Computer Simulation and Clinical Perspectives). HEARTS 2022. [DOI: 10.3390/hearts3010005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Atrial fibrillation (AF), a heart condition, has been a well-researched topic for the past few decades. This multidisciplinary field of study deals with signal processing, finite element analysis, mathematical modeling, optimization, and clinical procedure. This article is focused on a comprehensive review of journal articles published in the field of AF. Topics from the age-old fundamental concepts to specialized modern techniques involved in today’s AF research are discussed. It was found that a lot of research articles have already been published in modeling and simulation of AF. In comparison to that, the diagnosis and post-operative procedures for AF patients have not yet been totally understood or explored by the researchers. The simulation and modeling of AF have been investigated by many researchers in this field. Cellular model, tissue model, and geometric model among others have been used to simulate AF. Due to a very complex nature, the causes of AF have not been fully perceived to date, but the simulated results are validated with real-life patient data. Many algorithms have been proposed to detect the source of AF in human atria. There are many ablation strategies for AF patients, but the search for more efficient ablation strategies is still going on. AF management for patients with different stages of AF has been discussed in the literature as well but is somehow limited mostly to the patients with persistent AF. The authors hope that this study helps to find existing research gaps in the analysis and the diagnosis of AF.
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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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9
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Seno H, Yamazaki M, Shibata N, Sakuma I, Tomii N. In-Silico Deep Reinforcement Learning for Effective Cardiac Ablation Strategy. J Med Biol Eng 2021. [DOI: 10.1007/s40846-021-00664-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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10
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Wu Z, Liu Y, Tong L, Dong D, Deng D, Xia L. Current progress of computational modeling for guiding clinical atrial fibrillation ablation. J Zhejiang Univ Sci B 2021; 22:805-817. [PMID: 34636185 DOI: 10.1631/jzus.b2000727] [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] [Indexed: 01/17/2023]
Abstract
Atrial fibrillation (AF) is one of the most common arrhythmias, associated with high morbidity, mortality, and healthcare costs, and it places a significant burden on both individuals and society. Anti-arrhythmic drugs are the most commonly used strategy for treating AF. However, drug therapy faces challenges because of its limited efficacy and potential side effects. Catheter ablation is widely used as an alternative treatment for AF. Nevertheless, because the mechanism of AF is not fully understood, the recurrence rate after ablation remains high. In addition, the outcomes of ablation can vary significantly between medical institutions and patients, especially for persistent AF. Therefore, the issue of which ablation strategy is optimal is still far from settled. Computational modeling has the advantages of repeatable operation, low cost, freedom from risk, and complete control, and is a useful tool for not only predicting the results of different ablation strategies on the same model but also finding optimal personalized ablation targets for clinical reference and even guidance. This review summarizes three-dimensional computational modeling simulations of catheter ablation for AF, from the early-stage attempts such as Maze III or circumferential pulmonary vein isolation to the latest advances based on personalized substrate-guided ablation. Finally, we summarize current developments and challenges and provide our perspectives and suggestions for future directions.
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Affiliation(s)
- Zhenghong Wu
- College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou 310027, China
| | - Yunlong Liu
- School of Biomedical Engineering, Dalian University of Technology, Dalian 116024, China
| | - Lv Tong
- School of Biomedical Engineering, Dalian University of Technology, Dalian 116024, China
| | - Diandian Dong
- School of Biomedical Engineering, Dalian University of Technology, Dalian 116024, China
| | - Dongdong Deng
- School of Biomedical Engineering, Dalian University of Technology, Dalian 116024, China
| | - Ling Xia
- College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou 310027, China.
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11
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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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12
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Heijman J, Sutanto H, Crijns HJGM, Nattel S, Trayanova NA. Computational models of atrial fibrillation: achievements, challenges, and perspectives for improving clinical care. Cardiovasc Res 2021; 117:1682-1699. [PMID: 33890620 PMCID: PMC8208751 DOI: 10.1093/cvr/cvab138] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Indexed: 12/11/2022] Open
Abstract
Despite significant advances in its detection, understanding and management, atrial fibrillation (AF) remains a highly prevalent cardiac arrhythmia with a major impact on morbidity and mortality of millions of patients. AF results from complex, dynamic interactions between risk factors and comorbidities that induce diverse atrial remodelling processes. Atrial remodelling increases AF vulnerability and persistence, while promoting disease progression. The variability in presentation and wide range of mechanisms involved in initiation, maintenance and progression of AF, as well as its associated adverse outcomes, make the early identification of causal factors modifiable with therapeutic interventions challenging, likely contributing to suboptimal efficacy of current AF management. Computational modelling facilitates the multilevel integration of multiple datasets and offers new opportunities for mechanistic understanding, risk prediction and personalized therapy. Mathematical simulations of cardiac electrophysiology have been around for 60 years and are being increasingly used to improve our understanding of AF mechanisms and guide AF therapy. This narrative review focuses on the emerging and future applications of computational modelling in AF management. We summarize clinical challenges that may benefit from computational modelling, provide an overview of the different in silico approaches that are available together with their notable achievements, and discuss the major limitations that hinder the routine clinical application of these approaches. Finally, future perspectives are addressed. With the rapid progress in electronic technologies including computing, clinical applications of computational modelling are advancing rapidly. We expect that their application will progressively increase in prominence, especially if their added value can be demonstrated in clinical trials.
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Affiliation(s)
- Jordi Heijman
- Department of Cardiology, Cardiovascular Research Institute Maastricht, Faculty of Health, Medicine, and Life Sciences, Maastricht University, PO Box 616, 6200 MD Maastricht, The Netherlands
| | - Henry Sutanto
- Department of Cardiology, Cardiovascular Research Institute Maastricht, Faculty of Health, Medicine, and Life Sciences, Maastricht University, PO Box 616, 6200 MD Maastricht, The Netherlands
| | - Harry J G M Crijns
- Department of Cardiology, Cardiovascular Research Institute Maastricht, Faculty of Health, Medicine, and Life Sciences, Maastricht University, PO Box 616, 6200 MD Maastricht, The Netherlands
| | - Stanley Nattel
- Department of Medicine, Montreal Heart Institute and Université de Montréal, Montreal, Canada
- Department of Pharmacology and Therapeutics, McGill University, Montreal, Canada
- Institute of Pharmacology, West German Heart and Vascular Center, Faculty of Medicine, University Duisburg-Essen, Duisburg, Germany
- IHU Liryc and Fondation Bordeaux Université, Bordeaux, France
| | - Natalia A Trayanova
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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13
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Van Nieuwenhuyse E, Martinez-Mateu L, Saiz J, Panfilov AV, Vandersickel N. Directed graph mapping exceeds phase mapping in discriminating true and false rotors detected with a basket catheter in a complex in-silico excitation pattern. Comput Biol Med 2021; 133:104381. [PMID: 33901713 PMCID: PMC8204274 DOI: 10.1016/j.compbiomed.2021.104381] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 04/02/2021] [Accepted: 04/02/2021] [Indexed: 12/04/2022]
Abstract
Atrial fibrillation (AF) is the most frequently encountered arrhythmia in clinical practise. One of the major problems in the management of AF is the difficulty in identifying the arrhythmia sources from clinical recordings. That difficulty occurs because it is currently impossible to verify algorithms which determine these sources in clinical data, as high resolution true excitation patterns cannot be recorded in patients. Therefore, alternative approaches, like computer modelling are of great interest. In a recent published study such an approach was applied for the verification of one of the most commonly used algorithms, phase mapping (PM). A meandering rotor was simulated in the right atrium and a basket catheter was placed at 3 different locations: at the Superior Vena Cava (SVC), the Crista Terminalis (CT) and at the Coronary Sinus (CS). It was shown that although PM can identify the true source, it also finds several false sources due to the far-field effects and interpolation errors in all three positions. In addition, the detection efficiency strongly depended on the basket location. Recently, a novel tool was developed to analyse any arrhythmia called Directed Graph Mapping (DGM). DGM is based on network theory and creates a directed graph of the excitation pattern, from which the location and the source of the arrhythmia can be detected. Therefore, the objective of the current study was to compare the efficiency of DGM with PM on the basket dataset of this meandering rotor. The DGM-tool was applied for a wide variety of conduction velocities (minimal and maximal), which are input parameters of DGM. Overall we found that DGM was able to distinguish between the true rotor and false rotors for both the SVC and CT basket positions. For example, for the SVC position with a CVmin=0.01cmms, DGM detected the true core with a prevalence of 82% versus 94% for PM. Three false rotors where detected for 39.16% (DGM) versus 100% (PM); 22.64% (DGM) versus 100% (PM); and 0% (DGM) versus 57% (PM). Increasing CVmin to 0.02cmms had a stronger effect on the false rotors than on the true rotor. This led to a detection rate of 56.6% for the true rotor, while all the other false rotors disappeared. A similar trend was observed for the CT position. For the CS position, DGM already had a low performance for the true rotor for CVmin=0.01cmms (14.7%). For CVmin=0.02cmms the false and the true rotors could therefore not be distinguished. We can conclude that DGM can overcome some of the limitations of PM by varying one of its input parameters (CVmin). The true rotor is less dependent on this parameter than the false rotors, which disappear at a CVmin=0.02cmms. In order to increase to detection rate of the true rotor, one can decrease CVmin and discard the new rotors which also appear at lower values of CVmin.
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Affiliation(s)
| | - Laura Martinez-Mateu
- Departamento de Teoría de La Señal y Las Comunicaciones y Sistemas Telemáticos y Computación, Universidad Rey Juan Carlos, Madrid, Spain
| | - Javier Saiz
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, Valencia, Spain
| | - Alexander V Panfilov
- Department of Physics and Astronomy, Ghent University, Ghent, Belgium; Ural Federal University, Ekaterinburg, Russia; World-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov University, Moscow, Russia
| | - Nele Vandersickel
- Department of Physics and Astronomy, Ghent University, Ghent, Belgium
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14
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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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15
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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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16
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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: 4] [Impact Index Per Article: 1.0] [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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17
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Corrado C, Avezzù A, Lee AWC, Mendoca Costa C, Roney CH, Strocchi M, Bishop M, Niederer SA. Using cardiac ionic cell models to interpret clinical data. WIREs Mech Dis 2020; 13:e1508. [PMID: 33027553 DOI: 10.1002/wsbm.1508] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Revised: 08/27/2020] [Accepted: 09/04/2020] [Indexed: 01/24/2023]
Abstract
For over 100 years cardiac electrophysiology has been measured in the clinic. The electrical signals that can be measured span from noninvasive ECG and body surface potentials measurements through to detailed invasive measurements of local tissue electrophysiology. These electrophysiological measurements form a crucial component of patient diagnosis and monitoring; however, it remains challenging to quantitatively link changes in clinical electrophysiology measurements to biophysical cellular function. Multi-scale biophysical computational models represent one solution to this problem. These models provide a formal framework for linking cellular function through to emergent whole organ function and routine clinical diagnostic signals. In this review, we describe recent work on the use of computational models to interpret clinical electrophysiology signals. We review the simulation of human cardiac myocyte electrophysiology in the atria and the ventricles and how these models are being used to link organ scale function to patient disease mechanisms and therapy response in patients receiving implanted defibrillators, \cardiac resynchronisation therapy or suffering from atrial fibrillation and ventricular tachycardia. There is a growing use of multi-scale biophysical models to interpret clinical data. This allows cardiologists to link clinical observations with cellular mechanisms to better understand cardiopathophysiology and identify novel treatment strategies. This article is categorized under: Cardiovascular Diseases > Computational Models Cardiovascular Diseases > Biomedical Engineering Cardiovascular Diseases > Molecular and Cellular Physiology.
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18
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Roney CH, Beach ML, Mehta AM, Sim I, Corrado C, Bendikas R, Solis-Lemus JA, Razeghi O, Whitaker J, O’Neill L, Plank G, Vigmond E, Williams SE, O’Neill MD, Niederer SA. In silico Comparison of Left Atrial Ablation Techniques That Target the Anatomical, Structural, and Electrical Substrates of Atrial Fibrillation. Front Physiol 2020; 11:1145. [PMID: 33041850 PMCID: PMC7526475 DOI: 10.3389/fphys.2020.572874] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 08/18/2020] [Indexed: 12/17/2022] Open
Abstract
Catheter ablation therapy for persistent atrial fibrillation (AF) typically includes pulmonary vein isolation (PVI) and may include additional ablation lesions that target patient-specific anatomical, electrical, or structural features. Clinical centers employ different ablation strategies, which use imaging data together with electroanatomic mapping data, depending on data availability. The aim of this study was to compare ablation techniques across a virtual cohort of AF patients. We constructed 20 paroxysmal and 30 persistent AF patient-specific left atrial (LA) bilayer models incorporating fibrotic remodeling from late-gadolinium enhancement (LGE) MRI scans. AF was simulated and post-processed using phase mapping to determine electrical driver locations over 15 s. Six different ablation approaches were tested: (i) PVI alone, modeled as wide-area encirclement of the pulmonary veins; PVI together with: (ii) roof and inferior lines to model posterior wall box isolation; (iii) isolating the largest fibrotic area (identified by LGE-MRI); (iv) isolating all fibrotic areas; (v) isolating the largest driver hotspot region [identified as high simulated phase singularity (PS) density]; and (vi) isolating all driver hotspot regions. Ablation efficacy was assessed to predict optimal ablation therapies for individual patients. We subsequently trained a random forest classifier to predict ablation response using (a) imaging metrics alone, (b) imaging and electrical metrics, or (c) imaging, electrical, and ablation lesion metrics. The optimal ablation approach resulting in termination, or if not possible atrial tachycardia (AT), varied among the virtual patient cohort: (i) 20% PVI alone, (ii) 6% box ablation, (iii) 2% largest fibrosis area, (iv) 4% all fibrosis areas, (v) 2% largest driver hotspot, and (vi) 46% all driver hotspots. Around 20% of cases remained in AF for all ablation strategies. The addition of patient-specific and ablation pattern specific lesion metrics to the trained random forest classifier improved predictive capability from an accuracy of 0.73 to 0.83. The trained classifier results demonstrate that the surface areas of pre-ablation driver regions and of fibrotic tissue not isolated by the proposed ablation strategy are both important for predicting ablation outcome. Overall, our study demonstrates the need to select the optimal ablation strategy for each patient. It suggests that both patient-specific fibrosis properties and driver locations are important for planning ablation approaches, and the distribution of lesions is important for predicting an acute response.
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Affiliation(s)
- Caroline H. Roney
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Marianne L. Beach
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Arihant M. Mehta
- 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
| | - Cesare Corrado
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Rokas Bendikas
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Jose A. Solis-Lemus
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Orod Razeghi
- 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
| | - Louisa O’Neill
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Gernot Plank
- Department of Biophysics, Medical University of Graz, Graz, Austria
| | - Edward Vigmond
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Bordeaux, France
| | - Steven E. Williams
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Mark D. O’Neill
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Steven A. Niederer
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
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19
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Roy A, Varela M, Chubb H, MacLeod R, Hancox JC, Schaeffter T, Aslanidi O. Identifying locations of re-entrant drivers from patient-specific distribution of fibrosis in the left atrium. PLoS Comput Biol 2020; 16:e1008086. [PMID: 32966275 PMCID: PMC7535127 DOI: 10.1371/journal.pcbi.1008086] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 10/05/2020] [Accepted: 06/22/2020] [Indexed: 11/18/2022] Open
Abstract
Clinical evidence suggests a link between fibrosis in the left atrium (LA) and atrial fibrillation (AF), the most common sustained arrhythmia. Image-derived fibrosis is increasingly used for patient stratification and therapy guidance. However, locations of re-entrant drivers (RDs) sustaining AF are unknown and therapy success rates remain suboptimal. This study used image-derived LA models to explore the dynamics of RD stabilization in fibrotic regions and generate maps of RD locations. LA models with patient-specific geometry and fibrosis distribution were derived from late gadolinium enhanced magnetic resonance imaging of 6 AF patients. In each model, RDs were initiated at multiple locations, and their trajectories were tracked and overlaid on the LA fibrosis distributions to identify the most likely regions where the RDs stabilized. The simulations showed that the RD dynamics were strongly influenced by the amount and spatial distribution of fibrosis. In patients with fibrosis burden greater than 25%, RDs anchored to specific locations near large fibrotic patches. In patients with fibrosis burden below 25%, RDs either moved near small fibrotic patches or anchored to anatomical features. The patient-specific maps of RD locations showed that areas that harboured the RDs were much smaller than the entire fibrotic areas, indicating potential targets for ablation therapy. Ablating the predicted locations and connecting them to the existing pulmonary vein ablation lesions was the most effective in-silico ablation strategy.
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Affiliation(s)
- Aditi Roy
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King’s College London, St Thomas’ Hospital, London, United Kingdom
| | - Marta Varela
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King’s College London, St Thomas’ Hospital, London, United Kingdom
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Henry Chubb
- Cardiothoracic Surgery, Stanford University, United States of America
| | - Robert MacLeod
- Bioengineering Department, University of Utah, Salt Lake City, Utah, United States of America
| | - Jules C. Hancox
- School of Physiology and Pharmacology, Cardiovascular Research Laboratories, University of Bristol, Bristol, United Kingdom
| | | | - Oleg Aslanidi
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King’s College London, St Thomas’ Hospital, London, United Kingdom
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20
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Clayton RH, Aboelkassem Y, Cantwell CD, Corrado C, Delhaas T, Huberts W, Lei CL, Ni H, Panfilov AV, Roney C, dos Santos RW. An audit of uncertainty in multi-scale cardiac electrophysiology models. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2020; 378:20190335. [PMID: 32448070 PMCID: PMC7287340 DOI: 10.1098/rsta.2019.0335] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 03/16/2020] [Indexed: 05/21/2023]
Abstract
Models of electrical activation and recovery in cardiac cells and tissue have become valuable research tools, and are beginning to be used in safety-critical applications including guidance for clinical procedures and for drug safety assessment. As a consequence, there is an urgent need for a more detailed and quantitative understanding of the ways that uncertainty and variability influence model predictions. In this paper, we review the sources of uncertainty in these models at different spatial scales, discuss how uncertainties are communicated across scales, and begin to assess their relative importance. We conclude by highlighting important challenges that continue to face the cardiac modelling community, identifying open questions, and making recommendations for future studies. This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'.
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Affiliation(s)
- Richard H. Clayton
- Insigneo institute for in-silico Medicine and Department of Computer Science, University of Sheffield, Sheffield, UK
- e-mail:
| | - Yasser Aboelkassem
- Department of Bioengineering, University of California, San Diego, CA, USA
| | | | - Cesare Corrado
- Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, UK
| | - Tammo Delhaas
- School of Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
| | - Wouter Huberts
- School of Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Chon Lok Lei
- Computational Biology and Health Informatics, Department of Computer Science, University of Oxford, Oxford, UK
| | - Haibo Ni
- Department of Pharmacology, University of California, Davis, CA, USA
| | - Alexander V. Panfilov
- Department of Physics and Astronomy, University of Gent, Gent, Belgium
- Laboratory of Computational Biology and Medicine, Ural Federal University, Ekaterinburg, Russia
| | - Caroline Roney
- Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, UK
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21
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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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22
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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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23
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Bayer JD, Boukens BJ, Krul SPJ, Roney CH, Driessen AHG, Berger WR, van den Berg NWE, Verkerk AO, Vigmond EJ, Coronel R, de Groot JR. Acetylcholine Delays Atrial Activation to Facilitate Atrial Fibrillation. Front Physiol 2019; 10:1105. [PMID: 31551802 PMCID: PMC6737394 DOI: 10.3389/fphys.2019.01105] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Accepted: 08/09/2019] [Indexed: 11/13/2022] Open
Abstract
Background Acetylcholine (ACh) shortens action potential duration (APD) in human atria. APD shortening facilitates atrial fibrillation (AF) by reducing the wavelength for reentry. However, the influence of ACh on electrical conduction in human atria and its contribution to AF are unclear, particularly when combined with impaired conduction from interstitial fibrosis. Objective To investigate the effect of ACh on human atrial conduction and its role in AF with computational, experimental, and clinical approaches. Methods S1S2 pacing (S1 = 600 ms and S2 = variable cycle lengths) was applied to the following human AF computer models: a left atrial appendage (LAA) myocyte to quantify the effects of ACh on APD, maximum upstroke velocity (V max ), and resting membrane potential (RMP); a monolayer of LAA myocytes to quantify the effects of ACh on conduction; and 3) an intact left atrium (LA) to determine the effects of ACh on arrhythmogenicity. Heterogeneous ACh and interstitial fibrosis were applied to the monolayer and LA models. To corroborate the simulations, APD and RMP from isolated human atrial myocytes were recorded before and after 0.1 μM ACh. At the tissue level, LAAs from AF patients were optically mapped ex vivo using Di-4-ANEPPS. The difference in total activation time (AT) was determined between AT initially recorded with S1 pacing, and AT recorded during subsequent S1 pacing without (n = 6) or with (n = 7) 100 μM ACh. Results In LAA myocyte simulations, S1 pacing with 0.1 μM ACh shortened APD by 41 ms, hyperpolarized RMP by 7 mV, and increased V max by 27 mV/ms. In human atrial myocytes, 0.1 μM ACh shortened APD by 48 ms, hyperpolarized RMP by 3 mV, and increased V max by 6 mV/ms. In LAA monolayer simulations, S1 pacing with ACh hyperpolarized RMP to delay total AT by 32 ms without and 35 ms with fibrosis. This led to unidirectional conduction block and sustained reentry in fibrotic LA with heterogeneous ACh during S2 pacing. In AF patient LAAs, S1 pacing with ACh increased total AT from 39.3 ± 26 ms to 71.4 ± 31.2 ms (p = 0.036) compared to no change without ACh (56.7 ± 29.3 ms to 50.0 ± 21.9 ms, p = 0.140). Conclusion In fibrotic atria with heterogeneous parasympathetic activation, ACh facilitates AF by shortening APD and slowing conduction to promote unidirectional conduction block and reentry.
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Affiliation(s)
- Jason D Bayer
- Electrophysiology and Heart Modeling Institute (IHU-LIRYC), Bordeaux University Foundation, Bordeaux, France.,Institute of Mathematics of Bordeaux (U5251), University of Bordeaux, Bordeaux, France
| | - Bastiaan J Boukens
- Department of Medical Biology, Academic Medical Center, Amsterdam, Netherlands
| | - Sébastien P J Krul
- Department of Cardiology, Academic Medical Center, Amsterdam, Netherlands
| | - Caroline H Roney
- Division of Imaging Sciences and Bioengineering, King's College London, London, United Kingdom
| | | | - Wouter R Berger
- Department of Cardiology, Academic Medical Center, Amsterdam, Netherlands.,Department of Cardiology, Heart Center, OLVG, Amsterdam, Netherlands
| | | | - Arie O Verkerk
- Department of Medical Biology, Academic Medical Center, Amsterdam, Netherlands.,Department of Experimental Cardiology, Academic Medical Center, Amsterdam, Netherlands
| | - Edward J Vigmond
- Electrophysiology and Heart Modeling Institute (IHU-LIRYC), Bordeaux University Foundation, Bordeaux, France.,Institute of Mathematics of Bordeaux (U5251), University of Bordeaux, Bordeaux, France
| | - Ruben Coronel
- Electrophysiology and Heart Modeling Institute (IHU-LIRYC), Bordeaux University Foundation, Bordeaux, France.,Department of Experimental Cardiology, Academic Medical Center, Amsterdam, Netherlands
| | - Joris R de Groot
- Department of Cardiology, Academic Medical Center, Amsterdam, Netherlands
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24
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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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25
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Zhao J, Aslanidi O, Kuklik P, Lee G, Tse G, Niederer S, Vigmond EJ. Editorial: Recent Advances in Understanding the Basic Mechanisms of Atrial Fibrillation Using Novel Computational Approaches. Front Physiol 2019; 10:1065. [PMID: 31551796 PMCID: PMC6736575 DOI: 10.3389/fphys.2019.01065] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Accepted: 08/02/2019] [Indexed: 11/13/2022] Open
Affiliation(s)
- Jichao Zhao
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Oleg Aslanidi
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Pawel Kuklik
- Department of Cardiology, University Heart Center Hamburg, Hamburg, Germany
| | - Geoffrey Lee
- Royal Melbourne Hospital, Melbourne, VIC, Australia.,Department of Cardiology, University of Melbourne, Melbourne, VIC, Australia
| | - Gary Tse
- Department of Medicine and Therapeutics, Faculty of Medicine, Chinese University of Hong Kong, Hong Kong, China.,Faculty of Medicine, Li Ka Shing Institute of Health Sciences, Chinese University of Hong Kong, Hong Kong, China
| | - Steven Niederer
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - 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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26
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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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27
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Filos D, Tachmatzidis D, Maglaveras N, Vassilikos V, Chouvarda I. Understanding the Beat-to-Beat Variations of P-Waves Morphologies in AF Patients During Sinus Rhythm: A Scoping Review of the Atrial Simulation Studies. Front Physiol 2019; 10:742. [PMID: 31275161 PMCID: PMC6591370 DOI: 10.3389/fphys.2019.00742] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Accepted: 05/28/2019] [Indexed: 11/13/2022] Open
Abstract
The remarkable advances in high-performance computing and the resulting increase of the computational power have the potential to leverage computational cardiology toward improving our understanding of the pathophysiological mechanisms of arrhythmias, such as Atrial Fibrillation (AF). In AF, a complex interaction between various triggers and the atrial substrate is considered to be the leading cause of AF initiation and perpetuation. In electrocardiography (ECG), P-wave is supposed to reflect atrial depolarization. It has been found that even during sinus rhythm (SR), multiple P-wave morphologies are present in AF patients with a history of AF, suggesting a higher dispersion of the conduction route in this population. In this scoping review, we focused on the mechanisms which modify the electrical substrate of the atria in AF patients, while investigating the existence of computational models that simulate the propagation of the electrical signal through different routes. The adopted review methodology is based on a structured analytical framework which includes the extraction of the keywords based on an initial limited bibliographic search, the extensive literature search and finally the identification of relevant articles based on the reference list of the studies. The leading mechanisms identified were classified according to their scale, spanning from mechanisms in the cell, tissue or organ level, and the produced outputs. The computational modeling approaches for each of the factors that influence the initiation and the perpetuation of AF are presented here to provide a clear overview of the existing literature. Several levels of categorization were adopted while the studies which aim to translate their findings to ECG phenotyping are highlighted. The results denote the availability of multiple models, which are appropriate under specific conditions. However, the consideration of complex scenarios taking into account multiple spatiotemporal scales, personalization of electrophysiological and anatomical models and the reproducibility in terms of ECG phenotyping has only partially been tackled so far.
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Affiliation(s)
- Dimitrios Filos
- Lab of Computing, Medical Informatics and Biomedical Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | | | - Nicos Maglaveras
- Lab of Computing, Medical Informatics and Biomedical Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
- Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL, United States
| | - Vassilios Vassilikos
- 3rd Cardiology Department, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Ioanna Chouvarda
- Lab of Computing, Medical Informatics and Biomedical Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
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28
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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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29
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Computational modeling: What does it tell us about atrial fibrillation therapy? Int J Cardiol 2019; 287:155-161. [PMID: 30803891 DOI: 10.1016/j.ijcard.2019.01.077] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2018] [Revised: 12/09/2018] [Accepted: 01/22/2019] [Indexed: 12/19/2022]
Abstract
Atrial fibrillation (AF) is a complex cardiac arrhythmia with diverse etiology that negatively affects morbidity and mortality of millions of patients. Technological and experimental advances have provided a wealth of information on the pathogenesis of AF, highlighting a multitude of mechanisms involved in arrhythmia initiation and maintenance, and disease progression. However, it remains challenging to identify the predominant mechanisms for specific subgroups of AF patients, which, together with an incomplete understanding of the pleiotropic effects of antiarrhythmic therapies, likely contributes to the suboptimal efficacy of current antiarrhythmic approaches. Computer modeling of cardiac electrophysiology has advanced in parallel to experimental research and provides an integrative framework to attempt to overcome some of these challenges. Multi-scale cardiac modeling and simulation integrate structural and functional data from experimental and clinical work with knowledge of atrial electrophysiological mechanisms and dynamics, thereby improving our understanding of AF mechanisms and therapy. In this review, we describe recent advances in our quantitative understanding of AF through mathematical models. We discuss computational modeling of AF mechanisms and therapy using detailed, mechanistic cell/tissue-level models, including approaches to incorporate variability in patient populations. We also highlight efforts using whole-atria models to improve catheter ablation therapies. Finally, we describe recent efforts and suggest future extensions to model clinical concepts of AF using patient-level models.
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30
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Loewe A, Poremba E, Oesterlein T, Luik A, Schmitt C, Seemann G, Dössel O. Patient-Specific Identification of Atrial Flutter Vulnerability-A Computational Approach to Reveal Latent Reentry Pathways. Front Physiol 2019; 9:1910. [PMID: 30692934 PMCID: PMC6339942 DOI: 10.3389/fphys.2018.01910] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2018] [Accepted: 12/18/2018] [Indexed: 11/23/2022] Open
Abstract
Atypical atrial flutter (AFlut) is a reentrant arrhythmia which patients frequently develop after ablation for atrial fibrillation (AF). Indeed, substrate modifications during AF ablation can increase the likelihood to develop AFlut and it is clinically not feasible to reliably and sensitively test if a patient is vulnerable to AFlut. Here, we present a novel method based on personalized computational models to identify pathways along which AFlut can be sustained in an individual patient. We build a personalized model of atrial excitation propagation considering the anatomy as well as the spatial distribution of anisotropic conduction velocity and repolarization characteristics based on a combination of a priori knowledge on the population level and information derived from measurements performed in the individual patient. The fast marching scheme is employed to compute activation times for stimuli from all parts of the atria. Potential flutter pathways are then identified by tracing loops from wave front collision sites and constricting them using a geometric snake approach under consideration of the heterogeneous wavelength condition. In this way, all pathways along which AFlut can be sustained are identified. Flutter pathways can be instantiated by using an eikonal-diffusion phase extrapolation approach and a dynamic multifront fast marching simulation. In these dynamic simulations, the initial pattern eventually turns into the one driven by the dominant pathway, which is the only pathway that can be observed clinically. We assessed the sensitivity of the flutter pathway maps with respect to conduction velocity and its anisotropy. Moreover, we demonstrate the application of tailored models considering disease-specific repolarization properties (healthy, AF-remodeled, potassium channel mutations) as well as applicabiltiy on a clinical dataset. Finally, we tested how AFlut vulnerability of these substrates is modulated by exemplary antiarrhythmic drugs (amiodarone, dronedarone). Our novel method allows to assess the vulnerability of an individual patient to develop AFlut based on the personal anatomical, electrophysiological, and pharmacological characteristics. In contrast to clinical electrophysiological studies, our computational approach provides the means to identify all possible AFlut pathways and not just the currently dominant one. This allows to consider all relevant AFlut pathways when tailoring clinical ablation therapy in order to reduce the development and recurrence of AFlut.
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Affiliation(s)
- Axel Loewe
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Emanuel Poremba
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Tobias Oesterlein
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Armin Luik
- Medizinische Klinik IV, Städtisches Klinikum Karlsruhe, Karlsruhe, Germany
| | - Claus Schmitt
- Medizinische Klinik IV, Städtisches Klinikum Karlsruhe, Karlsruhe, Germany
| | - Gunnar Seemann
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
- Institute for Experimental Cardiovascular Medicine, University Heart Center Freiburg Bad Krozingen, Freiburg, Germany
- Faculty of Medicine, Albert-Ludwigs University, Freiburg, Germany
| | - Olaf Dössel
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
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31
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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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32
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Roney CH, Ng FS, Debney MT, Eichhorn C, Nachiappan A, Chowdhury RA, Qureshi NA, Cantwell CD, Tweedy JH, Niederer SA, Peters NS, Vigmond EJ. Determinants of new wavefront locations in cholinergic atrial fibrillation. Europace 2018; 20:iii3-iii15. [PMID: 30476057 PMCID: PMC6251188 DOI: 10.1093/europace/euy235] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2018] [Accepted: 10/10/2018] [Indexed: 01/10/2023] Open
Abstract
AIMS Atrial fibrillation (AF) wavefront dynamics are complex and difficult to interpret, contributing to uncertainty about the mechanisms that maintain AF. We aimed to investigate the interplay between rotors, wavelets, and focal sources during fibrillation. METHODS AND RESULTS Arrhythmia wavefront dynamics were analysed for four optically mapped canine cholinergic AF preparations. A bilayer computer model was tuned to experimental preparations, and varied to have (i) fibrosis in both layers or the epicardium only, (ii) different spatial acetylcholine distributions, (iii) different intrinsic action potential duration between layers, and (iv) varied interlayer connectivity. Phase singularities (PSs) were identified and tracked over time to identify rotational drivers. New focal wavefronts were identified using phase contours. Phase singularity density and new wavefront locations were calculated during AF. There was a single dominant mechanism for sustaining AF in each of the preparations, either a rotational driver or repetitive new focal wavefronts. High-density PS sites existed preferentially around the pulmonary vein junctions. Three of the four preparations exhibited stable preferential sites of new wavefronts. Computational simulations predict that only a small number of connections are functionally important in sustaining AF, with new wavefront locations determined by the interplay between fibrosis distribution, acetylcholine concentration, and heterogeneity in repolarization within layers. CONCLUSION We were able to identify preferential sites of new wavefront initiation and rotational activity, in order to determine the mechanisms sustaining AF. Electrical measurements should be interpreted differently according to whether they are endocardial or epicardial recordings.
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Affiliation(s)
- Caroline H Roney
- ElectroCardioMaths Programme, Imperial Centre for Cardiac Engineering, Imperial College London, London, UK
- LIRYC Electrophysiology and Heart Modeling Institute, Bordeaux Fondation, Avenue du Haut-Lévèque, Pessac, France
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Fu Siong Ng
- ElectroCardioMaths Programme, Imperial Centre for Cardiac Engineering, Imperial College London, London, UK
| | - Michael T Debney
- ElectroCardioMaths Programme, Imperial Centre for Cardiac Engineering, Imperial College London, London, UK
| | - Christian Eichhorn
- ElectroCardioMaths Programme, Imperial Centre for Cardiac Engineering, Imperial College London, London, UK
| | - Arun Nachiappan
- ElectroCardioMaths Programme, Imperial Centre for Cardiac Engineering, Imperial College London, London, UK
| | - Rasheda A Chowdhury
- ElectroCardioMaths Programme, Imperial Centre for Cardiac Engineering, Imperial College London, London, UK
| | - Norman A Qureshi
- ElectroCardioMaths Programme, Imperial Centre for Cardiac Engineering, Imperial College London, London, UK
| | - Chris D Cantwell
- ElectroCardioMaths Programme, Imperial Centre for Cardiac Engineering, Imperial College London, London, UK
| | - Jennifer H Tweedy
- ElectroCardioMaths Programme, Imperial Centre for Cardiac Engineering, Imperial College London, London, UK
| | - Steven A Niederer
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Nicholas S Peters
- ElectroCardioMaths Programme, Imperial Centre for Cardiac Engineering, Imperial College London, London, UK
| | - Edward J Vigmond
- LIRYC Electrophysiology and Heart Modeling Institute, Bordeaux Fondation, Avenue du Haut-Lévèque, Pessac, France
- Univ. Bordeaux, IMB UMR 5251, F-33400 Talence, France
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Nuñez-Garcia M, Camara O, O'Neill MD, Razavi R, Chubb H, Butakoff C. Mind the gap: Quantification of incomplete ablation patterns after pulmonary vein isolation using minimum path search. Med Image Anal 2018; 51:1-12. [PMID: 30347332 DOI: 10.1016/j.media.2018.10.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2017] [Revised: 10/02/2018] [Accepted: 10/05/2018] [Indexed: 10/28/2022]
Abstract
Pulmonary vein isolation (PVI) is a common procedure for the treatment of atrial fibrillation (AF) since the initial trigger for AF frequently originates in the pulmonary veins. A successful isolation produces a continuous lesion (scar) completely encircling the veins that stops activation waves from propagating to the atrial body. Unfortunately, the encircling lesion is often incomplete, becoming a combination of scar and gaps of healthy tissue. These gaps are potential causes of AF recurrence, which requires a redo of the isolation procedure. Late-gadolinium enhanced cardiac magnetic resonance (LGE-CMR) is a non-invasive method that may also be used to detect gaps, but it is currently a time-consuming process, prone to high inter-observer variability. In this paper, we present a method to semi-automatically identify and quantify ablation gaps. Gap quantification is performed through minimum path search in a graph where every node is a scar patch and the edges are the geodesic distances between patches. We propose the Relative Gap Measure (RGM) to estimate the percentage of gap around a vein, which is defined as the ratio of the overall gap length and the total length of the path that encircles the vein. Additionally, an advanced version of the RGM has been developed to integrate gap quantification estimates from different scar segmentation techniques into a single figure-of-merit. Population-based statistical and regional analysis of gap distribution was performed using a standardised parcellation of the left atrium. We have evaluated our method on synthetic and clinical data from 50 AF patients who underwent PVI with radiofrequency ablation. The population-based analysis concluded that the left superior PV is more prone to lesion gaps while the left inferior PV tends to have less gaps (p < .05 in both cases), in the processed data. This type of information can be very useful for the optimization and objective assessment of PVI interventions.
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Affiliation(s)
- Marta Nuñez-Garcia
- Physense, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
| | - Oscar Camara
- Physense, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Mark D O'Neill
- Division of Imaging Sciences and Biomedical Engineering, King's College London, UK
| | - Reza Razavi
- Division of Imaging Sciences and Biomedical Engineering, King's College London, UK
| | - Henry Chubb
- Division of Imaging Sciences and Biomedical Engineering, King's College London, UK
| | - Constantine Butakoff
- Physense, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
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Alessandrini M, Valinoti M, Unger L, Oesterlein T, Dössel O, Corsi C, Loewe A, Severi S. A Computational Framework to Benchmark Basket Catheter Guided Ablation in Atrial Fibrillation. Front Physiol 2018; 9:1251. [PMID: 30298012 PMCID: PMC6161611 DOI: 10.3389/fphys.2018.01251] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2018] [Accepted: 08/20/2018] [Indexed: 11/13/2022] Open
Abstract
Catheter ablation is a curative therapeutic approach for atrial fibrillation (AF). Ablation of rotational sources based on basket catheter measurements has been proposed as a promising approach in patients with persistent AF to complement pulmonary vein isolation. However, clinically reported success rates are equivocal calling for a mechanistic investigation under controlled conditions. We present a computational framework to benchmark ablation strategies considering the whole cycle from excitation propagation to electrogram acquisition and processing to virtual therapy. Fibrillation was induced in a patient-specific 3D volumetric model of the left atrium, which was homogeneously remodeled to sustain reentry. The resulting extracellular potential field was sampled using models of grid catheters as well as realistically deformed basket catheters considering the specific atrial anatomy. The virtual electrograms were processed to compute phase singularity density maps to target rotor tips with up to three circular ablations. Stable rotors were successfully induced in different regions of the homogeneously remodeled atrium showing that rotors are not constrained to unique anatomical structures or locations. Density maps of rotor tip trajectories correctly identified and located the rotors (deviation < 10 mm) based on catheter recordings only for sufficient resolution (inter-electrode distance ≤3 mm) and proximity to the wall (≤10 mm). Targeting rotor sites with ablation did not stop reentries in the homogeneously remodeled atria independent from lesion size (1-7 mm radius), from linearly connecting lesions with anatomical obstacles, and from the number of rotors targeted sequentially (≤3). Our results show that phase maps derived from intracardiac electrograms can be a powerful tool to map atrial activation patterns, yet they can also be misleading due to inaccurate localization of the rotor tip depending on electrode resolution and distance to the wall. This should be considered to avoid ablating regions that are in fact free of rotor sources of AF. In our experience, ablation of rotor sites was not successful to stop fibrillation. Our comprehensive simulation framework provides the means to holistically benchmark ablation strategies in silico under consideration of all steps involved in electrogram-based therapy and, in future, could be used to study more heterogeneously remodeled disease states as well.
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Affiliation(s)
- Martino Alessandrini
- Department of Electronic Engineering and Information Technology, University of Bologna, Cesena, Italy
| | - Maddalena Valinoti
- Department of Electronic Engineering and Information Technology, University of Bologna, Cesena, Italy
| | - Laura Unger
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Tobias Oesterlein
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Olaf Dössel
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Cristiana Corsi
- Department of Electronic Engineering and Information Technology, University of Bologna, Cesena, Italy
| | - Axel Loewe
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Stefano Severi
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology, Karlsruhe, Germany
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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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Rubenstein DS, Yin H, Azami SA. Compass Mapping, Double Potentials, Activation Patterns Can Identify and Track Rotational Activity Sites in the Left Atrium of Humans with Persistent Atrial Fibrillation. J Atr Fibrillation 2018; 11:2053. [PMID: 30505380 PMCID: PMC6244311 DOI: 10.4022/jafib.2053] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2018] [Revised: 06/28/2018] [Accepted: 07/25/2018] [Indexed: 11/10/2022]
Abstract
BACKGROUND Rotational circuits that occur between bipolar electrodes exhibit double potentials (DPs). It had been previously surmised that rotors could not be electrically tracked directly. PURPOSE Our purpose was twofold; first, to show that the use of compass mapping, one can regionally identify rotational activity; and second, to show that by combining simultaneous compass map recordings, standard narrow-adjacent bipolar, and unipolar recordings, that specific signature recording patterns emerge that allow one to identify the accurate time, location, and path of a rotational mechanism. METHODS This was an observational study in 20 patients with persistent atrial fibrillation in which the electrode configuration of a circular mapping catheter was changed to wide cross-circle electrode pairing (compass mapping). DPs were recorded and analyzed from 12 left atrial (LA) sites and identified electrical wavefront patterns and direction. A substudy analyzed transitions patterns with simultaneous narrow-adjacent bipolar and unipolar recordings. RESULTS Four wavefront patterns were identified: DPs, peripheral waves (PWs), distal peripheral waves and fibrillatory activity. DP wavefront patterns exhibited significantly shorter cycle lengths than PWs in 8 of 12 LA sites. Patients had 2.9± 2.1 regions that exhibited DPs. DPs of varying duration were found, few (25%) were of stable duration and location. Detailed electrical examination at the transition between a PW to a DP identified a highly consistent pattern of simultaneous reversal of activation sequence, a special form of Doppler effect for spiral waves as a rotor passes between 2 electrodes, and a ½ cycle drop-off of activation signals along the line of electrodes. CONCLUSION DP recordings in compass mode can provide a regional assessment for the existence of rotational activity. Simultaneous DP recordings in compass mode, narrow-adjacent bipolar, and unipolar recording provide an accurate assessment of the time, location, and path that a rotational mechanism breaches a perimeter of electrodes. Accurate time, location and path of perimeter breaches can be used to electrically track rotational mechanisms during atrial fibrillation.
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Affiliation(s)
- Donald S Rubenstein
- Greenville Health System, Greenville Health System, 701 Grove Road, Greenville, SC 29605
| | - Hang Yin
- Provident Sacred Heart Medical Center, 101 W 8th Ave, Spokane, WA 99204
| | - Sana A Azami
- Greenville Health System, 701 Grove Road, Greenville, SC 29605
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Roney CH, Bayer JD, Cochet H, Meo M, Dubois R, Jaïs P, Vigmond EJ. Variability in pulmonary vein electrophysiology and fibrosis determines arrhythmia susceptibility and dynamics. PLoS Comput Biol 2018; 14:e1006166. [PMID: 29795549 PMCID: PMC5997352 DOI: 10.1371/journal.pcbi.1006166] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Revised: 06/12/2018] [Accepted: 04/30/2018] [Indexed: 11/28/2022] Open
Abstract
Success rates for catheter ablation of persistent atrial fibrillation patients are currently low; however, there is a subset of patients for whom electrical isolation of the pulmonary veins alone is a successful treatment strategy. It is difficult to identify these patients because there are a multitude of factors affecting arrhythmia susceptibility and maintenance, and the individual contributions of these factors are difficult to determine clinically. We hypothesised that the combination of pulmonary vein (PV) electrophysiology and atrial body fibrosis determine driver location and effectiveness of pulmonary vein isolation (PVI). We used bilayer biatrial computer models based on patient geometries to investigate the effects of PV properties and atrial fibrosis on arrhythmia inducibility, maintenance mechanisms, and the outcome of PVI. Short PV action potential duration (APD) increased arrhythmia susceptibility, while longer PV APD was found to be protective. Arrhythmia inducibility increased with slower conduction velocity (CV) at the LA/PV junction, but not for cases with homogeneous CV changes or slower CV at the distal PV. Phase singularity (PS) density in the PV region for cases with PV fibrosis was increased. Arrhythmia dynamics depend on both PV properties and fibrosis distribution, varying from meandering rotors to PV reentry (in cases with baseline or long APD), to stable rotors at regions of high fibrosis density. Measurement of fibrosis and PV properties may indicate patient specific susceptibility to AF initiation and maintenance. PV PS density before PVI was higher for cases in which AF terminated or converted to a macroreentry; thus, high PV PS density may indicate likelihood of PVI success. Atrial fibrillation is the most commonly encountered cardiac arrhythmia, affecting a significant portion of the population. Currently, ablation is the most effective treatment but success rates are less than optimal, being 70% one-year post-treatment. There is a large effort to find better ablation strategies to permanently cure the condition. Pulmonary vein isolation by ablation is more or less the standard of care, but many questions remain since pulmonary vein ectopy by itself does not explain all of the clinical successes or failures. We used computer simulations to investigate how electrophysiological properties of the pulmonary veins can affect rotor formation and maintenance in patients suffering from atrial fibrillation. We used complex, biophysical representations of cellular electrophysiology in highly detailed geometries constructed from patient scans. We heterogeneously varied electrophysiological and structural properties to see their effects on rotor initiation and maintenance. Our study suggests a metric for indicating the likelihood of success of pulmonary vein isolation. Thus either measuring this clinically, or running patient-specific simulations to estimate this metric may suggest whether ablation in addition to pulmonary vein isolation should be performed. Our study provides motivation for a retrospective clinical study or experimental study into this metric.
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Affiliation(s)
- Caroline H. Roney
- IHU Liryc, Electrophysiology and Heart Modeling Institute, foundation Bordeaux Université, F-33600 Pessac- Bordeaux, France
- Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, United Kingdom
| | - Jason D. Bayer
- IHU Liryc, Electrophysiology and Heart Modeling Institute, foundation Bordeaux Université, F-33600 Pessac- Bordeaux, France
- Univ. Bordeaux, IMB UMR 5251, CNRS, F-33400 Talence, France
| | - Hubert Cochet
- IHU Liryc, Electrophysiology and Heart Modeling Institute, foundation Bordeaux Université, F-33600 Pessac- Bordeaux, France
- Hôpital Cardiologique du Haut-L’évêque, Université de Bordeaux, LIRYC Institute: IHU LIRYC ANR-10-IAHU-04 and Equipex MUSIC ANR-11-EQPX-0030, Bordeaux, France
| | - Marianna Meo
- IHU Liryc, Electrophysiology and Heart Modeling Institute, foundation Bordeaux Université, F-33600 Pessac- Bordeaux, France
| | - Rémi Dubois
- IHU Liryc, Electrophysiology and Heart Modeling Institute, foundation Bordeaux Université, F-33600 Pessac- Bordeaux, France
| | - Pierre Jaïs
- IHU Liryc, Electrophysiology and Heart Modeling Institute, foundation Bordeaux Université, F-33600 Pessac- Bordeaux, France
- Hôpital Cardiologique du Haut-L’évêque, Université de Bordeaux, LIRYC Institute: IHU LIRYC ANR-10-IAHU-04 and Equipex MUSIC ANR-11-EQPX-0030, Bordeaux, France
| | - Edward J. Vigmond
- IHU Liryc, Electrophysiology and Heart Modeling Institute, foundation Bordeaux Université, F-33600 Pessac- Bordeaux, France
- Univ. Bordeaux, IMB UMR 5251, CNRS, F-33400 Talence, France
- * E-mail:
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38
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Barichello S, Roberts JD, Backx P, Boyle PM, Laksman Z. Personalizing therapy for atrial fibrillation: the role of stem cell and in silico disease models. Cardiovasc Res 2018; 114:931-943. [DOI: 10.1093/cvr/cvy090] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/26/2017] [Accepted: 04/06/2018] [Indexed: 11/12/2022] Open
Affiliation(s)
- Scott Barichello
- University of British Columbia, 2329 West Mall, Vancouver, BC V6T 1Z4, Canada
| | - Jason D Roberts
- Section of Cardiac Electrophysiology, Division of Cardiology, Department of Medicine, Western University, London, ON, Canada
| | | | - Patrick M Boyle
- Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University
| | - Zachary Laksman
- Division of Cardiology, University of British Columbia, 211-1033 Davie Street Vancouver, BC V6E 1M7, Canada
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Affiliation(s)
- Jordi Heijman
- From the Department of Cardiology, Cardiovascular Research Institute Maastricht, Faculty of Health, Medicine, and Life Sciences, Maastricht University, The Netherlands (J.H.); Department of Medicine, Montreal Heart Institute and Université de Montréal, Canada (J.-B.G., S.N.); University Hospital of Saint-Étienne, University Jean Monnet, Saint-Étienne, France (J.-B.G.); Institute of Pharmacology, West German Heart and Vascular Center, Faculty of Medicine, University Duisburg-Essen (D.D., S.N.); and
| | - Jean-Baptiste Guichard
- From the Department of Cardiology, Cardiovascular Research Institute Maastricht, Faculty of Health, Medicine, and Life Sciences, Maastricht University, The Netherlands (J.H.); Department of Medicine, Montreal Heart Institute and Université de Montréal, Canada (J.-B.G., S.N.); University Hospital of Saint-Étienne, University Jean Monnet, Saint-Étienne, France (J.-B.G.); Institute of Pharmacology, West German Heart and Vascular Center, Faculty of Medicine, University Duisburg-Essen (D.D., S.N.); and
| | - Dobromir Dobrev
- From the Department of Cardiology, Cardiovascular Research Institute Maastricht, Faculty of Health, Medicine, and Life Sciences, Maastricht University, The Netherlands (J.H.); Department of Medicine, Montreal Heart Institute and Université de Montréal, Canada (J.-B.G., S.N.); University Hospital of Saint-Étienne, University Jean Monnet, Saint-Étienne, France (J.-B.G.); Institute of Pharmacology, West German Heart and Vascular Center, Faculty of Medicine, University Duisburg-Essen (D.D., S.N.); and
| | - Stanley Nattel
- From the Department of Cardiology, Cardiovascular Research Institute Maastricht, Faculty of Health, Medicine, and Life Sciences, Maastricht University, The Netherlands (J.H.); Department of Medicine, Montreal Heart Institute and Université de Montréal, Canada (J.-B.G., S.N.); University Hospital of Saint-Étienne, University Jean Monnet, Saint-Étienne, France (J.-B.G.); Institute of Pharmacology, West German Heart and Vascular Center, Faculty of Medicine, University Duisburg-Essen (D.D., S.N.); and
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40
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Loewe A, Dössel O. Commentary: Virtual In-Silico Modeling Guided Catheter Ablation Predicts Effective Linear Ablation Lesion Set for Longstanding Persistent Atrial Fibrillation: Multicenter Prospective Randomized Study. Front Physiol 2018; 8:1113. [PMID: 29313849 PMCID: PMC5744431 DOI: 10.3389/fphys.2017.01113] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2017] [Accepted: 12/15/2017] [Indexed: 11/13/2022] Open
Affiliation(s)
- Axel Loewe
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Olaf Dössel
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
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41
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Campana C, Akar FG. Commentary: Atrial Fibrillation Dynamics and Ionic Block Effects in Six Heterogeneous Human 3D Virtual Atria with Distinct Repolarization Dynamics. Front Bioeng Biotechnol 2017; 5:59. [PMID: 29057224 PMCID: PMC5635327 DOI: 10.3389/fbioe.2017.00059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2017] [Accepted: 09/20/2017] [Indexed: 11/26/2022] Open
Affiliation(s)
- Chiara Campana
- Icahn School of Medicine at Mount Sinai, The Cardiovascular Institute, New York, NY, United States
| | - Fadi G Akar
- Icahn School of Medicine at Mount Sinai, The Cardiovascular Institute, New York, NY, United States
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Deng D, Murphy MJ, Hakim JB, Franceschi WH, Zahid S, Pashakhanloo F, Trayanova NA, Boyle PM. Sensitivity of reentrant driver localization to electrophysiological parameter variability in image-based computational models of persistent atrial fibrillation sustained by a fibrotic substrate. CHAOS (WOODBURY, N.Y.) 2017; 27:093932. [PMID: 28964164 PMCID: PMC5605332 DOI: 10.1063/1.5003340] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2017] [Accepted: 09/04/2017] [Indexed: 05/30/2023]
Abstract
Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia, causing morbidity and mortality in millions worldwide. The atria of patients with persistent AF (PsAF) are characterized by the presence of extensive and distributed atrial fibrosis, which facilitates the formation of persistent reentrant drivers (RDs, i.e., spiral waves), which promote fibrillatory activity. Targeted catheter ablation of RD-harboring tissues has shown promise as a clinical treatment for PsAF, but the outcomes remain sub-par. Personalized computational modeling has been proposed as a means of non-invasively predicting optimal ablation targets in individual PsAF patients, but it remains unclear how RD localization dynamics are influenced by inter-patient variability in the spatial distribution of atrial fibrosis, action potential duration (APD), and conduction velocity (CV). Here, we conduct simulations in computational models of fibrotic atria derived from the clinical imaging of PsAF patients to characterize the sensitivity of RD locations to these three factors. We show that RDs consistently anchor to boundaries between fibrotic and non-fibrotic tissues, as delineated by late gadolinium-enhanced magnetic resonance imaging, but those changes in APD/CV can enhance or attenuate the likelihood that an RD will anchor to a specific site. These findings show that the level of uncertainty present in patient-specific atrial models reconstructed without any invasive measurements (i.e., incorporating each individual's unique distribution of fibrotic tissue from medical imaging alongside an average representation of AF-remodeled electrophysiology) is sufficiently high that a personalized ablation strategy based on targeting simulation-predicted RD trajectories alone may not produce the desired result.
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Affiliation(s)
- Dongdong Deng
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Michael J Murphy
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Joe B Hakim
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - William H Franceschi
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Sohail Zahid
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Farhad Pashakhanloo
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Natalia A Trayanova
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Patrick M Boyle
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
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Onal B, Gratz D, Hund TJ. Ca 2+/calmodulin-dependent kinase II-dependent regulation of atrial myocyte late Na + current, Ca 2+ cycling, and excitability: a mathematical modeling study. Am J Physiol Heart Circ Physiol 2017; 313:H1227-H1239. [PMID: 28842436 DOI: 10.1152/ajpheart.00185.2017] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Atrial fibrillation (AF) affects more than three million people per year in the United States and is associated with high morbidity and mortality. Both electrical and structural remodeling contribute to AF, but the molecular pathways underlying AF pathogenesis are not well understood. Recently, a role for Ca2+/calmodulin-dependent protein kinase II (CaMKII) in the regulation of persistent "late" Na+ current ( INa,L) has been identified. Although INa,L inhibition is emerging as a potential antiarrhythmic strategy in patients with AF, little is known about the mechanism linking INa,L to atrial arrhythmogenesis. A computational approach was used to test the hypothesis that increased CaMKII-activated INa,L in atrial myocytes disrupts Ca2+ homeostasis, promoting arrhythmogenic afterdepolarizations. Dynamic CaMKII activity and regulation of multiple downstream targets [ INa,L, L-type Ca2+ current, phospholamban, and the ryanodine receptor sarcoplasmic reticulum Ca2+-release channel (RyR2)] were incorporated into an existing well-validated computational model of the human atrial action potential. Model simulations showed that constitutive CaMKII-dependent phosphorylation of Nav1.5 and the subsequent increase in INa,L effectively disrupt intracellular atrial myocyte ion homeostasis and CaMKII signaling. Specifically, increased INa,L promotes intracellular Ca2+ overload via forward-mode Na+/Ca2+ exchange activity, which greatly increases RyR2 open probability beyond that observed for CaMKII-dependent phosphorylation of RyR2 alone. Increased INa,L promotes atrial myocyte repolarization defects (afterdepolarizations and alternans) in the setting of acute β-adrenergic stimulation. We anticipate that our modeling efforts will help identify new mechanisms for atrial NaV1.5 regulation with direct relevance for human AF. NEW & NOTEWORTHY Here, we present a novel computational model to study the effects of late Na+ current ( INa,L) in human atrial myocytes. Simulations predict that INa,L promotes intracellular accumulation of Ca2+, with subsequent dysregulation of Ca2+/calmodulin-dependent protein kinase II (CaMKII) signaling and ryanodine receptor 2-mediated Ca2+ release. Although INa,L plays a small role in regulating atrial myocyte excitability at baseline, CaMKII-dependent enhancement of the current promoted arrhythmogenic dynamics. Listen to this article's corresponding podcast at http://ajpheart.podbean.com/e/camkii-dependent-regulation-of-atrial-late-sodium-current-and-excitability/ .
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Affiliation(s)
- Birce Onal
- The Dorothy M. Davis Heart and Lung Research Institute, The Ohio State University Wexner Medical Center , Columbus, Ohio.,Department of Biomedical Engineering, College of Engineering, The Ohio State University , Columbus, Ohio
| | - Daniel Gratz
- The Dorothy M. Davis Heart and Lung Research Institute, The Ohio State University Wexner Medical Center , Columbus, Ohio.,Department of Biomedical Engineering, College of Engineering, The Ohio State University , Columbus, Ohio
| | - Thomas J Hund
- The Dorothy M. Davis Heart and Lung Research Institute, The Ohio State University Wexner Medical Center , Columbus, Ohio.,Department of Biomedical Engineering, College of Engineering, The Ohio State University , Columbus, Ohio.,Department of Internal Medicine, The Ohio State University Wexner Medical Center , Columbus, Ohio
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Boyle PM, Zahid S, Trayanova NA. Towards personalized computational modelling of the fibrotic substrate for atrial arrhythmia. Europace 2017; 18:iv136-iv145. [PMID: 28011841 DOI: 10.1093/europace/euw358] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2016] [Accepted: 07/28/2016] [Indexed: 11/13/2022] Open
Abstract
: Atrial arrhythmias involving a fibrotic substrate are an important cause of morbidity and mortality. In many cases, effective treatment of such rhythm disorders is severely hindered by a lack of mechanistic understanding relating features of fibrotic remodelling to dynamics of re-entrant arrhythmia. With the advent of clinical imaging modalities capable of resolving the unique fibrosis spatial pattern present in the atria of each individual patient, a promising new research trajectory has emerged in which personalized computational models are used to analyse mechanistic underpinnings of arrhythmia dynamics based on the distribution of fibrotic tissue. In this review, we first present findings that have yielded a robust and detailed biophysical representation of fibrotic substrate electrophysiological properties. Then, we summarize the results of several recent investigations seeking to use organ-scale models of the fibrotic human atria to derive new insights on mechanisms of arrhythmia perpetuation and to develop novel strategies for model-assisted individualized planning of catheter ablation procedures for atrial arrhythmias.
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Affiliation(s)
- Patrick M Boyle
- Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, 3400 N Charles St, 208 Hackerman Hall, Baltimore, MD 21218, USA
| | - Sohail Zahid
- Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, 3400 N Charles St, 208 Hackerman Hall, Baltimore, MD 21218, USA
| | - Natalia A Trayanova
- Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, 3400 N Charles St, 208 Hackerman Hall, Baltimore, MD 21218, USA
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Roney CH, Cantwell CD, Bayer JD, Qureshi NA, Lim PB, Tweedy JH, Kanagaratnam P, Peters NS, Vigmond EJ, Ng FS. Spatial Resolution Requirements for Accurate Identification of Drivers of Atrial Fibrillation. Circ Arrhythm Electrophysiol 2017; 10:e004899. [PMID: 28500175 PMCID: PMC5434962 DOI: 10.1161/circep.116.004899] [Citation(s) in RCA: 85] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2016] [Accepted: 04/11/2017] [Indexed: 11/25/2022]
Abstract
Supplemental Digital Content is available in the text. Background— Recent studies have demonstrated conflicting mechanisms underlying atrial fibrillation (AF), with the spatial resolution of data often cited as a potential reason for the disagreement. The purpose of this study was to investigate whether the variation in spatial resolution of mapping may lead to misinterpretation of the underlying mechanism in persistent AF. Methods and Results— Simulations of rotors and focal sources were performed to estimate the minimum number of recording points required to correctly identify the underlying AF mechanism. The effects of different data types (action potentials and unipolar or bipolar electrograms) and rotor stability on resolution requirements were investigated. We also determined the ability of clinically used endocardial catheters to identify AF mechanisms using clinically recorded and simulated data. The spatial resolution required for correct identification of rotors and focal sources is a linear function of spatial wavelength (the distance between wavefronts) of the arrhythmia. Rotor localization errors are larger for electrogram data than for action potential data. Stationary rotors are more reliably identified compared with meandering trajectories, for any given spatial resolution. All clinical high-resolution multipolar catheters are of sufficient resolution to accurately detect and track rotors when placed over the rotor core although the low-resolution basket catheter is prone to false detections and may incorrectly identify rotors that are not present. Conclusions— The spatial resolution of AF data can significantly affect the interpretation of the underlying AF mechanism. Therefore, the interpretation of human AF data must be taken in the context of the spatial resolution of the recordings.
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Affiliation(s)
- Caroline H Roney
- From the ElectroCardioMaths Programme (C.H.R., C.D.C., N.A.Q., P.B.L., P.K., N.S.P., F.S.N.), and the Department of Bioengineering (J.H.T.), Imperial College London, United Kingdom; IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Pessac-Bordeaux, France (J.D.B., E.J.V.); and Université de Bordeaux, IMB, UMR 5251, Talence, France (J.D.B., E.J.V.)
| | - Chris D Cantwell
- From the ElectroCardioMaths Programme (C.H.R., C.D.C., N.A.Q., P.B.L., P.K., N.S.P., F.S.N.), and the Department of Bioengineering (J.H.T.), Imperial College London, United Kingdom; IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Pessac-Bordeaux, France (J.D.B., E.J.V.); and Université de Bordeaux, IMB, UMR 5251, Talence, France (J.D.B., E.J.V.)
| | - Jason D Bayer
- From the ElectroCardioMaths Programme (C.H.R., C.D.C., N.A.Q., P.B.L., P.K., N.S.P., F.S.N.), and the Department of Bioengineering (J.H.T.), Imperial College London, United Kingdom; IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Pessac-Bordeaux, France (J.D.B., E.J.V.); and Université de Bordeaux, IMB, UMR 5251, Talence, France (J.D.B., E.J.V.)
| | - Norman A Qureshi
- From the ElectroCardioMaths Programme (C.H.R., C.D.C., N.A.Q., P.B.L., P.K., N.S.P., F.S.N.), and the Department of Bioengineering (J.H.T.), Imperial College London, United Kingdom; IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Pessac-Bordeaux, France (J.D.B., E.J.V.); and Université de Bordeaux, IMB, UMR 5251, Talence, France (J.D.B., E.J.V.)
| | - Phang Boon Lim
- From the ElectroCardioMaths Programme (C.H.R., C.D.C., N.A.Q., P.B.L., P.K., N.S.P., F.S.N.), and the Department of Bioengineering (J.H.T.), Imperial College London, United Kingdom; IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Pessac-Bordeaux, France (J.D.B., E.J.V.); and Université de Bordeaux, IMB, UMR 5251, Talence, France (J.D.B., E.J.V.)
| | - Jennifer H Tweedy
- From the ElectroCardioMaths Programme (C.H.R., C.D.C., N.A.Q., P.B.L., P.K., N.S.P., F.S.N.), and the Department of Bioengineering (J.H.T.), Imperial College London, United Kingdom; IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Pessac-Bordeaux, France (J.D.B., E.J.V.); and Université de Bordeaux, IMB, UMR 5251, Talence, France (J.D.B., E.J.V.)
| | - Prapa Kanagaratnam
- From the ElectroCardioMaths Programme (C.H.R., C.D.C., N.A.Q., P.B.L., P.K., N.S.P., F.S.N.), and the Department of Bioengineering (J.H.T.), Imperial College London, United Kingdom; IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Pessac-Bordeaux, France (J.D.B., E.J.V.); and Université de Bordeaux, IMB, UMR 5251, Talence, France (J.D.B., E.J.V.)
| | - Nicholas S Peters
- From the ElectroCardioMaths Programme (C.H.R., C.D.C., N.A.Q., P.B.L., P.K., N.S.P., F.S.N.), and the Department of Bioengineering (J.H.T.), Imperial College London, United Kingdom; IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Pessac-Bordeaux, France (J.D.B., E.J.V.); and Université de Bordeaux, IMB, UMR 5251, Talence, France (J.D.B., E.J.V.).
| | - Edward J Vigmond
- From the ElectroCardioMaths Programme (C.H.R., C.D.C., N.A.Q., P.B.L., P.K., N.S.P., F.S.N.), and the Department of Bioengineering (J.H.T.), Imperial College London, United Kingdom; IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Pessac-Bordeaux, France (J.D.B., E.J.V.); and Université de Bordeaux, IMB, UMR 5251, Talence, France (J.D.B., E.J.V.)
| | - Fu Siong Ng
- From the ElectroCardioMaths Programme (C.H.R., C.D.C., N.A.Q., P.B.L., P.K., N.S.P., F.S.N.), and the Department of Bioengineering (J.H.T.), Imperial College London, United Kingdom; IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Pessac-Bordeaux, France (J.D.B., E.J.V.); and Université de Bordeaux, IMB, UMR 5251, Talence, France (J.D.B., E.J.V.)
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Roney CH, Bayer JD, Zahid S, Meo M, Boyle PMJ, Trayanova NA, Haïssaguerre M, Dubois R, Cochet H, Vigmond EJ. Modelling methodology of atrial fibrosis affects rotor dynamics and electrograms. Europace 2017; 18:iv146-iv155. [PMID: 28011842 DOI: 10.1093/europace/euw365] [Citation(s) in RCA: 75] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2016] [Accepted: 06/28/2016] [Indexed: 11/14/2022] Open
Abstract
AIMS Catheter ablation is an effective technique for terminating atrial arrhythmia. However, given a high atrial fibrillation (AF) recurrence rate, optimal ablation strategies have yet to be defined. Computer modelling can be a powerful aid but modelling of fibrosis, a major factor associated with AF, is an open question. Several groups have proposed methodologies based on imaging data, but no comparison to determine which methodology best corroborates clinically observed reentrant behaviour has been performed. We examined several methodologies to determine the best method for capturing fibrillation dynamics. METHODS AND RESULTS Patient late gadolinium-enhanced magnetic resonance imaging data were transferred onto a bilayer atrial computer model and used to assign fibrosis distributions. Fibrosis was modelled as conduction disturbances (lower conductivity, edge splitting, or percolation), transforming growth factor-β1 ionic channel effects, myocyte-fibroblast coupling, and combinations of the preceding. Reentry was induced through pulmonary vein ectopy and the ensuing rotor dynamics characterized. Non-invasive electrocardiographic imaging data of the patients in AF was used for comparison. Electrograms were computed and the fractionation durations measured over the surface. Edge splitting produced more phase singularities from wavebreaks than the other representations. The number of phase singularities seen with percolation was closer to the clinical values. Addition of fibroblast coupling had an organizing effect on rotor dynamics. Simple tissue conductivity changes with ionic changes localized rotors over fibrosis which was not observed with clinical data. CONCLUSION The specific representation of fibrosis has a large effect on rotor dynamics and needs to be carefully considered for patient specific modelling.
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Affiliation(s)
- Caroline H Roney
- IHU Liryc, Electrophysiology and Heart Modeling Institute, foundation Bordeaux Université, F-33600 Pessac- Bordeaux, France.,Univ. Bordeaux, IMB UMR 5251, CNRS, F-33400 Talence, France
| | - Jason D Bayer
- IHU Liryc, Electrophysiology and Heart Modeling Institute, foundation Bordeaux Université, F-33600 Pessac- Bordeaux, France.,Univ. Bordeaux, IMB UMR 5251, CNRS, F-33400 Talence, France
| | - Sohail Zahid
- Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Marianna Meo
- IHU Liryc, Electrophysiology and Heart Modeling Institute, foundation Bordeaux Université, F-33600 Pessac- Bordeaux, France.,Univ. Bordeaux, Centre de recherche Cardio-Thoracique de Bordeaux, U1045, F- 33000, Bordeaux, France
| | - Patrick M J Boyle
- Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Natalia A Trayanova
- Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Michel Haïssaguerre
- IHU Liryc, Electrophysiology and Heart Modeling Institute, foundation Bordeaux Université, F-33600 Pessac- Bordeaux, France.,Univ. Bordeaux, Centre de recherche Cardio-Thoracique de Bordeaux, U1045, F- 33000, Bordeaux, France.,Bordeaux University Hospital (CHU), Electrophysiology and Ablation Unit, F-33600 Pessac, France
| | - Rémi Dubois
- IHU Liryc, Electrophysiology and Heart Modeling Institute, foundation Bordeaux Université, F-33600 Pessac- Bordeaux, France.,Univ. Bordeaux, Centre de recherche Cardio-Thoracique de Bordeaux, U1045, F- 33000, Bordeaux, France
| | - Hubert Cochet
- IHU Liryc, Electrophysiology and Heart Modeling Institute, foundation Bordeaux Université, F-33600 Pessac- Bordeaux, France.,Univ. Bordeaux, Centre de recherche Cardio-Thoracique de Bordeaux, U1045, F- 33000, Bordeaux, France.,Department of Cardiac Imaging, Bordeaux University Hospital, Bordeaux, France
| | - Edward J Vigmond
- IHU Liryc, Electrophysiology and Heart Modeling Institute, foundation Bordeaux Université, F-33600 Pessac- Bordeaux, France.,Univ. Bordeaux, IMB UMR 5251, CNRS, F-33400 Talence, France
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Boyle PM, Zahid S, Trayanova NA. Using personalized computer models to custom-tailor ablation procedures for atrial fibrillation patients: are we there yet? Expert Rev Cardiovasc Ther 2017; 15:339-341. [PMID: 28395557 DOI: 10.1080/14779072.2017.1317593] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Patrick M Boyle
- a Institute for Computational Medicine and Department of Biomedical Engineering , Johns Hopkins University , Baltimore , USA
| | - Sohail Zahid
- a Institute for Computational Medicine and Department of Biomedical Engineering , Johns Hopkins University , Baltimore , USA
| | - Natalia A Trayanova
- a Institute for Computational Medicine and Department of Biomedical Engineering , Johns Hopkins University , Baltimore , USA
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Lee YS, Song JS, Hwang M, Lim B, Joung B, Pak HN. A New Efficient Method for Detecting Phase Singularity in Cardiac Fibrillation. PLoS One 2016; 11:e0167567. [PMID: 27907144 PMCID: PMC5131933 DOI: 10.1371/journal.pone.0167567] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2016] [Accepted: 11/16/2016] [Indexed: 12/31/2022] Open
Abstract
Background The point of phase singularity (PS) is considered to represent a spiral wave core or a rotor in cardiac fibrillation. Computational efficiency is important for detection of PS in clinical electrophysiology. We developed a novel algorithm for highly efficient and robust detection of PS. Methods In contrast to the conventional method, which calculates PS based on the line integral of the phase around a PS point equal to ±2π (the Iyer-Gray method), the proposed algorithm (the location-centric method) looks for the phase discontinuity point at which PS actually occurs. We tested the efficiency and robustness of these two methods in a two-dimensional mathematical model of atrial fibrillation (AF), with and without remodeling of ionic currents. Results 1. There was a significant association, in terms of the Hausdorff distance (3.30 ± 0.0 mm), between the PS points measured using the Iyer-Gray and location-centric methods, with almost identical PS trajectories generated by the two methods. 2. For the condition of electrical remodeling of AF (0.3 × ICaL), the PS points calculated by the two methods were satisfactorily co-localized (with the Hausdorff distance of 1.64 ± 0.09 mm). 3. The proposed location-centric method was substantially more efficient than the Iyer-Gray method, with a 28.6-fold and 28.2-fold shorter run times for the control and remodeling scenarios, respectively. Conclusion We propose a new location-centric method for calculating PS, which is robust and more efficient compared with the conventionally used method.
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Affiliation(s)
| | | | - Minki Hwang
- Yonsei University Health System, Seoul, Korea
| | | | | | - Hui-Nam Pak
- Yonsei University Health System, Seoul, Korea
- * E-mail:
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