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Sharp AJ, Betts TR, Banerjee A. Leveraging 3D Atrial Geometry for the Evaluation of Atrial Fibrillation: A Comprehensive Review. J Clin Med 2024; 13:4442. [PMID: 39124709 PMCID: PMC11313299 DOI: 10.3390/jcm13154442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2024] [Revised: 07/19/2024] [Accepted: 07/23/2024] [Indexed: 08/12/2024] Open
Abstract
Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia associated with significant morbidity and mortality. Managing risk of stroke and AF burden are pillars of AF management. Atrial geometry has long been recognized as a useful measure in achieving these goals. However, traditional diagnostic approaches often overlook the complex spatial dynamics of the atria. This review explores the emerging role of three-dimensional (3D) atrial geometry in the evaluation and management of AF. Advancements in imaging technologies and computational modeling have enabled detailed reconstructions of atrial anatomy, providing insights into the pathophysiology of AF that were previously unattainable. We examine current methodologies for interpreting 3D atrial data, including qualitative, basic quantitative, global quantitative, and statistical shape modeling approaches. We discuss their integration into clinical practice, highlighting potential benefits such as personalized treatment strategies, improved outcome prediction, and informed treatment approaches. Additionally, we discuss the challenges and limitations associated with current approaches, including technical constraints and variable interpretations, and propose future directions for research and clinical applications. This comprehensive review underscores the transformative potential of leveraging 3D atrial geometry in the evaluation and management of AF, advocating for its broader adoption in clinical practice.
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Affiliation(s)
- Alexander J. Sharp
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, UK
| | - Timothy R. Betts
- Cardiology Department, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
| | - Abhirup Banerjee
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, UK
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 9DU, UK
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2
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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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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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Masè M, Cristoforetti A, Pelloni S, Ravelli F. Systematic in-silico evaluation of fibrosis effects on re-entrant wave dynamics in atrial tissue. Sci Rep 2024; 14:11427. [PMID: 38763959 DOI: 10.1038/s41598-024-62002-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 05/13/2024] [Indexed: 05/21/2024] Open
Abstract
Despite the key role of fibrosis in atrial fibrillation (AF), the effects of different spatial distributions and textures of fibrosis on wave propagation mechanisms in AF are not fully understood. To clarify these aspects, we performed a systematic computational study to assess fibrosis effects on the characteristics and stability of re-entrant waves in electrically-remodelled atrial tissues. A stochastic algorithm, which generated fibrotic distributions with controlled overall amount, average size, and orientation of fibrosis elements, was implemented on a monolayer spheric atrial model. 245 simulations were run at changing fibrosis parameters. The emerging propagation patterns were quantified in terms of rate, regularity, and coupling by frequency-domain analysis of correspondent synthetic bipolar electrograms. At the increase of fibrosis amount, the rate of reentrant waves significantly decreased and higher levels of regularity and coupling were observed (p < 0.0001). Higher spatial variability and pattern stochasticity over repetitions was observed for larger amount of fibrosis, especially in the presence of patchy and compact fibrosis. Overall, propagation slowing and organization led to higher stability of re-entrant waves. These results strengthen the evidence that the amount and spatial distribution of fibrosis concur in dictating re-entry dynamics in remodeled tissue and represent key factors in AF maintenance.
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Affiliation(s)
- Michela Masè
- Laboratory of Biophysics and Translational Cardiology, Department of Cellular, Computational and Integrative Biology-CIBIO, University of Trento, Via Sommarive 18, 38123, Povo, Trento, Italy.
| | - Alessandro Cristoforetti
- Laboratory of Biophysics and Translational Cardiology, Department of Cellular, Computational and Integrative Biology-CIBIO, University of Trento, Via Sommarive 18, 38123, Povo, Trento, Italy
| | - Samuele Pelloni
- Laboratory of Biophysics and Translational Cardiology, Department of Cellular, Computational and Integrative Biology-CIBIO, University of Trento, Via Sommarive 18, 38123, Povo, Trento, Italy
| | - Flavia Ravelli
- Laboratory of Biophysics and Translational Cardiology, Department of Cellular, Computational and Integrative Biology-CIBIO, University of Trento, Via Sommarive 18, 38123, Povo, Trento, Italy
- CISMed-Centre for Medical Sciences, University of Trento, 38122, Trento, Italy
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5
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Saha S, Linz D, Saha D, McEwan A, Baumert M. Overcoming Uncertainties in Electrogram-Based Atrial Fibrillation Mapping: A Review. Cardiovasc Eng Technol 2024; 15:52-64. [PMID: 37962813 DOI: 10.1007/s13239-023-00696-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 10/31/2023] [Indexed: 11/15/2023]
Abstract
In clinical rhythmology, intracardiac bipolar electrograms (EGMs) play a critical role in investigating the triggers and substrates inducing and perpetuating atrial fibrillation (AF). However, the interpretation of bipolar EGMs is ambiguous due to several aspects of electrodes, mapping algorithms and wave propagation dynamics, so it requires several variables to describe the effects of these uncertainties on EGM analysis. In this narrative review, we critically evaluate the potential impact of such uncertainties on the design of cardiac mapping tools on AF-related substrate characterization. Literature suggest uncertainties are due to several variables, including the wave propagation vector, the wave's incidence angle, inter-electrode spacing, electrode size and shape, and tissue contact. The preprocessing of the EGM signals and mapping density will impact the electro-anatomical representation and the features extracted from the local electrical activities. The superposition of multiple waves further complicates EGM interpretation. The inclusion of these uncertainties is a nontrivial problem but their consideration will yield a better interpretation of the intra-atrial dynamics in local activation patterns. From a translational perspective, this review provides a concise but complete overview of the critical variables for developing more precise cardiac mapping tools.
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Affiliation(s)
- Simanto Saha
- School of Biomedical Engineering, The University of Sydney, Sydney, NSW, 2008, Australia.
| | - Dominik Linz
- Centre for Heart Rhythm Disorders, The University of Adelaide, Adelaide, SA, 5000, Australia
| | - Dyuti Saha
- Kumudini Women's Medical College, The University of Dhaka, Tangail, 1940, Dhaka, Bangladesh
| | - Alistair McEwan
- School of Biomedical Engineering, The University of Sydney, Sydney, NSW, 2008, Australia
| | - Mathias Baumert
- School of Electrical and Mechanical Engineering, The University of Adelaide, Adelaide, SA, 5000, Australia
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Trayanova NA, Prakosa A. Up digital and personal: How heart digital twins can transform heart patient care. Heart Rhythm 2024; 21:89-99. [PMID: 37871809 PMCID: PMC10872898 DOI: 10.1016/j.hrthm.2023.10.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 10/12/2023] [Accepted: 10/15/2023] [Indexed: 10/25/2023]
Abstract
Precision medicine is the vision of health care where therapy is tailored to each patient. As part of this vision, digital twinning technology promises to deliver a digital representation of organs or even patients by using tools capable of simulating personal health conditions and predicting patient or disease trajectories on the basis of relationships learned both from data and from biophysics knowledge. Such virtual replicas would update themselves with data from monitoring devices and medical tests and assessments, reflecting dynamically the changes in our health conditions and the responses to treatment. In precision cardiology, the concepts and initial applications of heart digital twins have slowly been gaining popularity and the trust of the clinical community. In this article, we review the advancement in heart digital twinning and its initial translation to the management of heart rhythm disorders.
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Affiliation(s)
- Natalia A Trayanova
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland.
| | - Adityo Prakosa
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland
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7
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Galappaththige S, Pathmanathan P, Gray RA. A computational modeling framework for pre-clinical evaluation of cardiac mapping systems. Front Physiol 2023; 14:1074527. [PMID: 37485068 PMCID: PMC10358980 DOI: 10.3389/fphys.2023.1074527] [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: 10/19/2022] [Accepted: 05/31/2023] [Indexed: 07/25/2023] Open
Abstract
There are a variety of difficulties in evaluating clinical cardiac mapping systems, most notably the inability to record the transmembrane potential throughout the entire heart during patient procedures which prevents the comparison to a relevant "gold standard". Cardiac mapping systems are comprised of hardware and software elements including sophisticated mathematical algorithms, both of which continue to undergo rapid innovation. The purpose of this study is to develop a computational modeling framework to evaluate the performance of cardiac mapping systems. The framework enables rigorous evaluation of a mapping system's ability to localize and characterize (i.e., focal or reentrant) arrhythmogenic sources in the heart. The main component of our tool is a library of computer simulations of various dynamic patterns throughout the entire heart in which the type and location of the arrhythmogenic sources are known. Our framework allows for performance evaluation for various electrode configurations, heart geometries, arrhythmias, and electrogram noise levels and involves blind comparison of mapping systems against a "silver standard" comprised of computer simulations in which the precise transmembrane potential patterns throughout the heart are known. A feasibility study was performed using simulations of patterns in the human left atria and three hypothetical virtual catheter electrode arrays. Activation times (AcT) and patterns (AcP) were computed for three virtual electrode arrays: two basket arrays with good and poor contact and one high-resolution grid with uniform spacing. The average root mean squared difference of AcTs of electrograms and those of the nearest endocardial action potential was less than 1 ms and therefore appears to be a poor performance metric. In an effort to standardize performance evaluation of mapping systems a novel performance metric is introduced based on the number of AcPs identified correctly and those considered spurious as well as misclassifications of arrhythmia type; spatial and temporal localization accuracy of correctly identified patterns was also quantified. This approach provides a rigorous quantitative analysis of cardiac mapping system performance. Proof of concept of this computational evaluation framework suggests that it could help safeguard that mapping systems perform as expected as well as provide estimates of system accuracy.
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Ogiermann D, Perotti LE, Balzani D. A simple and efficient adaptive time stepping technique for low-order operator splitting schemes applied to cardiac electrophysiology. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2023; 39:e3670. [PMID: 36510350 DOI: 10.1002/cnm.3670] [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: 12/20/2021] [Revised: 10/25/2022] [Accepted: 12/04/2022] [Indexed: 06/17/2023]
Abstract
We present a simple, yet efficient adaptive time stepping scheme for cardiac electrophysiology (EP) simulations based on standard operator splitting techniques. The general idea is to exploit the relation between the splitting error and the reaction's magnitude-found in a previous one-dimensional analytical study by Spiteri and Ziaratgahi-to construct the new time step controller for three-dimensional problems. Accordingly, we propose to control the time step length of the operator splitting scheme as a function of the reaction magnitude, in addition to the common approach of adapting the reaction time step. This conforms with observations in numerical experiments supporting the need for a significantly smaller time step length during depolarization than during repolarization. The proposed scheme is compared with classical proportional-integral-differential controllers using state-of-the-art error estimators, which are also presented in details as they have not been previously applied in the context of cardiac EP with operator splitting techniques. Benchmarks show that choosing the time step as a sigmoidal function of the reaction magnitude is highly efficient and full cardiac cycles can be computed with precision even in a realistic biventricular setup. The proposed scheme outperforms common adaptive time stepping techniques, while depending on fewer tuning parameters.
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Affiliation(s)
- Dennis Ogiermann
- Chair of Continuum Mechanics, Ruhr University Bochum, Bochum, Germany
| | - Luigi E Perotti
- Mechanical and Aerospace Engineering Department, University of Central Florida, Orlando, Florida, USA
| | - Daniel Balzani
- Chair of Continuum Mechanics, Ruhr University Bochum, Bochum, Germany
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9
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Rossi S, Abdala L, Woodward A, Vavalle JP, Henriquez CS, Griffith BE. Rule-based definition of muscle bundles in patient-specific models of the left atrium. Front Physiol 2022; 13:912947. [PMID: 36311246 PMCID: PMC9597256 DOI: 10.3389/fphys.2022.912947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 06/29/2022] [Indexed: 11/13/2022] Open
Abstract
Atrial fibrillation (AF) is the most common arrhythmia encountered clinically, and as the population ages, its prevalence is increasing. Although the CHA2DS2- VASc score is the most used risk-stratification system for stroke risk in AF, it lacks personalization. Patient-specific computer models of the atria can facilitate personalized risk assessment and treatment planning. However, a challenge faced in creating such models is the complexity of the atrial muscle arrangement and its influence on the atrial fiber architecture. This work proposes a semi-automated rule-based algorithm to generate the local fiber orientation in the left atrium (LA). We use the solutions of several harmonic equations to decompose the LA anatomy into subregions. Solution gradients define a two-layer fiber field in each subregion. The robustness of our approach is demonstrated by recreating the fiber orientation on nine models of the LA obtained from AF patients who underwent WATCHMAN device implantation. This cohort of patients encompasses a variety of morphology variants of the left atrium, both in terms of the left atrial appendages (LAAs) and the number of pulmonary veins (PVs). We test the fiber construction algorithm by performing electrophysiology (EP) simulations. Furthermore, this study is the first to compare its results with other rule-based algorithms for the LA fiber architecture definition available in the literature. This analysis suggests that a multi-layer fiber architecture is important to capture complex electrical activation patterns. A notable advantage of our approach is the ability to reconstruct the main LA fiber bundles in a variety of morphologies while solving for a small number of harmonic fields, leading to a comparatively straightforward and reproducible approach.
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Affiliation(s)
- Simone Rossi
- Department of Mathematics, UNC Chapel Hill, Chapel Hill, NC, United States
| | - Laryssa Abdala
- Department of Mathematics, UNC Chapel Hill, Chapel Hill, NC, United States
| | - Andrew Woodward
- Advanced Medical Imaging Lab, UNC Chapel Hill, Chapel Hill, NC, United States
| | - John P. Vavalle
- Department of Medicine, UNC Chapel Hill, Chapel Hill, NC, United States
| | - Craig S. Henriquez
- Department of Biomedical Engineering, Duke University, Durham, NC, United States
| | - Boyce E. Griffith
- Department of Mathematics, UNC Chapel Hill, Chapel Hill, NC, United States
- Department of Biomedical Engineering, UNC Chapel Hill, Chapel Hill, NC, United States
- McAllister Heart Institute, UNC Chapel Hill, Chapel Hill, NC, United States
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Falkenberg M, Coleman JA, Dobson S, Hickey DJ, Terrill L, Ciacci A, Thomas B, Sau A, Ng FS, Zhao J, Peters NS, Christensen K. Identifying locations susceptible to micro-anatomical reentry using a spatial network representation of atrial fibre maps. PLoS One 2022; 17:e0267166. [PMID: 35737662 PMCID: PMC9223322 DOI: 10.1371/journal.pone.0267166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 04/03/2022] [Indexed: 11/18/2022] Open
Abstract
Micro-anatomical reentry has been identified as a potential driver of atrial fibrillation (AF). In this paper, we introduce a novel computational method which aims to identify which atrial regions are most susceptible to micro-reentry. The approach, which considers the structural basis for micro-reentry only, is based on the premise that the accumulation of electrically insulating interstitial fibrosis can be modelled by simulating percolation-like phenomena on spatial networks. Our results suggest that at high coupling, where micro-reentry is rare, the micro-reentrant substrate is highly clustered in areas where the atrial walls are thin and have convex wall morphology, likely facilitating localised treatment via ablation. However, as transverse connections between fibres are removed, mimicking the accumulation of interstitial fibrosis, the substrate becomes less spatially clustered, and the bias to forming in thin, convex regions of the atria is reduced, possibly restricting the efficacy of localised ablation. Comparing our algorithm on image-based models with and without atrial fibre structure, we find that strong longitudinal fibre coupling can suppress the micro-reentrant substrate, whereas regions with disordered fibre orientations have an enhanced risk of micro-reentry. With further development, these methods may be useful for modelling the temporal development of the fibrotic substrate on an individualised basis.
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Affiliation(s)
- Max Falkenberg
- Centre for Complexity Science, Imperial College London, London, United Kingdom
- Department of Physics, Imperial College London, London, United Kingdom
- ElectroCardioMaths Programme, Imperial Centre for Cardiac Engineering, National Heart & Lung Institute, Imperial College London, London, United Kingdom
| | - James A. Coleman
- Department of Physics, Imperial College London, London, United Kingdom
| | - Sam Dobson
- Department of Physics, Imperial College London, London, United Kingdom
| | - David J. Hickey
- Department of Physics, Imperial College London, London, United Kingdom
| | - Louie Terrill
- Department of Physics, Imperial College London, London, United Kingdom
| | - Alberto Ciacci
- Centre for Complexity Science, Imperial College London, London, United Kingdom
- Department of Physics, Imperial College London, London, United Kingdom
- ElectroCardioMaths Programme, Imperial Centre for Cardiac Engineering, National Heart & Lung Institute, Imperial College London, London, United Kingdom
| | - Belvin Thomas
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Arunashis Sau
- ElectroCardioMaths Programme, Imperial Centre for Cardiac Engineering, National Heart & Lung Institute, Imperial College London, London, United Kingdom
| | - Fu Siong Ng
- ElectroCardioMaths Programme, Imperial Centre for Cardiac Engineering, National Heart & Lung Institute, Imperial College London, London, United Kingdom
| | - Jichao Zhao
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Nicholas S. Peters
- ElectroCardioMaths Programme, Imperial Centre for Cardiac Engineering, National Heart & Lung Institute, Imperial College London, London, United Kingdom
| | - Kim Christensen
- Centre for Complexity Science, Imperial College London, London, United Kingdom
- Department of Physics, Imperial College London, London, United Kingdom
- ElectroCardioMaths Programme, Imperial Centre for Cardiac Engineering, National Heart & Lung Institute, Imperial College London, London, United Kingdom
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11
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Alinezhad L, Ghalichi F, Ahmadlouydarab M, Chenaghlou M. Left atrial appendage shape impacts on the left atrial flow hemodynamics: A numerical hypothesis generating study on two cases. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 213:106506. [PMID: 34752960 DOI: 10.1016/j.cmpb.2021.106506] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Accepted: 10/25/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVES The left atrial appendage (LAA) is the most common region for thrombus formation in atrial fibrillation (AF). Morphological parameters such as shape, size, and LAA volume can cause insufficient effectiveness of available therapeutic options. This study aimed to examine blood flow inside LAA and its removal effects. Computational fluid dynamic (CFD) simulations were carried out on two patients with different morphologies. METHODS Two patients' CT was used to reconstruct the 3D geometries of the left atrium (LA) and left atrial appendage (LAA). Then, the geometries were refined in the mentioned software, and the LAA in some models was removed. Next, in generated 3D volume mesh, sinus rhythm (SR) and atrial fibrillation (AF) outflow velocity were imposed at the mitral valve as boundary conditions. Finally, CFD simulation was conducted to analyzing blood flow within LA with/without LA. RESULTS The results confirmed that velocity and vorticity decreased under AF conditions inside the LA domain for both patients. However, removing LAA may cause unpredictable consequences, due to different shape and volume of LAA. LAA removal had insignificant effects on velocity and vorticity within LA in SR-mitral outflow. However, removing LAA increased the blood flow rate by 9.15% and vorticity by 7.27% for patient one under AF rhythm (SR)-outflow. In contrast, for patient two, LAA removal in both AF and SR decreased velocity and vorticity within the LA domain. In SR-mitral outflow, velocity dropped by 18.8 %, and vorticity by 13.2%. Also, under AF velocity and vorticity decreased by 23.33% and 18.6% respectively. Meanwhile, the results indicated that the vorticity magnitude increased inside the LAA under AF associated with the risk of thrombus formation, particularly for patient one under AF. The distal part of LAA in both patients was the most common region for blood stasis because of the lowest velocity magnitude. CONCLUSION Overall, the morphology of LAA could be the critical parameter to determine the possibility of thrombosis formation, particularly under AF conditions. High volume, low blood flow velocity and two-lobe-appendage are more likely to have blood stasis. Furthermore, the morphology difference can affect the LAA removal result and make it more complicated. So, it could be challenging to generalize LAA removal as a therapeutic option for different patients. The implication of this CFD observation needs more investigation.
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Affiliation(s)
- Lida Alinezhad
- Department of Biomedical Engineering, Division of Biomechanics, Sahand University of Technology, Tabriz, Iran
| | - Farzan Ghalichi
- Department of Biomedical Engineering, Division of Biomechanics, Sahand University of Technology, Tabriz, Iran
| | - Majid Ahmadlouydarab
- Faculty of Chemical & Petroleum Engineering, University of Tabriz, Tabriz, Iran.
| | - Maryam Chenaghlou
- Cardiovascular Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
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12
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Seno H, Yamazaki M, Shibata N, Sakuma I, Tomii N. In-Silico Deep Reinforcement Learning for Effective Cardiac Ablation Strategy. J Med Biol Eng 2021. [DOI: 10.1007/s40846-021-00664-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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13
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Nagarajan VD, Lee SL, Robertus JL, Nienaber CA, Trayanova NA, Ernst S. Artificial intelligence in the diagnosis and management of arrhythmias. Eur Heart J 2021; 42:3904-3916. [PMID: 34392353 PMCID: PMC8497074 DOI: 10.1093/eurheartj/ehab544] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2020] [Revised: 01/06/2021] [Accepted: 07/27/2021] [Indexed: 01/05/2023] Open
Abstract
The field of cardiac electrophysiology (EP) had adopted simple artificial intelligence (AI) methodologies for decades. Recent renewed interest in deep learning techniques has opened new frontiers in electrocardiography analysis including signature identification of diseased states. Artificial intelligence advances coupled with simultaneous rapid growth in computational power, sensor technology, and availability of web-based platforms have seen the rapid growth of AI-aided applications and big data research. Changing lifestyles with an expansion of the concept of internet of things and advancements in telecommunication technology have opened doors to population-based detection of atrial fibrillation in ways, which were previously unimaginable. Artificial intelligence-aided advances in 3D cardiac imaging heralded the concept of virtual hearts and the simulation of cardiac arrhythmias. Robotics, completely non-invasive ablation therapy, and the concept of extended realities show promise to revolutionize the future of EP. In this review, we discuss the impact of AI and recent technological advances in all aspects of arrhythmia care.
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Affiliation(s)
- Venkat D Nagarajan
- Department of Cardiology, Royal Brompton and Harefield NHS Foundation Trust, Sydney Street, London SW3 6NP, UK.,Department of Cardiology, Doncaster and Bassetlaw Hospitals, NHS Foundation Trust, Thorne Road, Doncaster DN2 5LT, UK
| | - Su-Lin Lee
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), UCL, Foley Street, London W1W 7TS, UK
| | - Jan-Lukas Robertus
- Department of Pathology, Royal Brompton and Harefield NHS Foundation Trust, Sydney Street, London SW3 6NP, UK.,National Heart and Lung Institute, Imperial College London, Guy Scadding Building, Dovehouse St, London SW3 6LY, UK
| | - Christoph A Nienaber
- Department of Cardiology, Royal Brompton and Harefield NHS Foundation Trust, Sydney Street, London SW3 6NP, UK.,National Heart and Lung Institute, Imperial College London, Guy Scadding Building, Dovehouse St, London SW3 6LY, UK
| | - Natalia A Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Charles Street, Baltimore, MD 21218, USA
| | - Sabine Ernst
- Department of Cardiology, Royal Brompton and Harefield NHS Foundation Trust, Sydney Street, London SW3 6NP, UK.,National Heart and Lung Institute, Imperial College London, Guy Scadding Building, Dovehouse St, London SW3 6LY, UK
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14
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Bai J, Lu Y, Zhu Y, Wang H, Yin D, Zhang H, Franco D, Zhao J. Understanding PITX2-Dependent Atrial Fibrillation Mechanisms through Computational Models. Int J Mol Sci 2021; 22:7681. [PMID: 34299303 PMCID: PMC8307824 DOI: 10.3390/ijms22147681] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 07/14/2021] [Accepted: 07/16/2021] [Indexed: 01/11/2023] Open
Abstract
Atrial fibrillation (AF) is a common arrhythmia. Better prevention and treatment of AF are needed to reduce AF-associated morbidity and mortality. Several major mechanisms cause AF in patients, including genetic predispositions to AF development. Genome-wide association studies have identified a number of genetic variants in association with AF populations, with the strongest hits clustering on chromosome 4q25, close to the gene for the homeobox transcription PITX2. Because of the inherent complexity of the human heart, experimental and basic research is insufficient for understanding the functional impacts of PITX2 variants on AF. Linking PITX2 properties to ion channels, cells, tissues, atriums and the whole heart, computational models provide a supplementary tool for achieving a quantitative understanding of the functional role of PITX2 in remodelling atrial structure and function to predispose to AF. It is hoped that computational approaches incorporating all we know about PITX2-related structural and electrical remodelling would provide better understanding into its proarrhythmic effects leading to development of improved anti-AF therapies. In the present review, we discuss advances in atrial modelling and focus on the mechanistic links between PITX2 and AF. Challenges in applying models for improving patient health are described, as well as a summary of future perspectives.
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Affiliation(s)
- Jieyun Bai
- College of Information Science and Technology, Jinan University, Guangzhou 510632, China; (Y.L.); (Y.Z.)
- Auckland Bioengineering Institute, University of Auckland, Auckland 1010, New Zealand
| | - Yaosheng Lu
- College of Information Science and Technology, Jinan University, Guangzhou 510632, China; (Y.L.); (Y.Z.)
| | - Yijie Zhu
- College of Information Science and Technology, Jinan University, Guangzhou 510632, China; (Y.L.); (Y.Z.)
| | - Huijin Wang
- College of Information Science and Technology, Jinan University, Guangzhou 510632, China; (Y.L.); (Y.Z.)
| | - Dechun Yin
- Department of Cardiology, First Affiliated Hospital of Harbin Medical University, Harbin 150000, China;
| | - Henggui Zhang
- Biological Physics Group, School of Physics & Astronomy, The University of Manchester, Manchester M13 9PL, UK;
| | - Diego Franco
- Department of Experimental Biology, University of Jaen, 23071 Jaen, Spain;
| | - Jichao Zhao
- Auckland Bioengineering Institute, University of Auckland, Auckland 1010, New Zealand
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15
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Nedios S, Lindemann F, Heijman J, Crijns HJGM, Bollmann A, Hindricks G. Atrial remodeling and atrial fibrillation recurrence after catheter ablation : Past, present, and future developments. Herz 2021; 46:312-317. [PMID: 34223914 DOI: 10.1007/s00059-021-05050-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/25/2021] [Indexed: 12/30/2022]
Abstract
The term "atrial remodeling" is used to describe the electrical, mechanical, and structural changes associated with the presence of an arrhythmogenic substrate for atrial fibrillation. Rhythm control therapy may slow down or even reverse progressive atrial remodeling. Atrial remodeling has long been recognized as an important predictor of clinical outcomes and therapeutic success, but recent advances have highlighted its clinical relevance and revealed the implications of specific anatomical changes such as atrial asymmetry or shape. This has opened the path to computational precision medicine that captures these data in detail and combines them with other factors, to provide patient-specific solutions. The goal of precision medicine lies in improving clinical outcomes, reducing costs, and avoiding unnecessary procedures. In this article, we review the history of atrial remodeling and we summarize the insights from our research on anatomical atrial remodeling and its association with rhythm outcomes after catheter ablation. Finally, we present recent advances in the field, reflecting the beginning of a new technological era that will enable us to improve patient care by personalized patient-specific medicine.
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Affiliation(s)
- Sotirios Nedios
- Department of Electrophysiology, Heart Center at University of Leipzig, Strümpellstr. 39, 04289, Leipzig, Germany.
| | - Frank Lindemann
- Department of Electrophysiology, Heart Center at University of Leipzig, Strümpellstr. 39, 04289, Leipzig, Germany
| | - Jordi Heijman
- Department of Cardiology, CardiovascularResearch Institute Maastricht (CARIM), Maastricht University Medical Center, Maastricht, The Netherlands
| | - Harry J G M Crijns
- Department of Cardiology, CardiovascularResearch Institute Maastricht (CARIM), Maastricht University Medical Center, Maastricht, The Netherlands
| | - Andreas Bollmann
- Department of Electrophysiology, Heart Center at University of Leipzig, Strümpellstr. 39, 04289, Leipzig, Germany
| | - Gerhard Hindricks
- Department of Electrophysiology, Heart Center at University of Leipzig, Strümpellstr. 39, 04289, Leipzig, Germany
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16
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Heijman J, Sutanto H, Crijns HJGM, Nattel S, Trayanova NA. Computational models of atrial fibrillation: achievements, challenges, and perspectives for improving clinical care. Cardiovasc Res 2021; 117:1682-1699. [PMID: 33890620 PMCID: PMC8208751 DOI: 10.1093/cvr/cvab138] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Indexed: 12/11/2022] Open
Abstract
Despite significant advances in its detection, understanding and management, atrial fibrillation (AF) remains a highly prevalent cardiac arrhythmia with a major impact on morbidity and mortality of millions of patients. AF results from complex, dynamic interactions between risk factors and comorbidities that induce diverse atrial remodelling processes. Atrial remodelling increases AF vulnerability and persistence, while promoting disease progression. The variability in presentation and wide range of mechanisms involved in initiation, maintenance and progression of AF, as well as its associated adverse outcomes, make the early identification of causal factors modifiable with therapeutic interventions challenging, likely contributing to suboptimal efficacy of current AF management. Computational modelling facilitates the multilevel integration of multiple datasets and offers new opportunities for mechanistic understanding, risk prediction and personalized therapy. Mathematical simulations of cardiac electrophysiology have been around for 60 years and are being increasingly used to improve our understanding of AF mechanisms and guide AF therapy. This narrative review focuses on the emerging and future applications of computational modelling in AF management. We summarize clinical challenges that may benefit from computational modelling, provide an overview of the different in silico approaches that are available together with their notable achievements, and discuss the major limitations that hinder the routine clinical application of these approaches. Finally, future perspectives are addressed. With the rapid progress in electronic technologies including computing, clinical applications of computational modelling are advancing rapidly. We expect that their application will progressively increase in prominence, especially if their added value can be demonstrated in clinical trials.
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Affiliation(s)
- Jordi Heijman
- Department of Cardiology, Cardiovascular Research Institute Maastricht, Faculty of Health, Medicine, and Life Sciences, Maastricht University, PO Box 616, 6200 MD Maastricht, The Netherlands
| | - Henry Sutanto
- Department of Cardiology, Cardiovascular Research Institute Maastricht, Faculty of Health, Medicine, and Life Sciences, Maastricht University, PO Box 616, 6200 MD Maastricht, The Netherlands
| | - Harry J G M Crijns
- Department of Cardiology, Cardiovascular Research Institute Maastricht, Faculty of Health, Medicine, and Life Sciences, Maastricht University, PO Box 616, 6200 MD Maastricht, The Netherlands
| | - Stanley Nattel
- Department of Medicine, Montreal Heart Institute and Université de Montréal, Montreal, Canada
- Department of Pharmacology and Therapeutics, McGill University, Montreal, Canada
- Institute of Pharmacology, West German Heart and Vascular Center, Faculty of Medicine, University Duisburg-Essen, Duisburg, Germany
- IHU Liryc and Fondation Bordeaux Université, Bordeaux, France
| | - Natalia A Trayanova
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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17
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Dmour BA, Miftode RS, Iliescu Halitchi D, Anton-Paduraru DT, Iliescu Halitchi CO, Miftode IL, Mitu O, Costache AD, Stafie CS, Costache II. Latest Insights into Mechanisms behind Atrial Cardiomyopathy: It Is Not always about Ventricular Function. Diagnostics (Basel) 2021; 11:diagnostics11030449. [PMID: 33807827 PMCID: PMC8001077 DOI: 10.3390/diagnostics11030449] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2021] [Revised: 02/27/2021] [Accepted: 02/27/2021] [Indexed: 12/19/2022] Open
Abstract
Atrial cardiomyopathy (ACM) represents a constantly evolving concept, with increasing importance in contemporary research and clinical practice. A better understanding of the mechanisms involved in atrial remodeling and its clinical correlations especially with atrial fibrillation (AF) and other cardiometabolic comorbidities may induce a significant impact on the diagnosis, prognosis, and therapeutic approach of ACM-related comorbidities. Although initially described several decades ago, investigators have only recently highlighted that several renal, metabolic, and cardiovascular diseases are determining factors for atrial remodeling and subsequent ACM. Based on data from multiple recent studies, our research emphasizes the correlations between ACM and other coexisting pathologies including cardiovascular, respiratory, or metabolic diseases, with fibrosis being the most incriminated pathophysiological mechanism. In addition to the usual tests, the paraclinical assessment of ACM is increasingly based on the use of various cardiac biomarkers, while the cardiac magnetic resonance (CMR) has become an increasingly tempting diagnostic too for describing morphofunctional aspects of the heart chambers, with the gadolinium contrast enhanced CMR (LGE-CMR) emerging as a commonly used technique aiming to identify and quantify the precise extent of atrial fibrosis. Further research should be conducted in order to clarify our knowledge regarding atrial remodeling and, therefore, to develop new and improved therapeutic approaches in these patients.
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Affiliation(s)
- Bianca-Ana Dmour
- Department of Internal Medicine I (Internal Medicine), Faculty of Medicine, University of Medicine and Pharmacy “Gr. T. Popa”, 700115 Iasi, Romania;
| | - Radu-Stefan Miftode
- Department of Internal Medicine I (Cardiology), Faculty of Medicine, University of Medicine and Pharmacy “Gr. T. Popa”, 700115 Iasi, Romania; (D.I.H.); (O.M.); (A.-D.C.); (I.I.C.)
- Correspondence:
| | - Dan Iliescu Halitchi
- Department of Internal Medicine I (Cardiology), Faculty of Medicine, University of Medicine and Pharmacy “Gr. T. Popa”, 700115 Iasi, Romania; (D.I.H.); (O.M.); (A.-D.C.); (I.I.C.)
| | - Dana Teodora Anton-Paduraru
- Department of Mother and Child Medicine, Faculty of Medicine, University of Medicine and Pharmacy “Gr. T. Popa”, 700115 Iasi, Romania; (D.T.A.-P.); (C.-O.I.H.)
| | - Codruta-Olimpiada Iliescu Halitchi
- Department of Mother and Child Medicine, Faculty of Medicine, University of Medicine and Pharmacy “Gr. T. Popa”, 700115 Iasi, Romania; (D.T.A.-P.); (C.-O.I.H.)
| | - Ionela-Larisa Miftode
- Department of Infectious Diseases (Internal Medicine II), Faculty of Medicine, University of Medicine and Pharmacy “Gr. T. Popa”, 700115 Iasi, Romania;
| | - Ovidiu Mitu
- Department of Internal Medicine I (Cardiology), Faculty of Medicine, University of Medicine and Pharmacy “Gr. T. Popa”, 700115 Iasi, Romania; (D.I.H.); (O.M.); (A.-D.C.); (I.I.C.)
| | - Alexandru-Dan Costache
- Department of Internal Medicine I (Cardiology), Faculty of Medicine, University of Medicine and Pharmacy “Gr. T. Popa”, 700115 Iasi, Romania; (D.I.H.); (O.M.); (A.-D.C.); (I.I.C.)
| | - Celina-Silvia Stafie
- Department of Preventive Medicine and Interdisciplinarity, Faculty of Medicine, University of Medicine and Pharmacy “Gr. T. Popa”, 700115 Iasi, Romania;
| | - Irina Iuliana Costache
- Department of Internal Medicine I (Cardiology), Faculty of Medicine, University of Medicine and Pharmacy “Gr. T. Popa”, 700115 Iasi, Romania; (D.I.H.); (O.M.); (A.-D.C.); (I.I.C.)
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18
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Shade JK, Ali RL, Basile D, Popescu D, Akhtar T, Marine JE, Spragg DD, Calkins H, Trayanova NA. Preprocedure Application of Machine Learning and Mechanistic Simulations Predicts Likelihood of Paroxysmal Atrial Fibrillation Recurrence Following Pulmonary Vein Isolation. Circ Arrhythm Electrophysiol 2020; 13:e008213. [PMID: 32536204 DOI: 10.1161/circep.119.008213] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
BACKGROUND Pulmonary vein isolation (PVI) is an effective treatment strategy for patients with atrial fibrillation (AF), but many experience AF recurrence and require repeat ablation procedures. The goal of this study was to develop and evaluate a methodology that combines machine learning (ML) and personalized computational modeling to predict, before PVI, which patients are most likely to experience AF recurrence after PVI. METHODS This single-center retrospective proof-of-concept study included 32 patients with documented paroxysmal AF who underwent PVI and had preprocedural late gadolinium enhanced magnetic resonance imaging. For each patient, a personalized computational model of the left atrium simulated AF induction via rapid pacing. Features were derived from pre-PVI late gadolinium enhanced magnetic resonance images and from results of simulations of AF induction. The most predictive features were used as input to a quadratic discriminant analysis ML classifier, which was trained, optimized, and evaluated with 10-fold nested cross-validation to predict the probability of AF recurrence post-PVI. RESULTS In our cohort, the ML classifier predicted probability of AF recurrence with an average validation sensitivity and specificity of 82% and 89%, respectively, and a validation area under the curve of 0.82. Dissecting the relative contributions of simulations of AF induction and raw images to the predictive capability of the ML classifier, we found that when only features from simulations of AF induction were used to train the ML classifier, its performance remained similar (validation area under the curve, 0.81). However, when only features extracted from raw images were used for training, the validation area under the curve significantly decreased (0.47). CONCLUSIONS ML and personalized computational modeling can be used together to accurately predict, using only pre-PVI late gadolinium enhanced magnetic resonance imaging scans as input, whether a patient is likely to experience AF recurrence following PVI, even when the patient cohort is small.
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Affiliation(s)
- Julie K Shade
- Alliance for Cardiovascular Diagnostic and Treatment Innovation (J.K.S., R.L.A., D.B., D.P., H.C., N.A.T.), Johns Hopkins University, Baltimore, MD.,Department of Biomedical Engineering (J.K.S., D.B., N.A.T.), Johns Hopkins University, Baltimore, MD
| | - Rheeda L Ali
- Alliance for Cardiovascular Diagnostic and Treatment Innovation (J.K.S., R.L.A., D.B., D.P., H.C., N.A.T.), Johns Hopkins University, Baltimore, MD
| | - Dante Basile
- Alliance for Cardiovascular Diagnostic and Treatment Innovation (J.K.S., R.L.A., D.B., D.P., H.C., N.A.T.), Johns Hopkins University, Baltimore, MD.,Department of Biomedical Engineering (J.K.S., D.B., N.A.T.), Johns Hopkins University, Baltimore, MD
| | - Dan Popescu
- Alliance for Cardiovascular Diagnostic and Treatment Innovation (J.K.S., R.L.A., D.B., D.P., H.C., N.A.T.), Johns Hopkins University, Baltimore, MD.,Department of Applied Math and Statistics (D.P.), Johns Hopkins University, Baltimore, MD
| | - Tauseef Akhtar
- Division of Cardiology, Department of Medicine (T.A., J.E.M., D.D.S., H.C.), Johns Hopkins University School of Medicine, Baltimore, MD
| | - Joseph E Marine
- Division of Cardiology, Department of Medicine (T.A., J.E.M., D.D.S., H.C.), Johns Hopkins University School of Medicine, Baltimore, MD
| | - David D Spragg
- Division of Cardiology, Department of Medicine (T.A., J.E.M., D.D.S., H.C.), Johns Hopkins University School of Medicine, Baltimore, MD
| | - Hugh Calkins
- Alliance for Cardiovascular Diagnostic and Treatment Innovation (J.K.S., R.L.A., D.B., D.P., H.C., N.A.T.), Johns Hopkins University, Baltimore, MD.,Division of Cardiology, Department of Medicine (T.A., J.E.M., D.D.S., H.C.), Johns Hopkins University School of Medicine, Baltimore, MD
| | - Natalia A Trayanova
- Alliance for Cardiovascular Diagnostic and Treatment Innovation (J.K.S., R.L.A., D.B., D.P., H.C., N.A.T.), Johns Hopkins University, Baltimore, MD.,Department of Biomedical Engineering (J.K.S., D.B., N.A.T.), Johns Hopkins University, Baltimore, MD.,Department of Medicine (N.A.T.), Johns Hopkins University School of Medicine, Baltimore, MD
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19
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Ciaccio EJ, Wan EY, Saluja DS, Acharya UR, Peters NS, Garan H. Addressing challenges of quantitative methodologies and event interpretation in the study of atrial fibrillation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 178:113-122. [PMID: 31416540 PMCID: PMC6748794 DOI: 10.1016/j.cmpb.2019.06.017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2019] [Revised: 05/21/2019] [Accepted: 06/14/2019] [Indexed: 05/06/2023]
Abstract
Atrial fibrillation (AF) is the commonest arrhythmia, yet the mechanisms of its onset and persistence are incompletely known. Although techniques for quantitative assessment have been investigated, there have been few attempts to integrate this information to advance disease treatment protocols. In this review, key quantitative methods for AF analysis are described, and suggestions are provided for the coordination of the available information, and to develop foci and directions for future research efforts. Quantitative biologists may have an interest in this topic in order to develop machine learning and tools for arrhythmia characterization, but they may perhaps have a minimal background in the clinical methodology and in the types of observed events and mechanistic hypotheses that have thus far been developed. We attempt to address these issues via exploration of the published literature. Although no new data is presented in this review, examples are shown of current lines of investigation, and in particular, how electrogram analysis and whole-chamber quantitative modeling of the left atrium may be useful to characterize fibrillatory patterns of activity, so as to propose avenues for more efficacious acquisition and interpretation of AF data.
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Affiliation(s)
- Edward J Ciaccio
- Department of Medicine - Division of Cardiology, Columbia University Medical Center, New York, NY, USA; ElectroCardioMaths Programme, Imperial Centre for Cardiac Engineering, Imperial College London, London, UK.
| | - Elaine Y Wan
- Department of Medicine - Division of Cardiology, Columbia University Medical Center, New York, NY, USA
| | - Deepak S Saluja
- Department of Medicine - Division of Cardiology, Columbia University Medical Center, New York, NY, USA
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | - Nicholas S Peters
- ElectroCardioMaths Programme, Imperial Centre for Cardiac Engineering, Imperial College London, London, UK
| | - Hasan Garan
- Department of Medicine - Division of Cardiology, Columbia University Medical Center, New York, NY, USA
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20
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Aronis KN, Ali RL, Liang JA, Zhou S, Trayanova NA. Understanding AF Mechanisms Through Computational Modelling and Simulations. Arrhythm Electrophysiol Rev 2019; 8:210-219. [PMID: 31463059 PMCID: PMC6702471 DOI: 10.15420/aer.2019.28.2] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Accepted: 06/17/2019] [Indexed: 12/21/2022] Open
Abstract
AF is a progressive disease of the atria, involving complex mechanisms related to its initiation, maintenance and progression. Computational modelling provides a framework for integration of experimental and clinical findings, and has emerged as an essential part of mechanistic research in AF. The authors summarise recent advancements in development of multi-scale AF models and focus on the mechanistic links between alternations in atrial structure and electrophysiology with AF. Key AF mechanisms that have been explored using atrial modelling are pulmonary vein ectopy; atrial fibrosis and fibrosis distribution; atrial wall thickness heterogeneity; atrial adipose tissue infiltration; development of repolarisation alternans; cardiac ion channel mutations; and atrial stretch with mechano-electrical feedback. They review modelling approaches that capture variability at the cohort level and provide cohort-specific mechanistic insights. The authors conclude with a summary of future perspectives, as envisioned for the contributions of atrial modelling in the mechanistic understanding of AF.
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Affiliation(s)
- Konstantinos N Aronis
- Department of Biomedical Engineering and the Institute for Computational Medicine, Johns Hopkins UniversityBaltimore, MD, US
- Division of Cardiology, Johns Hopkins HospitalBaltimore, MD, US
| | - Rheeda L Ali
- Department of Biomedical Engineering and the Institute for Computational Medicine, Johns Hopkins UniversityBaltimore, MD, US
| | - Jialiu A Liang
- Department of Biomedical Engineering and the Institute for Computational Medicine, Johns Hopkins UniversityBaltimore, MD, US
| | - Shijie Zhou
- Department of Biomedical Engineering and the Institute for Computational Medicine, Johns Hopkins UniversityBaltimore, MD, US
| | - Natalia A Trayanova
- Department of Biomedical Engineering and the Institute for Computational Medicine, Johns Hopkins UniversityBaltimore, MD, US
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