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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, Pope MT, Wijesurendra RS, Betts TR, Sachetto R, Bueno‐Orovio A, Rodriguez B. What determines the optimal pharmacological treatment of atrial fibrillation? Insights from in silico trials in 800 virtual atria. J Physiol 2023; 601:4013-4032. [PMID: 37475475 PMCID: PMC10952228 DOI: 10.1113/jp284730] [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: 03/22/2023] [Accepted: 07/05/2023] [Indexed: 07/22/2023] Open
Abstract
The best pharmacological treatment for each atrial fibrillation (AF) patient is unclear. We aim to exploit AF simulations in 800 virtual atria to identify key patient characteristics that guide the optimal selection of anti-arrhythmic drugs. The virtual cohort considered variability in electrophysiology and low voltage areas (LVA) and was developed and validated against experimental and clinical data from ionic currents to ECG. AF sustained in 494 (62%) atria, with large inward rectifier K+ current (IK1 ) and Na+ /K+ pump (INaK ) densities (IK1 0.11 ± 0.03 vs. 0.07 ± 0.03 S mF-1 ; INaK 0.68 ± 0.15 vs. 0.38 ± 26 S mF-1 ; sustained vs. un-sustained AF). In severely remodelled left atrium, with LVA extensions of more than 40% in the posterior wall, higher IK1 (median density 0.12 ± 0.02 S mF-1 ) was required for AF maintenance, and rotors localized in healthy right atrium. For lower LVA extensions, rotors could also anchor to LVA, in atria presenting short refractoriness (median L-type Ca2+ current, ICaL , density 0.08 ± 0.03 S mF-1 ). This atrial refractoriness, modulated by ICaL and fast Na+ current (INa ), determined pharmacological treatment success for both small and large LVA. Vernakalant was effective in atria presenting long refractoriness (median ICaL density 0.13 ± 0.05 S mF-1 ). For short refractoriness, atria with high INa (median density 8.92 ± 2.59 S mF-1 ) responded more favourably to amiodarone than flecainide, and the opposite was found in atria with low INa (median density 5.33 ± 1.41 S mF-1 ). In silico drug trials in 800 human atria identify inward currents as critical for optimal stratification of AF patient to pharmacological treatment and, together with the left atrial LVA extension, for accurately phenotyping AF dynamics. KEY POINTS: Atrial fibrillation (AF) maintenance is facilitated by small L-type Ca2+ current (ICaL ) and large inward rectifier K+ current (IK1 ) and Na+ /K+ pump. In severely remodelled left atrium, with low voltage areas (LVA) covering more than 40% of the posterior wall, sustained AF requires higher IK1 and rotors localize in healthy right atrium. For lower LVA extensions, rotors can also anchor to LVA, if the atria present short refractoriness (low ICaL ) Vernakalant is effective in atria presenting long refractoriness (high ICaL ). For short refractoriness, atria with fast Na+ current (INa ) up-regulation respond more favourably to amiodarone than flecainide, and the opposite is found in atria with low INa . The inward currents (ICaL and INa ) are critical for optimal stratification of AF patient to pharmacological treatment and, together with the left atrial LVA extension, for accurately phenotyping AF dynamics.
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Affiliation(s)
- Albert Dasí
- Department of Computer ScienceUniversity of OxfordOxfordUK
| | - Michael T.B. Pope
- Department of CardiologyOxford University Hospitals NHS Foundation TrustOxfordUK
- Department for Human Development and HealthUniversity of SouthamptonSouthamptonUK
| | - Rohan S. Wijesurendra
- Department of CardiologyOxford University Hospitals NHS Foundation TrustOxfordUK
- Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of MedicineUniversity of OxfordOxfordUK
| | - Tim R. Betts
- Department of CardiologyOxford University Hospitals NHS Foundation TrustOxfordUK
| | - Rafael Sachetto
- Departamento de Ciência da ComputaçãoUniversidade Federal de São João del‐ReiSão João del‐ReiBrazil
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Magtibay K, Massé S, Nanthakumar K, Umapathy K. Pro-arrhythmic role of adrenergic spatial densities in the human atria: An in-silico study. PLoS One 2023; 18:e0290676. [PMID: 37624832 PMCID: PMC10456151 DOI: 10.1371/journal.pone.0290676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 08/13/2023] [Indexed: 08/27/2023] Open
Abstract
Chronic stress among young patients (≤ 45 years old) could result in autonomic dysfunction. Autonomic dysfunction could be exhibited via sympathetic hyperactivity, sympathetic nerve sprouting, and diffuse adrenergic stimulation in the atria. Adrenergic spatial densities could alter atrial electrophysiology and increase arrhythmic susceptibility. Therefore, we examined the role of adrenergic spatial densities in creating arrhythmogenic substrates in silico. We simulated three 25 cm2 atrial sheets with varying adrenergic spatial densities (ASD), activation rates, and external transmembrane currents. We measured their effects on spatial and temporal heterogeneity of action potential durations (APD) at 50% and 20%. Increasing ASD shortens overall APD, and maximum spatial heterogeneity (31%) is achieved at 15% ASD. The addition of a few (5% to 10%) adrenergic elements decreases the excitation threshold, below 18 μA/cm2, while ASDs greater than 10% increase their excitation threshold up to 22 μA/cm2. Increase in ASD during rapid activation increases APD50 and APD20 by 21% and 41%, respectively. Activation times of captured beats during rapid activation could change by as much as 120 ms from the baseline cycle length. Rapidly activated atrial sheets with high ASDs significantly increase temporal heterogeneity of APD50 and APD20. Rapidly activated atrial sheets with 10% ASD have a high likelihood (0.7 ± 0.06) of fragmenting otherwise uniform wavefronts due to the transient inexcitability of adrenergically stimulated elements, producing an effective functional block. The likelihood of wave fragmentation due to ASD highly correlates with the spatial variations of APD20 (ρ = 0.90, p = 0.04). Our simulations provide a novel insight into the contributions of ASD to spatial and temporal heterogeneities of APDs, changes in excitation thresholds, and a potential explanation for wave fragmentation in the human atria due to sympathetic hyperactivity. Our work may aid in elucidating an electrophysiological link to arrhythmia initiation due to chronic stress among young patients.
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Affiliation(s)
- Karl Magtibay
- Biomedical Signal and Image Processing Laboratory, Faculty of Engineering and Architectural Science, Toronto Metropolitan University, Toronto, Ontario, Canada
| | - Stéphane Massé
- Toby Hull Cardiac Fibrillation Management Laboratory, Department of Medicine/Cardiology, Toronto General Hospital, University Health Network, Toronto, Ontario, Canada
| | - Kumaraswamy Nanthakumar
- Toby Hull Cardiac Fibrillation Management Laboratory, Department of Medicine/Cardiology, Toronto General Hospital, University Health Network, Toronto, Ontario, Canada
| | - Karthikeyan Umapathy
- Biomedical Signal and Image Processing Laboratory, Department of Electrical, Computer, and Biomedical Engineering, Faculty of Engineering and Architectural Science, Toronto Metropolitan University, Toronto, Ontario, Canada
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Dasí A, Roy A, Sachetto R, Camps J, Bueno-Orovio A, Rodriguez B. In-silico drug trials for precision medicine in atrial fibrillation: From ionic mechanisms to electrocardiogram-based predictions in structurally-healthy human atria. Front Physiol 2022; 13:966046. [PMID: 36187798 PMCID: PMC9522526 DOI: 10.3389/fphys.2022.966046] [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: 06/10/2022] [Accepted: 08/23/2022] [Indexed: 11/13/2022] Open
Abstract
Atrial fibrillation (AF) inducibility, sustainability and response to pharmacological treatment of individual patients are expected to be determined by their ionic current properties, especially in structurally-healthy atria. Mechanisms underlying AF and optimal cardioversion are however still unclear. In this study, in-silico drug trials were conducted using a population of human structurally-healthy atria models to 1) identify key ionic current properties determining AF inducibility, maintenance and pharmacological cardioversion, and 2) compare the prognostic value for predicting individual AF cardioversion of ionic current properties and electrocardiogram (ECG) metrics. In the population of structurally-healthy atria, 477 AF episodes were induced in ionic current profiles with both steep action potential duration (APD) restitution (eliciting APD alternans), and high excitability (enabling propagation at fast rates that transformed alternans into discordant). High excitability also favored 211 sustained AF episodes, so its decrease, through prolonged refractoriness, explained pharmacological cardioversion. In-silico trials over 200 AF episodes, 100 ionic profiles and 10 antiarrhythmic compounds were consistent with previous clinical trials, and identified optimal treatments for individual electrophysiological properties of the atria. Algorithms trained on 211 simulated AF episodes exhibited >70% accuracy in predictions of cardioversion for individual treatments using either ionic current profiles or ECG metrics. In structurally-healthy atria, AF inducibility and sustainability are enabled by discordant alternans, under high excitability and steep restitution conditions. Successful pharmacological cardioversion is predicted with 70% accuracy from either ionic or ECG properties, and it is optimal for treatments maximizing refractoriness (thus reducing excitability) for the given ionic current profile of the atria.
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Affiliation(s)
- Albert Dasí
- Department of Computer Science, University of Oxford, Oxford, United Kingdom,*Correspondence: Blanca Rodriguez, ; Albert Dasí,
| | - Aditi Roy
- Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | - Rafael Sachetto
- Departamento de Ciência da Computação, Universidade Federal De São João Del-Rei, São João del Rei, Brazil
| | - Julia Camps
- Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | | | - Blanca Rodriguez
- Department of Computer Science, University of Oxford, Oxford, United Kingdom,*Correspondence: Blanca Rodriguez, ; Albert Dasí,
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Cellular heterogeneity and repolarisation across the atria: an in silico study. Med Biol Eng Comput 2022; 60:3153-3168. [PMID: 36104609 PMCID: PMC9537222 DOI: 10.1007/s11517-022-02640-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 07/28/2022] [Indexed: 11/08/2022]
Abstract
Mechanisms of atrial fibrillation and the susceptibility to reentries can be impacted by the repolarization across the atria. Studies into atrial fibrillation ignore cell-to-cell heterogeneity due to electrotonic coupling. Recent studies show that cellular variability may have a larger impact on electrophysiological behaviour than assumed. This paper aims to determine the impact of cellular heterogeneity on the repolarization phase across the AF remodelled atria. Using a population of models approach, 10 anatomically identical atrial models were created to include cellular heterogeneity. Atrial models were compared with an equivalent homogenous model. Activation, APD90, and repolarization maps were used to compare models. The impact of electrotonic coupling in the tissue was determined through a comparison of RMP, APD20, APD50, APD90, and triangulation between regional atrial tissue and the single cell populations. After calibration, cellular heterogeneity does not impact atrial depolarization. Repolarization patterns were significantly impacted by cellular heterogeneity, with the APD90 across the LA increasing due to heterogeneity and the reverse occurring in the RA. Electrotonic coupling caused a reduction in variability across all biomarkers but did not fully remove variability. Electrotonic coupling resulted in an increase in APD20 and APD50, and reduced triangulation compared to isolated cell populations. Heterogeneity also caused a reduction in triangulation compared with regionally homogeneous atria.
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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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Farquhar ME, Burrage K, Weber Dos Santos R, Bueno-Orovio A, Lawson BA. Graph-based homogenisation for modelling cardiac fibrosis. JOURNAL OF COMPUTATIONAL PHYSICS 2022; 459:None. [PMID: 35959500 PMCID: PMC9352598 DOI: 10.1016/j.jcp.2022.111126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Revised: 02/15/2022] [Accepted: 03/02/2022] [Indexed: 05/02/2023]
Abstract
Fibrosis, the excess of extracellular matrix, can affect, and even block, propagation of action potential in cardiac tissue. This can result in deleterious effects on heart function, but the nature and severity of these effects depend strongly on the localisation of fibrosis and its by-products in cardiac tissue, such as collagen scar formation. Computer simulation is an important means of understanding the complex effects of fibrosis on activation patterns in the heart, but concerns of computational cost place restrictions on the spatial resolution of these simulations. In this work, we present a novel numerical homogenisation technique that uses both Eikonal and graph approaches to allow fine-scale heterogeneities in conductivity to be incorporated into a coarser mesh. Homogenisation achieves this by deriving effective conductivity tensors so that a coarser mesh can then be used for numerical simulation. By taking a graph-based approach, our homogenisation technique functions naturally on irregular grids and does not rely upon any assumptions of periodicity, even implicitly. We present results of action potential propagation through fibrotic tissue in two dimensions that show the graph-based homogenisation technique is an accurate and effective way to capture fine-scale domain information on coarser meshes in the context of sharp-fronted travelling waves of activation. As test problems, we consider excitation propagation in tissue with diffuse fibrosis and through a tunnel-like structure designed to test homogenisation, interaction of an excitation wave with a scar region, and functional re-entry.
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Affiliation(s)
- Megan E. Farquhar
- Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers, School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia
| | - Kevin Burrage
- Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers, School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia
- Department of Computer Science, Oxford University, Oxford, United Kingdom
| | - Rodrigo Weber Dos Santos
- Department of Computer Science and Program on Computational Modeling, Universidade Federal de Juiz de Fora, Juiz de Fora, Brazil
| | | | - Brodie A.J. Lawson
- Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers, School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia
- Centre for Data Science, Queensland University of Technology, Brisbane, Australia
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Gharaviri A, Pezzuto S, Potse M, Conte G, Zeemering S, Sobota V, Verheule S, Krause R, Auricchio A, Schotten U. Synergistic antiarrhythmic effect of inward rectifier current inhibition and pulmonary vein isolation in a 3D computer model for atrial fibrillation. Europace 2021; 23:i161-i168. [PMID: 33751085 DOI: 10.1093/europace/euaa413] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 12/15/2020] [Indexed: 12/16/2022] Open
Abstract
AIMS Recent clinical studies showed that antiarrhythmic drug (AAD) treatment and pulmonary vein isolation (PVI) synergistically reduce atrial fibrillation (AF) recurrences after initially successful ablation. Among newly developed atrial-selective AADs, inhibitors of the G-protein-gated acetylcholine-activated inward rectifier current (IKACh) were shown to effectively suppress AF in an experimental model but have not yet been evaluated clinically. We tested in silico whether inhibition of inward rectifier current or its combination with PVI reduces AF inducibility. METHODS AND RESULTS We simulated the effect of inward rectifier current blockade (IK blockade), PVI, and their combination on AF inducibility in a detailed three-dimensional model of the human atria with different degrees of fibrosis. IK blockade was simulated with a 30% reduction of its conductivity. Atrial fibrillation was initiated using incremental pacing applied at 20 different locations, in both atria. IK blockade effectively prevented AF induction in simulations without fibrosis as did PVI in simulations without fibrosis and with moderate fibrosis. Both interventions lost their efficacy in severe fibrosis. The combination of IK blockade and PVI prevented AF in simulations without fibrosis, with moderate fibrosis, and even with severe fibrosis. The combined therapy strongly decreased the number of fibrillation waves, due to a synergistic reduction of wavefront generation rate while the wavefront lifespan remained unchanged. CONCLUSION Newly developed blockers of atrial-specific inward rectifier currents, such as IKAch, might prevent AF occurrences and when combined with PVI effectively supress AF recurrences in human.
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Affiliation(s)
- Ali Gharaviri
- Center for Computational Medicine in Cardiology, Institute of Computational Science, Università della Svizzera italiana, Lugano, Switzerland
| | - Simone Pezzuto
- Center for Computational Medicine in Cardiology, Institute of Computational Science, Università della Svizzera italiana, Lugano, Switzerland
| | - Mark Potse
- Carmen Team, Inria Bordeaux-Sud-Ouest, Talence, France.,Université de Bordeaux, IMB, UMR 5251, F-33400, Talence, France.,IHU Liryc, Electrophysiology and Heart Modeling Institute, Foundation Bordeaux Université, Bordeaux, France
| | - Giulio Conte
- Center for Computational Medicine in Cardiology, Institute of Computational Science, Università della Svizzera italiana, Lugano, Switzerland.,Division of Cardiology, Fondazione Cardiocentro Ticino, Via Tesserete 48, 6900 Lugano, Switzerland
| | - Stef Zeemering
- Department of Physiology, Maastricht University, Maastricht, The Netherlands
| | - Vladimír Sobota
- Department of Physiology, Maastricht University, Maastricht, The Netherlands
| | - Sander Verheule
- Department of Physiology, Maastricht University, Maastricht, The Netherlands
| | - Rolf Krause
- Center for Computational Medicine in Cardiology, Institute of Computational Science, Università della Svizzera italiana, Lugano, Switzerland
| | - Angelo Auricchio
- Center for Computational Medicine in Cardiology, Institute of Computational Science, Università della Svizzera italiana, Lugano, Switzerland.,Division of Cardiology, Fondazione Cardiocentro Ticino, Via Tesserete 48, 6900 Lugano, Switzerland
| | - Ulrich Schotten
- Department of Physiology, Maastricht University, Maastricht, The Netherlands
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A Comparison of Regional Classification Strategies Implemented for the Population Based Approach to Modelling Atrial Fibrillation. MATHEMATICS 2021. [DOI: 10.3390/math9141686] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
(1) Background: in silico models are increasingly relied upon to study the mechanisms of atrial fibrillation. Due to the complexity associated with atrial models, cellular variability is often ignored. Recent studies have shown that cellular variability may have a larger impact on electrophysiological behaviour than previously expected. This paper compares two methods for AF remodelling using regional populations. (2) Methods: using 200,000 action potentials, experimental data was used to calibrate healthy atrial regional populations with two cellular models. AF remodelling was applied by directly adjusting maximum channel conductances. AF remodelling was also applied through adjusting biomarkers. The methods were compared upon replication of experimental data. (3) Results: compared to the percentage method, the biomarker approach resulted in smaller changes. RMP, APD20, APD50, and APD90 were changed in the percentage method by up to 11%, 500%, 50%, and 60%, respectively. In the biomarker approach, RMP, APD20, APD50, and APD90 were changed by up to 4.5%, 132%, 50%, and 35%, respectively. (4) Conclusion: applying AF remodelling through biomarker-based clustering resulted in channel conductance changes that were consistent with experimental data, while maintaining the highly non-linear relationships between channel conductances and biomarkers. Directly changing conductances in the healthy regional populations impacted the non-linear relationships and resulted in non-physiological APD20 and APD50 values.
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11
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Corral-Acero J, Margara F, Marciniak M, Rodero C, Loncaric F, Feng Y, Gilbert A, Fernandes JF, Bukhari HA, Wajdan A, Martinez MV, Santos MS, Shamohammdi M, Luo H, Westphal P, Leeson P, DiAchille P, Gurev V, Mayr M, Geris L, Pathmanathan P, Morrison T, Cornelussen R, Prinzen F, Delhaas T, Doltra A, Sitges M, Vigmond EJ, Zacur E, Grau V, Rodriguez B, Remme EW, Niederer S, Mortier P, McLeod K, Potse M, Pueyo E, Bueno-Orovio A, Lamata P. The 'Digital Twin' to enable the vision of precision cardiology. Eur Heart J 2020; 41:4556-4564. [PMID: 32128588 PMCID: PMC7774470 DOI: 10.1093/eurheartj/ehaa159] [Citation(s) in RCA: 185] [Impact Index Per Article: 46.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Revised: 11/29/2019] [Accepted: 02/24/2020] [Indexed: 12/26/2022] Open
Abstract
Providing therapies tailored to each patient is the vision of precision medicine, enabled by the increasing ability to capture extensive data about individual patients. In this position paper, we argue that the second enabling pillar towards this vision is the increasing power of computers and algorithms to learn, reason, and build the 'digital twin' of a patient. Computational models are boosting the capacity to draw diagnosis and prognosis, and future treatments will be tailored not only to current health status and data, but also to an accurate projection of the pathways to restore health by model predictions. The early steps of the digital twin in the area of cardiovascular medicine are reviewed in this article, together with a discussion of the challenges and opportunities ahead. We emphasize the synergies between mechanistic and statistical models in accelerating cardiovascular research and enabling the vision of precision medicine.
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Affiliation(s)
| | - Francesca Margara
- Department of Computer Science, British Heart Foundation Centre of Research Excellence, University of Oxford, Oxford, UK
| | - Maciej Marciniak
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, UK
| | - Cristobal Rodero
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, UK
| | - Filip Loncaric
- Institut Clínic Cardiovascular, Hospital Clínic, Universitat de Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Yingjing Feng
- IHU Liryc, Electrophysiology and Heart Modeling Institute, fondation Bordeaux Université, Pessac-Bordeaux F-33600, France
- IMB, UMR 5251, University of Bordeaux, Talence F-33400, France
| | | | - Joao F Fernandes
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, UK
| | - Hassaan A Bukhari
- IMB, UMR 5251, University of Bordeaux, Talence F-33400, France
- Aragón Institute of Engineering Research, Universidad de Zaragoza, IIS Aragón, Zaragoza, Spain
| | - Ali Wajdan
- The Intervention Centre, Oslo University Hospital, Rikshospitalet, Oslo, Norway
| | | | | | - Mehrdad Shamohammdi
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
| | - Hongxing Luo
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
| | - Philip Westphal
- Medtronic PLC, Bakken Research Center, Maastricht, the Netherlands
| | - Paul Leeson
- Radcliffe Department of Medicine, Division of Cardiovascular Medicine, Oxford Cardiovascular Clinical Research Facility, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Paolo DiAchille
- Healthcare and Life Sciences Research, IBM T.J. Watson Research Center, Yorktown Heights, NY, USA
| | - Viatcheslav Gurev
- Healthcare and Life Sciences Research, IBM T.J. Watson Research Center, Yorktown Heights, NY, USA
| | - Manuel Mayr
- King’s British Heart Foundation Centre, King’s College London, London, UK
| | - Liesbet Geris
- Virtual Physiological Human Institute, Leuven, Belgium
| | - Pras Pathmanathan
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, USA
| | - Tina Morrison
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, USA
| | | | - Frits Prinzen
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
| | - Tammo Delhaas
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
| | - Ada Doltra
- Institut Clínic Cardiovascular, Hospital Clínic, Universitat de Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Marta Sitges
- Institut Clínic Cardiovascular, Hospital Clínic, Universitat de Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- CIBERCV, Instituto de Salud Carlos III, (CB16/11/00354), CERCA Programme/Generalitat de, Catalunya, Spain
| | - Edward J Vigmond
- IHU Liryc, Electrophysiology and Heart Modeling Institute, fondation Bordeaux Université, Pessac-Bordeaux F-33600, France
- IMB, UMR 5251, University of Bordeaux, Talence F-33400, France
| | - Ernesto Zacur
- Department of Engineering Science, University of Oxford, Oxford, UK
| | - Vicente Grau
- Department of Engineering Science, University of Oxford, Oxford, UK
| | - Blanca Rodriguez
- Department of Computer Science, British Heart Foundation Centre of Research Excellence, University of Oxford, Oxford, UK
| | - Espen W Remme
- The Intervention Centre, Oslo University Hospital, Rikshospitalet, Oslo, Norway
| | - Steven Niederer
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, UK
| | | | | | - Mark Potse
- IHU Liryc, Electrophysiology and Heart Modeling Institute, fondation Bordeaux Université, Pessac-Bordeaux F-33600, France
- IMB, UMR 5251, University of Bordeaux, Talence F-33400, France
- Inria Bordeaux Sud-Ouest, CARMEN team, Talence F-33400, France
| | - Esther Pueyo
- Aragón Institute of Engineering Research, Universidad de Zaragoza, IIS Aragón, Zaragoza, Spain
- CIBER in Bioengineering, Biomaterials and Nanomedicine (CIBER‐BBN), Madrid, Spain
| | - Alfonso Bueno-Orovio
- Department of Computer Science, British Heart Foundation Centre of Research Excellence, University of Oxford, Oxford, UK
| | - Pablo Lamata
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, UK
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12
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Celotto C, Sanchez C, Mountris KA, Laguna P, Pueyo E. SK Channel Block and Adrenergic Stimulation Counteract Acetylcholine-Induced Arrhythmogenic Effects in Human Atria. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:2303-2306. [PMID: 33018468 DOI: 10.1109/embc44109.2020.9175886] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
There is increasing evidence on the role of the autonomic nervous system in the pathogenesis of atrial fibrillation. Interventions targeting autonomic modulation of atrial electrical activity have been shown to reduce the incidence of atrial arrhythmias. Additionally, recent investigations have proved that pharmacological therapies inhibiting small-conductance calcium-activated potassium (SK) channels are able to lessen cholinergic effects in the atria.In this study we use computational modeling and simulation to test individual and combined effects of SK channel block and adrenergic stimulation in counteracting detrimental effects induced by the parasympathetic neurotransmitter acetylcholine (ACh) on human atrial electrophysiology. Cell and tissue models are built that incorporate descriptions of SK channels as well as of isoproterenol (Iso)- and ACh-mediated regulation of the atrial action potential (AP). Three different cellular AP models, representing a range of physiological AP shapes, are considered and both homogeneous and heterogeneous ACh distributions in atrial tissue are simulated.At the cellular level, SK channel block is demonstrated to partially revert shortening of AP duration (APD) mediated by ACh at various doses, whereas 1 µM Iso has a variable response depending on the AP shape. The combination of SK block and Iso is in all cases able to take APD back to baseline levels, recovering between 82% and 120% of the APD shortening induced by 0.1 µM ACh. At the tissue level, SK block and Iso alone or in combination do not exert remarkable effects on conduction velocity, but the combination of the two is able to notably prolong the ACh-mediated APD shortening, thus increasing the wavelength for reentry.In conclusion, the results from this study support the combination of SK channel block and adrenergic stimulation as a potential option to counteract parasympathetically-mediated proarrhythmic effects in the human atria.
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13
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Bragard JR, Camara O, Echebarria B, Gerardo Giorda L, Pueyo E, Saiz J, Sebastián R, Soudah E, Vázquez M. Cardiac computational modelling. ACTA ACUST UNITED AC 2020; 74:65-71. [PMID: 32807708 DOI: 10.1016/j.rec.2020.05.024] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Accepted: 05/25/2020] [Indexed: 12/26/2022]
Abstract
Cardiovascular diseases currently have a major social and economic impact, constituting one of the leading causes of mortality and morbidity. Personalized computational models of the heart are demonstrating their usefulness both to help understand the mechanisms underlying cardiac disease, and to optimize their treatment and predict the patient's response. Within this framework, the Spanish Research Network for Cardiac Computational Modelling (VHeart-SN) has been launched. The general objective of the VHeart-SN network is the development of an integrated, modular and multiscale multiphysical computational model of the heart. This general objective is addressed through the following specific objectives: a) to integrate the different numerical methods and models taking into account the specificity of patients; b) to assist in advancing knowledge of the mechanisms associated with cardiac and vascular diseases; and c) to support the application of different personalized therapies. This article presents the current state of cardiac computational modelling and different scientific works conducted by the members of the network to gain greater understanding of the characteristics and usefulness of these models.
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Affiliation(s)
- Jean R Bragard
- Grupo de Biofísica (BIOFIS), Departamento de Física y Matemática Aplicada, Universidad de Navarra, Pamplona, Navarra, Spain
| | - Oscar Camara
- Sensing in Physiology and Biomedicine (PhySense), Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Blas Echebarria
- Grupo de Biología Computacional y Sistemas Complejos (BIOCOM-SC), Universitat Politècnica de Catalunya, Barcelona, Spain
| | | | - Esther Pueyo
- Biomedical Signal Interpretation and Computational Simulation (BSICoS), Universidad de Zaragoza, CIBER-BBN, Zaragoza, Spain
| | - Javier Saiz
- Centro de Investigación e Innovación en Bioingeniería (Ci2B), Universitat Politècnica de València, Valencia, Spain.
| | - Rafael Sebastián
- Computational Multiscale Simulation Lab (CoMMLab), Universitat de València, Burjassot, Valencia, Spain
| | - Eduardo Soudah
- International Centre for Numerical Methods in Engineering (CIMNE), Universitat Politècnica de Catalunya, Barcelona, Spain
| | - Mariano Vázquez
- Barcelona Supercomputing Center & ELEM Biotech, Barcelona, Spain
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14
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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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15
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Filos D, Tachmatzidis D, Maglaveras N, Vassilikos V, Chouvarda I. Understanding the Beat-to-Beat Variations of P-Waves Morphologies in AF Patients During Sinus Rhythm: A Scoping Review of the Atrial Simulation Studies. Front Physiol 2019; 10:742. [PMID: 31275161 PMCID: PMC6591370 DOI: 10.3389/fphys.2019.00742] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Accepted: 05/28/2019] [Indexed: 11/13/2022] Open
Abstract
The remarkable advances in high-performance computing and the resulting increase of the computational power have the potential to leverage computational cardiology toward improving our understanding of the pathophysiological mechanisms of arrhythmias, such as Atrial Fibrillation (AF). In AF, a complex interaction between various triggers and the atrial substrate is considered to be the leading cause of AF initiation and perpetuation. In electrocardiography (ECG), P-wave is supposed to reflect atrial depolarization. It has been found that even during sinus rhythm (SR), multiple P-wave morphologies are present in AF patients with a history of AF, suggesting a higher dispersion of the conduction route in this population. In this scoping review, we focused on the mechanisms which modify the electrical substrate of the atria in AF patients, while investigating the existence of computational models that simulate the propagation of the electrical signal through different routes. The adopted review methodology is based on a structured analytical framework which includes the extraction of the keywords based on an initial limited bibliographic search, the extensive literature search and finally the identification of relevant articles based on the reference list of the studies. The leading mechanisms identified were classified according to their scale, spanning from mechanisms in the cell, tissue or organ level, and the produced outputs. The computational modeling approaches for each of the factors that influence the initiation and the perpetuation of AF are presented here to provide a clear overview of the existing literature. Several levels of categorization were adopted while the studies which aim to translate their findings to ECG phenotyping are highlighted. The results denote the availability of multiple models, which are appropriate under specific conditions. However, the consideration of complex scenarios taking into account multiple spatiotemporal scales, personalization of electrophysiological and anatomical models and the reproducibility in terms of ECG phenotyping has only partially been tackled so far.
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Affiliation(s)
- Dimitrios Filos
- Lab of Computing, Medical Informatics and Biomedical Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | | | - Nicos Maglaveras
- Lab of Computing, Medical Informatics and Biomedical Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
- Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL, United States
| | - Vassilios Vassilikos
- 3rd Cardiology Department, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Ioanna Chouvarda
- Lab of Computing, Medical Informatics and Biomedical Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
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16
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Computational modeling: What does it tell us about atrial fibrillation therapy? Int J Cardiol 2019; 287:155-161. [PMID: 30803891 DOI: 10.1016/j.ijcard.2019.01.077] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2018] [Revised: 12/09/2018] [Accepted: 01/22/2019] [Indexed: 12/19/2022]
Abstract
Atrial fibrillation (AF) is a complex cardiac arrhythmia with diverse etiology that negatively affects morbidity and mortality of millions of patients. Technological and experimental advances have provided a wealth of information on the pathogenesis of AF, highlighting a multitude of mechanisms involved in arrhythmia initiation and maintenance, and disease progression. However, it remains challenging to identify the predominant mechanisms for specific subgroups of AF patients, which, together with an incomplete understanding of the pleiotropic effects of antiarrhythmic therapies, likely contributes to the suboptimal efficacy of current antiarrhythmic approaches. Computer modeling of cardiac electrophysiology has advanced in parallel to experimental research and provides an integrative framework to attempt to overcome some of these challenges. Multi-scale cardiac modeling and simulation integrate structural and functional data from experimental and clinical work with knowledge of atrial electrophysiological mechanisms and dynamics, thereby improving our understanding of AF mechanisms and therapy. In this review, we describe recent advances in our quantitative understanding of AF through mathematical models. We discuss computational modeling of AF mechanisms and therapy using detailed, mechanistic cell/tissue-level models, including approaches to incorporate variability in patient populations. We also highlight efforts using whole-atria models to improve catheter ablation therapies. Finally, we describe recent efforts and suggest future extensions to model clinical concepts of AF using patient-level models.
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Cluitmans M, Brooks DH, MacLeod R, Dössel O, Guillem MS, van Dam PM, Svehlikova J, He B, Sapp J, Wang L, Bear L. Validation and Opportunities of Electrocardiographic Imaging: From Technical Achievements to Clinical Applications. Front Physiol 2018; 9:1305. [PMID: 30294281 PMCID: PMC6158556 DOI: 10.3389/fphys.2018.01305] [Citation(s) in RCA: 68] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2018] [Accepted: 08/29/2018] [Indexed: 11/23/2022] Open
Abstract
Electrocardiographic imaging (ECGI) reconstructs the electrical activity of the heart from a dense array of body-surface electrocardiograms and a patient-specific heart-torso geometry. Depending on how it is formulated, ECGI allows the reconstruction of the activation and recovery sequence of the heart, the origin of premature beats or tachycardia, the anchors/hotspots of re-entrant arrhythmias and other electrophysiological quantities of interest. Importantly, these quantities are directly and non-invasively reconstructed in a digitized model of the patient's three-dimensional heart, which has led to clinical interest in ECGI's ability to personalize diagnosis and guide therapy. Despite considerable development over the last decades, validation of ECGI is challenging. Firstly, results depend considerably on implementation choices, which are necessary to deal with ECGI's ill-posed character. Secondly, it is challenging to obtain (invasive) ground truth data of high quality. In this review, we discuss the current status of ECGI validation as well as the major challenges remaining for complete adoption of ECGI in clinical practice. Specifically, showing clinical benefit is essential for the adoption of ECGI. Such benefit may lie in patient outcome improvement, workflow improvement, or cost reduction. Future studies should focus on these aspects to achieve broad adoption of ECGI, but only after the technical challenges have been solved for that specific application/pathology. We propose 'best' practices for technical validation and highlight collaborative efforts recently organized in this field. Continued interaction between engineers, basic scientists, and physicians remains essential to find a hybrid between technical achievements, pathological mechanisms insights, and clinical benefit, to evolve this powerful technique toward a useful role in clinical practice.
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Affiliation(s)
- Matthijs Cluitmans
- Department of Cardiology, Cardiovascular Research Institute Maastricht Maastricht University, Maastricht, Netherlands
| | - Dana H. Brooks
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, United States
| | - Rob MacLeod
- Biomedical Engineering Department, Scientific Computing and Imaging Institute (SCI), and Cardiovascular Research and Training Institute (CVRTI), The University of Utah, Salt Lake City, UT, United States
| | - Olaf Dössel
- Karlsruhe Institute of Technology, Karlsruhe, Germany
| | | | - Peter M. van Dam
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Center, Nijmegen, Netherlands
| | - Jana Svehlikova
- Institute of Measurement Science, Slovak Academy of Sciences, Bratislava, Slovakia
| | - Bin He
- Department of Biomedical Engineering Carnegie Mellon University, Pittsburgh, PA, United States
| | - John Sapp
- QEII Health Sciences Centre and Department of Medicine, Dalhousie University, Halifax, NS, Canada
| | - Linwei Wang
- Rochester Institute of Technology, Rochester, NY, United States
| | - Laura Bear
- IHU LIRYC, Fondation Bordeaux Université, Inserm U1045 and Université de Bordeaux, Bordeaux, France
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18
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Colman MA, Saxena P, Kettlewell S, Workman AJ. Description of the Human Atrial Action Potential Derived From a Single, Congruent Data Source: Novel Computational Models for Integrated Experimental-Numerical Study of Atrial Arrhythmia Mechanisms. Front Physiol 2018; 9:1211. [PMID: 30245635 PMCID: PMC6137999 DOI: 10.3389/fphys.2018.01211] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2018] [Accepted: 08/13/2018] [Indexed: 11/13/2022] Open
Abstract
Introduction: The development of improved diagnosis, management, and treatment strategies for human atrial fibrillation (AF) is a significant and important challenge in order to improve quality of life for millions and reduce the substantial social-economic costs of the condition. As a complex condition demonstrating high variability and relation to other cardiac conditions, the study of AF requires approaches from multiple disciplines including single-cell experimental electrophysiology and computational modeling. Models of human atrial cells are less well parameterized than those of the human ventricle or other mammal species, largely due to the inherent challenges in patch clamping human atrial cells. Such challenges include, frequently, unphysiologically depolarized resting potentials and thus injection of a compensatory hyperpolarizing current, as well as detecting certain ion currents which may be disrupted by the cell isolation process. The aim of this study was to develop a laboratory specific model of human atrial electrophysiology which reproduces exactly the conditions of isolated-cell experiments, including testing of multiple experimental interventions. Methods: Formulations for the primary ion currents characterized by isolated-cell experiments in the Workman laboratory were fit directly to voltage-clamp data; the fast sodium-current was parameterized based on experiments relating resting membrane potential to maximal action potential upstroke velocity; compensatory hyperpolarizing current was included as a constant applied current. These formulations were integrated with three independent human atrial cell models to provide a family of novel models. Extrapolated intact-cell models were developed through removal of the hyperpolarizing current and introduction of terminal repolarization potassium currents. Results: The isolated-cell models quantitatively reproduced experimentally measured properties of excitation in both control and pharmacological and dynamic-clamp interventions. Comparison of isolated and intact-cell models highlighted the importance of reproducing this cellular environment when comparing experimental and simulation data. Conclusion: We have developed a laboratory specific model of the human atrial cell which directly reproduces the experimental isolated-cell conditions and captures human atrial excitation properties. The model may be particularly useful for directly relating model to experiment, and offers a complementary tool to the available set of human atrial cell models with specific advantages resulting from the congruent input data source.
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Affiliation(s)
- Michael A Colman
- Leeds Computational Physiology Lab, School of Biomedical Sciences, University of Leeds, Leeds, United Kingdom
| | - Priyanka Saxena
- Institute of Cardiovascular and Medical Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom
| | - Sarah Kettlewell
- Institute of Cardiovascular and Medical Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom
| | - Antony J Workman
- Institute of Cardiovascular and Medical Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom
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19
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Vagos M, van Herck IGM, Sundnes J, Arevalo HJ, Edwards AG, Koivumäki JT. Computational Modeling of Electrophysiology and Pharmacotherapy of Atrial Fibrillation: Recent Advances and Future Challenges. Front Physiol 2018; 9:1221. [PMID: 30233399 PMCID: PMC6131668 DOI: 10.3389/fphys.2018.01221] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2018] [Accepted: 08/13/2018] [Indexed: 12/19/2022] Open
Abstract
The pathophysiology of atrial fibrillation (AF) is broad, with components related to the unique and diverse cellular electrophysiology of atrial myocytes, structural complexity, and heterogeneity of atrial tissue, and pronounced disease-associated remodeling of both cells and tissue. A major challenge for rational design of AF therapy, particularly pharmacotherapy, is integrating these multiscale characteristics to identify approaches that are both efficacious and independent of ventricular contraindications. Computational modeling has long been touted as a basis for achieving such integration in a rapid, economical, and scalable manner. However, computational pipelines for AF-specific drug screening are in their infancy, and while the field is progressing quite rapidly, major challenges remain before computational approaches can fill the role of workhorse in rational design of AF pharmacotherapies. In this review, we briefly detail the unique aspects of AF pathophysiology that determine requirements for compounds targeting AF rhythm control, with emphasis on delimiting mechanisms that promote AF triggers from those providing substrate or supporting reentry. We then describe modeling approaches that have been used to assess the outcomes of drugs acting on established AF targets, as well as on novel promising targets including the ultra-rapidly activating delayed rectifier potassium current, the acetylcholine-activated potassium current and the small conductance calcium-activated potassium channel. Finally, we describe how heterogeneity and variability are being incorporated into AF-specific models, and how these approaches are yielding novel insights into the basic physiology of disease, as well as aiding identification of the important molecular players in the complex AF etiology.
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Affiliation(s)
- Márcia Vagos
- Computational Physiology Department, Simula Research Laboratory, Lysaker, Norway
- Department of Informatics, University of Oslo, Oslo, Norway
| | - Ilsbeth G. M. van Herck
- Computational Physiology Department, Simula Research Laboratory, Lysaker, Norway
- Department of Informatics, University of Oslo, Oslo, Norway
| | - Joakim Sundnes
- Computational Physiology Department, Simula Research Laboratory, Lysaker, Norway
- Center for Cardiological Innovation, Oslo, Norway
| | - Hermenegild J. Arevalo
- Computational Physiology Department, Simula Research Laboratory, Lysaker, Norway
- Center for Cardiological Innovation, Oslo, Norway
| | - Andrew G. Edwards
- Computational Physiology Department, Simula Research Laboratory, Lysaker, Norway
- Center for Cardiological Innovation, Oslo, Norway
| | - Jussi T. Koivumäki
- BioMediTech Institute and Faculty of Biomedical Sciences and Engineering, Tampere University of Technology, Tampere, Finland
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
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20
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Luengo D, Rios-Munoz G, Elvira V, Sanchez C, Artes-Rodriguez A. Hierarchical Algorithms for Causality Retrieval in Atrial Fibrillation Intracavitary Electrograms. IEEE J Biomed Health Inform 2018; 23:143-155. [PMID: 29994646 DOI: 10.1109/jbhi.2018.2805773] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Multichannel intracavitary electrograms (EGMs) are acquired at the electrophysiology laboratory to guide radio frequency catheter ablation of patients suffering from atrial fibrillation. These EGMs are used by cardiologists to determine candidate areas for ablation (e.g., areas corresponding to high dominant frequencies or complex fractionated electrograms). In this paper, we introduce two hierarchical algorithms to retrieve the causal interactions among these multiple EGMs. Both algorithms are based on Granger causality, but other causality measures can be easily incorporated. In both cases, they start by selecting a root node, but they differ on the way in which they explore the set of signals to determine their cause-effect relationships: either testing the full set of unexplored signals (GS-CaRe) or performing a local search only among the set of neighbor EGMs (LS-CaRe). The ensuing causal model provides important information about the propagation of the electrical signals inside the atria, uncovering wavefronts and activation patterns that can guide cardiologists towards candidate areas for catheter ablation. Numerical experiments, on both synthetic signals and annotated real-world signals, show the good performance of the two proposed approaches.
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21
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Ríos-Muñoz GR, Arenal Á, Artés-Rodríguez A. Real-Time Rotational Activity Detection in Atrial Fibrillation. Front Physiol 2018; 9:208. [PMID: 29593566 PMCID: PMC5859379 DOI: 10.3389/fphys.2018.00208] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2017] [Accepted: 02/23/2018] [Indexed: 12/31/2022] Open
Abstract
Rotational activations, or spiral waves, are one of the proposed mechanisms for atrial fibrillation (AF) maintenance. We present a system for assessing the presence of rotational activity from intracardiac electrograms (EGMs). Our system is able to operate in real-time with multi-electrode catheters of different topologies in contact with the atrial wall, and it is based on new local activation time (LAT) estimation and rotational activity detection methods. The EGM LAT estimation method is based on the identification of the highest sustained negative slope of unipolar signals. The method is implemented as a linear filter whose output is interpolated on a regular grid to match any catheter topology. Its operation is illustrated on selected signals and compared to the classical Hilbert-Transform-based phase analysis. After the estimation of the LAT on the regular grid, the detection of rotational activity in the atrium is done by a novel method based on the optical flow of the wavefront dynamics, and a rotation pattern match. The methods have been validated using in silico and real AF signals.
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Affiliation(s)
- Gonzalo R Ríos-Muñoz
- Signal Theory and Communications Department, Universidad Carlos III de Madrid, Madrid, Spain.,Gregorio Marañón Health Research Institute, Madrid, Spain
| | - Ángel Arenal
- Gregorio Marañón Health Research Institute, Madrid, Spain.,Department of Cardiology, Hospital General Universitario Gregorio Marañón, Madrid, Spain
| | - Antonio Artés-Rodríguez
- Signal Theory and Communications Department, Universidad Carlos III de Madrid, Madrid, Spain.,Gregorio Marañón Health Research Institute, Madrid, Spain
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22
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23
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Campana C, Akar FG. Commentary: Atrial Fibrillation Dynamics and Ionic Block Effects in Six Heterogeneous Human 3D Virtual Atria with Distinct Repolarization Dynamics. Front Bioeng Biotechnol 2017; 5:59. [PMID: 29057224 PMCID: PMC5635327 DOI: 10.3389/fbioe.2017.00059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2017] [Accepted: 09/20/2017] [Indexed: 11/26/2022] Open
Affiliation(s)
- Chiara Campana
- Icahn School of Medicine at Mount Sinai, The Cardiovascular Institute, New York, NY, United States
| | - Fadi G Akar
- Icahn School of Medicine at Mount Sinai, The Cardiovascular Institute, New York, NY, United States
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