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Trayanova NA, Lyon A, Shade J, Heijman J. Computational modeling of cardiac electrophysiology and arrhythmogenesis: toward clinical translation. Physiol Rev 2024; 104:1265-1333. [PMID: 38153307 DOI: 10.1152/physrev.00017.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 12/19/2023] [Accepted: 12/21/2023] [Indexed: 12/29/2023] Open
Abstract
The complexity of cardiac electrophysiology, involving dynamic changes in numerous components across multiple spatial (from ion channel to organ) and temporal (from milliseconds to days) scales, makes an intuitive or empirical analysis of cardiac arrhythmogenesis challenging. Multiscale mechanistic computational models of cardiac electrophysiology provide precise control over individual parameters, and their reproducibility enables a thorough assessment of arrhythmia mechanisms. This review provides a comprehensive analysis of models of cardiac electrophysiology and arrhythmias, from the single cell to the organ level, and how they can be leveraged to better understand rhythm disorders in cardiac disease and to improve heart patient care. Key issues related to model development based on experimental data are discussed, and major families of human cardiomyocyte models and their applications are highlighted. An overview of organ-level computational modeling of cardiac electrophysiology and its clinical applications in personalized arrhythmia risk assessment and patient-specific therapy of atrial and ventricular arrhythmias is provided. The advancements presented here highlight how patient-specific computational models of the heart reconstructed from patient data have achieved success in predicting risk of sudden cardiac death and guiding optimal treatments of heart rhythm disorders. Finally, an outlook toward potential future advances, including the combination of mechanistic modeling and machine learning/artificial intelligence, is provided. As the field of cardiology is embarking on a journey toward precision medicine, personalized modeling of the heart is expected to become a key technology to guide pharmaceutical therapy, deployment of devices, and surgical interventions.
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Affiliation(s)
- Natalia A Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland, United States
| | - Aurore Lyon
- Department of Biomedical Engineering, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
- Division of Heart and Lungs, Department of Medical Physiology, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Julie Shade
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland, United States
| | - Jordi Heijman
- Department of Cardiology, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
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2
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Dasí A, Nagel C, Pope MTB, Wijesurendra RS, Betts TR, Sachetto R, Loewe A, Bueno-Orovio A, Rodriguez B. In Silico TRials guide optimal stratification of ATrIal FIbrillation patients to Catheter Ablation and pharmacological medicaTION: the i-STRATIFICATION study. Europace 2024; 26:euae150. [PMID: 38870348 PMCID: PMC11184207 DOI: 10.1093/europace/euae150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Accepted: 05/23/2024] [Indexed: 06/15/2024] Open
Abstract
AIMS Patients with persistent atrial fibrillation (AF) experience 50% recurrence despite pulmonary vein isolation (PVI), and no consensus is established for secondary treatments. The aim of our i-STRATIFICATION study is to provide evidence for stratifying patients with AF recurrence after PVI to optimal pharmacological and ablation therapies, through in silico trials. METHODS AND RESULTS A cohort of 800 virtual patients, with variability in atrial anatomy, electrophysiology, and tissue structure (low-voltage areas, LVAs), was developed and validated against clinical data from ionic currents to electrocardiogram. Virtual patients presenting AF post-PVI underwent 12 secondary treatments. Sustained AF developed in 522 virtual patients after PVI. Second ablation procedures involving left atrial ablation alone showed 55% efficacy, only succeeding in the small right atria (<60 mL). When additional cavo-tricuspid isthmus ablation was considered, Marshall-PLAN sufficed (66% efficacy) for the small left atria (<90 mL). For the bigger left atria, a more aggressive ablation approach was required, such as anterior mitral line (75% efficacy) or posterior wall isolation plus mitral isthmus ablation (77% efficacy). Virtual patients with LVAs greatly benefited from LVA ablation in the left and right atria (100% efficacy). Conversely, in the absence of LVAs, synergistic ablation and pharmacotherapy could terminate AF. In the absence of ablation, the patient's ionic current substrate modulated the response to antiarrhythmic drugs, being the inward currents critical for optimal stratification to amiodarone or vernakalant. CONCLUSION In silico trials identify optimal strategies for AF treatment based on virtual patient characteristics, evidencing the power of human modelling and simulation as a clinical assisting tool.
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Affiliation(s)
- Albert Dasí
- Department of Computer Science, University of Oxford, Wolfson Building, Parks Road, Oxford OX1 3QD, UK
| | - Claudia Nagel
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Michael T B Pope
- Department of Cardiology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- Department for Human Development and Health, University of Southampton, Southampton, UK
| | - Rohan S Wijesurendra
- Department of Cardiology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Timothy R Betts
- Department of Cardiology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Rafael Sachetto
- Departamento de Ciência da Computação, Universidade Federal de São João del Rei, São João del Rei, MG, Brazil
| | - Axel Loewe
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Alfonso Bueno-Orovio
- Department of Computer Science, University of Oxford, Wolfson Building, Parks Road, Oxford OX1 3QD, UK
| | - Blanca Rodriguez
- Department of Computer Science, University of Oxford, Wolfson Building, Parks Road, Oxford OX1 3QD, UK
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3
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Jaffery OA, Melki L, Slabaugh G, Good WW, Roney CH. A Review of Personalised Cardiac Computational Modelling Using Electroanatomical Mapping Data. Arrhythm Electrophysiol Rev 2024; 13:e08. [PMID: 38807744 PMCID: PMC11131150 DOI: 10.15420/aer.2023.25] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 12/27/2023] [Indexed: 05/30/2024] Open
Abstract
Computational models of cardiac electrophysiology have gradually matured during the past few decades and are now being personalised to provide patient-specific therapy guidance for improving suboptimal treatment outcomes. The predictive features of these personalised electrophysiology models hold the promise of providing optimal treatment planning, which is currently limited in the clinic owing to reliance on a population-based or average patient approach. The generation of a personalised electrophysiology model entails a sequence of steps for which a range of activation mapping, calibration methods and therapy simulation pipelines have been suggested. However, the optimal methods that can potentially constitute a clinically relevant in silico treatment are still being investigated and face limitations, such as uncertainty of electroanatomical data recordings, generation and calibration of models within clinical timelines and requirements to validate or benchmark the recovered tissue parameters. This paper is aimed at reporting techniques on the personalisation of cardiac computational models, with a focus on calibrating cardiac tissue conductivity based on electroanatomical mapping data.
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Affiliation(s)
- Ovais A Jaffery
- School of Engineering and Materials Science, Queen Mary University of London London, UK
| | - Lea Melki
- R&D Algorithms, Acutus Medical Carlsbad, CA, US
| | - Gregory Slabaugh
- Digital Environment Research Institute, Queen Mary University of London London, UK
| | | | - Caroline H Roney
- School of Engineering and Materials Science, Queen Mary University of London London, UK
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4
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Riaz Gondal MU, Atta Mehdi H, Khenhrani RR, Kumari N, Ali MF, Kumar S, Faraz M, Malik J. Role of Machine Learning and Artificial Intelligence in Arrhythmias and Electrophysiology. Cardiol Rev 2024:00045415-990000000-00270. [PMID: 38761137 DOI: 10.1097/crd.0000000000000715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/20/2024]
Abstract
Machine learning (ML), a subset of artificial intelligence (AI) centered on machines learning from extensive datasets, stands at the forefront of a technological revolution shaping various facets of society. Cardiovascular medicine has emerged as a key domain for ML applications, with considerable efforts to integrate these innovations into routine clinical practice. Within cardiac electrophysiology, ML applications, especially in the automated interpretation of electrocardiograms, have garnered substantial attention in existing literature. However, less recognized are the diverse applications of ML in cardiac electrophysiology and arrhythmias, spanning basic science research on arrhythmia mechanisms, both experimental and computational, as well as contributions to enhanced techniques for mapping cardiac electrical function and translational research related to arrhythmia management. This comprehensive review delves into various ML applications within the scope of this journal, organized into 3 parts. The first section provides a fundamental understanding of general ML principles and methodologies, serving as a foundational resource for readers interested in exploring ML applications in arrhythmia research. The second part offers an in-depth review of studies in arrhythmia and electrophysiology that leverage ML methodologies, showcasing the broad potential of ML approaches. Each subject is thoroughly outlined, accompanied by a review of notable ML research advancements. Finally, the review delves into the primary challenges and future perspectives surrounding ML-driven cardiac electrophysiology and arrhythmias research.
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Affiliation(s)
| | - Hassan Atta Mehdi
- Department of Medicine, Jinnah Postgraduate Medical Centre, Karachi, Pakistan
| | - Raja Ram Khenhrani
- Department of Medicine, Internal Medicine Fellow, Shaheed Mohtarma Benazir Bhutto Medical College and Lyari General Hospital, Karachi, Pakistan
| | - Neha Kumari
- Department of Medicine, Jinnah Postgraduate Medical Centre, Karachi, Pakistan
| | - Muhammad Faizan Ali
- Department of Medicine, Jinnah Postgraduate Medical Centre, Karachi, Pakistan
| | - Sooraj Kumar
- Department of Medicine, Jinnah Sindh Medical University, Karachi, Pakistan; and
| | - Maria Faraz
- Department of Cardiovascular Medicine, Cardiovascular Analytics Group, Rawalpindi, Pakistan
| | - Jahanzeb Malik
- Department of Cardiovascular Medicine, Cardiovascular Analytics Group, Rawalpindi, Pakistan
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5
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Laubenbacher R, Mehrad B, Shmulevich I, Trayanova N. Digital twins in medicine. NATURE COMPUTATIONAL SCIENCE 2024; 4:184-191. [PMID: 38532133 PMCID: PMC11102043 DOI: 10.1038/s43588-024-00607-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 02/12/2024] [Indexed: 03/28/2024]
Abstract
Medical digital twins, which are potentially vital for personalized medicine, have become a recent focus in medical research. Here we present an overview of the state of the art in medical digital twin development, especially in oncology and cardiology, where it is most advanced. We discuss major challenges, such as data integration and privacy, and provide an outlook on future advancements. Emphasizing the importance of this technology in healthcare, we highlight the potential for substantial improvements in patient-specific treatments and diagnostics.
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Affiliation(s)
- R Laubenbacher
- Department of Medicine, University of Florida, Gainesville, FL, USA.
| | - B Mehrad
- Department of Medicine, University of Florida, Gainesville, FL, USA
| | | | - N Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
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6
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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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7
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Roney CH, Solis Lemus JA, Lopez Barrera C, Zolotarev A, Ulgen O, Kerfoot E, Bevis L, Misghina S, Vidal Horrach C, Jaffery OA, Ehnesh M, Rodero C, Dharmaprani D, Ríos-Muñoz GR, Ganesan A, Good WW, Neic A, Plank G, Hopman LHGA, Götte MJW, Honarbakhsh S, Narayan SM, Vigmond E, Niederer S. Constructing bilayer and volumetric atrial models at scale. Interface Focus 2023; 13:20230038. [PMID: 38106921 PMCID: PMC10722212 DOI: 10.1098/rsfs.2023.0038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 11/15/2023] [Indexed: 12/19/2023] Open
Abstract
To enable large in silico trials and personalized model predictions on clinical timescales, it is imperative that models can be constructed quickly and reproducibly. First, we aimed to overcome the challenges of constructing cardiac models at scale through developing a robust, open-source pipeline for bilayer and volumetric atrial models. Second, we aimed to investigate the effects of fibres, fibrosis and model representation on fibrillatory dynamics. To construct bilayer and volumetric models, we extended our previously developed coordinate system to incorporate transmurality, atrial regions and fibres (rule-based or data driven diffusion tensor magnetic resonance imaging (MRI)). We created a cohort of 1000 biatrial bilayer and volumetric models derived from computed tomography (CT) data, as well as models from MRI, and electroanatomical mapping. Fibrillatory dynamics diverged between bilayer and volumetric simulations across the CT cohort (correlation coefficient for phase singularity maps: left atrial (LA) 0.27 ± 0.19, right atrial (RA) 0.41 ± 0.14). Adding fibrotic remodelling stabilized re-entries and reduced the impact of model type (LA: 0.52 ± 0.20, RA: 0.36 ± 0.18). The choice of fibre field has a small effect on paced activation data (less than 12 ms), but a larger effect on fibrillatory dynamics. Overall, we developed an open-source user-friendly pipeline for generating atrial models from imaging or electroanatomical mapping data enabling in silico clinical trials at scale (https://github.com/pcmlab/atrialmtk).
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Affiliation(s)
- Caroline H. Roney
- School of Engineering and Materials Science, Queen Mary University of London, London, UK
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK
| | - Jose Alonso Solis Lemus
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Carlos Lopez Barrera
- School of Engineering and Materials Science, Queen Mary University of London, London, UK
- Center for Research in Advanced Materials S.C (CIMAV), Chihuahua, Mexico
| | - Alexander Zolotarev
- School of Engineering and Materials Science, Queen Mary University of London, London, UK
| | - Onur Ulgen
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK
| | - Eric Kerfoot
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK
| | - Laura Bevis
- School of Engineering and Materials Science, Queen Mary University of London, London, UK
| | - Semhar Misghina
- School of Engineering and Materials Science, Queen Mary University of London, London, UK
| | - Caterina Vidal Horrach
- School of Engineering and Materials Science, Queen Mary University of London, London, UK
| | - Ovais A. Jaffery
- School of Engineering and Materials Science, Queen Mary University of London, London, UK
| | - Mahmoud Ehnesh
- School of Engineering and Materials Science, Queen Mary University of London, London, UK
| | - Cristobal Rodero
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Dhani Dharmaprani
- College of Medicine and Public Health, Flinders University, Adelaide, Australia
| | - Gonzalo R. Ríos-Muñoz
- Bioengineering Department, Universidad Carlos III de Madrid, Madrid 28911, Spain
- Department of Cardiology, Gregorio Marañón Health Research Institute (IiSGM), Hospital General Universitario Gregorio Marañón, Madrid 28007, Spain
- Center for Biomedical Research in Cardiovascular Disease Network (CIBERCV), Madrid 28029, Spain
| | - Anand Ganesan
- College of Medicine and Public Health, Flinders University, Adelaide, Australia
| | | | | | - Gernot Plank
- Gottfried Schatz Research Center-Biophysics, Medical University of Graz, Graz, Austria
- BioTechMed-Graz, Graz, Austria
| | | | | | - Shohreh Honarbakhsh
- Electrophysiology Department, Barts Heart Centre, Barts Health NHS Trust, London, UK
| | - Sanjiv M. Narayan
- Department of Medicine and Cardiovascular Institute, Stanford University, Palo Alto, CA, USA
| | - Edward Vigmond
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Bordeaux, France
- IMB, UMR 5251, University Bordeaux, Talence 33400, France
| | - Steven Niederer
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
- Turing Research and Innovation Cluster in Digital Twins (TRIC: DT), The Alan Turing Institute, London, UK
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8
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Hermans BJ, Bijvoet GP, Holtackers RJ, Mihl C, Luermans JG, Maesen B, Vernooy K, Linz D, Chaldoupi SM, Schotten U. Multi-modal characterization of the left atrium by a fully automated integration of pre-procedural cardiac imaging and electro-anatomical mapping. IJC HEART & VASCULATURE 2023; 49:101276. [PMID: 37854978 PMCID: PMC10579959 DOI: 10.1016/j.ijcha.2023.101276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 09/25/2023] [Accepted: 09/27/2023] [Indexed: 10/20/2023]
Abstract
Background The combination of information obtained from pre-procedural cardiac imaging and electro-anatomical mapping (EAM) can potentially help to locate new ablation targets. In this study we developed and evaluated a fully automated technique to align left atrial (LA) anatomies obtained from CT- and MRI-scans with LA anatomies obtained from EAM. Methods Twenty-one patients scheduled for a pulmonary vein (PV) isolation with a pre-procedural MRI were enrolled. Additionally, a recent computed tomography (CT) scan was available in 12 patients. LA anatomies were segmented from MRI-scans using ADAS-AF (Galgo Medical, Barcelona) and from the CT-scans using Slicer3D. MRI and CT anatomies were aligned with the EAM anatomy using an iterative closest plane-to-plane algorithm. Initially, the algorithm included the PVs, LA appendage and mitral valve anulus as they are the most distinctive landmarks. Subsequently, the algorithm was applied again, excluding these structures, with only three iterative steps to refine the alignment of the true LA surface. The result of the alignments was quantified by the Euclidian distance between the aligned anatomies after excluding PVs, LA appendage and mitral anulus. Results Our algorithm successfully aligned 20/21 MRI anatomies and 11/12 CT anatomies with the corresponding EAM anatomies. The average median residual distances were 1.9 ± 0.6 mm and 2.5 ± 0.8 mm for MRI and CT anatomies respectively. The average LA surface with a residual distance less than 5.00 mm was 89 ± 9% and 89 ± 10% for MRI and CT anatomies respectively. Conclusion An iterative closest plane-to-plane algorithm is a reliable method to automatically align pre-procedural cardiac images with anatomies acquired during ablation procedures.
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Affiliation(s)
- Ben J.M. Hermans
- Department of Physiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, the Netherlands
| | - Geertruida P. Bijvoet
- Department of Cardiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Center (MUMC+), Maastricht, the Netherlands
| | - Robert J. Holtackers
- Department of Radiology and Nuclear Medicine, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Center (MUMC+), Maastricht, the Netherlands
| | - Casper Mihl
- Department of Radiology and Nuclear Medicine, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Center (MUMC+), Maastricht, the Netherlands
| | - Justin G.L.M. Luermans
- Department of Cardiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Center (MUMC+), Maastricht, the Netherlands
| | - Bart Maesen
- Department of Cardiothoracic Surgery, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Center (MUMC+), Maastricht, the Netherlands
| | - Kevin Vernooy
- Department of Cardiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Center (MUMC+), Maastricht, the Netherlands
| | - Dominik Linz
- Department of Cardiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Center (MUMC+), Maastricht, the Netherlands
| | - Sevasti-Maria Chaldoupi
- Department of Cardiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Center (MUMC+), Maastricht, the Netherlands
| | - Ulrich Schotten
- Department of Physiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, the Netherlands
- Department of Cardiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Center (MUMC+), Maastricht, the Netherlands
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Azzolin L, Eichenlaub M, Nagel C, Nairn D, Sánchez J, Unger L, Arentz T, Westermann D, Dössel O, Jadidi A, Loewe A. AugmentA: Patient-specific augmented atrial model generation tool. Comput Med Imaging Graph 2023; 108:102265. [PMID: 37392493 DOI: 10.1016/j.compmedimag.2023.102265] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 01/07/2023] [Accepted: 06/03/2023] [Indexed: 07/03/2023]
Abstract
Digital twins of patients' hearts are a promising tool to assess arrhythmia vulnerability and to personalize therapy. However, the process of building personalized computational models can be challenging and requires a high level of human interaction. We propose a patient-specific Augmented Atria generation pipeline (AugmentA) as a highly automated framework which, starting from clinical geometrical data, provides ready-to-use atrial personalized computational models. AugmentA identifies and labels atrial orifices using only one reference point per atrium. If the user chooses to fit a statistical shape model to the input geometry, it is first rigidly aligned with the given mean shape before a non-rigid fitting procedure is applied. AugmentA automatically generates the fiber orientation and finds local conduction velocities by minimizing the error between the simulated and clinical local activation time (LAT) map. The pipeline was tested on a cohort of 29 patients on both segmented magnetic resonance images (MRI) and electroanatomical maps of the left atrium. Moreover, the pipeline was applied to a bi-atrial volumetric mesh derived from MRI. The pipeline robustly integrated fiber orientation and anatomical region annotations in 38.4 ± 5.7 s. In conclusion, AugmentA offers an automated and comprehensive pipeline delivering atrial digital twins from clinical data in procedural time.
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Affiliation(s)
- Luca Azzolin
- Institute of Biomedical Engineering at Karlsruhe Institute of Technology, Karlsruhe, Germany.
| | - Martin Eichenlaub
- University Heart Center Freiburg-Bad Krozingen, Bad Krozingen, Germany
| | - Claudia Nagel
- Institute of Biomedical Engineering at Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Deborah Nairn
- Institute of Biomedical Engineering at Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Jorge Sánchez
- Institute of Biomedical Engineering at Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Laura Unger
- Institute of Biomedical Engineering at Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Thomas Arentz
- University Heart Center Freiburg-Bad Krozingen, Bad Krozingen, Germany
| | - Dirk Westermann
- University Heart Center Freiburg-Bad Krozingen, Bad Krozingen, Germany
| | - Olaf Dössel
- Institute of Biomedical Engineering at Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Amir Jadidi
- University Heart Center Freiburg-Bad Krozingen, Bad Krozingen, Germany
| | - Axel Loewe
- Institute of Biomedical Engineering at Karlsruhe Institute of Technology, Karlsruhe, Germany
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10
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Solís-Lemus JA, Baptiste T, Barrows R, Sillett C, Gharaviri A, Raffaele G, Razeghi O, Strocchi M, Sim I, Kotadia I, Bodagh N, O'Hare D, O'Neill M, Williams SE, Roney C, Niederer S. Evaluation of an open-source pipeline to create patient-specific left atrial models: A reproducibility study. Comput Biol Med 2023; 162:107009. [PMID: 37301099 PMCID: PMC10790305 DOI: 10.1016/j.compbiomed.2023.107009] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 04/11/2023] [Accepted: 05/03/2023] [Indexed: 06/12/2023]
Abstract
This work presents an open-source software pipeline to create patient-specific left atrial models with fibre orientations and a fibrDEFAULTosis map, suitable for electrophysiology simulations, and quantifies the intra and inter observer reproducibility of the model creation. The semi-automatic pipeline takes as input a contrast enhanced magnetic resonance angiogram, and a late gadolinium enhanced (LGE) contrast magnetic resonance (CMR). Five operators were allocated 20 cases each from a set of 50 CMR datasets to create a total of 100 models to evaluate inter and intra-operator variability. Each output model consisted of: (1) a labelled surface mesh open at the pulmonary veins and mitral valve, (2) fibre orientations mapped from a diffusion tensor MRI (DTMRI) human atlas, (3) fibrosis map extracted from the LGE-CMR scan, and (4) simulation of local activation time (LAT) and phase singularity (PS) mapping. Reproducibility in our pipeline was evaluated by comparing agreement in shape of the output meshes, fibrosis distribution in the left atrial body, and fibre orientations. Reproducibility in simulations outputs was evaluated in the LAT maps by comparing the total activation times, and the mean conduction velocity (CV). PS maps were compared with the structural similarity index measure (SSIM). The users processed in total 60 cases for inter and 40 cases for intra-operator variability. Our workflow allows a single model to be created in 16.72 ± 12.25 min. Similarity was measured with shape, percentage of fibres oriented in the same direction, and intra-class correlation coefficient (ICC) for the fibrosis calculation. Shape differed noticeably only with users' selection of the mitral valve and the length of the pulmonary veins from the ostia to the distal end; fibrosis agreement was high, with ICC of 0.909 (inter) and 0.999 (intra); fibre orientation agreement was high with 60.63% (inter) and 71.77% (intra). The LAT showed good agreement, where the median ± IQR of the absolute difference of the total activation times was 2.02 ± 2.45 ms for inter, and 1.37 ± 2.45 ms for intra. Also, the average ± sd of the mean CV difference was -0.00404 ± 0.0155 m/s for inter, and 0.0021 ± 0.0115 m/s for intra. Finally, the PS maps showed a moderately good agreement in SSIM for inter and intra, where the mean ± sd SSIM for inter and intra were 0.648 ± 0.21 and 0.608 ± 0.15, respectively. Although we found notable differences in the models, as a consequence of user input, our tests show that the uncertainty caused by both inter and intra-operator variability is comparable with uncertainty due to estimated fibres, and image resolution accuracy of segmentation tools.
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Affiliation(s)
- José Alonso Solís-Lemus
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas Hospital, London, SE1 7EH, UK.
| | - Tiffany Baptiste
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas Hospital, London, SE1 7EH, UK
| | - Rosie Barrows
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas Hospital, London, SE1 7EH, UK
| | - Charles Sillett
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas Hospital, London, SE1 7EH, UK
| | - Ali Gharaviri
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas Hospital, London, SE1 7EH, UK; Centre for Cardiovascular Science, University of Edinburgh, Old College, South Bridge, Edinburgh, EH8 9YL, Scotland, UK
| | - Giulia Raffaele
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas Hospital, London, SE1 7EH, UK; School of Medical Education, King's College London, St Thomas Hospital, London, SE1 7EH, UK
| | - Orod Razeghi
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas Hospital, London, SE1 7EH, UK; Department of Haematology, NHS Blood and Transplant Centre, University of Cambridge, Cambridge, UK
| | - Marina Strocchi
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas Hospital, London, SE1 7EH, UK
| | - Iain Sim
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas Hospital, London, SE1 7EH, UK
| | - Irum Kotadia
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas Hospital, London, SE1 7EH, UK
| | - Neil Bodagh
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas Hospital, London, SE1 7EH, UK
| | - Daniel O'Hare
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas Hospital, London, SE1 7EH, UK
| | - Mark O'Neill
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas Hospital, London, SE1 7EH, UK
| | - Steven E Williams
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas Hospital, London, SE1 7EH, UK; Centre for Cardiovascular Science, University of Edinburgh, Old College, South Bridge, Edinburgh, EH8 9YL, Scotland, UK
| | - Caroline Roney
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas Hospital, London, SE1 7EH, UK; Queen Mary University of London, Mile End Rd, Bethnal Green, London, E1 4NS, UK
| | - Steven Niederer
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas Hospital, London, SE1 7EH, UK; Alan Turing Institute, British Library, 96 Euston Rd, London, NW1 2DB, UK
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11
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Vila M, Rivolta MW, Barrios Espinosa CA, Unger LA, Luik A, Loewe A, Sassi R. Recommender system for ablation lines to treat complex atrial tachycardia. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 231:107406. [PMID: 36787660 DOI: 10.1016/j.cmpb.2023.107406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 01/30/2023] [Accepted: 02/03/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVE Planning the optimal ablation strategy for the treatment of complex atrial tachycardia (CAT) is a time consuming task and is error-prone. Recently, directed network mapping, a technology based on graph theory, proved to efficiently identify CAT based solely on data of clinical interventions. Briefly, a directed network was used to model the atrial electrical propagation and reentrant activities were identified by looking for closed-loop paths in the network. In this study, we propose a recommender system, built as an optimization problem, able to suggest the optimal ablation strategy for the treatment of CAT. METHODS The optimization problem modeled the optimal ablation strategy as that one interrupting all reentrant mechanisms while minimizing the ablated atrial surface. The problem was designed on top of directed network mapping. Considering the exponential complexity of finding the optimal solution of the problem, we introduced a heuristic algorithm with polynomial complexity. The proposed algorithm was applied to the data of i) 6 simulated scenarios including both left and right atrial flutter; and ii) 10 subjects that underwent a clinical routine. RESULTS The recommender system suggested the optimal strategy in 4 out of 6 simulated scenarios. On clinical data, the recommended ablation lines were found satisfactory on 67% of the cases according to the clinician's opinion, while they were correctly located in 89%. The algorithm made use of only data collected during mapping and was able to process them nearly real-time. CONCLUSIONS The first recommender system for the identification of the optimal ablation lines for CAT, based solely on the data collected during the intervention, is presented. The study may open up interesting scenarios for the application of graph theory for the treatment of CAT.
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Affiliation(s)
- Muhamed Vila
- Università degli Studi di Milano, Via Celoria 18, Milan, 20133, Italy
| | - Massimo W Rivolta
- Università degli Studi di Milano, Via Celoria 18, Milan, 20133, Italy.
| | - Cristian A Barrios Espinosa
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, Karlsruhe, 76131, Germany
| | - Laura A Unger
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, Karlsruhe, 76131, Germany
| | - Armin Luik
- Medizinische Klinik IV, Städtisches Klinikum Karlsruhe, Moltkestraße 90, Karlsruhe, 76133, Germany
| | - Axel Loewe
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, Karlsruhe, 76131, Germany
| | - Roberto Sassi
- Università degli Studi di Milano, Via Celoria 18, Milan, 20133, Italy
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12
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Ogbomo-Harmitt S, Muffoletto M, Zeidan A, Qureshi A, King AP, Aslanidi O. Exploring interpretability in deep learning prediction of successful ablation therapy for atrial fibrillation. Front Physiol 2023; 14:1054401. [PMID: 36998987 PMCID: PMC10043207 DOI: 10.3389/fphys.2023.1054401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 02/28/2023] [Indexed: 03/16/2023] Open
Abstract
Background: Radiofrequency catheter ablation (RFCA) therapy is the first-line treatment for atrial fibrillation (AF), the most common type of cardiac arrhythmia globally. However, the procedure currently has low success rates in dealing with persistent AF, with a reoccurrence rate of ∼50% post-ablation. Therefore, deep learning (DL) has increasingly been applied to improve RFCA treatment for AF. However, for a clinician to trust the prediction of a DL model, its decision process needs to be interpretable and have biomedical relevance.Aim: This study explores interpretability in DL prediction of successful RFCA therapy for AF and evaluates if pro-arrhythmogenic regions in the left atrium (LA) were used in its decision process.Methods: AF and its termination by RFCA have been simulated in MRI-derived 2D LA tissue models with segmented fibrotic regions (n = 187). Three ablation strategies were applied for each LA model: pulmonary vein isolation (PVI), fibrosis-based ablation (FIBRO) and a rotor-based ablation (ROTOR). The DL model was trained to predict the success of each RFCA strategy for each LA model. Three feature attribution (FA) map methods were then used to investigate interpretability of the DL model: GradCAM, Occlusions and LIME.Results: The developed DL model had an AUC (area under the receiver operating characteristic curve) of 0.78 ± 0.04 for predicting the success of the PVI strategy, 0.92 ± 0.02 for FIBRO and 0.77 ± 0.02 for ROTOR. GradCAM had the highest percentage of informative regions in the FA maps (62% for FIBRO and 71% for ROTOR) that coincided with the successful RFCA lesions known from the 2D LA simulations, but unseen by the DL model. Moreover, GradCAM had the smallest coincidence of informative regions of the FA maps with non-arrhythmogenic regions (25% for FIBRO and 27% for ROTOR).Conclusion: The most informative regions of the FA maps coincided with pro-arrhythmogenic regions, suggesting that the DL model leveraged structural features of MRI images to identify such regions and make its prediction. In the future, this technique could provide a clinician with a trustworthy decision support tool.
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13
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Lin WH, Zhu Z, Ravikumar V, Sharma V, Tolkacheva EG, McAlpine MC, Ogle BM. A Bionic Testbed for Cardiac Ablation Tools. Int J Mol Sci 2022; 23:ijms232214444. [PMID: 36430922 PMCID: PMC9692733 DOI: 10.3390/ijms232214444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 11/03/2022] [Accepted: 11/09/2022] [Indexed: 11/22/2022] Open
Abstract
Bionic-engineered tissues have been proposed for testing the performance of cardiovascular medical devices and predicting clinical outcomes ex vivo. Progress has been made in the development of compliant electronics that are capable of monitoring treatment parameters and being coupled to engineered tissues; however, the scale of most engineered tissues is too small to accommodate the size of clinical-grade medical devices. Here, we show substantial progress toward bionic tissues for evaluating cardiac ablation tools by generating a centimeter-scale human cardiac disk and coupling it to a hydrogel-based soft-pressure sensor. The cardiac tissue with contiguous electromechanical function was made possible by our recently established method to 3D bioprint human pluripotent stem cells in an extracellular matrix-based bioink that allows for in situ cell expansion prior to cardiac differentiation. The pressure sensor described here utilized electrical impedance tomography to enable the real-time spatiotemporal mapping of pressure distribution. A cryoablation tip catheter was applied to the composite bionic tissues with varied pressure. We found a close correlation between the cell response to ablation and the applied pressure. Under some conditions, cardiomyocytes could survive in the ablated region with more rounded morphology compared to the unablated controls, and connectivity was disrupted. This is the first known functional characterization of living human cardiomyocytes following an ablation procedure that suggests several mechanisms by which arrhythmia might redevelop following an ablation. Thus, bionic-engineered testbeds of this type can be indicators of tissue health and function and provide unique insight into human cell responses to ablative interventions.
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Affiliation(s)
- Wei-Han Lin
- Department of Biomedical Engineering, University of Minnesota—Twin Cities, Minneapolis, MN 55455, USA
- Stem Cell Institute, University of Minnesota—Twin Cities, Minneapolis, MN 55455, USA
| | - Zhijie Zhu
- Department of Mechanical Engineering, University of Minnesota—Twin Cities, Minneapolis, MN 55455, USA
| | - Vasanth Ravikumar
- Department of Electrical Engineering, University of Minnesota—Twin Cities, Minneapolis, MN 55455, USA
| | - Vinod Sharma
- Cardiac Rhythm and Heart Failure Division, Medtronic Inc., Minneapolis, MN 55432, USA
| | - Elena G. Tolkacheva
- Department of Biomedical Engineering, University of Minnesota—Twin Cities, Minneapolis, MN 55455, USA
- Lillehei Heart Institute, University of Minnesota—Twin Cities, Minneapolis, MN 55455, USA
- Institute for Engineering in Medicine, University of Minnesota—Twin Cities, Minneapolis, MN 55455, USA
| | - Michael C. McAlpine
- Department of Mechanical Engineering, University of Minnesota—Twin Cities, Minneapolis, MN 55455, USA
- Institute for Engineering in Medicine, University of Minnesota—Twin Cities, Minneapolis, MN 55455, USA
- Correspondence: (M.C.M.); (B.M.O.)
| | - Brenda M. Ogle
- Department of Biomedical Engineering, University of Minnesota—Twin Cities, Minneapolis, MN 55455, USA
- Stem Cell Institute, University of Minnesota—Twin Cities, Minneapolis, MN 55455, USA
- Lillehei Heart Institute, University of Minnesota—Twin Cities, Minneapolis, MN 55455, USA
- Institute for Engineering in Medicine, University of Minnesota—Twin Cities, Minneapolis, MN 55455, USA
- Masonic Cancer Center, University of Minnesota—Twin Cities, Minneapolis, MN 55455, USA
- Correspondence: (M.C.M.); (B.M.O.)
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14
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Yamamoto C, Trayanova NA. Atrial fibrillation: Insights from animal models, computational modeling, and clinical studies. EBioMedicine 2022; 85:104310. [PMID: 36309006 PMCID: PMC9619190 DOI: 10.1016/j.ebiom.2022.104310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 09/25/2022] [Accepted: 10/04/2022] [Indexed: 11/11/2022] Open
Abstract
Atrial fibrillation (AF) is the most common human arrhythmia, affecting millions of patients worldwide. A combination of risk factors and comorbidities results in complex atrial remodeling, which increases AF vulnerability and persistence. Insights from animal models, clinical studies, and computational modeling have advanced the understanding of the mechanisms and pathophysiology of AF. Areas of heterogeneous pathological remodeling, as well as altered electrophysiological properties, serve as a substrate for AF drivers and spontaneous activations. The complex and individualized presentation of this arrhythmia suggests that mechanisms-based personalized approaches will likely be needed to overcome current challenges in AF management. In this paper, we review the insights on the mechanisms of AF obtained from animal models and clinical studies and how computational models integrate this knowledge to advance AF clinical management. We also assess the challenges that need to be overcome to implement these mechanistic models in clinical practice.
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Affiliation(s)
- Carolyna Yamamoto
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Natalia A. Trayanova
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA,Alliance for Cardiovascular Diagnostic and Treatment Innovation (ADVANCE), Johns Hopkins University, Baltimore, MD, USA,Corresponding author. Johns Hopkins, Johns Hopkins University, United States.
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Abstract
The global burden caused by cardiovascular disease is substantial, with heart disease representing the most common cause of death around the world. There remains a need to develop better mechanistic models of cardiac function in order to combat this health concern. Heart rhythm disorders, or arrhythmias, are one particular type of disease which has been amenable to quantitative investigation. Here we review the application of quantitative methodologies to explore dynamical questions pertaining to arrhythmias. We begin by describing single-cell models of cardiac myocytes, from which two and three dimensional models can be constructed. Special focus is placed on results relating to pattern formation across these spatially-distributed systems, especially the formation of spiral waves of activation. Next, we discuss mechanisms which can lead to the initiation of arrhythmias, focusing on the dynamical state of spatially discordant alternans, and outline proposed mechanisms perpetuating arrhythmias such as fibrillation. We then review experimental and clinical results related to the spatio-temporal mapping of heart rhythm disorders. Finally, we describe treatment options for heart rhythm disorders and demonstrate how statistical physics tools can provide insights into the dynamics of heart rhythm disorders.
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Affiliation(s)
- Wouter-Jan Rappel
- Department of Physics, University of California San Diego, La Jolla, CA 92037
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16
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Bai J, Lu Y, Wang H, Zhao J. How synergy between mechanistic and statistical models is impacting research in atrial fibrillation. Front Physiol 2022; 13:957604. [PMID: 36111152 PMCID: PMC9468674 DOI: 10.3389/fphys.2022.957604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 08/08/2022] [Indexed: 11/13/2022] Open
Abstract
Atrial fibrillation (AF) with multiple complications, high morbidity and mortality, and low cure rates, has become a global public health problem. Although significant progress has been made in the treatment methods represented by anti-AF drugs and radiofrequency ablation, the therapeutic effect is not as good as expected. The reason is mainly because of our lack of understanding of AF mechanisms. This field has benefited from mechanistic and (or) statistical methodologies. Recent renewed interest in digital twin techniques by synergizing between mechanistic and statistical models has opened new frontiers in AF analysis. In the review, we briefly present findings that gave rise to the AF pathophysiology and current therapeutic modalities. We then summarize the achievements of digital twin technologies in three aspects: understanding AF mechanisms, screening anti-AF drugs and optimizing ablation strategies. Finally, we discuss the challenges that hinder the clinical application of the digital twin heart. With the rapid progress in data reuse and sharing, we expect their application to realize the transition from AF description to response prediction.
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Affiliation(s)
- Jieyun Bai
- Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Information Technology, Jinan University, Guangzhou, China
- College of Information Science and Technology, Jinan University, Guangzhou, China
- *Correspondence: Jieyun Bai, ; Jichao Zhao,
| | - Yaosheng Lu
- Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Information Technology, Jinan University, Guangzhou, China
- College of Information Science and Technology, Jinan University, Guangzhou, China
| | - Huijin Wang
- College of Information Science and Technology, Jinan University, Guangzhou, China
| | - Jichao Zhao
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
- *Correspondence: Jieyun Bai, ; Jichao Zhao,
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17
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Azzolin L, Eichenlaub M, Nagel C, Nairn D, Sanchez J, Unger L, Dössel O, Jadidi A, Loewe A. Personalized ablation vs. conventional ablation strategies to terminate atrial fibrillation and prevent recurrence. Europace 2022; 25:211-222. [PMID: 35943361 PMCID: PMC9907752 DOI: 10.1093/europace/euac116] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 06/17/2022] [Indexed: 11/14/2022] Open
Abstract
AIMS The long-term success rate of ablation therapy is still sub-optimal in patients with persistent atrial fibrillation (AF), mostly due to arrhythmia recurrence originating from arrhythmogenic sites outside the pulmonary veins. Computational modelling provides a framework to integrate and augment clinical data, potentially enabling the patient-specific identification of AF mechanisms and of the optimal ablation sites. We developed a technology to tailor ablations in anatomical and functional digital atrial twins of patients with persistent AF aiming to identify the most successful ablation strategy. METHODS AND RESULTS Twenty-nine patient-specific computational models integrating clinical information from tomographic imaging and electro-anatomical activation time and voltage maps were generated. Areas sustaining AF were identified by a personalized induction protocol at multiple locations. State-of-the-art anatomical and substrate ablation strategies were compared with our proposed Personalized Ablation Lines (PersonAL) plan, which consists of iteratively targeting emergent high dominant frequency (HDF) regions, to identify the optimal ablation strategy. Localized ablations were connected to the closest non-conductive barrier to prevent recurrence of AF or atrial tachycardia. The first application of the HDF strategy had a success of >98% and isolated only 5-6% of the left atrial myocardium. In contrast, conventional ablation strategies targeting anatomical or structural substrate resulted in isolation of up to 20% of left atrial myocardium. After a second iteration of the HDF strategy, no further arrhythmia episode could be induced in any of the patient-specific models. CONCLUSION The novel PersonAL in silico technology allows to unveil all AF-perpetuating areas and personalize ablation by leveraging atrial digital twins.
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Affiliation(s)
- Luca Azzolin
- Corresponding author. Tel: +393381319986, E-mail address:
| | | | - Claudia Nagel
- Institute of Biomedical Engineering at Karlsruhe Institute of Technology, Building 30.33, Fritz-Haber-Weg 1, 76131 Karlsruhe, Germany
| | - Deborah Nairn
- Institute of Biomedical Engineering at Karlsruhe Institute of Technology, Building 30.33, Fritz-Haber-Weg 1, 76131 Karlsruhe, Germany
| | - Jorge Sanchez
- Institute of Biomedical Engineering at Karlsruhe Institute of Technology, Building 30.33, Fritz-Haber-Weg 1, 76131 Karlsruhe, Germany
| | - Laura Unger
- Institute of Biomedical Engineering at Karlsruhe Institute of Technology, Building 30.33, Fritz-Haber-Weg 1, 76131 Karlsruhe, Germany
| | - Olaf Dössel
- Institute of Biomedical Engineering at Karlsruhe Institute of Technology, Building 30.33, Fritz-Haber-Weg 1, 76131 Karlsruhe, Germany
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18
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Atrial Fibrillation Global Changes after Pulmonary Vein and Posterior Wall Isolation: A Charge Density Mapping Study. J Clin Med 2022; 11:jcm11102948. [PMID: 35629074 PMCID: PMC9145946 DOI: 10.3390/jcm11102948] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Revised: 05/09/2022] [Accepted: 05/19/2022] [Indexed: 02/04/2023] Open
Abstract
Background: Non-contact charge density (CD) mapping allows a global visualization of left atrium (LA) activation and of activation patterns during atrial fibrillation (AF). The aim of this study was to analyze, with CD mapping, the changes in persistent AF induced by pulmonary vein isolation (PVI) and LA posterior wall isolation (LAPWI). Methods: Patients undergoing PVI + LAPWI using the Arctic Front Advance PROTM cryoballoon system were included in the study. CD maps were created during AF at baseline, after PVI and after LAPWI. Three distinct activation patterns were identified in the CD maps: localized irregular activation (LIA), localized rotational activation (LRA) and focal centrifugal activation (FCA). LA maps were divided into the following regions: anterior, septal, lateral, roof, posterior, inferior. Results: Eleven patients were included, with a total of 33 maps and 198 AF regions analyzed. Global and regional AF cycle lengths significantly increased after PVI and LAPWI. Baseline analysis demonstrated higher LIA, LRA and FCA numbers in the posterior and anterior regions. After PVI, there was no change in LIA, LRA and FCA occurrence. After PVI + LAPWI, a significant decrease in LRA was observed with no difference in LIA and FCA occurrence. In the regional analysis, there was a significant reduction in the LIA number in the inferior region, in the LRA number in the roof and posterior regions and in the FCA number in the lateral region. Conclusions: A global reduction in the LRA number was observed only after PVI + LAPWI; it was driven by a reduction in rotational activity in the roof and posterior regions.
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19
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Molinari L, Zaltieri M, Massaroni C, Filippi S, Gizzi A, Schena E. Multiscale and Multiphysics Modeling of Anisotropic Cardiac RFCA: Experimental-Based Model Calibration via Multi-Point Temperature Measurements. Front Physiol 2022; 13:845896. [PMID: 35514332 PMCID: PMC9062295 DOI: 10.3389/fphys.2022.845896] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 03/22/2022] [Indexed: 11/13/2022] Open
Abstract
Radiofrequency catheter ablation (RFCA) is the mainstream treatment for drug-refractory cardiac fibrillation. Multiple studies demonstrated that incorrect dosage of radiofrequency energy to the myocardium could lead to uncontrolled tissue damage or treatment failure, with the consequent need for unplanned reoperations. Monitoring tissue temperature during thermal therapy and predicting the extent of lesions may improve treatment efficacy. Cardiac computational modeling represents a viable tool for identifying optimal RFCA settings, though predictability issues still limit a widespread usage of such a technology in clinical scenarios. We aim to fill this gap by assessing the influence of the intrinsic myocardial microstructure on the thermo-electric behavior at the tissue level. By performing multi-point temperature measurements on ex-vivo swine cardiac tissue samples, the experimental characterization of myocardial thermal anisotropy allowed us to assemble a fine-tuned thermo-electric material model of the cardiac tissue. We implemented a multiphysics and multiscale computational framework, encompassing thermo-electric anisotropic conduction, phase-lagging for heat transfer, and a three-state dynamical system for cellular death and lesion estimation. Our analysis resulted in a remarkable agreement between ex-vivo measurements and numerical results. Accordingly, we identified myocardium anisotropy as the driving effect on the outcomes of hyperthermic treatments. Furthermore, we characterized the complex nonlinear couplings regulating tissue behavior during RFCA, discussing model calibration, limitations, and perspectives.
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Affiliation(s)
- Leonardo Molinari
- Department of Mathematics and Computer Science, Emory University, Atlanta, GA, United States
| | - Martina Zaltieri
- Laboratory of Measurement and Biomedical Instrumentation, Department of Engineering, University of Rome Campus Bio-Medico, Rome, Italy
| | - Carlo Massaroni
- Laboratory of Measurement and Biomedical Instrumentation, Department of Engineering, University of Rome Campus Bio-Medico, Rome, Italy
| | - Simonetta Filippi
- Nonlinear Physics and Mathematical Modeling Lab, Department of Engineering, University of Rome Campus Bio-Medico, Rome, Italy
| | - Alessio Gizzi
- Nonlinear Physics and Mathematical Modeling Lab, Department of Engineering, University of Rome Campus Bio-Medico, Rome, Italy
| | - Emiliano Schena
- Laboratory of Measurement and Biomedical Instrumentation, Department of Engineering, University of Rome Campus Bio-Medico, Rome, Italy
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20
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Li L, Zimmer VA, Schnabel JA, Zhuang X. Medical image analysis on left atrial LGE MRI for atrial fibrillation studies: A review. Med Image Anal 2022; 77:102360. [PMID: 35124370 PMCID: PMC7614005 DOI: 10.1016/j.media.2022.102360] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 11/04/2021] [Accepted: 01/10/2022] [Indexed: 02/08/2023]
Abstract
Late gadolinium enhancement magnetic resonance imaging (LGE MRI) is commonly used to visualize and quantify left atrial (LA) scars. The position and extent of LA scars provide important information on the pathophysiology and progression of atrial fibrillation (AF). Hence, LA LGE MRI computing and analysis are essential for computer-assisted diagnosis and treatment stratification of AF patients. Since manual delineations can be time-consuming and subject to intra- and inter-expert variability, automating this computing is highly desired, which nevertheless is still challenging and under-researched. This paper aims to provide a systematic review on computing methods for LA cavity, wall, scar, and ablation gap segmentation and quantification from LGE MRI, and the related literature for AF studies. Specifically, we first summarize AF-related imaging techniques, particularly LGE MRI. Then, we review the methodologies of the four computing tasks in detail and summarize the validation strategies applied in each task as well as state-of-the-art results on public datasets. Finally, the possible future developments are outlined, with a brief survey on the potential clinical applications of the aforementioned methods. The review indicates that the research into this topic is still in the early stages. Although several methods have been proposed, especially for the LA cavity segmentation, there is still a large scope for further algorithmic developments due to performance issues related to the high variability of enhancement appearance and differences in image acquisition.
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Affiliation(s)
- Lei Li
- School of Data Science, Fudan University, Shanghai, China; School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China; School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Veronika A Zimmer
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK; Department of Informatics, Technical University of Munich, Germany
| | - Julia A Schnabel
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK; Department of Informatics, Technical University of Munich, Germany; Helmholtz Center Munich, Germany
| | - Xiahai Zhuang
- School of Data Science, Fudan University, Shanghai, China.
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21
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Roney CH, Sim I, Yu J, Beach M, Mehta A, Alonso Solis-Lemus J, Kotadia I, Whitaker J, Corrado C, Razeghi O, Vigmond E, Narayan SM, O’Neill M, Williams SE, Niederer SA. Predicting Atrial Fibrillation Recurrence by Combining Population Data and Virtual Cohorts of Patient-Specific Left Atrial Models. Circ Arrhythm Electrophysiol 2022; 15:e010253. [PMID: 35089057 PMCID: PMC8845531 DOI: 10.1161/circep.121.010253] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 01/03/2022] [Indexed: 01/14/2023]
Abstract
BACKGROUND Current ablation therapy for atrial fibrillation is suboptimal, and long-term response is challenging to predict. Clinical trials identify bedside properties that provide only modest prediction of long-term response in populations, while patient-specific models in small cohorts primarily explain acute response to ablation. We aimed to predict long-term atrial fibrillation recurrence after ablation in large cohorts, by using machine learning to complement biophysical simulations by encoding more interindividual variability. METHODS Patient-specific models were constructed for 100 atrial fibrillation patients (43 paroxysmal, 41 persistent, and 16 long-standing persistent), undergoing first ablation. Patients were followed for 1 year using ambulatory ECG monitoring. Each patient-specific biophysical model combined differing fibrosis patterns, fiber orientation maps, electrical properties, and ablation patterns to capture uncertainty in atrial properties and to test the ability of the tissue to sustain fibrillation. These simulation stress tests of different model variants were postprocessed to calculate atrial fibrillation simulation metrics. Machine learning classifiers were trained to predict atrial fibrillation recurrence using features from the patient history, imaging, and atrial fibrillation simulation metrics. RESULTS We performed 1100 atrial fibrillation ablation simulations across 100 patient-specific models. Models based on simulation stress tests alone showed a maximum accuracy of 0.63 for predicting long-term fibrillation recurrence. Classifiers trained to history, imaging, and simulation stress tests (average 10-fold cross-validation area under the curve, 0.85±0.09; recall, 0.80±0.13; precision, 0.74±0.13) outperformed those trained to history and imaging (area under the curve, 0.66±0.17) or history alone (area under the curve, 0.61±0.14). CONCLUSION A novel computational pipeline accurately predicted long-term atrial fibrillation recurrence in individual patients by combining outcome data with patient-specific acute simulation response. This technique could help to personalize selection for atrial fibrillation ablation.
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Affiliation(s)
- Caroline H. Roney
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (C.H.R., I.S., J.Y., M.B., A.M., J.A.S.-L., I.K., J.W., C.C., O.R., M.O., S.E.W., S.A.N.)
- School of Engineering and Materials Science, Queen Mary University of London, United Kingdom (C.H.R.)
| | - Iain Sim
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (C.H.R., I.S., J.Y., M.B., A.M., J.A.S.-L., I.K., J.W., C.C., O.R., M.O., S.E.W., S.A.N.)
| | - Jin Yu
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (C.H.R., I.S., J.Y., M.B., A.M., J.A.S.-L., I.K., J.W., C.C., O.R., M.O., S.E.W., S.A.N.)
| | - Marianne Beach
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (C.H.R., I.S., J.Y., M.B., A.M., J.A.S.-L., I.K., J.W., C.C., O.R., M.O., S.E.W., S.A.N.)
| | - Arihant Mehta
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (C.H.R., I.S., J.Y., M.B., A.M., J.A.S.-L., I.K., J.W., C.C., O.R., M.O., S.E.W., S.A.N.)
| | - Jose Alonso Solis-Lemus
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (C.H.R., I.S., J.Y., M.B., A.M., J.A.S.-L., I.K., J.W., C.C., O.R., M.O., S.E.W., S.A.N.)
| | - Irum Kotadia
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (C.H.R., I.S., J.Y., M.B., A.M., J.A.S.-L., I.K., J.W., C.C., O.R., M.O., S.E.W., S.A.N.)
| | - John Whitaker
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (C.H.R., I.S., J.Y., M.B., A.M., J.A.S.-L., I.K., J.W., C.C., O.R., M.O., S.E.W., S.A.N.)
- The Department of Internal Medicine, Cardiovascular Division, Brigham and Women’s Hospital, Boston, MA (J.W.)
| | - Cesare Corrado
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (C.H.R., I.S., J.Y., M.B., A.M., J.A.S.-L., I.K., J.W., C.C., O.R., M.O., S.E.W., S.A.N.)
| | - Orod Razeghi
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (C.H.R., I.S., J.Y., M.B., A.M., J.A.S.-L., I.K., J.W., C.C., O.R., M.O., S.E.W., S.A.N.)
| | - Edward Vigmond
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, France (E.V.)
- Univ. Bordeaux, IMB, UMR 5251, F-33400 Talence, France (E.V.)
| | - Sanjiv M. Narayan
- Department of Medicine and Cardiovascular Institute, Stanford University, Palo Alto, CA (S.M.N.)
| | - Mark O’Neill
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (C.H.R., I.S., J.Y., M.B., A.M., J.A.S.-L., I.K., J.W., C.C., O.R., M.O., S.E.W., S.A.N.)
| | - Steven E. Williams
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (C.H.R., I.S., J.Y., M.B., A.M., J.A.S.-L., I.K., J.W., C.C., O.R., M.O., S.E.W., S.A.N.)
- Centre for Cardiovascular Science, College of Medicine and Veterinary Medicine, University of Edinburgh (S.E.W.)
| | - Steven A. Niederer
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (C.H.R., I.S., J.Y., M.B., A.M., J.A.S.-L., I.K., J.W., C.C., O.R., M.O., S.E.W., S.A.N.)
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22
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Jenkins EV, Dharmaprani D, Schopp M, Quah JX, Tiver K, Mitchell L, Xiong F, Aguilar M, Pope K, Akar FG, Roney CH, Niederer SA, Nattel S, Nash MP, Clayton RH, Ganesan AN. The inspection paradox: An important consideration in the evaluation of rotor lifetimes in cardiac fibrillation. Front Physiol 2022; 13:920788. [PMID: 36148313 PMCID: PMC9486478 DOI: 10.3389/fphys.2022.920788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 08/10/2022] [Indexed: 11/18/2022] Open
Abstract
Background and Objective: Renewal theory is a statistical approach to model the formation and destruction of phase singularities (PS), which occur at the pivots of spiral waves. A common issue arising during observation of renewal processes is an inspection paradox, due to oversampling of longer events. The objective of this study was to characterise the effect of a potential inspection paradox on the perception of PS lifetimes in cardiac fibrillation. Methods: A multisystem, multi-modality study was performed, examining computational simulations (Aliev-Panfilov (APV) model, Courtmanche-Nattel model), experimentally acquired optical mapping Atrial and Ventricular Fibrillation (AF/VF) data, and clinically acquired human AF and VF. Distributions of all PS lifetimes across full epochs of AF, VF, or computational simulations, were compared with distributions formed from lifetimes of PS existing at 10,000 simulated commencement timepoints. Results: In all systems, an inspection paradox led towards oversampling of PS with longer lifetimes. In APV computational simulations there was a mean PS lifetime shift of +84.9% (95% CI, ± 0.3%) (p < 0.001 for observed vs overall), in Courtmanche-Nattel simulations of AF +692.9% (95% CI, ±57.7%) (p < 0.001), in optically mapped rat AF +374.6% (95% CI, ± 88.5%) (p = 0.052), in human AF mapped with basket catheters +129.2% (95% CI, ±4.1%) (p < 0.05), human AF-HD grid catheters 150.8% (95% CI, ± 9.0%) (p < 0.001), in optically mapped rat VF +171.3% (95% CI, ±15.6%) (p < 0.001), in human epicardial VF 153.5% (95% CI, ±15.7%) (p < 0.001). Conclusion: Visual inspection of phase movies has the potential to systematically oversample longer lasting PS, due to an inspection paradox. An inspection paradox is minimised by consideration of the overall distribution of PS lifetimes.
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Affiliation(s)
- Evan V Jenkins
- College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia
| | - Dhani Dharmaprani
- College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia.,College of Science and Engineering, Flinders University, Adelaide, SA, Australia
| | - Madeline Schopp
- College of Science and Engineering, Flinders University, Adelaide, SA, Australia
| | - Jing Xian Quah
- College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia.,Department of Cardiovascular Medicine, Flinders Medical Centre, Adelaide, SA, Australia
| | - Kathryn Tiver
- Department of Cardiovascular Medicine, Flinders Medical Centre, Adelaide, SA, Australia
| | - Lewis Mitchell
- School of Mathematical Sciences, University of Adelaide, Adelaide, SA, Australia
| | - Feng Xiong
- Montréal Heart Institute and Université de Montréal, Montréal, QC, Canada
| | - Martin Aguilar
- Montréal Heart Institute and Université de Montréal, Montréal, QC, Canada
| | - Kenneth Pope
- College of Science and Engineering, Flinders University, Adelaide, SA, Australia
| | - Fadi G Akar
- School of Medicine, Yale University, New Haven, CT, United States
| | - Caroline H Roney
- School of Engineering and Materials Science, Queen Mary University of London, London, United Kingdom
| | - Steven A Niederer
- School of Biomedical Engineering and Imaging Sciences, Kings College London, London, United Kingdom
| | - Stanley Nattel
- Montréal Heart Institute and Université de Montréal, Montréal, QC, Canada
| | - Martyn P Nash
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Richard H Clayton
- Insigneo Institute for in Silico Medicine and Department of Computer Science, University of Sheffield, Sheffield, United Kingdom
| | - Anand N Ganesan
- College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia.,Department of Cardiovascular Medicine, Flinders Medical Centre, Adelaide, SA, Australia
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23
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Roney CH, Sillett C, Whitaker J, Lemus JAS, Sim I, Kotadia I, O'Neill M, Williams SE, Niederer SA. Applications of multimodality imaging for left atrial catheter ablation. Eur Heart J Cardiovasc Imaging 2021; 23:31-41. [PMID: 34747450 PMCID: PMC8685603 DOI: 10.1093/ehjci/jeab205] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Indexed: 11/13/2022] Open
Abstract
Atrial arrhythmias, including atrial fibrillation and atrial flutter, may be treated through catheter ablation. The process of atrial arrhythmia catheter ablation, which includes patient selection, pre-procedural planning, intra-procedural guidance, and post-procedural assessment, is typically characterized by the use of several imaging modalities to sequentially inform key clinical decisions. Increasingly, advanced imaging modalities are processed via specialized image analysis techniques and combined with intra-procedural electrical measurements to inform treatment approaches. Here, we review the use of multimodality imaging for left atrial ablation procedures. The article first outlines how imaging modalities are routinely used in the peri-ablation period. We then describe how advanced imaging techniques may inform patient selection for ablation and ablation targets themselves. Ongoing research directions for improving catheter ablation outcomes by using imaging combined with advanced analyses for personalization of ablation targets are discussed, together with approaches for their integration in the standard clinical environment. Finally, we describe future research areas with the potential to improve catheter ablation outcomes.
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Affiliation(s)
- Caroline H Roney
- School of Biomedical Engineering and Imaging Sciences, King's College, London, UK
| | - Charles Sillett
- School of Biomedical Engineering and Imaging Sciences, King's College, London, UK
| | - John Whitaker
- School of Biomedical Engineering and Imaging Sciences, King's College, London, UK
| | | | - Iain Sim
- School of Biomedical Engineering and Imaging Sciences, King's College, London, UK
| | - Irum Kotadia
- School of Biomedical Engineering and Imaging Sciences, King's College, London, UK
| | - Mark O'Neill
- School of Biomedical Engineering and Imaging Sciences, King's College, London, UK
| | - Steven E Williams
- School of Biomedical Engineering and Imaging Sciences, King's College, London, UK
- Centre for Cardiovascular Science, The University of Edinburgh, Scotland, UK
| | - Steven A Niederer
- School of Biomedical Engineering and Imaging Sciences, King's College, London, UK
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24
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Nakamura T, Sasano T. Artificial intelligence and cardiology: Current status and perspective: Artificial Intelligence and Cardiology. J Cardiol 2021; 79:326-333. [PMID: 34895982 DOI: 10.1016/j.jjcc.2021.11.017] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 10/27/2021] [Indexed: 12/19/2022]
Abstract
The development of artificial intelligence (AI) began in the mid-20th century but has been rapidly accelerating in the past decade. Reflecting the development of digital health over the past few years, this trend is also seen in medicine. The field of cardiovascular medicine uses a wide variety and a large amount of biosignals, so there are many situations where AI can contribute. The development of AI is in progress for all aspects of the healthcare system, including the prevention, screening, and treatment of diseases and the prediction of the prognosis. AI is expected to be used to provide specialist-level medical care, even in a situation where medical resources are scarce. However, like other medical devices, the concept and mechanism of AI must be fully understood when used; otherwise, it may be used inappropriately, resulting in detriment to the patient. Therefore, it is important to understand what we need to know as a cardiologist handling AI. This review introduces the basics and principles of AI, then shows how far the current development of AI has come, and finally gives a brief introduction of how to start the AI development for those who want to develop their own AI.
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Affiliation(s)
- Tomofumi Nakamura
- Department of Cardiovascular Medicine, Tokyo Medical and Dental University, Tokyo, Japan
| | - Tetsuo Sasano
- Department of Cardiovascular Medicine, Tokyo Medical and Dental University, Tokyo, Japan.
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25
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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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26
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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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27
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Heijman J, Sutanto H, Crijns HJGM, Nattel S, Trayanova NA. Computational models of atrial fibrillation: achievements, challenges, and perspectives for improving clinical care. Cardiovasc Res 2021; 117:1682-1699. [PMID: 33890620 PMCID: PMC8208751 DOI: 10.1093/cvr/cvab138] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Indexed: 12/11/2022] Open
Abstract
Despite significant advances in its detection, understanding and management, atrial fibrillation (AF) remains a highly prevalent cardiac arrhythmia with a major impact on morbidity and mortality of millions of patients. AF results from complex, dynamic interactions between risk factors and comorbidities that induce diverse atrial remodelling processes. Atrial remodelling increases AF vulnerability and persistence, while promoting disease progression. The variability in presentation and wide range of mechanisms involved in initiation, maintenance and progression of AF, as well as its associated adverse outcomes, make the early identification of causal factors modifiable with therapeutic interventions challenging, likely contributing to suboptimal efficacy of current AF management. Computational modelling facilitates the multilevel integration of multiple datasets and offers new opportunities for mechanistic understanding, risk prediction and personalized therapy. Mathematical simulations of cardiac electrophysiology have been around for 60 years and are being increasingly used to improve our understanding of AF mechanisms and guide AF therapy. This narrative review focuses on the emerging and future applications of computational modelling in AF management. We summarize clinical challenges that may benefit from computational modelling, provide an overview of the different in silico approaches that are available together with their notable achievements, and discuss the major limitations that hinder the routine clinical application of these approaches. Finally, future perspectives are addressed. With the rapid progress in electronic technologies including computing, clinical applications of computational modelling are advancing rapidly. We expect that their application will progressively increase in prominence, especially if their added value can be demonstrated in clinical trials.
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Affiliation(s)
- Jordi Heijman
- Department of Cardiology, Cardiovascular Research Institute Maastricht, Faculty of Health, Medicine, and Life Sciences, Maastricht University, PO Box 616, 6200 MD Maastricht, The Netherlands
| | - Henry Sutanto
- Department of Cardiology, Cardiovascular Research Institute Maastricht, Faculty of Health, Medicine, and Life Sciences, Maastricht University, PO Box 616, 6200 MD Maastricht, The Netherlands
| | - Harry J G M Crijns
- Department of Cardiology, Cardiovascular Research Institute Maastricht, Faculty of Health, Medicine, and Life Sciences, Maastricht University, PO Box 616, 6200 MD Maastricht, The Netherlands
| | - Stanley Nattel
- Department of Medicine, Montreal Heart Institute and Université de Montréal, Montreal, Canada
- Department of Pharmacology and Therapeutics, McGill University, Montreal, Canada
- Institute of Pharmacology, West German Heart and Vascular Center, Faculty of Medicine, University Duisburg-Essen, Duisburg, Germany
- IHU Liryc and Fondation Bordeaux Université, Bordeaux, France
| | - Natalia A Trayanova
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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28
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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: 1] [Impact Index Per Article: 0.3] [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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29
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Precision Medicine Approaches to Cardiac Arrhythmias: JACC Focus Seminar 4/5. J Am Coll Cardiol 2021; 77:2573-2591. [PMID: 34016268 DOI: 10.1016/j.jacc.2021.03.325] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 03/31/2021] [Indexed: 12/15/2022]
Abstract
In the initial 3 papers in this Focus Seminar series, the fundamentals and key concepts of precision medicine were reviewed, followed by a focus on precision medicine in the context of vascular disease and cardiomyopathy. For the remaining 2 papers, we focus on precision medicine in the context of arrhythmias. Specifically, in this fourth paper we focus on long QT syndrome, Brugada syndrome, and atrial fibrillation. The final (fifth) paper will deal with catecholaminergic polymorphic ventricular tachycardia. These arrhythmias represent a spectrum of disease ranging from common to relatively rare, with very different genetic and environmental causative factors, and with differing clinical manifestations that range from almost no consequences to lethality in childhood or adolescence if untreated. Accordingly, the emerging precision medicine approaches to these arrhythmias vary significantly, but several common themes include increased use of genetic testing, avoidance of triggers, and personalized risk stratification to guide the use of arrhythmia-specific therapies.
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30
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Carrick RT, Benson BE, Bates ORJ, Spector PS. Competitive Drivers of Atrial Fibrillation: The Interplay Between Focal Drivers and Multiwavelet Reentry. Front Physiol 2021; 12:633643. [PMID: 33796028 PMCID: PMC8007783 DOI: 10.3389/fphys.2021.633643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 02/22/2021] [Indexed: 11/15/2022] Open
Abstract
Background There is debate whether human atrial fibrillation is driven by focal drivers or multiwavelet reentry. We propose that the changing activation sequences surrounding a focal driver can at times self-sustain in the absence of that driver. Further, the relationship between focal drivers and surrounding chaotic activation is bidirectional; focal drivers can generate chaotic activation, which may affect the dynamics of focal drivers. Methods and Results In a propagation model, we generated tissues that support structural micro-reentry and moving functional reentrant circuits. We qualitatively assessed (1) the tissue’s ability to support self-sustaining fibrillation after elimination of the focal driver, (2) the impact that structural-reentrant substrate has on the duration of fibrillation, the impact that micro-reentrant (3) frequency, (4) excitable gap, and (5) exposure to surrounding fibrillation have on micro-reentry in the setting of chaotic activation, and finally the likelihood fibrillation will end in structural reentry based on (6) the distance between and (7) the relative lengths of an ablated tissue’s inner and outer boundaries. We found (1) focal drivers produced chaotic activation when waves encountered heterogeneous refractoriness; chaotic activation could then repeatedly initiate and terminate micro-reentry. Perpetuation of fibrillation following elimination of micro-reentry was predicted by tissue properties. (2) Duration of fibrillation was increased by the presence of a structural micro-reentrant substrate only when surrounding tissue had a low propensity to support self-sustaining chaotic activation. Likelihood of micro-reentry around the structural reentrant substrate increased as (3) the frequency of structural reentry increased relative to the frequency of fibrillation in the surrounding tissue, (4) the excitable gap of micro-reentry increased, and (5) the exposure of the structural circuit to the surrounding tissue decreased. Likelihood of organized tachycardia following termination of fibrillation increased with (6) decreasing distance and (7) disparity of size between focal obstacle and external boundary. Conclusion Focal drivers such as structural micro-reentry and the chaotic activation they produce are continuously interacting with one another. In order to accurately describe cardiac tissue’s propensity to support fibrillation, the relative characteristics of both stationary and moving drivers must be taken into account.
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Affiliation(s)
- Richard T Carrick
- College of Medicine, University of Vermont, Burlington, VT, United States.,College of Engineering and Mathematical Sciences, University of Vermont, Burlington, VT, United States
| | - Bryce E Benson
- College of Engineering and Mathematical Sciences, University of Vermont, Burlington, VT, United States
| | - Oliver R J Bates
- College of Engineering, Boston University, Boston, MA, United States
| | - Peter S Spector
- College of Medicine, University of Vermont, Burlington, VT, United States.,College of Engineering and Mathematical Sciences, University of Vermont, Burlington, VT, United States
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31
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Azzolin L, Schuler S, Dössel O, Loewe A. A Reproducible Protocol to Assess Arrhythmia Vulnerability in silico: Pacing at the End of the Effective Refractory Period. Front Physiol 2021; 12:656411. [PMID: 33868025 PMCID: PMC8047415 DOI: 10.3389/fphys.2021.656411] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 03/10/2021] [Indexed: 01/31/2023] Open
Abstract
In both clinical and computational studies, different pacing protocols are used to induce arrhythmia and non-inducibility is often considered as the endpoint of treatment. The need for a standardized methodology is urgent since the choice of the protocol used to induce arrhythmia could lead to contrasting results, e.g., in assessing atrial fibrillation (AF) vulnerabilty. Therefore, we propose a novel method-pacing at the end of the effective refractory period (PEERP)-and compare it to state-of-the-art protocols, such as phase singularity distribution (PSD) and rapid pacing (RP) in a computational study. All methods were tested by pacing from evenly distributed endocardial points at 1 cm inter-point distance in two bi-atrial geometries. Seven different atrial models were implemented: five cases without specific AF-induced remodeling but with decreasing global conduction velocity and two persistent AF cases with an increasing amount of fibrosis resembling different substrate remodeling stages. Compared with PSD and RP, PEERP induced a larger variety of arrhythmia complexity requiring, on average, only 2.7 extra-stimuli and 3 s of simulation time to initiate reentry. Moreover, PEERP and PSD were the protocols which unveiled a larger number of areas vulnerable to sustain stable long living reentries compared to RP. Finally, PEERP can foster standardization and reproducibility, since, in contrast to the other protocols, it is a parameter-free method. Furthermore, we discuss its clinical applicability. We conclude that the choice of the inducing protocol has an influence on both initiation and maintenance of AF and we propose and provide PEERP as a reproducible method to assess arrhythmia vulnerability.
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Affiliation(s)
- Luca Azzolin
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
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32
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Corrado C, Williams S, Roney C, Plank G, O'Neill M, Niederer S. Using machine learning to identify local cellular properties that support re-entrant activation in patient-specific models of atrial fibrillation. Europace 2021; 23:i12-i20. [PMID: 33437987 PMCID: PMC7943361 DOI: 10.1093/europace/euaa386] [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: 11/26/2020] [Accepted: 12/15/2020] [Indexed: 11/17/2022] Open
Abstract
AIMS Atrial fibrillation (AF) is sustained by re-entrant activation patterns. Ablation strategies have been proposed that target regions of tissue that may support re-entrant activation patterns. We aimed to characterize the tissue properties associated with regions that tether re-entrant activation patterns in a validated virtual patient cohort. METHODS AND RESULTS Atrial fibrillation patient-specific models (seven paroxysmal and three persistent) were generated and validated against local activation time (LAT) measurements during an S1-S2 pacing protocol from the coronary sinus and high right atrium, respectively. Atrial models were stimulated with burst pacing from three locations in the proximity of each pulmonary vein to initiate re-entrant activation patterns. Five atria exhibited sustained activation patterns for at least 80 s. Models with short maximum action potential durations (APDs) were associated with sustained activation. Phase singularities were mapped across the atria sustained activation patterns. Regions with a low maximum conduction velocity (CV) were associated with tethering of phase singularities. A support vector machine (SVM) was trained on maximum local conduction velocity and action potential duration to identify regions that tether phase singularities. The SVM identified regions of tissue that could support tethering with 91% accuracy. This accuracy increased to 95% when the SVM was also trained on surface area. CONCLUSION In a virtual patient cohort, local tissue properties, that can be measured (CV) or estimated (APD; using effective refractory period as a surrogate) clinically, identified regions of tissue that tether phase singularities. Combing CV and APD with atrial surface area further improved the accuracy in identifying regions that tether phase singularities.
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Affiliation(s)
- Cesare Corrado
- Department of Biomedical Engineering, King's College London, 4th floor North Wing St Thomas' Hospital, Westminster Bridge Road, London SE17EH, UK
| | - Steven Williams
- Department of Biomedical Engineering, King's College London, 4th floor North Wing St Thomas' Hospital, Westminster Bridge Road, London SE17EH, UK
| | - Caroline Roney
- Department of Biomedical Engineering, King's College London, 4th floor North Wing St Thomas' Hospital, Westminster Bridge Road, London SE17EH, UK
| | - Gernot Plank
- Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Mark O'Neill
- Department of Biomedical Engineering, King's College London, 4th floor North Wing St Thomas' Hospital, Westminster Bridge Road, London SE17EH, UK
| | - Steven Niederer
- Department of Biomedical Engineering, King's College London, 4th floor North Wing St Thomas' Hospital, Westminster Bridge Road, London SE17EH, UK
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Abstract
Machine learning (ML), a branch of artificial intelligence, where machines learn from big data, is at the crest of a technological wave of change sweeping society. Cardiovascular medicine is at the forefront of many ML applications, and there is a significant effort to bring them into mainstream clinical practice. In the field of cardiac electrophysiology, ML applications have also seen a rapid growth and popularity, particularly the use of ML in the automatic interpretation of ECGs, which has been extensively covered in the literature. Much lesser known are the other aspects of ML application in cardiac electrophysiology and arrhythmias, such as those in basic science research on arrhythmia mechanisms, both experimental and computational; in the development of better techniques for mapping of cardiac electrical function; and in translational research related to arrhythmia management. In the current review, we examine comprehensively such ML applications as they match the scope of this journal. The current review is organized in 3 parts. The first provides an overview of general ML principles and methodologies that will afford readers of the necessary information on the subject, serving as the foundation for inviting further ML applications in arrhythmia research. The basic information we provide can serve as a guide on how one might design and conduct an ML study. The second part is a review of arrhythmia and electrophysiology studies in which ML has been utilized, highlighting the broad potential of ML approaches. For each subject, we outline comprehensively the general topics, while reviewing some of the research advances utilizing ML under the subject. Finally, we discuss the main challenges and the perspectives for ML-driven cardiac electrophysiology and arrhythmia research.
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Affiliation(s)
- Natalia A. Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, USA 21218
- Alliance for Cardiovascular Diagnosis and Treatment Innovation, Whiting School of Engineering and School of Medicine, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, USA 21218
- Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 733 North Broadway, Baltimore, MD, USA 21205
| | - Dan M. Popescu
- Alliance for Cardiovascular Diagnosis and Treatment Innovation, Whiting School of Engineering and School of Medicine, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, USA 21218
- Department of Applied Mathematics and Statistics, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, USA 21218
| | - Julie K. Shade
- Department of Biomedical Engineering, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, USA 21218
- Alliance for Cardiovascular Diagnosis and Treatment Innovation, Whiting School of Engineering and School of Medicine, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, USA 21218
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