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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 PMCID: PMC11381036 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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Sakata K, Bradley RP, Prakosa A, Yamamoto CAP, Ali SY, Loeffler S, Tice BM, Boyle PM, Kholmovski EG, Yadav R, Sinha SK, Marine JE, Calkins H, Spragg DD, Trayanova NA. Assessing the arrhythmogenic propensity of fibrotic substrate using digital twins to inform a mechanisms-based atrial fibrillation ablation strategy. NATURE CARDIOVASCULAR RESEARCH 2024; 3:857-868. [PMID: 39157719 PMCID: PMC11329066 DOI: 10.1038/s44161-024-00489-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 05/15/2024] [Indexed: 08/20/2024]
Abstract
Atrial fibrillation (AF), the most common heart rhythm disorder, may cause stroke and heart failure. For patients with persistent AF with fibrosis proliferation, the standard AF treatment-pulmonary vein isolation-has poor outcomes, necessitating redo procedures, owing to insufficient understanding of what constitutes good targets in fibrotic substrates. Here we present a prospective clinical and personalized digital twin study that characterizes the arrhythmogenic properties of persistent AF substrates and uncovers locations possessing rotor-attracting capabilities. Among these, a portion needs to be ablated to render the substrate not inducible for rotors, but the rest (37%) lose rotor-attracting capabilities when another location is ablated. Leveraging digital twin mechanistic insights, we suggest ablation targets that eliminate arrhythmia propensity with minimum lesions while also minimizing the risk of iatrogenic tachycardia and AF recurrence. Our findings provide further evidence regarding the appropriate substrate ablation targets in persistent AF, opening the door for effective strategies to mitigate patients' AF burden.
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Affiliation(s)
- Kensuke Sakata
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA
| | - Ryan P. Bradley
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA
- Research Computing, Lehigh University, Bethlehem, PA, USA
| | - Adityo Prakosa
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA
| | | | - Syed Yusuf Ali
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Shane Loeffler
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA
| | - Brock M. Tice
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA
| | - Patrick M. Boyle
- Center for Cardiovascular Biology, University of Washington, Seattle, WA, USA
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA
| | - Eugene G. Kholmovski
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Ritu Yadav
- Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Sunil Kumar Sinha
- Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Joseph E. Marine
- Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Hugh Calkins
- Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - David D. Spragg
- Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Natalia A. Trayanova
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
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3
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Bifulco SF, Boyle PM. Computational Modeling and Simulation of the Fibrotic Human Atria. Methods Mol Biol 2024; 2735:105-115. [PMID: 38038845 DOI: 10.1007/978-1-0716-3527-8_6] [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] [Indexed: 12/02/2023]
Abstract
Patient-specific modeling of atrial electrical activity enables the execution of simulations that can provide mechanistic insights and provide novel solutions to vexing clinical problems. The geometry and fibrotic remodeling of the heart can be reconstructed from clinical-grade medical scans and used to inform personalized models with detail incorporated at the cell- and tissue-scale to represent changes in image-identified diseased regions. Here, we provide a rubric for the reconstruction of realistic atrial models from pre-segmented 3D renderings of the left atrium with fibrotic tissue regions delineated, which are the output from clinical-grade systems for quantifying fibrosis. We then provide a roadmap for using those models to carry out patient-specific characterization of the fibrotic substrate in terms of its potential to harbor reentrant drivers via cardiac electrophysiology simulations.
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Affiliation(s)
- Savannah F Bifulco
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Patrick M Boyle
- Department of Bioengineering, University of Washington, Seattle, WA, USA.
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA.
- Center for Cardiovascular Biology, University of Washington, Seattle, WA, USA.
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4
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Bifulco SF, Macheret F, Scott GD, Akoum N, Boyle PM. Explainable Machine Learning to Predict Anchored Reentry Substrate Created by Persistent Atrial Fibrillation Ablation in Computational Models. J Am Heart Assoc 2023; 12:e030500. [PMID: 37581387 PMCID: PMC10492949 DOI: 10.1161/jaha.123.030500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Accepted: 07/21/2023] [Indexed: 08/16/2023]
Abstract
Background Postablation arrhythmia recurrence occurs in ~40% of patients with persistent atrial fibrillation. Fibrotic remodeling exacerbates arrhythmic activity in persistent atrial fibrillation and can play a key role in reentrant arrhythmia, but emergent interaction between nonconductive ablation-induced scar and native fibrosis (ie, residual fibrosis) is poorly understood. Methods and Results We conducted computational simulations in pre- and postablation left atrial models reconstructed from late gadolinium enhanced magnetic resonance imaging scans to test the hypothesis that ablation in patients with persistent atrial fibrillation creates new substrate conducive to recurrent arrhythmia mediated by anchored reentry. We trained a random forest machine learning classifier to accurately pinpoint specific nonconductive tissue regions (ie, areas of ablation-delivered scar or vein/valve boundaries) with the capacity to serve as substrate for anchored reentry-driven recurrent arrhythmia (area under the curve: 0.91±0.03). Our analysis suggests there is a distinctive nonconductive tissue pattern prone to serving as arrhythmogenic substrate in postablation models, defined by a specific size and proximity to residual fibrosis. Conclusions Overall, this suggests persistent atrial fibrillation ablation transforms substrate that favors functional reentry (ie, rotors meandering in excitable tissue) into an arrhythmogenic milieu more conducive to anchored reentry. Our work also indicates that explainable machine learning and computational simulations can be combined to effectively probe mechanisms of recurrent arrhythmia.
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Affiliation(s)
| | - Fima Macheret
- Division of CardiologyUniversity of WashingtonSeattleWAUSA
| | - Griffin D. Scott
- Department of BioengineeringUniversity of WashingtonSeattleWAUSA
| | - Nazem Akoum
- Department of BioengineeringUniversity of WashingtonSeattleWAUSA
- Division of CardiologyUniversity of WashingtonSeattleWAUSA
| | - Patrick M. Boyle
- Department of BioengineeringUniversity of WashingtonSeattleWAUSA
- Institute for Stem Cell and Regenerative MedicineUniversity of WashingtonSeattleWAUSA
- Center for Cardiovascular BiologyUniversity of WashingtonSeattleWAUSA
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5
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Giorgios T, Antonio F, Limite LR, Felicia L, Zweiker D, Cireddu M, Vlachos K, Hadjis A, D'Angelo G, Baratto F, Bisceglia C, Vergara P, Marzi A, Peretto G, Paglino G, Radinovic A, Gulletta S, Sala S, Mazzone P, Bella PD. Bi‐atrial characterization of the electrical substrate in patients with atrial fibrillation. Pacing Clin Electrophysiol 2022; 45:752-760. [PMID: 35403246 PMCID: PMC9322275 DOI: 10.1111/pace.14490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 02/19/2022] [Accepted: 03/11/2022] [Indexed: 11/30/2022]
Abstract
Background Methods Results Conclusion
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Affiliation(s)
| | | | | | | | - David Zweiker
- Department of Arrhythmology San Raffaele Hospital Milan Italy
| | - Manuela Cireddu
- Department of Arrhythmology San Raffaele Hospital Milan Italy
| | | | - Alexios Hadjis
- Department of Arrhythmology San Raffaele Hospital Milan Italy
| | | | | | | | | | | | | | | | | | - Simone Gulletta
- Department of Arrhythmology San Raffaele Hospital Milan Italy
| | - Simone Sala
- Department of Arrhythmology San Raffaele Hospital Milan Italy
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6
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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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7
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de Groot NMS, Shah D, Boyle PM, Anter E, Clifford GD, Deisenhofer I, Deneke T, van Dessel P, Doessel O, Dilaveris P, Heinzel FR, Kapa S, Lambiase PD, Lumens J, Platonov PG, Ngarmukos T, Martinez JP, Sanchez AO, Takahashi Y, Valdigem BP, van der Veen AJ, Vernooy K, Casado-Arroyo Co-Chair R. Critical appraisal of technologies to assess electrical activity during atrial fibrillation: a position paper from the European Heart Rhythm Association and European Society of Cardiology Working Group on eCardiology in collaboration with the Heart Rhythm Society, Asia Pacific Heart Rhythm Society, Latin American Heart Rhythm Society and Computing in Cardiology. Europace 2021; 24:313-330. [PMID: 34878119 DOI: 10.1093/europace/euab254] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 09/21/2021] [Indexed: 11/13/2022] Open
Abstract
We aim to provide a critical appraisal of basic concepts underlying signal recording and processing technologies applied for (i) atrial fibrillation (AF) mapping to unravel AF mechanisms and/or identifying target sites for AF therapy and (ii) AF detection, to optimize usage of technologies, stimulate research aimed at closing knowledge gaps, and developing ideal AF recording and processing technologies. Recording and processing techniques for assessment of electrical activity during AF essential for diagnosis and guiding ablative therapy including body surface electrocardiograms (ECG) and endo- or epicardial electrograms (EGM) are evaluated. Discussion of (i) differences in uni-, bi-, and multi-polar (omnipolar/Laplacian) recording modes, (ii) impact of recording technologies on EGM morphology, (iii) global or local mapping using various types of EGM involving signal processing techniques including isochronal-, voltage- fractionation-, dipole density-, and rotor mapping, enabling derivation of parameters like atrial rate, entropy, conduction velocity/direction, (iv) value of epicardial and optical mapping, (v) AF detection by cardiac implantable electronic devices containing various detection algorithms applicable to stored EGMs, (vi) contribution of machine learning (ML) to further improvement of signals processing technologies. Recording and processing of EGM (or ECG) are the cornerstones of (body surface) mapping of AF. Currently available AF recording and processing technologies are mainly restricted to specific applications or have technological limitations. Improvements in AF mapping by obtaining highest fidelity source signals (e.g. catheter-electrode combinations) for signal processing (e.g. filtering, digitization, and noise elimination) is of utmost importance. Novel acquisition instruments (multi-polar catheters combined with improved physical modelling and ML techniques) will enable enhanced and automated interpretation of EGM recordings in the near future.
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Affiliation(s)
- Natasja M S de Groot
- Department of Cardiology, Erasmus University Medical Centre, Rotterdam, Delft University of Technology, Delft the Netherlands
| | - Dipen Shah
- Cardiology Service, University Hospitals Geneva, Geneva, Switzerland
| | - Patrick M Boyle
- Department of Bioengineering, University of Washington, Seattle, Washington, USA
| | - Elad Anter
- Cardiac Electrophysiology Section, Department of Cardiovascular Medicine, Cleveland Clinic, Cleveland, Ohio, USA
| | - Gari D Clifford
- Department of Biomedical Informatics, Emory University, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, USA
| | - Isabel Deisenhofer
- Department of Electrophysiology, German Heart Center Munich and Technical University of Munich, Munich, Germany
| | - Thomas Deneke
- Department of Cardiology, Rhon-klinikum Campus Bad Neustadt, Germany
| | - Pascal van Dessel
- Department of Cardiology, Medisch Spectrum Twente, Twente, the Netherlands
| | - Olaf Doessel
- Karlsruher Institut für Technologie (KIT), Karlsruhe, Germany
| | - Polychronis Dilaveris
- 1st University Department of Cardiology, National & Kapodistrian University of Athens School of Medicine, Hippokration Hospital, Athens, Greece
| | - Frank R Heinzel
- Department of Internal Medicine and Cardiology, Charité-Universitätsmedizin Berlin, Campus Virchow-Klinikum and DZHK (German Centre for Cardiovascular Research), Berlin, Germany
| | - Suraj Kapa
- Department of Cardiology, Mayo Clinic, Rochester, USA
| | | | - Joost Lumens
- Cardiovascular Research Institute Maastricht (CARIM) Maastricht University, Maastricht, the Netherlands
| | - Pyotr G Platonov
- Department of Cardiology, Clinical Sciences, Lund University, Lund, Sweden
| | - Tachapong Ngarmukos
- Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Juan Pablo Martinez
- Aragon Institute of Engineering Research/IIS-Aragon and University of Zaragoza, Zaragoza, Spain, CIBER Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Zaragoza, Spain
| | - Alejandro Olaya Sanchez
- Department of Cardiology, Hospital San José, Fundacion Universitaia de Ciencas de la Salud, Bogota, Colombia
| | - Yoshihide Takahashi
- Department of Cardiovascular Medicine, Tokyo Medical and Dental University, Tokyo, Japan
| | - Bruno P Valdigem
- Department of Cardiology, Hospital Rede D'or São Luiz, hospital Albert einstein and Dante pazzanese heart institute, São Paulo, Brasil
| | - Alle-Jan van der Veen
- Department Circuits and Systems, Delft University of Technology, Delft, the Netherlands
| | - Kevin Vernooy
- Department of Cardiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Centre, Maastricht, the Netherlands
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8
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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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9
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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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10
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Boyle PM, Ochs AR, Ali RL, Paliwal N, Trayanova NA. Characterizing the arrhythmogenic substrate in personalized models of atrial fibrillation: sensitivity to mesh resolution and pacing protocol in AF models. Europace 2021; 23:i3-i11. [PMID: 33751074 DOI: 10.1093/europace/euaa385] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Accepted: 12/03/2020] [Indexed: 11/13/2022] Open
Abstract
AIMS Computationally guided persistent atrial fibrillation (PsAF) ablation has emerged as an alternative to conventional treatment planning. To make this approach scalable, computational cost and the time required to conduct simulations must be minimized while maintaining predictive accuracy. Here, we assess the sensitivity of the process to finite-element mesh resolution. We also compare methods for pacing site distribution used to evaluate inducibility arrhythmia sustained by re-entrant drivers (RDs). METHODS AND RESULTS Simulations were conducted in low- and high-resolution models (average edge lengths: 400/350 µm) reconstructed from PsAF patients' late gadolinium enhancement magnetic resonance imaging scans. Pacing was simulated from 80 sites to assess RD inducibility. When pacing from the same site led to different outcomes in low-/high-resolution models, we characterized divergence dynamics by analysing dissimilarity index over time. Pacing site selection schemes prioritizing even spatial distribution and proximity to fibrotic tissue were evaluated. There were no RD sites observed in low-resolution models but not high-resolution models, or vice versa. Dissimilarity index analysis suggested that differences in simulation outcome arising from differences in discretization were the result of isolated conduction block incidents in one model but not the other; this never led to RD sites unique to one mesh resolution. Pacing site selection based on fibrosis proximity led to the best observed trade-off between number of stimulation locations and predictive accuracy. CONCLUSION Simulations conducted in meshes with 400 µm average edge length and ∼40 pacing sites proximal to fibrosis are sufficient to reveal the most comprehensive possible list of RD sites, given feasibility constraints.
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Affiliation(s)
- Patrick M Boyle
- Department of Bioengineering, University of Washington, Seattle, Foege N310H UW Mailbox 355061, WA 98195, USA.,Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA 98195, USA.,Center for Cardiovascular Biology, University of Washington, Seattle, WA 98195, USA
| | - Alexander R Ochs
- Department of Bioengineering, University of Washington, Seattle, Foege N310H UW Mailbox 355061, WA 98195, USA
| | - Rheeda L Ali
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Hackerman 216, 3400 N Charles St, Baltimore, MD 21218, USA
| | - Nikhil Paliwal
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Hackerman 216, 3400 N Charles St, Baltimore, MD 21218, USA
| | - Natalia A Trayanova
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Hackerman 216, 3400 N Charles St, Baltimore, MD 21218, USA.,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
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11
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Schopp M, Dharmaprani D, Kuklik P, Quah J, Lahiri A, Tiver K, Meyer C, Willems S, McGavigan AD, Ganesan AN. Spatial concentration and distribution of phase singularities in human atrial fibrillation: Insights for the AF mechanism. J Arrhythm 2021; 37:922-930. [PMID: 34386118 PMCID: PMC8339121 DOI: 10.1002/joa3.12547] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 04/10/2021] [Accepted: 04/14/2021] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND Atrial fibrillation (AF) is characterized by the repetitive regeneration of unstable rotational events, the pivot of which are known as phase singularities (PSs). The spatial concentration and distribution of PSs have not been systematically investigated using quantitative statistical approaches. OBJECTIVES We utilized a geospatial statistical approach to determine the presence of local spatial concentration and global clustering of PSs in biatrial human AF recordings. METHODS 64-electrode conventional basket (~5 min, n = 18 patients, persistent AF) recordings were studied. Phase maps were produced using a Hilbert-transform based approach. PSs were characterized spatially using the following approaches: (i) local "hotspots" of high phase singularity (PS) concentration using Getis-Ord Gi* (Z ≥ 1.96, P ≤ .05) and (ii) global spatial clustering using Moran's I (inverse distance matrix). RESULTS Episodes of AF were analyzed from basket catheter recordings (H: 41 epochs, 120 000 s, n = 18 patients). The Getis-Ord Gi* statistic showed local PS hotspots in 12/41 basket recordings. As a metric of spatial clustering, Moran's I showed an overall mean of 0.033 (95% CI: 0.0003-0.065), consistent with the notion of complete spatial randomness. CONCLUSION Using a systematic, quantitative geospatial statistical approach, evidence for the existence of spatial concentrations ("hotspots") of PSs were detectable in human AF, along with evidence of spatial clustering. Geospatial statistical approaches offer a new approach to map and ablate PS clusters using substrate-based approaches.
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Affiliation(s)
- Madeline Schopp
- College of Science and EngineeringFlinders University of South AustraliaAdelaideSAAustralia
| | - Dhani Dharmaprani
- College of Science and EngineeringFlinders University of South AustraliaAdelaideSAAustralia
- College of Medicine and Public HealthFlinders University of South AustraliaAdelaideSAAustralia
| | - Pawel Kuklik
- Department of CardiologyUniversity Medical CentreHamburgGermany
| | - Jing Quah
- College of Medicine and Public HealthFlinders University of South AustraliaAdelaideSAAustralia
- Department of Cardiovascular MedicineFlinders Medical CentreAdelaideSAAustralia
| | - Anandaroop Lahiri
- Department of Cardiovascular MedicineFlinders Medical CentreAdelaideSAAustralia
| | - Kathryn Tiver
- College of Medicine and Public HealthFlinders University of South AustraliaAdelaideSAAustralia
- Department of Cardiovascular MedicineFlinders Medical CentreAdelaideSAAustralia
| | - Christian Meyer
- Department of CardiologyUniversity Medical CentreHamburgGermany
| | - Stephan Willems
- Department of CardiologyUniversity Medical CentreHamburgGermany
| | - Andrew D. McGavigan
- Department of Cardiovascular MedicineFlinders Medical CentreAdelaideSAAustralia
| | - Anand N. Ganesan
- College of Medicine and Public HealthFlinders University of South AustraliaAdelaideSAAustralia
- Department of Cardiovascular MedicineFlinders Medical CentreAdelaideSAAustralia
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12
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Pagani S, Dede' L, Frontera A, Salvador M, Limite LR, Manzoni A, Lipartiti F, Tsitsinakis G, Hadjis A, Della Bella P, Quarteroni A. A Computational Study of the Electrophysiological Substrate in Patients Suffering From Atrial Fibrillation. Front Physiol 2021; 12:673612. [PMID: 34305637 PMCID: PMC8297688 DOI: 10.3389/fphys.2021.673612] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Accepted: 05/28/2021] [Indexed: 12/19/2022] Open
Abstract
In the context of cardiac electrophysiology, we propose a novel computational approach to highlight and explain the long-debated mechanisms behind atrial fibrillation (AF) and to reliably numerically predict its induction and sustainment. A key role is played, in this respect, by a new way of setting a parametrization of electrophysiological mathematical models based on conduction velocities; these latter are estimated from high-density mapping data, which provide a detailed characterization of patients' electrophysiological substrate during sinus rhythm. We integrate numerically approximated conduction velocities into a mathematical model consisting of a coupled system of partial and ordinary differential equations, formed by the monodomain equation and the Courtemanche-Ramirez-Nattel model. Our new model parametrization is then adopted to predict the formation and self-sustainment of localized reentries characterizing atrial fibrillation, by numerically simulating the onset of ectopic beats from the pulmonary veins. We investigate the paroxysmal and the persistent form of AF starting from electro-anatomical maps of two patients. The model's response to stimulation shows how substrate characteristics play a key role in inducing and sustaining these arrhythmias. Localized reentries are less frequent and less stable in case of paroxysmal AF, while they tend to anchor themselves in areas affected by severe slow conduction in case of persistent AF.
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Affiliation(s)
- S Pagani
- MOX-Department of Mathematics, Politecnico di Milano, Milan, Italy
| | - L Dede'
- MOX-Department of Mathematics, Politecnico di Milano, Milan, Italy
| | - A Frontera
- Department of Arrhythmology, San Raffaele Hospital, Milan, Italy
| | - M Salvador
- MOX-Department of Mathematics, Politecnico di Milano, Milan, Italy
| | - L R Limite
- Department of Arrhythmology, San Raffaele Hospital, Milan, Italy
| | - A Manzoni
- MOX-Department of Mathematics, Politecnico di Milano, Milan, Italy
| | - F Lipartiti
- Department of Arrhythmology, San Raffaele Hospital, Milan, Italy
| | - G Tsitsinakis
- Department of Arrhythmology, San Raffaele Hospital, Milan, Italy
| | - A Hadjis
- Department of Arrhythmology, San Raffaele Hospital, Milan, Italy
| | - P Della Bella
- Department of Arrhythmology, San Raffaele Hospital, Milan, Italy
| | - A Quarteroni
- MOX-Department of Mathematics, Politecnico di Milano, Milan, Italy.,Institute of Mathematics, EPFL, Lausanne, Switzerland
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13
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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: 38] [Impact Index Per Article: 12.7] [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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14
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Bifulco SF, Scott GD, Sarairah S, Birjandian Z, Roney CH, Niederer SA, Mahnkopf C, Kuhnlein P, Mitlacher M, Tirschwell D, Longstreth WT, Akoum N, Boyle PM. Computational modeling identifies embolic stroke of undetermined source patients with potential arrhythmic substrate. eLife 2021; 10:e64213. [PMID: 33942719 PMCID: PMC8143793 DOI: 10.7554/elife.64213] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Accepted: 04/16/2021] [Indexed: 12/25/2022] Open
Abstract
Cardiac magnetic resonance imaging (MRI) has revealed fibrosis in embolic stroke of undetermined source (ESUS) patients comparable to levels seen in atrial fibrillation (AFib). We used computational modeling to understand the absence of arrhythmia in ESUS despite the presence of putatively pro-arrhythmic fibrosis. MRI-based atrial models were reconstructed for 45 ESUS and 45 AFib patients. The fibrotic substrate's arrhythmogenic capacity in each patient was assessed computationally. Reentrant drivers were induced in 24/45 (53%) ESUS and 22/45 (49%) AFib models. Inducible models had more fibrosis (16.7 ± 5.45%) than non-inducible models (11.07 ± 3.61%; p<0.0001); however, inducible subsets of ESUS and AFib models had similar fibrosis levels (p=0.90), meaning that the intrinsic pro-arrhythmic substrate properties of fibrosis in ESUS and AFib are indistinguishable. This suggests that some ESUS patients have latent pre-clinical fibrotic substrate that could be a future source of arrhythmogenicity. Thus, our work prompts the hypothesis that ESUS patients with fibrotic atria are spared from AFib due to an absence of arrhythmia triggers.
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Affiliation(s)
- Savannah F Bifulco
- Department of Bioengineering, University of WashingtonSeattleUnited States
| | - Griffin D Scott
- Department of Bioengineering, University of WashingtonSeattleUnited States
| | - Sakher Sarairah
- Division of Cardiology, University of WashingtonSeattleUnited States
| | - Zeinab Birjandian
- Division of Cardiology, University of WashingtonSeattleUnited States
- Department of Neurology, University of WashingtonSeattleUnited States
| | - Caroline H Roney
- School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
| | - Steven A Niederer
- School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
| | | | | | | | - David Tirschwell
- Department of Neurology, University of WashingtonSeattleUnited States
| | - WT Longstreth
- Department of Neurology, University of WashingtonSeattleUnited States
- Department of Epidemiology, University of WashingtonSeattleUnited States
| | - Nazem Akoum
- Division of Cardiology, University of WashingtonSeattleUnited States
| | - Patrick M Boyle
- Department of Bioengineering, University of WashingtonSeattleUnited States
- Center for Cardiovascular Biology, University of WashingtonSeattleUnited States
- Institute for Stem Cell and Regenerative Medicine, University of WashingtonSeattleUnited States
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15
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Salinet J, Molero R, Schlindwein FS, Karel J, Rodrigo M, Rojo-Álvarez JL, Berenfeld O, Climent AM, Zenger B, Vanheusden F, Paredes JGS, MacLeod R, Atienza F, Guillem MS, Cluitmans M, Bonizzi P. Electrocardiographic Imaging for Atrial Fibrillation: A Perspective From Computer Models and Animal Experiments to Clinical Value. Front Physiol 2021; 12:653013. [PMID: 33995122 PMCID: PMC8120164 DOI: 10.3389/fphys.2021.653013] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Accepted: 03/22/2021] [Indexed: 01/16/2023] Open
Abstract
Electrocardiographic imaging (ECGI) is a technique to reconstruct non-invasively the electrical activity on the heart surface from body-surface potential recordings and geometric information of the torso and the heart. ECGI has shown scientific and clinical value when used to characterize and treat both atrial and ventricular arrhythmias. Regarding atrial fibrillation (AF), the characterization of the electrical propagation and the underlying substrate favoring AF is inherently more challenging than for ventricular arrhythmias, due to the progressive and heterogeneous nature of the disease and its manifestation, the small volume and wall thickness of the atria, and the relatively large role of microstructural abnormalities in AF. At the same time, ECGI has the advantage over other mapping technologies of allowing a global characterization of atrial electrical activity at every atrial beat and non-invasively. However, since ECGI is time-consuming and costly and the use of electrical mapping to guide AF ablation is still not fully established, the clinical value of ECGI for AF is still under assessment. Nonetheless, AF is known to be the manifestation of a complex interaction between electrical and structural abnormalities and therefore, true electro-anatomical-structural imaging may elucidate important key factors of AF development, progression, and treatment. Therefore, it is paramount to identify which clinical questions could be successfully addressed by ECGI when it comes to AF characterization and treatment, and which questions may be beyond its technical limitations. In this manuscript we review the questions that researchers have tried to address on the use of ECGI for AF characterization and treatment guidance (for example, localization of AF triggers and sustaining mechanisms), and we discuss the technological requirements and validation. We address experimental and clinical results, limitations, and future challenges for fruitful application of ECGI for AF understanding and management. We pay attention to existing techniques and clinical application, to computer models and (animal or human) experiments, to challenges of methodological and clinical validation. The overall objective of the study is to provide a consensus on valuable directions that ECGI research may take to provide future improvements in AF characterization and treatment guidance.
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Affiliation(s)
- João Salinet
- Biomedical Engineering, Centre for Engineering, Modelling and Applied Social Sciences (CECS), Federal University of ABC, São Bernardo do Campo, Brazil
| | - Rubén Molero
- ITACA Institute, Universitat Politècnica de València, València, Spain
| | - Fernando S. Schlindwein
- School of Engineering, University of Leicester, United Kingdom and National Institute for Health Research, Leicester Cardiovascular Biomedical Research Centre, Glenfield Hospital, Leicester, United Kingdom
| | - Joël Karel
- Department of Data Science and Knowledge Engineering, Maastricht University, Maastricht, Netherlands
| | - Miguel Rodrigo
- Electronic Engineering Department, Universitat de València, València, Spain
| | - José Luis Rojo-Álvarez
- Department of Signal Theory and Communications and Telematic Systems and Computation, University Rey Juan Carlos, Madrid, Spain
| | - Omer Berenfeld
- Center for Arrhythmia Research, University of Michigan, Ann Arbor, MI, United States
| | - Andreu M. Climent
- ITACA Institute, Universitat Politècnica de València, València, Spain
| | - Brian Zenger
- Biomedical Engineering Department, Scientific Computing and Imaging Institute (SCI), and Cardiovascular Research and Training Institute (CVRTI), The University of Utah, Salt Lake City, UT, United States
| | - Frederique Vanheusden
- Department of Engineering, School of Science and Technology, Nottingham Trent University, Nottingham, United Kingdom
| | - Jimena Gabriela Siles Paredes
- Biomedical Engineering, Centre for Engineering, Modelling and Applied Social Sciences (CECS), Federal University of ABC, São Bernardo do Campo, Brazil
| | - Rob MacLeod
- Biomedical Engineering Department, Scientific Computing and Imaging Institute (SCI), and Cardiovascular Research and Training Institute (CVRTI), The University of Utah, Salt Lake City, UT, United States
| | - Felipe Atienza
- Cardiology Department, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón, and Facultad de Medicina, Universidad Complutense de Madrid, Madrid, Spain
| | - María S. Guillem
- ITACA Institute, Universitat Politècnica de València, València, Spain
| | - Matthijs Cluitmans
- Department of Cardiology, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, Netherlands
| | - Pietro Bonizzi
- Department of Data Science and Knowledge Engineering, Maastricht University, Maastricht, Netherlands
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16
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Boyle PM, Yu J, Klimas A, Williams JC, Trayanova NA, Entcheva E. OptoGap is an optogenetics-enabled assay for quantification of cell-cell coupling in multicellular cardiac tissue. Sci Rep 2021; 11:9310. [PMID: 33927252 PMCID: PMC8085001 DOI: 10.1038/s41598-021-88573-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 03/31/2021] [Indexed: 12/23/2022] Open
Abstract
Intercellular electrical coupling is an essential means of communication between cells. It is important to obtain quantitative knowledge of such coupling between cardiomyocytes and non-excitable cells when, for example, pathological electrical coupling between myofibroblasts and cardiomyocytes yields increased arrhythmia risk or during the integration of donor (e.g., cardiac progenitor) cells with native cardiomyocytes in cell-therapy approaches. Currently, there is no direct method for assessing heterocellular coupling within multicellular tissue. Here we demonstrate experimentally and computationally a new contactless assay for electrical coupling, OptoGap, based on selective illumination of inexcitable cells that express optogenetic actuators and optical sensing of the response of coupled excitable cells (e.g., cardiomyocytes) that are light-insensitive. Cell-cell coupling is quantified by the energy required to elicit an action potential via junctional current from the light-stimulated cell(s). The proposed technique is experimentally validated against the standard indirect approach, GapFRAP, using light-sensitive cardiac fibroblasts and non-transformed cardiomyocytes in a two-dimensional setting. Its potential applicability to the complex three-dimensional setting of the native heart is corroborated by computational modelling and proper calibration. Lastly, the sensitivity of OptoGap to intrinsic cell-scale excitability is robustly characterized via computational analysis.
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Affiliation(s)
- Patrick M Boyle
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA
- Center for Cardiovascular Biology, University of Washington, Seattle, WA, USA
| | - Jinzhu Yu
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
| | - Aleksandra Klimas
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
- Department of Biomedical Engineering, George Washington University, 800 22nd Street NW, Suite 5000, Washington, DC, 20052, USA
| | - John C Williams
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
| | - Natalia A Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA
| | - Emilia Entcheva
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA.
- Department of Biomedical Engineering, George Washington University, 800 22nd Street NW, Suite 5000, Washington, DC, 20052, USA.
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17
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Substrate mapping of the left atrium in persistent atrial fibrillation: spatial correlation of localized complex conduction patterns in global charge-density maps to low-voltage areas in 3D contact bipolar voltage maps. J Interv Card Electrophysiol 2021; 62:539-547. [PMID: 33420713 PMCID: PMC8645534 DOI: 10.1007/s10840-020-00926-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Accepted: 12/27/2020] [Indexed: 12/02/2022]
Abstract
Purpose This study aimed to investigate the spatial relationship between low-voltage areas (LVAs) in bipolar voltage mapping (BVM) and localized complex conduction (LCC)-cores in a global, non-contact, charge-density-based imaging, and mapping system (AcM). Methods Patients with history of index PVI for PsAF and scheduled for a repeat ablation procedure for recurrence of the same arrhythmia were enrolled between August 2018 and February 2020. All patients underwent both substrate mappings of the left atrium (LA) with the CARTO 3D map-ping system and with AcM. Results Ten patients where included in our analysis. All presented with persistency of PVI in all veins at the moment of repeat procedure. There was no linear relationship in BVM maps between SR and CSd (correlation coefficient 0.31 ± 0.15), SR and CSp (0.36 ± 0.12) and CSd and CSp (0.43 ± 0.10). The % overlap of localized irregular activation (LIA), localized rotational activation (LRA) and Focal (F) regions with LVA was lower at 0.2 mV compared to 0.5 mV (4.97 ± 7.39%, 3.27 ± 5.25%, 1.09 ± 1.92% and 12.59 ± 11.81%, 7.8 ± 9.20%, 4.62 ± 5.27%). Sensitivity and specificity are not significantly different when comparing composite maps with different LVA cut-offs. AURC was 0.46, 0.48, and 0.39 for LIA, LRA, and Focal, respectively. Conclusion Due to wave front direction dependency, LVAs mapped with BVM in sinus rhythm and during coronary sinus pacing only partially overlap in patients with PsAF. LCC-cores mapped during PsAF partially co-localize with LVAs.
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18
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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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19
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Boyle PM, Del Álamo JC, Akoum N. Fibrosis, atrial fibrillation and stroke: clinical updates and emerging mechanistic models. Heart 2020; 107:99-105. [PMID: 33097562 DOI: 10.1136/heartjnl-2020-317455] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 09/21/2020] [Accepted: 09/25/2020] [Indexed: 02/06/2023] Open
Abstract
The current paradigm of stroke risk assessment and mitigation in patients with atrial fibrillation (AF) is centred around clinical risk factors which, in the presence of AF, lead to thrombus formation. The mechanisms by which these clinical risk factors lead to thromboembolism, including any role played by atrial fibrosis, are not understood. In patients who had embolic stroke of undetermined source (ESUS), the problem is compounded by the absence of AF in a majority of patients despite long-term monitoring. Atrial fibrosis has emerged as a unifying mechanism that independently provides a substrate for arrhythmia and thrombus formation. Fibrosis-based computational models of AF initiation and maintenance promise to identify therapeutic targets in catheter ablation. In ESUS, fibrosis is also increasingly recognised as a major risk factor, but the underlying mechanism of this correlation is unclear. Simulations have uncovered potential vulnerability to arrhythmia induction in patients who had ESUS. Likewise, computational models of fluid dynamics representing blood flow in the left atrium and left atrium appendage have improved our understanding of thrombus formation, in particular left atrium appendage shapes and blood flow changes influenced by atrial remodelling. Multiscale modelling of blood flow dynamics based on structural fibrotic and morphological changes with associated cellular and tissue electrical remodelling leading to electromechanical abnormalities holds tremendous promise in providing a mechanistic understanding of the clinical problem of thromboembolisation. We present a review of clinical knowledge alongside computational modelling frameworks and conclude with a vision of a future paradigm integrating simulations in formulating personalised treatment plans for each patient.
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Affiliation(s)
- Patrick M Boyle
- Bioengineering, University of Washington, Seattle, Washington, USA
| | - Juan Carlos Del Álamo
- Mechanical Engineering, University of Washington College of Engineering, Seattle, Washington, USA
| | - Nazem Akoum
- Cardiology, University of Washington School of Medicine, Seattle, Washington, USA
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20
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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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21
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Ali RL, Hakim JB, Boyle PM, Zahid S, Sivasambu B, Marine JE, Calkins H, Trayanova NA, Spragg DD. Arrhythmogenic propensity of the fibrotic substrate after atrial fibrillation ablation: a longitudinal study using magnetic resonance imaging-based atrial models. Cardiovasc Res 2020; 115:1757-1765. [PMID: 30977811 DOI: 10.1093/cvr/cvz083] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2018] [Revised: 01/31/2019] [Accepted: 04/08/2019] [Indexed: 12/19/2022] Open
Abstract
AIMS Inadequate modification of the atrial fibrotic substrate necessary to sustain re-entrant drivers (RDs) may explain atrial fibrillation (AF) recurrence following failed pulmonary vein isolation (PVI). Personalized computational models of the fibrotic atrial substrate derived from late gadolinium enhanced (LGE)-magnetic resonance imaging (MRI) can be used to non-invasively determine the presence of RDs. The objective of this study is to assess the changes of the arrhythmogenic propensity of the fibrotic substrate after PVI. METHODS AND RESULTS Pre- and post-ablation individualized left atrial models were constructed from 12 AF patients who underwent pre- and post-PVI LGE-MRI, in six of whom PVI failed. Pre-ablation AF sustained by RDs was induced in 10 models. RDs in the post-ablation models were classified as either preserved or emergent. Pre-ablation models derived from patients for whom the procedure failed exhibited a higher number of RDs and larger areas defined as promoting RD formation when compared with atrial models from patients who had successful ablation, 2.6 ± 0.9 vs. 1.8 ± 0.2 and 18.9 ± 1.6% vs. 13.8 ± 1.5%, respectively. In cases of successful ablation, PVI eliminated completely the RDs sustaining AF. Preserved RDs unaffected by ablation were documented only in post-ablation models of patients who experienced recurrent AF (2/5 models); all of these models had also one or more emergent RDs at locations distinct from those of pre-ablation RDs. Emergent RDs occurred in regions that had the same characteristics of the fibrosis spatial distribution (entropy and density) as regions that harboured RDs in pre-ablation models. CONCLUSION Recurrent AF after PVI in the fibrotic atria may be attributable to both preserved RDs that sustain AF pre- and post-ablation, and the emergence of new RDs following ablation. The same levels of fibrosis entropy and density underlie the pro-RD propensity in both pre- and post-ablation substrates.
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Affiliation(s)
- Rheeda L Ali
- Institute for Computational Medicine, Johns Hopkins University, 3400 N Charles Street, 208 Hackerman, Baltimore, MD, USA
| | - Joe B Hakim
- Institute for Computational Medicine, Johns Hopkins University, 3400 N Charles Street, 208 Hackerman, Baltimore, MD, USA
| | - Patrick M Boyle
- Institute for Computational Medicine, Johns Hopkins University, 3400 N Charles Street, 208 Hackerman, Baltimore, MD, USA.,Department of Biomedical Engineering, Johns Hopkins University, 3400 N Charles Street, 208 Hackerman, Baltimore, MD, USA.,Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Sohail Zahid
- Department of Biomedical Engineering, Johns Hopkins University, 3400 N Charles Street, 208 Hackerman, Baltimore, MD, USA
| | - Bhradeev Sivasambu
- Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Joseph E Marine
- Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Hugh Calkins
- Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Natalia A Trayanova
- Institute for Computational Medicine, Johns Hopkins University, 3400 N Charles Street, 208 Hackerman, Baltimore, MD, USA.,Department of Biomedical Engineering, Johns Hopkins University, 3400 N Charles Street, 208 Hackerman, Baltimore, MD, USA.,Department of Medicine, Johns Hopkins University School of Medicine, USA
| | - David D Spragg
- Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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22
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Shade JK, Ali RL, Basile D, Popescu D, Akhtar T, Marine JE, Spragg DD, Calkins H, Trayanova NA. Preprocedure Application of Machine Learning and Mechanistic Simulations Predicts Likelihood of Paroxysmal Atrial Fibrillation Recurrence Following Pulmonary Vein Isolation. Circ Arrhythm Electrophysiol 2020; 13:e008213. [PMID: 32536204 DOI: 10.1161/circep.119.008213] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
BACKGROUND Pulmonary vein isolation (PVI) is an effective treatment strategy for patients with atrial fibrillation (AF), but many experience AF recurrence and require repeat ablation procedures. The goal of this study was to develop and evaluate a methodology that combines machine learning (ML) and personalized computational modeling to predict, before PVI, which patients are most likely to experience AF recurrence after PVI. METHODS This single-center retrospective proof-of-concept study included 32 patients with documented paroxysmal AF who underwent PVI and had preprocedural late gadolinium enhanced magnetic resonance imaging. For each patient, a personalized computational model of the left atrium simulated AF induction via rapid pacing. Features were derived from pre-PVI late gadolinium enhanced magnetic resonance images and from results of simulations of AF induction. The most predictive features were used as input to a quadratic discriminant analysis ML classifier, which was trained, optimized, and evaluated with 10-fold nested cross-validation to predict the probability of AF recurrence post-PVI. RESULTS In our cohort, the ML classifier predicted probability of AF recurrence with an average validation sensitivity and specificity of 82% and 89%, respectively, and a validation area under the curve of 0.82. Dissecting the relative contributions of simulations of AF induction and raw images to the predictive capability of the ML classifier, we found that when only features from simulations of AF induction were used to train the ML classifier, its performance remained similar (validation area under the curve, 0.81). However, when only features extracted from raw images were used for training, the validation area under the curve significantly decreased (0.47). CONCLUSIONS ML and personalized computational modeling can be used together to accurately predict, using only pre-PVI late gadolinium enhanced magnetic resonance imaging scans as input, whether a patient is likely to experience AF recurrence following PVI, even when the patient cohort is small.
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Affiliation(s)
- Julie K Shade
- Alliance for Cardiovascular Diagnostic and Treatment Innovation (J.K.S., R.L.A., D.B., D.P., H.C., N.A.T.), Johns Hopkins University, Baltimore, MD.,Department of Biomedical Engineering (J.K.S., D.B., N.A.T.), Johns Hopkins University, Baltimore, MD
| | - Rheeda L Ali
- Alliance for Cardiovascular Diagnostic and Treatment Innovation (J.K.S., R.L.A., D.B., D.P., H.C., N.A.T.), Johns Hopkins University, Baltimore, MD
| | - Dante Basile
- Alliance for Cardiovascular Diagnostic and Treatment Innovation (J.K.S., R.L.A., D.B., D.P., H.C., N.A.T.), Johns Hopkins University, Baltimore, MD.,Department of Biomedical Engineering (J.K.S., D.B., N.A.T.), Johns Hopkins University, Baltimore, MD
| | - Dan Popescu
- Alliance for Cardiovascular Diagnostic and Treatment Innovation (J.K.S., R.L.A., D.B., D.P., H.C., N.A.T.), Johns Hopkins University, Baltimore, MD.,Department of Applied Math and Statistics (D.P.), Johns Hopkins University, Baltimore, MD
| | - Tauseef Akhtar
- Division of Cardiology, Department of Medicine (T.A., J.E.M., D.D.S., H.C.), Johns Hopkins University School of Medicine, Baltimore, MD
| | - Joseph E Marine
- Division of Cardiology, Department of Medicine (T.A., J.E.M., D.D.S., H.C.), Johns Hopkins University School of Medicine, Baltimore, MD
| | - David D Spragg
- Division of Cardiology, Department of Medicine (T.A., J.E.M., D.D.S., H.C.), Johns Hopkins University School of Medicine, Baltimore, MD
| | - Hugh Calkins
- Alliance for Cardiovascular Diagnostic and Treatment Innovation (J.K.S., R.L.A., D.B., D.P., H.C., N.A.T.), Johns Hopkins University, Baltimore, MD.,Division of Cardiology, Department of Medicine (T.A., J.E.M., D.D.S., H.C.), Johns Hopkins University School of Medicine, Baltimore, MD
| | - Natalia A Trayanova
- Alliance for Cardiovascular Diagnostic and Treatment Innovation (J.K.S., R.L.A., D.B., D.P., H.C., N.A.T.), Johns Hopkins University, Baltimore, MD.,Department of Biomedical Engineering (J.K.S., D.B., N.A.T.), Johns Hopkins University, Baltimore, MD.,Department of Medicine (N.A.T.), Johns Hopkins University School of Medicine, Baltimore, MD
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23
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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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24
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Hakim JB, Murphy MJ, Trayanova NA, Boyle PM. Arrhythmia dynamics in computational models of the atria following virtual ablation of re-entrant drivers. Europace 2019; 20:iii45-iii54. [PMID: 30476053 DOI: 10.1093/europace/euy234] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2018] [Accepted: 09/18/2018] [Indexed: 12/19/2022] Open
Abstract
Aims Efforts to improve ablation success rates in persistent atrial fibrillation (AF) patients by targeting re-entrant driver (RD) sites have been hindered by weak mechanistic understanding regarding emergent RDs localization following initial fibrotic substrate modification. This study aimed to systematically assess arrhythmia dynamics after virtual ablation of RD sites in computational models. Methods and results Simulations were conducted in 12 patient-specific atrial models reconstructed from pre-procedure late gadolinium-enhanced magnetic resonance imaging scans. In a previous study involving these same models, we comprehensively characterized pre-ablation RDs in simulations conducted with either 'average human AF'-based electrophysiology (i.e. EPavg) or ±10% action potential duration or conduction velocity (i.e. EPvar). Re-entrant drivers seen under the EPavg condition were virtually ablated and the AF initiation protocol was re-applied. Twenty-one emergent RDs were observed in 9/12 atrial models (1.75 ± 1.35 emergent RDs per model); these dynamically localized to boundary regions between fibrotic and non-fibrotic tissue. Most emergent RD locations (15/21, 71.4%) were within 0.1 cm of sites where RDs were seen pre-ablation in simulations under EPvar conditions. Importantly, this suggests that the level of uncertainty in our models' ability to predict patient-specific ablation targets can be substantially mitigated by running additional simulations that include virtual ablation of RDs. In 7/12 atrial models, at least one episode of macro-reentry around ablation lesion(s) was observed. Conclusion Arrhythmia episodes after virtual RD ablation are perpetuated by both emergent RDs and by macro-reentrant circuits formed around lesions. Custom-tailoring of ablation procedures based on models should take steps to mitigate these sources of AF recurrence.
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Affiliation(s)
- Joe B Hakim
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, 3400 N. Charles St, 208 Hackerman Hall, Baltimore, MD, USA
| | - Michael J Murphy
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, 3400 N. Charles St, 208 Hackerman Hall, Baltimore, MD, USA
| | - Natalia A Trayanova
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, 3400 N. Charles St, 208 Hackerman Hall, Baltimore, MD, USA
| | - Patrick M Boyle
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, 3400 N. Charles St, 208 Hackerman Hall, Baltimore, MD, USA.,Department of Bioengineering, University of Washington, N310H Foege, Box 355061, Seattle WA, USA
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25
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Computationally guided personalized targeted ablation of persistent atrial fibrillation. Nat Biomed Eng 2019; 3:870-879. [PMID: 31427780 PMCID: PMC6842421 DOI: 10.1038/s41551-019-0437-9] [Citation(s) in RCA: 153] [Impact Index Per Article: 30.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Accepted: 07/03/2019] [Indexed: 12/12/2022]
Abstract
Atrial fibrillation (AF) — the most common arrhythmia — significantly increases the risk of stroke and heart failure. Although catheter ablation can restore normal heart rhythms, patients with persistent AF who develop atrial fibrosis often undergo multiple failed ablations and thus increased procedural risks. Here, we present personalized computational modelling for the reliable predetermination of ablation targets, which are then used to guide the ablation procedure in patients with persistent AF and atrial fibrosis. We first show that a computational model of the atria of patients identifies fibrotic tissue that if ablated will not sustain AF. We then integrated the target-ablation sites in a clinical-mapping system, and tested its feasibility in 10 patients with persistent AF. The computational prediction of ablation targets avoids lengthy electrical mapping and could improve the accuracy and efficacy of targeted AF ablation in patients whilst eliminating the need for repeat procedures.
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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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Perez Alday EA, Whittaker DG, Benson AP, Colman MA. Effects of Heart Rate and Ventricular Wall Thickness on Non-invasive Mapping: An in silico Study. Front Physiol 2019; 10:308. [PMID: 31024330 PMCID: PMC6460935 DOI: 10.3389/fphys.2019.00308] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Accepted: 03/07/2019] [Indexed: 01/08/2023] Open
Abstract
Background: Non-invasive cardiac mapping—also known as Electrocardiographic imaging (ECGi)—is a novel, painless and relatively economic method to map the electrical activation and repolarization patterns of the heart, providing a valuable tool for early identification and diagnosis of conduction abnormalities and arrhythmias. Moreover, the ability to obtain information on cardiac electrical activity non-invasively using ECGi provides the potential for a priori information to guide invasive surgical procedures, improving success rates, and reducing procedure time. Previous studies have shown the influence of clinical variables, such as heart rate, heart size, endocardial wall, and body composition on surface electrocardiogram (ECG) measurements. The influence of clinical variables on the ECG variability has provided information on cardiovascular control and its abnormalities in various pathologies. However, the effects of such clinical variables on the Body Surface Potential (BSP) and ECGi maps have yet to be systematically investigated. Methods: In this study we investigated the effects of heart size, intracardiac thickness, and heart rate on BSP and ECGi maps using a previously-developed 3D electrophysiologically-detailed ventricles-torso model. The inverse solution was solved using the three different Tikhonov regularization methods. Results: Through comparison of multiple measures of error/accuracy on the ECGi reconstructions, our results showed that using different heart geometries to solve the forward and inverse problems produced a larger estimated focal excitation location. An increase of ~2 mm in the Euclidean distance error was observed for an increase in the heart size. However, the estimation of the location of focal activity was still able to be obtained. Similarly, a Euclidean distance increase was observed when the order of regularization was reduced. For the case of activation maps reconstructed at the same ectopic focus location but different heart rates, an increase in the errors and Euclidean distance was observed when the heart rate was increased. Conclusions: Non-invasive cardiac mapping can still provide useful information about cardiac activation patterns for the cases when a different geometry is used for the inverse problem compared to the one used for the forward solution; rapid pacing rates can induce order-dependent errors in the accuracy of reconstruction.
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Affiliation(s)
- Erick Andres Perez Alday
- Knight Cardiovascular Institute, Oregon Health & Science University, Portland, OR, United States
| | - Dominic G Whittaker
- School of Biomedical Science and Multidisciplinary Cardiovascular Research Centre, University of Leeds, Leeds, United Kingdom
| | - Alan P Benson
- School of Biomedical Science and Multidisciplinary Cardiovascular Research Centre, University of Leeds, Leeds, United Kingdom
| | - Michael A Colman
- School of Biomedical Science and Multidisciplinary Cardiovascular Research Centre, University of Leeds, Leeds, United Kingdom
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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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Cantwell CD, Mohamied Y, Tzortzis KN, Garasto S, Houston C, Chowdhury RA, Ng FS, Bharath AA, Peters NS. Rethinking multiscale cardiac electrophysiology with machine learning and predictive modelling. Comput Biol Med 2019; 104:339-351. [PMID: 30442428 PMCID: PMC6334203 DOI: 10.1016/j.compbiomed.2018.10.015] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2018] [Revised: 10/04/2018] [Accepted: 10/14/2018] [Indexed: 11/17/2022]
Abstract
We review some of the latest approaches to analysing cardiac electrophysiology data using machine learning and predictive modelling. Cardiac arrhythmias, particularly atrial fibrillation, are a major global healthcare challenge. Treatment is often through catheter ablation, which involves the targeted localised destruction of regions of the myocardium responsible for initiating or perpetuating the arrhythmia. Ablation targets are either anatomically defined, or identified based on their functional properties as determined through the analysis of contact intracardiac electrograms acquired with increasing spatial density by modern electroanatomic mapping systems. While numerous quantitative approaches have been investigated over the past decades for identifying these critical curative sites, few have provided a reliable and reproducible advance in success rates. Machine learning techniques, including recent deep-learning approaches, offer a potential route to gaining new insight from this wealth of highly complex spatio-temporal information that existing methods struggle to analyse. Coupled with predictive modelling, these techniques offer exciting opportunities to advance the field and produce more accurate diagnoses and robust personalised treatment. We outline some of these methods and illustrate their use in making predictions from the contact electrogram and augmenting predictive modelling tools, both by more rapidly predicting future states of the system and by inferring the parameters of these models from experimental observations.
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Affiliation(s)
- Chris D Cantwell
- ElectroCardioMaths Group, Imperial College Centre for Cardiac Engineering, Imperial College London, London, UK; Department of Aeronautics, Imperial College London, South Kensington Campus, London, UK.
| | - Yumnah Mohamied
- ElectroCardioMaths Group, Imperial College Centre for Cardiac Engineering, Imperial College London, London, UK; National Heart and Lung Institute, Imperial College London, South Kensington Campus, London, UK
| | - Konstantinos N Tzortzis
- ElectroCardioMaths Group, Imperial College Centre for Cardiac Engineering, Imperial College London, London, UK; National Heart and Lung Institute, Imperial College London, South Kensington Campus, London, UK
| | - Stef Garasto
- ElectroCardioMaths Group, Imperial College Centre for Cardiac Engineering, Imperial College London, London, UK; Department of Bioengineering, Imperial College London, South Kensington Campus, London, UK
| | - Charles Houston
- ElectroCardioMaths Group, Imperial College Centre for Cardiac Engineering, Imperial College London, London, UK; National Heart and Lung Institute, Imperial College London, South Kensington Campus, London, UK
| | - Rasheda A Chowdhury
- ElectroCardioMaths Group, Imperial College Centre for Cardiac Engineering, Imperial College London, London, UK; National Heart and Lung Institute, Imperial College London, South Kensington Campus, London, UK
| | - Fu Siong Ng
- ElectroCardioMaths Group, Imperial College Centre for Cardiac Engineering, Imperial College London, London, UK; National Heart and Lung Institute, Imperial College London, South Kensington Campus, London, UK
| | - Anil A Bharath
- ElectroCardioMaths Group, Imperial College Centre for Cardiac Engineering, Imperial College London, London, UK; Department of Bioengineering, Imperial College London, South Kensington Campus, London, UK
| | - Nicholas S Peters
- ElectroCardioMaths Group, Imperial College Centre for Cardiac Engineering, Imperial College London, London, UK; National Heart and Lung Institute, Imperial College London, South Kensington Campus, London, UK
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31
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Ammar-Busch S, Reents T, Knecht S, Rostock T, Arentz T, Duytschaever M, Neumann T, Cauchemez B, Albenque JP, Hessling G, Deisenhofer I. Correlation between atrial fibrillation driver locations and complex fractionated atrial electrograms in patients with persistent atrial fibrillation. PACING AND CLINICAL ELECTROPHYSIOLOGY: PACE 2018; 41:1279-1285. [DOI: 10.1111/pace.13483] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2018] [Revised: 06/13/2018] [Accepted: 06/15/2018] [Indexed: 12/01/2022]
Affiliation(s)
- Sonia Ammar-Busch
- Klinikum Coburg; Coburg Germany
- Department of Electrophysiology, Deutsches Herzzentrum München; Technische Universitaet; Munich Germany
| | - Tilko Reents
- Department of Electrophysiology, Deutsches Herzzentrum München; Technische Universitaet; Munich Germany
| | | | - Thomas Rostock
- Johannes Gutenberg-University Mainz; University Hospital; II. Medical Clinic Germany
| | - Thomas Arentz
- Universitäts-Herzzentrum Freiburg-Bad Krozingen; Bad Krozingen Germany
| | | | | | | | | | - Gabriele Hessling
- Department of Electrophysiology, Deutsches Herzzentrum München; Technische Universitaet; Munich Germany
| | - Isabel Deisenhofer
- Department of Electrophysiology, Deutsches Herzzentrum München; Technische Universitaet; Munich Germany
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32
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Vidmar D, Alhusseini MI, Narayan SM, Rappel WJ. Characterizing Electrogram Signal Fidelity and the Effects of Signal Contamination on Mapping Human Persistent Atrial Fibrillation. Front Physiol 2018; 9:1232. [PMID: 30237766 PMCID: PMC6135945 DOI: 10.3389/fphys.2018.01232] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2018] [Accepted: 08/15/2018] [Indexed: 11/30/2022] Open
Abstract
Objective: Determining accurate intracardiac maps of atrial fibrillation (AF) in humans can be difficult, owing primarily to various sources of contamination in electrogram signals. The goal of this study is to develop a measure for signal fidelity and to develop methods to quantify robustness of observed rotational activity in phase maps subject to signal contamination. Methods: We identified rotational activity in phase maps of human persistent AF using the Hilbert transform of sinusoidally recomposed signals, where localized ablation at rotational sites terminated fibrillation. A novel measure of signal fidelity was developed to quantify signal quality. Contamination is then introduced to the underlying electrograms by removing signals at random, adding noise to computations of cycle length, and adding realistic far-field signals. Mean tip number N and tip density δ, defined as the proportion of time a region contains a tip, at the termination site are computed to compare the effects of contamination. Results: Domains of low signal fidelity correspond to the location of rotational cores. Removing signals and altering cycle length accounted for minor changes in tip density, while targeted removal of low fidelity electrograms can result in a significant increase in tip density and stability. Far-field contamination was found to obscure rotation at the termination site. Conclusion: Rotational activity in clinical AF can produce domains of low fidelity electrogram recordings at rotational cores. Observed rotational patterns in phase maps appear most sensitive to far-field activation. These results may inform novel methods to map AF in humans which can be tested directly in patients at electrophysiological study and ablation.
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Affiliation(s)
- David Vidmar
- Department of Physics, University of California, San Diego, San Diego, CA, United States
| | - Mahmood I. Alhusseini
- Division of Cardiology, Department of Medicine, Stanford University, Stanford, CA, United States
| | - Sanjiv M. Narayan
- Division of Cardiology, Department of Medicine, Stanford University, Stanford, CA, United States
| | - Wouter-Jan Rappel
- Department of Physics, University of California, San Diego, San Diego, CA, United States
- *Correspondence: Wouter-Jan Rappel
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Boyle PM, Hakim JB, Zahid S, Franceschi WH, Murphy MJ, Prakosa A, Aronis KN, Zghaib T, Balouch M, Ipek EG, Chrispin J, Berger RD, Ashikaga H, Marine JE, Calkins H, Nazarian S, Spragg DD, Trayanova NA. The Fibrotic Substrate in Persistent Atrial Fibrillation Patients: Comparison Between Predictions From Computational Modeling and Measurements From Focal Impulse and Rotor Mapping. Front Physiol 2018; 9:1151. [PMID: 30210356 PMCID: PMC6123380 DOI: 10.3389/fphys.2018.01151] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Accepted: 07/31/2018] [Indexed: 12/19/2022] Open
Abstract
Focal impulse and rotor mapping (FIRM) involves intracardiac detection and catheter ablation of re-entrant drivers (RDs), some of which may contribute to arrhythmia perpetuation in persistent atrial fibrillation (PsAF). Patient-specific computational models derived from late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) has the potential to non-invasively identify all areas of the fibrotic substrate where RDs could potentially be sustained, including locations where RDs may not manifest during mapped AF episodes. The objective of this study was to carry out multi-modal assessment of the arrhythmogenic propensity of the fibrotic substrate in PsAF patients by comparing locations of RD-harboring regions found in simulations and detected by FIRM (RDsim and RDFIRM) and analyze implications for ablation strategies predicated on targeting RDs. For 11 PsAF patients who underwent pre-procedure LGE-MRI and FIRM-guided ablation, we retrospectively simulated AF in individualized atrial models, with geometry and fibrosis distribution reconstructed from pre-ablation LGE-MRI scans, and identified RDsim sites. Regions harboring RDsim and RDFIRM were compared. RDsim were found in 38 atrial regions (median [inter-quartile range (IQR)] = 4 [3; 4] per model). RDFIRM were identified and subsequently ablated in 24 atrial regions (2 [1; 3] per patient), which was significantly fewer than the number of RDsim-harboring regions in corresponding models (p < 0.05). Computational modeling predicted RDsim in 20 of 24 (83%) atrial regions identified as RDFIRM-harboring during clinical mapping. In a large number of cases, we uncovered RDsim-harboring regions in which RDFIRM were never observed (18/22 regions that differed between the two modalities; 82%); we termed such cases “latent” RDsim sites. During follow-up (230 [180; 326] days), AF recurrence occurred in 7/11 (64%) individuals. Interestingly, latent RDsim sites were observed in all seven computational models corresponding to patients who experienced recurrent AF (2 [2; 2] per patient); in contrast, latent RDsim sites were only discovered in two of four patients who were free from AF during follow-up (0.5 [0; 1.5] per patient; p < 0.05 vs. patients with AF recurrence). We conclude that substrate-based ablation based on computational modeling could improve outcomes.
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Affiliation(s)
- Patrick M Boyle
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Joe B Hakim
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Sohail Zahid
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - William H Franceschi
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Michael J Murphy
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Adityo Prakosa
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
| | | | - Tarek Zghaib
- Department of Cardiology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Muhammed Balouch
- Department of Cardiology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Esra G Ipek
- Department of Cardiology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Jonathan Chrispin
- Department of Cardiology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Ronald D Berger
- Department of Cardiology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Hiroshi Ashikaga
- Department of Cardiology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Joseph E Marine
- Department of Cardiology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Hugh Calkins
- Department of Cardiology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Saman Nazarian
- Penn Heart & Vascular Center, University of Pennsylvania, Philadelphia, PA, United States
| | - David D Spragg
- Department of Cardiology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Natalia A Trayanova
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
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