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Ye T, Yang J, Liu Z, Yu Y, Zhang C, Guo Y, Yu F, Zhou Y, Song Z, Shi J, Wang L, Yang B, Wang X. Inhibition of the P2X7 receptor prevents atrial proarrhythmic remodeling in experimental post-operative atrial fibrillation. Int Immunopharmacol 2024; 129:111536. [PMID: 38320354 DOI: 10.1016/j.intimp.2024.111536] [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: 10/18/2023] [Revised: 01/10/2024] [Accepted: 01/10/2024] [Indexed: 02/08/2024]
Abstract
BACKGROUND Post-operative atrial fibrillation (POAF) is a common complication in patients undergoing cardiac surgery. The purinergic receptor P2X7 (P2X7R) is involved in some cardiovascular diseases, whereas its effects on atrial fibrillation (AF) are unclear. OBJECTIVE This study was to assess the effect of P2X7R on atrial arrhythmogenic remodeling in the rat model of sterile pericarditis (SP). METHODS Male Sprague-Dawley (SD) rats were used to induce the SP model. Electrocardiogram, atrial electrophysiological protocol, histology, mRNA sequencing, real-time quantitative PCR, western blot, and Elisa assay were performed. RESULTS SP significantly up-regulated P2X7R expression; increased AF susceptibility; reduced the protein expression of ion channels including Nav1.5, Cav1.2, Kv4.2, Kv4.3, and Kv1.5; caused atrial fibrosis; increased norepinephrine (NE) level in plasma; promoted the production of inflammatory cytokines such as TNF-α, IL-1β, and IL-6; increased the accumulation of immune cells (CD68- and MPO- positive cells); and activated NLRP3 inflammasome signaling pathway. P2X7R antagonist Brilliant Blue G (BBG) mitigated SP-induced alterations. The mRNA sequencing demonstrated that BBG prevented POAF mainly by regulating the immune system. In addition, another selective P2X7R antagonist A740003, and IL-1R antagonist anakinra also reduced AF inducibility in the SP model. CONCLUSIONS P2X7R inhibition prevents SP-induced atrial proarrhythmic remodeling, which is closely associated with the improvement of inflammatory changes, ion channel expression, atrial fibrosis, and sympathetic activation. The findings point to P2X7R inhibition as a promising target for AF (particularly POAF) and perhaps other conditions.
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Affiliation(s)
- Tianxin Ye
- Department of Cardiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China
| | - Jinxiu Yang
- Department of Cardiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China
| | - Zhangchi Liu
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan 430060, PR China; Cardiovascular Research Institute, Wuhan University, Wuhan 430060, PR China; Hubei Key Laboratory of Cardiology, Wuhan 430060, PR China
| | - Yi Yu
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan 430060, PR China; Cardiovascular Research Institute, Wuhan University, Wuhan 430060, PR China; Hubei Key Laboratory of Cardiology, Wuhan 430060, PR China
| | - Cui Zhang
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan 430060, PR China; Cardiovascular Research Institute, Wuhan University, Wuhan 430060, PR China; Hubei Key Laboratory of Cardiology, Wuhan 430060, PR China
| | - Yan Guo
- Department of Cardiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China
| | - Fangcong Yu
- Department of Cardiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China
| | - Yunping Zhou
- Department of Cardiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China
| | - Zhuonan Song
- Department of Cardiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China
| | - Jiaran Shi
- Department of Cardiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China
| | - Longbo Wang
- Department of Cardiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China
| | - Bo Yang
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan 430060, PR China; Cardiovascular Research Institute, Wuhan University, Wuhan 430060, PR China; Hubei Key Laboratory of Cardiology, Wuhan 430060, PR China.
| | - Xingxiang Wang
- Department of Cardiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China.
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2
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Ciuffo L, Tung M, Dukes JW, Vittinghoff E, Moss JD, Lee RJ, Lee BK, Tseng ZH, Vedantham V, Olgin JE, Scheinman MM, Hsia H, Ramchandani VA, Gerstenfeld EP, Marcus GM. Acute alcohol exposure and electrocardiographic changes: Finding from the HOLIDAY trial. J Electrocardiol 2024; 83:26-29. [PMID: 38295539 DOI: 10.1016/j.jelectrocard.2024.01.003] [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: 10/23/2023] [Revised: 12/30/2023] [Accepted: 01/12/2024] [Indexed: 02/02/2024]
Abstract
BACKGROUND Alcohol consumption is associated with a higher increased risk of atrial fibrillation (AF), but the acute effects on cardiac electrophysiology in humans remain poorly understood. The HOw ALcohol InDuces Atrial TachYarrhythmias (HOLIDAY) Trial revealed that alcohol shortened pulmonary vein atrial effective refractory periods, but more global electrophysiologic changes gleaned from the surface ECG have not yet been reported. METHODS This was a secondary analysis of the HOLIDAY Trial. During AF ablation procedures, 100 adults were randomized to intravenous alcohol titrated to 0.08% blood alcohol concentration versus a volume and osmolarity-matched, masked, placebo. Intervals measured from 12‑lead ECGs were compared between pre infusion and at infusion steady state (20 min). RESULTS The average age was 60 years and 11% were female. No significant differences in the P-wave duration, PR, QRS or QT intervals, were present between alcohol and placebo arms. However, infusion of alcohol was associated with a statistically significant relative shortening of the JT interval (r: -14.73, p = 0.048) after multivariable adjustment. CONCLUSION Acute exposure to alcohol was associated with a relative reduction in the JT interval, reflecting shortening of ventricular repolarization. These acute changes may reflect a more global shortening of refractoriness, suggesting immediate proarrhythmic effects pertinent to the atria and ventricles.
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Affiliation(s)
- Luisa Ciuffo
- Division of Cardiology, University of California San Francisco, San Francisco, CA, USA
| | - Monica Tung
- Division of Cardiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | | | - Eric Vittinghoff
- Department of Epidemiology and Biostatistics, University of California-San Francisco, San Francisco, CA, USA
| | - Joshua D Moss
- Division of Cardiology, University of California San Francisco, San Francisco, CA, USA
| | - Randall J Lee
- Division of Cardiology, University of California San Francisco, San Francisco, CA, USA
| | - Byron K Lee
- Division of Cardiology, University of California San Francisco, San Francisco, CA, USA
| | - Zian H Tseng
- Division of Cardiology, University of California San Francisco, San Francisco, CA, USA
| | - Vasanth Vedantham
- Division of Cardiology, University of California San Francisco, San Francisco, CA, USA
| | - Jeffrey E Olgin
- Division of Cardiology, University of California San Francisco, San Francisco, CA, USA
| | - Melvin M Scheinman
- Division of Cardiology, University of California San Francisco, San Francisco, CA, USA
| | - Henry Hsia
- Division of Cardiology, University of California San Francisco, San Francisco, CA, USA
| | | | - Edward P Gerstenfeld
- Division of Cardiology, University of California San Francisco, San Francisco, CA, USA
| | - Gregory M Marcus
- Division of Cardiology, University of California San Francisco, San Francisco, CA, USA.
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3
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Ye T, Zhou Y, Yang J, Yu F, Song Z, Shi J, Wang L, Huang Z, Yang B, Wang X. P2X7 receptor inhibition prevents atrial fibrillation in rodent models of depression. Europace 2024; 26:euae022. [PMID: 38261756 PMCID: PMC10873709 DOI: 10.1093/europace/euae022] [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: 10/05/2023] [Accepted: 12/07/2023] [Indexed: 01/25/2024] Open
Abstract
AIMS Depression, the most prevalent psychiatric disorder, is associated with the occurrence and development of atrial fibrillation (AF). P2X7 receptor (P2X7R) activation participates in the development of depression, but little attention has been given to its role in AF. This study was to investigate the effects of P2X7R on AF in depression models. METHODS AND RESULTS Lipopolysaccharide (LPS) and chronic unpredictable stress (CUS) were carried out to induce depression in rodents. Behavioural assessments, atrial electrophysiological parameters, electrocardiogram (ECG) parameters, western blot, and histology were performed. Atrial fibrillation inducibility was increased in both LPS- and CUS-induced depression, along with the up-regulation of P2X7R in atria. CUS facilitated atrial fibrosis. CUS reduced heart rate variability (HRV) and increased the expression of TH and GAP43, representing autonomic dysfunction. Down-regulation of Nav1.5, Cav1.2, Kv1.5, Kv4.3, Cx40, and Cx43 in CUS indicated the abnormalities in ion channels. In addition, the expression levels of TLR4, P65, P-P65, NLRP3, ASC, caspase-1, and IL-1β were elevated in depression models. Pharmacological inhibitor (Brilliant Blue G, BBG) or genetic deficiency of P2X7R significantly mitigated depressive-like behaviours; ameliorated electrophysiological deterioration and autonomic dysfunction; improved ion channel expression and atrial fibrosis; and prevented atrial NLRP3 inflammasome activation in the pathophysiological process of AF in depression models. CONCLUSION LPS or CUS induces AF and promotes P2X7R-dependent activation of NLRP3 inflammasome, whereas pharmacological P2X7R inhibition or P2X7R genetic deficiency prevents atrial remodelling without interrupting normal atrial physiological functions. Our results point to P2X7R as an important factor in the pathology of AF in depression.
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Affiliation(s)
- Tianxin Ye
- Department of Cardiology, The First Affiliated Hospital, Zhejiang University School of Medicine, #79 Qingchun Road, Hangzhou, Zhejiang Province 310003, PR China
| | - Yunping Zhou
- Department of Cardiology, The First Affiliated Hospital, Zhejiang University School of Medicine, #79 Qingchun Road, Hangzhou, Zhejiang Province 310003, PR China
| | - Jinxiu Yang
- Department of Cardiology, The First Affiliated Hospital, Zhejiang University School of Medicine, #79 Qingchun Road, Hangzhou, Zhejiang Province 310003, PR China
| | - Fangcong Yu
- Department of Cardiology, The First Affiliated Hospital, Zhejiang University School of Medicine, #79 Qingchun Road, Hangzhou, Zhejiang Province 310003, PR China
| | - Zhuonan Song
- Department of Cardiology, The First Affiliated Hospital, Zhejiang University School of Medicine, #79 Qingchun Road, Hangzhou, Zhejiang Province 310003, PR China
| | - Jiaran Shi
- Department of Cardiology, The First Affiliated Hospital, Zhejiang University School of Medicine, #79 Qingchun Road, Hangzhou, Zhejiang Province 310003, PR China
| | - Longbo Wang
- Department of Cardiology, The First Affiliated Hospital, Zhejiang University School of Medicine, #79 Qingchun Road, Hangzhou, Zhejiang Province 310003, PR China
| | - Zhouqing Huang
- Department of Cardiology, The First Affiliated Hospital of Wenzhou Medical University, 2 Fuxue Road, Wenzhou, Zhejiang Province 325000, PR China
| | - Bo Yang
- Department of Cardiology, Renmin Hospital of Wuhan University, 238 Jiefang Road, Wuchang District, Wuhan 430060, PR China
- Cardiovascular Research Institute, Wuhan University, 238 Jiefang Road, Wuchang District, Wuhan 430060, PR China
- Hubei Key Laboratory of Cardiology, Wuhan 430060, PR China
| | - Xingxiang Wang
- Department of Cardiology, The First Affiliated Hospital, Zhejiang University School of Medicine, #79 Qingchun Road, Hangzhou, Zhejiang Province 310003, PR China
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4
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Bifulco SF, Boyle PM. Computational Modeling and Simulation of the Fibrotic Human Atria. Methods Mol Biol 2024; 2735:105-115. [PMID: 38038845 DOI: 10.1007/978-1-0716-3527-8_6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2023]
Abstract
Patient-specific modeling of atrial electrical activity enables the execution of simulations that can provide mechanistic insights and provide novel solutions to vexing clinical problems. The geometry and fibrotic remodeling of the heart can be reconstructed from clinical-grade medical scans and used to inform personalized models with detail incorporated at the cell- and tissue-scale to represent changes in image-identified diseased regions. Here, we provide a rubric for the reconstruction of realistic atrial models from pre-segmented 3D renderings of the left atrium with fibrotic tissue regions delineated, which are the output from clinical-grade systems for quantifying fibrosis. We then provide a roadmap for using those models to carry out patient-specific characterization of the fibrotic substrate in terms of its potential to harbor reentrant drivers via cardiac electrophysiology simulations.
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Affiliation(s)
- Savannah F Bifulco
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Patrick M Boyle
- Department of Bioengineering, University of Washington, Seattle, WA, USA.
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA.
- Center for Cardiovascular Biology, University of Washington, Seattle, WA, USA.
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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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6
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Xiong LI, Garfinkel A. Are physiological oscillations physiological? J Physiol 2023. [PMID: 37622389 DOI: 10.1113/jp285015] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 08/03/2023] [Indexed: 08/26/2023] Open
Abstract
Despite widespread and striking examples of physiological oscillations, their functional role is often unclear. Even glycolysis, the paradigm example of oscillatory biochemistry, has seen questions about its oscillatory function. Here, we take a systems approach to argue that oscillations play critical physiological roles, such as enabling systems to avoid desensitization, to avoid chronically high and therefore toxic levels of chemicals, and to become more resistant to noise. Oscillation also enables complex physiological systems to reconcile incompatible conditions such as oxidation and reduction, by cycling between them, and to synchronize the oscillations of many small units into one large effect. In pancreatic β-cells, glycolytic oscillations synchronize with calcium and mitochondrial oscillations to drive pulsatile insulin release, critical for liver regulation of glucose. In addition, oscillation can keep biological time, essential for embryonic development in promoting cell diversity and pattern formation. The functional importance of oscillatory processes requires a re-thinking of the traditional doctrine of homeostasis, holding that physiological quantities are maintained at constant equilibrium values, a view that has largely failed in the clinic. A more dynamic approach will initiate a paradigm shift in our view of health and disease. A deeper look into the mechanisms that create, sustain and abolish oscillatory processes requires the language of nonlinear dynamics, well beyond the linearization techniques of equilibrium control theory. Nonlinear dynamics enables us to identify oscillatory ('pacemaking') mechanisms at the cellular, tissue and system levels.
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Affiliation(s)
- Lingyun Ivy Xiong
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine of the University of Southern California, Los Angeles, CA, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
| | - Alan Garfinkel
- Departments of Medicine (Cardiology) and Integrative Biology and Physiology, University of California, Los Angeles, CA, USA
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7
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Bifulco SF, Macheret F, Scott GD, Akoum N, Boyle PM. Explainable Machine Learning to Predict Anchored Reentry Substrate Created by Persistent Atrial Fibrillation Ablation in Computational Models. J Am Heart Assoc 2023; 12:e030500. [PMID: 37581387 PMCID: PMC10492949 DOI: 10.1161/jaha.123.030500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Accepted: 07/21/2023] [Indexed: 08/16/2023]
Abstract
Background Postablation arrhythmia recurrence occurs in ~40% of patients with persistent atrial fibrillation. Fibrotic remodeling exacerbates arrhythmic activity in persistent atrial fibrillation and can play a key role in reentrant arrhythmia, but emergent interaction between nonconductive ablation-induced scar and native fibrosis (ie, residual fibrosis) is poorly understood. Methods and Results We conducted computational simulations in pre- and postablation left atrial models reconstructed from late gadolinium enhanced magnetic resonance imaging scans to test the hypothesis that ablation in patients with persistent atrial fibrillation creates new substrate conducive to recurrent arrhythmia mediated by anchored reentry. We trained a random forest machine learning classifier to accurately pinpoint specific nonconductive tissue regions (ie, areas of ablation-delivered scar or vein/valve boundaries) with the capacity to serve as substrate for anchored reentry-driven recurrent arrhythmia (area under the curve: 0.91±0.03). Our analysis suggests there is a distinctive nonconductive tissue pattern prone to serving as arrhythmogenic substrate in postablation models, defined by a specific size and proximity to residual fibrosis. Conclusions Overall, this suggests persistent atrial fibrillation ablation transforms substrate that favors functional reentry (ie, rotors meandering in excitable tissue) into an arrhythmogenic milieu more conducive to anchored reentry. Our work also indicates that explainable machine learning and computational simulations can be combined to effectively probe mechanisms of recurrent arrhythmia.
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Affiliation(s)
| | - Fima Macheret
- Division of CardiologyUniversity of WashingtonSeattleWAUSA
| | - Griffin D. Scott
- Department of BioengineeringUniversity of WashingtonSeattleWAUSA
| | - Nazem Akoum
- Department of BioengineeringUniversity of WashingtonSeattleWAUSA
- Division of CardiologyUniversity of WashingtonSeattleWAUSA
| | - Patrick M. Boyle
- Department of BioengineeringUniversity of WashingtonSeattleWAUSA
- Institute for Stem Cell and Regenerative MedicineUniversity of WashingtonSeattleWAUSA
- Center for Cardiovascular BiologyUniversity of WashingtonSeattleWAUSA
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8
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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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9
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Calibrating cardiac electrophysiology models using latent Gaussian processes on atrial manifolds. Sci Rep 2022; 12:16572. [PMID: 36195766 PMCID: PMC9532401 DOI: 10.1038/s41598-022-20745-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 09/19/2022] [Indexed: 11/24/2022] Open
Abstract
Models of electrical excitation and recovery in the heart have become increasingly detailed, but have yet to be used routinely in the clinical setting to guide personalized intervention in patients. One of the main challenges is calibrating models from the limited measurements that can be made in a patient during a standard clinical procedure. In this work, we propose a novel framework for the probabilistic calibration of electrophysiology parameters on the left atrium of the heart using local measurements of cardiac excitability. Parameter fields are represented as Gaussian processes on manifolds and are linked to measurements via surrogate functions that map from local parameter values to measurements. The posterior distribution of parameter fields is then obtained. We show that our method can recover parameter fields used to generate localised synthetic measurements of effective refractory period. Our methodology is applicable to other measurement types collected with clinical protocols, and more generally for calibration where model parameters vary over a manifold.
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10
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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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Choi Y, Lim B, Yang SY, Yang SH, Kwon OS, Kim D, Kim YG, Park JW, Yu HT, Kim TH, Yang PS, Uhm JS, Shim J, Kim SH, Sung JH, Choi JI, Joung B, Lee MH, Kim YH, Oh YS, Pak HN. Clinical Usefulness of Virtual Ablation Guided Catheter Ablation of Atrial Fibrillation Targeting Restitution Parameter-Guided Catheter Ablation: CUVIA-REGAB Prospective Randomized Study. Korean Circ J 2022; 52:699-711. [PMID: 35927040 PMCID: PMC9470491 DOI: 10.4070/kcj.2022.0113] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 05/18/2022] [Accepted: 06/08/2022] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND AND OBJECTIVES We investigated whether extra-pulmonary vein (PV) ablation targeting a high maximal slope of the action potential duration restitution curve (Smax) improves the rhythm outcome of persistent atrial fibrillation (PeAF) ablation. METHODS In this open-label, multi-center, randomized, and controlled trial, 178 PeAF patients were randomized with 1:1 ratio to computational modeling-guided virtual Smax ablation (V-Smax) or empirical ablation (E-ABL) groups. Smax maps were generated by computational modeling based on atrial substrate maps acquired during clinical procedures in sinus rhythm. Smax maps were generated during the clinical PV isolation (PVI). The V-Smax group underwent an additional extra-PV ablation after PVI targeting the virtual high Smax sites. RESULTS After a mean follow-up period of 12.3±5.2 months, the clinical recurrence rates (25.6% vs. 23.9% in the V-Smax and the E-ABL group, p=0.880) or recurrence appearing as atrial tachycardia (11.1% vs. 5.7%, p=0.169) did not differ between the 2 groups. The post-ablation cardioversion rate was higher in the V-Smax group than E-ABL group (14.4% vs. 5.7%, p=0.027). Among antiarrhythmic drug-free patients (n=129), the AF freedom rate was 78.7% in the V-Smax group and 80.9% in the E-ABL group (p=0.776). The total procedure time was longer in the V-Smax group (p=0.008), but no significant difference was found in the major complication rates (p=0.497) between the groups. CONCLUSIONS Unlike a dominant frequency ablation, the computational modeling-guided V-Smax ablation did not improve the rhythm outcome of the PeAF ablation and had a longer procedure time. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT02558699.
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Affiliation(s)
- Young Choi
- Division of Cardiology, Department of Internal Medicine, Seoul St. Mary Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Byounghyun Lim
- Department of Cardiology, Yonsei University College of Medicine, Yonsei University Health System, Seoul, Korea
| | - Song-Yi Yang
- Department of Cardiology, Yonsei University College of Medicine, Yonsei University Health System, Seoul, Korea
| | - So-Hyun Yang
- Department of Cardiology, Yonsei University College of Medicine, Yonsei University Health System, Seoul, Korea
| | - Oh-Seok Kwon
- Department of Cardiology, Yonsei University College of Medicine, Yonsei University Health System, Seoul, Korea
| | - Daehoon Kim
- Department of Cardiology, Yonsei University College of Medicine, Yonsei University Health System, Seoul, Korea
| | - Yun Gi Kim
- Department of Cardiology, Korea University Cardiovascular Center, Korea University, Seoul, Korea
| | - Je-Wook Park
- Department of Cardiology, Yonsei University College of Medicine, Yonsei University Health System, Seoul, Korea
| | - Hee Tae Yu
- Department of Cardiology, Yonsei University College of Medicine, Yonsei University Health System, Seoul, Korea
| | - Tae-Hoon Kim
- Department of Cardiology, Yonsei University College of Medicine, Yonsei University Health System, Seoul, Korea
| | - Pil-Sung Yang
- Department of Cardiology, Bundang CHA Hospital, CHA College of Medicine, Seoul, Korea
| | - Jae-Sun Uhm
- Department of Cardiology, Yonsei University College of Medicine, Yonsei University Health System, Seoul, Korea
| | - Jamin Shim
- Department of Cardiology, Korea University Cardiovascular Center, Korea University, Seoul, Korea
| | - Sung Hwan Kim
- Division of Cardiology, Department of Internal Medicine, Seoul St. Mary Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Jung-Hoon Sung
- Department of Cardiology, Bundang CHA Hospital, CHA College of Medicine, Seoul, Korea
| | - Jong-Il Choi
- Department of Cardiology, Korea University Cardiovascular Center, Korea University, Seoul, Korea
| | - Boyoung Joung
- Department of Cardiology, Yonsei University College of Medicine, Yonsei University Health System, Seoul, Korea
| | - Moon-Hyoung Lee
- Department of Cardiology, Yonsei University College of Medicine, Yonsei University Health System, Seoul, Korea
| | - Young-Hoon Kim
- Department of Cardiology, Korea University Cardiovascular Center, Korea University, Seoul, Korea
| | - Yong-Seog Oh
- Division of Cardiology, Department of Internal Medicine, Seoul St. Mary Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea.
| | - Hui-Nam Pak
- Department of Cardiology, Yonsei University College of Medicine, Yonsei University Health System, Seoul, Korea.
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12
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A Review on Atrial Fibrillation (Computer Simulation and Clinical Perspectives). HEARTS 2022. [DOI: 10.3390/hearts3010005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Atrial fibrillation (AF), a heart condition, has been a well-researched topic for the past few decades. This multidisciplinary field of study deals with signal processing, finite element analysis, mathematical modeling, optimization, and clinical procedure. This article is focused on a comprehensive review of journal articles published in the field of AF. Topics from the age-old fundamental concepts to specialized modern techniques involved in today’s AF research are discussed. It was found that a lot of research articles have already been published in modeling and simulation of AF. In comparison to that, the diagnosis and post-operative procedures for AF patients have not yet been totally understood or explored by the researchers. The simulation and modeling of AF have been investigated by many researchers in this field. Cellular model, tissue model, and geometric model among others have been used to simulate AF. Due to a very complex nature, the causes of AF have not been fully perceived to date, but the simulated results are validated with real-life patient data. Many algorithms have been proposed to detect the source of AF in human atria. There are many ablation strategies for AF patients, but the search for more efficient ablation strategies is still going on. AF management for patients with different stages of AF has been discussed in the literature as well but is somehow limited mostly to the patients with persistent AF. The authors hope that this study helps to find existing research gaps in the analysis and the diagnosis of AF.
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Roney CH, Sim I, Yu J, Beach M, Mehta A, Alonso Solis-Lemus J, Kotadia I, Whitaker J, Corrado C, Razeghi O, Vigmond E, Narayan SM, O’Neill M, Williams SE, Niederer SA. Predicting Atrial Fibrillation Recurrence by Combining Population Data and Virtual Cohorts of Patient-Specific Left Atrial Models. Circ Arrhythm Electrophysiol 2022; 15:e010253. [PMID: 35089057 PMCID: PMC8845531 DOI: 10.1161/circep.121.010253] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 01/03/2022] [Indexed: 01/14/2023]
Abstract
BACKGROUND Current ablation therapy for atrial fibrillation is suboptimal, and long-term response is challenging to predict. Clinical trials identify bedside properties that provide only modest prediction of long-term response in populations, while patient-specific models in small cohorts primarily explain acute response to ablation. We aimed to predict long-term atrial fibrillation recurrence after ablation in large cohorts, by using machine learning to complement biophysical simulations by encoding more interindividual variability. METHODS Patient-specific models were constructed for 100 atrial fibrillation patients (43 paroxysmal, 41 persistent, and 16 long-standing persistent), undergoing first ablation. Patients were followed for 1 year using ambulatory ECG monitoring. Each patient-specific biophysical model combined differing fibrosis patterns, fiber orientation maps, electrical properties, and ablation patterns to capture uncertainty in atrial properties and to test the ability of the tissue to sustain fibrillation. These simulation stress tests of different model variants were postprocessed to calculate atrial fibrillation simulation metrics. Machine learning classifiers were trained to predict atrial fibrillation recurrence using features from the patient history, imaging, and atrial fibrillation simulation metrics. RESULTS We performed 1100 atrial fibrillation ablation simulations across 100 patient-specific models. Models based on simulation stress tests alone showed a maximum accuracy of 0.63 for predicting long-term fibrillation recurrence. Classifiers trained to history, imaging, and simulation stress tests (average 10-fold cross-validation area under the curve, 0.85±0.09; recall, 0.80±0.13; precision, 0.74±0.13) outperformed those trained to history and imaging (area under the curve, 0.66±0.17) or history alone (area under the curve, 0.61±0.14). CONCLUSION A novel computational pipeline accurately predicted long-term atrial fibrillation recurrence in individual patients by combining outcome data with patient-specific acute simulation response. This technique could help to personalize selection for atrial fibrillation ablation.
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Affiliation(s)
- Caroline H. Roney
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (C.H.R., I.S., J.Y., M.B., A.M., J.A.S.-L., I.K., J.W., C.C., O.R., M.O., S.E.W., S.A.N.)
- School of Engineering and Materials Science, Queen Mary University of London, United Kingdom (C.H.R.)
| | - Iain Sim
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (C.H.R., I.S., J.Y., M.B., A.M., J.A.S.-L., I.K., J.W., C.C., O.R., M.O., S.E.W., S.A.N.)
| | - Jin Yu
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (C.H.R., I.S., J.Y., M.B., A.M., J.A.S.-L., I.K., J.W., C.C., O.R., M.O., S.E.W., S.A.N.)
| | - Marianne Beach
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (C.H.R., I.S., J.Y., M.B., A.M., J.A.S.-L., I.K., J.W., C.C., O.R., M.O., S.E.W., S.A.N.)
| | - Arihant Mehta
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (C.H.R., I.S., J.Y., M.B., A.M., J.A.S.-L., I.K., J.W., C.C., O.R., M.O., S.E.W., S.A.N.)
| | - Jose Alonso Solis-Lemus
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (C.H.R., I.S., J.Y., M.B., A.M., J.A.S.-L., I.K., J.W., C.C., O.R., M.O., S.E.W., S.A.N.)
| | - Irum Kotadia
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (C.H.R., I.S., J.Y., M.B., A.M., J.A.S.-L., I.K., J.W., C.C., O.R., M.O., S.E.W., S.A.N.)
| | - John Whitaker
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (C.H.R., I.S., J.Y., M.B., A.M., J.A.S.-L., I.K., J.W., C.C., O.R., M.O., S.E.W., S.A.N.)
- The Department of Internal Medicine, Cardiovascular Division, Brigham and Women’s Hospital, Boston, MA (J.W.)
| | - Cesare Corrado
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (C.H.R., I.S., J.Y., M.B., A.M., J.A.S.-L., I.K., J.W., C.C., O.R., M.O., S.E.W., S.A.N.)
| | - Orod Razeghi
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (C.H.R., I.S., J.Y., M.B., A.M., J.A.S.-L., I.K., J.W., C.C., O.R., M.O., S.E.W., S.A.N.)
| | - Edward Vigmond
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, France (E.V.)
- Univ. Bordeaux, IMB, UMR 5251, F-33400 Talence, France (E.V.)
| | - Sanjiv M. Narayan
- Department of Medicine and Cardiovascular Institute, Stanford University, Palo Alto, CA (S.M.N.)
| | - Mark O’Neill
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (C.H.R., I.S., J.Y., M.B., A.M., J.A.S.-L., I.K., J.W., C.C., O.R., M.O., S.E.W., S.A.N.)
| | - Steven E. Williams
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (C.H.R., I.S., J.Y., M.B., A.M., J.A.S.-L., I.K., J.W., C.C., O.R., M.O., S.E.W., S.A.N.)
- Centre for Cardiovascular Science, College of Medicine and Veterinary Medicine, University of Edinburgh (S.E.W.)
| | - Steven A. Niederer
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (C.H.R., I.S., J.Y., M.B., A.M., J.A.S.-L., I.K., J.W., C.C., O.R., M.O., S.E.W., S.A.N.)
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14
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Kwon OS, Hwang I, Pak HN. Computational modeling of atrial fibrillation. INTERNATIONAL JOURNAL OF ARRHYTHMIA 2021. [DOI: 10.1186/s42444-021-00051-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
AbstractWith the aging society, the prevalence of atrial fibrillation (AF) continues to increase. Nevertheless, there are still limitations in antiarrhythmic drugs (AAD) or catheter interventions for AF. If it is possible to predict the outcome of AF management according to various AADs or ablation lesion sets through computational modeling, it will be of great clinical help. AF computational modeling has been utilized for in-silico arrhythmia research and enabled high-density entire chamber mapping, reproducible condition control, virtual intervention, not possible clinically or experimentally, in-depth mechanistic research. With the recent development of computer science and technology, more sophisticated and faster computational modeling has become available for clinical application. In particular, it can be applied to determine the extra-PV target of persistent AF catheter ablation or to select the AAD with the best effect. AF computational modeling combined with artificial intelligence is expected to contribute to precision medicine for more diverse uses in the future. Therefore, in this review, we will deal with the history, development, and various applications of computation modeling.
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15
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Identifying Atrial Fibrillation Mechanisms for Personalized Medicine. J Clin Med 2021; 10:jcm10235679. [PMID: 34884381 PMCID: PMC8658178 DOI: 10.3390/jcm10235679] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 11/27/2021] [Accepted: 11/28/2021] [Indexed: 01/02/2023] Open
Abstract
Atrial fibrillation (AF) is a major cause of heart failure and stroke. The early maintenance of sinus rhythm has been shown to reduce major cardiovascular endpoints, yet is difficult to achieve. For instance, it is unclear how discoveries at the genetic and cellular level can be used to tailor pharmacotherapy. For non-pharmacologic therapy, pulmonary vein isolation (PVI) remains the cornerstone of rhythm control, yet has suboptimal success. Improving these therapies will likely require a multifaceted approach that personalizes therapy based on mechanisms measured in individuals across biological scales. We review AF mechanisms from cell-to-organ-to-patient from this perspective of personalized medicine, linking them to potential clinical indices and biomarkers, and discuss how these data could influence therapy. We conclude by describing approaches to improve ablation, including the emergence of several mapping systems that are in use today.
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16
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Ochs AR, Karathanos TV, Trayanova NA, Boyle PM. Optogenetic Stimulation Using Anion Channelrhodopsin (GtACR1) Facilitates Termination of Reentrant Arrhythmias With Low Light Energy Requirements: A Computational Study. Front Physiol 2021; 12:718622. [PMID: 34526912 PMCID: PMC8435849 DOI: 10.3389/fphys.2021.718622] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 07/23/2021] [Indexed: 12/24/2022] Open
Abstract
Optogenetic defibrillation of hearts expressing light-sensitive cation channels (e.g., ChR2) has been proposed as an alternative to conventional electrotherapy. Past modeling work has shown that ChR2 stimulation can depolarize enough myocardium to interrupt arrhythmia, but its efficacy is limited by light attenuation and high energy needs. These shortcomings may be mitigated by using new optogenetic proteins like Guillardia theta Anion Channelrhodopsin (GtACR1), which produces a repolarizing outward current upon illumination. Accordingly, we designed a study to assess the feasibility of GtACR1-based optogenetic arrhythmia termination in human hearts. We conducted electrophysiological simulations in MRI-based atrial or ventricular models (n = 3 each), with pathological remodeling from atrial fibrillation or ischemic cardiomyopathy, respectively. We simulated light sensitization via viral gene delivery of three different opsins (ChR2, red-shifted ChR2, GtACR1) and uniform endocardial illumination at the appropriate wavelengths (blue, red, or green light, respectively). To analyze consistency of arrhythmia termination, we varied pulse timing (three evenly spaced intervals spanning the reentrant cycle) and intensity (atrial: 0.001–1 mW/mm2; ventricular: 0.001–10 mW/mm2). In atrial models, GtACR1 stimulation with 0.005 mW/mm2 green light consistently terminated reentry; this was 10–100x weaker than the threshold levels for ChR2-mediated defibrillation. In ventricular models, defibrillation was observed in 2/3 models for GtACR1 stimulation at 0.005 mW/mm2 (100–200x weaker than ChR2 cases). In the third ventricular model, defibrillation failed in nearly all cases, suggesting that attenuation issues and patient-specific organ/scar geometry may thwart termination in some cases. Across all models, the mechanism of GtACR1-mediated defibrillation was voltage forcing of illuminated tissue toward the modeled channel reversal potential of −40 mV, which made propagation through affected regions impossible. Thus, our findings suggest GtACR1-based optogenetic defibrillation of the human heart may be feasible with ≈2–3 orders of magnitude less energy than ChR2.
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Affiliation(s)
- Alexander R Ochs
- Department of Bioengineering, University of Washington, Seattle, WA, United States
| | - Thomas V Karathanos
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Natalia A Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States.,Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, United States
| | - Patrick M Boyle
- Department of Bioengineering, University of Washington, Seattle, WA, United States.,Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, United States.,Center for Cardiovascular Biology, University of Washington, Seattle, WA, United States
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17
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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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18
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Bifulco SF, Scott GD, Sarairah S, Birjandian Z, Roney CH, Niederer SA, Mahnkopf C, Kuhnlein P, Mitlacher M, Tirschwell D, Longstreth WT, Akoum N, Boyle PM. Computational modeling identifies embolic stroke of undetermined source patients with potential arrhythmic substrate. eLife 2021; 10:e64213. [PMID: 33942719 PMCID: PMC8143793 DOI: 10.7554/elife.64213] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Accepted: 04/16/2021] [Indexed: 12/25/2022] Open
Abstract
Cardiac magnetic resonance imaging (MRI) has revealed fibrosis in embolic stroke of undetermined source (ESUS) patients comparable to levels seen in atrial fibrillation (AFib). We used computational modeling to understand the absence of arrhythmia in ESUS despite the presence of putatively pro-arrhythmic fibrosis. MRI-based atrial models were reconstructed for 45 ESUS and 45 AFib patients. The fibrotic substrate's arrhythmogenic capacity in each patient was assessed computationally. Reentrant drivers were induced in 24/45 (53%) ESUS and 22/45 (49%) AFib models. Inducible models had more fibrosis (16.7 ± 5.45%) than non-inducible models (11.07 ± 3.61%; p<0.0001); however, inducible subsets of ESUS and AFib models had similar fibrosis levels (p=0.90), meaning that the intrinsic pro-arrhythmic substrate properties of fibrosis in ESUS and AFib are indistinguishable. This suggests that some ESUS patients have latent pre-clinical fibrotic substrate that could be a future source of arrhythmogenicity. Thus, our work prompts the hypothesis that ESUS patients with fibrotic atria are spared from AFib due to an absence of arrhythmia triggers.
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Affiliation(s)
- Savannah F Bifulco
- Department of Bioengineering, University of WashingtonSeattleUnited States
| | - Griffin D Scott
- Department of Bioengineering, University of WashingtonSeattleUnited States
| | - Sakher Sarairah
- Division of Cardiology, University of WashingtonSeattleUnited States
| | - Zeinab Birjandian
- Division of Cardiology, University of WashingtonSeattleUnited States
- Department of Neurology, University of WashingtonSeattleUnited States
| | - Caroline H Roney
- School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
| | - Steven A Niederer
- School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
| | | | | | | | - David Tirschwell
- Department of Neurology, University of WashingtonSeattleUnited States
| | - WT Longstreth
- Department of Neurology, University of WashingtonSeattleUnited States
- Department of Epidemiology, University of WashingtonSeattleUnited States
| | - Nazem Akoum
- Division of Cardiology, University of WashingtonSeattleUnited States
| | - Patrick M Boyle
- Department of Bioengineering, University of WashingtonSeattleUnited States
- Center for Cardiovascular Biology, University of WashingtonSeattleUnited States
- Institute for Stem Cell and Regenerative Medicine, University of WashingtonSeattleUnited States
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19
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Azzolin L, Schuler S, Dössel O, Loewe A. A Reproducible Protocol to Assess Arrhythmia Vulnerability in silico: Pacing at the End of the Effective Refractory Period. Front Physiol 2021; 12:656411. [PMID: 33868025 PMCID: PMC8047415 DOI: 10.3389/fphys.2021.656411] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 03/10/2021] [Indexed: 01/31/2023] Open
Abstract
In both clinical and computational studies, different pacing protocols are used to induce arrhythmia and non-inducibility is often considered as the endpoint of treatment. The need for a standardized methodology is urgent since the choice of the protocol used to induce arrhythmia could lead to contrasting results, e.g., in assessing atrial fibrillation (AF) vulnerabilty. Therefore, we propose a novel method-pacing at the end of the effective refractory period (PEERP)-and compare it to state-of-the-art protocols, such as phase singularity distribution (PSD) and rapid pacing (RP) in a computational study. All methods were tested by pacing from evenly distributed endocardial points at 1 cm inter-point distance in two bi-atrial geometries. Seven different atrial models were implemented: five cases without specific AF-induced remodeling but with decreasing global conduction velocity and two persistent AF cases with an increasing amount of fibrosis resembling different substrate remodeling stages. Compared with PSD and RP, PEERP induced a larger variety of arrhythmia complexity requiring, on average, only 2.7 extra-stimuli and 3 s of simulation time to initiate reentry. Moreover, PEERP and PSD were the protocols which unveiled a larger number of areas vulnerable to sustain stable long living reentries compared to RP. Finally, PEERP can foster standardization and reproducibility, since, in contrast to the other protocols, it is a parameter-free method. Furthermore, we discuss its clinical applicability. We conclude that the choice of the inducing protocol has an influence on both initiation and maintenance of AF and we propose and provide PEERP as a reproducible method to assess arrhythmia vulnerability.
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Affiliation(s)
- Luca Azzolin
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
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Ho G, Lin AY, Krummen DE. Linking Electrical Drivers With Atrial Cardiomyopathy for the Targeted Treatment of Atrial Fibrillation. Front Physiol 2020; 11:570740. [PMID: 33281614 PMCID: PMC7689158 DOI: 10.3389/fphys.2020.570740] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 09/22/2020] [Indexed: 12/19/2022] Open
Abstract
The relationship between atrial fibrillation (AF) and underlying functional and structural abnormalities has received substantial attention in the research literature over the past decade. Significant progress has been made in identifying these changes using non-invasive imaging, voltage mapping, and electrical recordings. Advances in computed tomography and cardiac magnetic resonance imaging can now provide insight regarding the presence and extent of cardiac fibrosis. Additionally, multiple technologies able to identify electrical targets during AF have emerged. However, an organized strategy to employ these resources in the targeted treatment of AF remains elusive. In this work, we will discuss the basis for mechanistic importance of atrial fibrosis and scar as potential sites promoting AF and emerging technologies to identify and target these structural and functional substrates in the electrophysiology laboratory. We also propose an approach to the use of such technologies to serve as a basis for ongoing work in the field.
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Affiliation(s)
- Gordon Ho
- Division of Cardiology, Department of Medicine, University of California, San Diego, San Diego, CA, United States
- Division of Cardiology, Veterans Affairs San Diego Medical Center, San Diego, CA, United States
| | - Andrew Y. Lin
- Division of Cardiology, Department of Medicine, University of California, San Diego, San Diego, CA, United States
- Division of Cardiology, Veterans Affairs San Diego Medical Center, San Diego, CA, United States
| | - David E. Krummen
- Division of Cardiology, Department of Medicine, University of California, San Diego, San Diego, CA, United States
- Division of Cardiology, Veterans Affairs San Diego Medical Center, San Diego, CA, United States
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Corrado C, Avezzù A, Lee AWC, Mendoca Costa C, Roney CH, Strocchi M, Bishop M, Niederer SA. Using cardiac ionic cell models to interpret clinical data. WIREs Mech Dis 2020; 13:e1508. [PMID: 33027553 DOI: 10.1002/wsbm.1508] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Revised: 08/27/2020] [Accepted: 09/04/2020] [Indexed: 01/24/2023]
Abstract
For over 100 years cardiac electrophysiology has been measured in the clinic. The electrical signals that can be measured span from noninvasive ECG and body surface potentials measurements through to detailed invasive measurements of local tissue electrophysiology. These electrophysiological measurements form a crucial component of patient diagnosis and monitoring; however, it remains challenging to quantitatively link changes in clinical electrophysiology measurements to biophysical cellular function. Multi-scale biophysical computational models represent one solution to this problem. These models provide a formal framework for linking cellular function through to emergent whole organ function and routine clinical diagnostic signals. In this review, we describe recent work on the use of computational models to interpret clinical electrophysiology signals. We review the simulation of human cardiac myocyte electrophysiology in the atria and the ventricles and how these models are being used to link organ scale function to patient disease mechanisms and therapy response in patients receiving implanted defibrillators, \cardiac resynchronisation therapy or suffering from atrial fibrillation and ventricular tachycardia. There is a growing use of multi-scale biophysical models to interpret clinical data. This allows cardiologists to link clinical observations with cellular mechanisms to better understand cardiopathophysiology and identify novel treatment strategies. This article is categorized under: Cardiovascular Diseases > Computational Models Cardiovascular Diseases > Biomedical Engineering Cardiovascular Diseases > Molecular and Cellular Physiology.
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22
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Hansen BJ, Zhao J, Helfrich KM, Li N, Iancau A, Zolotarev AM, Zakharkin SO, Kalyanasundaram A, Subr M, Dastagir N, Sharma R, Artiga EJ, Salgia N, Houmsse MM, Kahaly O, Janssen PML, Mohler PJ, Mokadam NA, Whitson BA, Afzal MR, Simonetti OP, Hummel JD, Fedorov VV. Unmasking Arrhythmogenic Hubs of Reentry Driving Persistent Atrial Fibrillation for Patient-Specific Treatment. J Am Heart Assoc 2020; 9:e017789. [PMID: 33006292 PMCID: PMC7792422 DOI: 10.1161/jaha.120.017789] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Background Atrial fibrillation (AF) driver mechanisms are obscured to clinical multielectrode mapping approaches that provide partial, surface‐only visualization of unstable 3‐dimensional atrial conduction. We hypothesized that transient modulation of refractoriness by pharmacologic challenge during multielectrode mapping improves visualization of hidden paths of reentrant AF drivers for targeted ablation. Methods and Results Pharmacologic challenge with adenosine was tested in ex vivo human hearts with a history of AF and cardiac diseases by multielectrode and high‐resolution subsurface near‐infrared optical mapping, integrated with 3‐dimensional structural imaging and heart‐specific computational simulations. Adenosine challenge was also studied on acutely terminated AF drivers in 10 patients with persistent AF. Ex vivo, adenosine stabilized reentrant driver paths within arrhythmogenic fibrotic hubs and improved visualization of reentrant paths, previously seen as focal or unstable breakthrough activation pattern, for targeted AF ablation. Computational simulations suggested that shortening of atrial refractoriness by adenosine may (1) improve driver stability by annihilating spatially unstable functional blocks and tightening reentrant circuits around fibrotic substrates, thus unmasking the common reentrant path; and (2) destabilize already stable reentrant drivers along fibrotic substrates by accelerating competing fibrillatory wavelets or secondary drivers. In patients with persistent AF, adenosine challenge unmasked hidden common reentry paths (9/15 AF drivers, 41±26% to 68±25% visualization), but worsened visualization of previously visible reentry paths (6/15, 74±14% to 34±12%). AF driver ablation led to acute termination of AF. Conclusions Our ex vivo to in vivo human translational study suggests that transiently altering atrial refractoriness can stabilize reentrant paths and unmask arrhythmogenic hubs to guide targeted AF driver ablation treatment.
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Affiliation(s)
- Brian J Hansen
- Department of Physiology & Cell Biology and Frick Center for Heart Failure and Arrhythmia The Ohio State University Wexner Medical Center Columbus OH.,Davis Heart & Lung Research InstituteThe Ohio State University Wexner Medical Center Columbus OH
| | | | - Katelynn M Helfrich
- Department of Physiology & Cell Biology and Frick Center for Heart Failure and Arrhythmia The Ohio State University Wexner Medical Center Columbus OH.,Davis Heart & Lung Research InstituteThe Ohio State University Wexner Medical Center Columbus OH
| | - Ning Li
- Department of Physiology & Cell Biology and Frick Center for Heart Failure and Arrhythmia The Ohio State University Wexner Medical Center Columbus OH.,Davis Heart & Lung Research InstituteThe Ohio State University Wexner Medical Center Columbus OH
| | - Alexander Iancau
- Department of Physiology & Cell Biology and Frick Center for Heart Failure and Arrhythmia The Ohio State University Wexner Medical Center Columbus OH
| | - Alexander M Zolotarev
- Department of Physiology & Cell Biology and Frick Center for Heart Failure and Arrhythmia The Ohio State University Wexner Medical Center Columbus OH.,Skolkovo Institute of Science and Technology Moscow Russia
| | - Stanislav O Zakharkin
- Department of Physiology & Cell Biology and Frick Center for Heart Failure and Arrhythmia The Ohio State University Wexner Medical Center Columbus OH
| | - Anuradha Kalyanasundaram
- Department of Physiology & Cell Biology and Frick Center for Heart Failure and Arrhythmia The Ohio State University Wexner Medical Center Columbus OH.,Davis Heart & Lung Research InstituteThe Ohio State University Wexner Medical Center Columbus OH
| | - Megan Subr
- Department of Physiology & Cell Biology and Frick Center for Heart Failure and Arrhythmia The Ohio State University Wexner Medical Center Columbus OH
| | | | | | - Esthela J Artiga
- Department of Physiology & Cell Biology and Frick Center for Heart Failure and Arrhythmia The Ohio State University Wexner Medical Center Columbus OH.,Davis Heart & Lung Research InstituteThe Ohio State University Wexner Medical Center Columbus OH
| | - Nicholas Salgia
- Department of Physiology & Cell Biology and Frick Center for Heart Failure and Arrhythmia The Ohio State University Wexner Medical Center Columbus OH
| | - Mustafa M Houmsse
- Department of Physiology & Cell Biology and Frick Center for Heart Failure and Arrhythmia The Ohio State University Wexner Medical Center Columbus OH
| | - Omar Kahaly
- Davis Heart & Lung Research InstituteThe Ohio State University Wexner Medical Center Columbus OH.,Department of Internal Medicine The Ohio State University Wexner Medical Center Columbus OH
| | - Paul M L Janssen
- Department of Physiology & Cell Biology and Frick Center for Heart Failure and Arrhythmia The Ohio State University Wexner Medical Center Columbus OH.,Davis Heart & Lung Research InstituteThe Ohio State University Wexner Medical Center Columbus OH
| | - Peter J Mohler
- Department of Physiology & Cell Biology and Frick Center for Heart Failure and Arrhythmia The Ohio State University Wexner Medical Center Columbus OH.,Davis Heart & Lung Research InstituteThe Ohio State University Wexner Medical Center Columbus OH
| | - Nahush A Mokadam
- Davis Heart & Lung Research InstituteThe Ohio State University Wexner Medical Center Columbus OH.,Division of Cardiac Surgery The Ohio State University Wexner Medical Center Columbus OH
| | - Bryan A Whitson
- Davis Heart & Lung Research InstituteThe Ohio State University Wexner Medical Center Columbus OH.,Division of Cardiac Surgery The Ohio State University Wexner Medical Center Columbus OH
| | - Muhammad R Afzal
- Davis Heart & Lung Research InstituteThe Ohio State University Wexner Medical Center Columbus OH.,Department of Internal Medicine The Ohio State University Wexner Medical Center Columbus OH
| | - Orlando P Simonetti
- Davis Heart & Lung Research InstituteThe Ohio State University Wexner Medical Center Columbus OH.,Department of Biomedical Engineering The Ohio State University Wexner Medical Center Columbus OH
| | - John D Hummel
- Davis Heart & Lung Research InstituteThe Ohio State University Wexner Medical Center Columbus OH.,Department of Internal Medicine The Ohio State University Wexner Medical Center Columbus OH
| | - Vadim V Fedorov
- Department of Physiology & Cell Biology and Frick Center for Heart Failure and Arrhythmia The Ohio State University Wexner Medical Center Columbus OH.,Davis Heart & Lung Research InstituteThe Ohio State University Wexner Medical Center Columbus OH
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23
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Ganglionated Plexi Ablation for the Treatment of Atrial Fibrillation. J Clin Med 2020; 9:jcm9103081. [PMID: 32987820 PMCID: PMC7598705 DOI: 10.3390/jcm9103081] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 09/15/2020] [Accepted: 09/23/2020] [Indexed: 01/11/2023] Open
Abstract
Atrial fibrillation (AF) is the most common type of cardiac arrhythmia and is associated with significant morbidity and mortality. The autonomic nervous system (ANS) plays an important role in the initiation and development of AF, causing alterations in atrial structure and electrophysiological defects. The intrinsic ANS of the heart consists of multiple ganglionated plexi (GP), commonly nestled in epicardial fat pads. These GPs contain both parasympathetic and sympathetic afferent and efferent neuronal circuits that control the electrophysiological properties of the myocardium. Pulmonary vein isolation and other cardiac catheter ablation targets including GP ablation can disrupt the fibers connecting GPs or directly damage the GPs, mediating the benefits of the ablation procedure. Ablation of GPs has been evaluated over the past decade as an adjunctive procedure for the treatment of patients suffering from AF. The success rate of GP ablation is strongly associated with specific ablation sites, surgical techniques, localization techniques, method of access and the incorporation of additional interventions. In this review, we present the current data on the clinical utility of GP ablation and its significance in AF elimination and the restoration of normal sinus rhythm in humans.
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24
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Roney CH, Beach ML, Mehta AM, Sim I, Corrado C, Bendikas R, Solis-Lemus JA, Razeghi O, Whitaker J, O’Neill L, Plank G, Vigmond E, Williams SE, O’Neill MD, Niederer SA. In silico Comparison of Left Atrial Ablation Techniques That Target the Anatomical, Structural, and Electrical Substrates of Atrial Fibrillation. Front Physiol 2020; 11:1145. [PMID: 33041850 PMCID: PMC7526475 DOI: 10.3389/fphys.2020.572874] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 08/18/2020] [Indexed: 12/17/2022] Open
Abstract
Catheter ablation therapy for persistent atrial fibrillation (AF) typically includes pulmonary vein isolation (PVI) and may include additional ablation lesions that target patient-specific anatomical, electrical, or structural features. Clinical centers employ different ablation strategies, which use imaging data together with electroanatomic mapping data, depending on data availability. The aim of this study was to compare ablation techniques across a virtual cohort of AF patients. We constructed 20 paroxysmal and 30 persistent AF patient-specific left atrial (LA) bilayer models incorporating fibrotic remodeling from late-gadolinium enhancement (LGE) MRI scans. AF was simulated and post-processed using phase mapping to determine electrical driver locations over 15 s. Six different ablation approaches were tested: (i) PVI alone, modeled as wide-area encirclement of the pulmonary veins; PVI together with: (ii) roof and inferior lines to model posterior wall box isolation; (iii) isolating the largest fibrotic area (identified by LGE-MRI); (iv) isolating all fibrotic areas; (v) isolating the largest driver hotspot region [identified as high simulated phase singularity (PS) density]; and (vi) isolating all driver hotspot regions. Ablation efficacy was assessed to predict optimal ablation therapies for individual patients. We subsequently trained a random forest classifier to predict ablation response using (a) imaging metrics alone, (b) imaging and electrical metrics, or (c) imaging, electrical, and ablation lesion metrics. The optimal ablation approach resulting in termination, or if not possible atrial tachycardia (AT), varied among the virtual patient cohort: (i) 20% PVI alone, (ii) 6% box ablation, (iii) 2% largest fibrosis area, (iv) 4% all fibrosis areas, (v) 2% largest driver hotspot, and (vi) 46% all driver hotspots. Around 20% of cases remained in AF for all ablation strategies. The addition of patient-specific and ablation pattern specific lesion metrics to the trained random forest classifier improved predictive capability from an accuracy of 0.73 to 0.83. The trained classifier results demonstrate that the surface areas of pre-ablation driver regions and of fibrotic tissue not isolated by the proposed ablation strategy are both important for predicting ablation outcome. Overall, our study demonstrates the need to select the optimal ablation strategy for each patient. It suggests that both patient-specific fibrosis properties and driver locations are important for planning ablation approaches, and the distribution of lesions is important for predicting an acute response.
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Affiliation(s)
- Caroline H. Roney
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Marianne L. Beach
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Arihant M. Mehta
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Iain Sim
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Cesare Corrado
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Rokas Bendikas
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Jose A. Solis-Lemus
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Orod Razeghi
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - John Whitaker
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Louisa O’Neill
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Gernot Plank
- Department of Biophysics, Medical University of Graz, Graz, Austria
| | - Edward Vigmond
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Bordeaux, France
| | - Steven E. Williams
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Mark D. O’Neill
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Steven A. Niederer
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
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25
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Ali RL, Hakim JB, Boyle PM, Zahid S, Sivasambu B, Marine JE, Calkins H, Trayanova NA, Spragg DD. Arrhythmogenic propensity of the fibrotic substrate after atrial fibrillation ablation: a longitudinal study using magnetic resonance imaging-based atrial models. Cardiovasc Res 2020; 115:1757-1765. [PMID: 30977811 DOI: 10.1093/cvr/cvz083] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2018] [Revised: 01/31/2019] [Accepted: 04/08/2019] [Indexed: 12/19/2022] Open
Abstract
AIMS Inadequate modification of the atrial fibrotic substrate necessary to sustain re-entrant drivers (RDs) may explain atrial fibrillation (AF) recurrence following failed pulmonary vein isolation (PVI). Personalized computational models of the fibrotic atrial substrate derived from late gadolinium enhanced (LGE)-magnetic resonance imaging (MRI) can be used to non-invasively determine the presence of RDs. The objective of this study is to assess the changes of the arrhythmogenic propensity of the fibrotic substrate after PVI. METHODS AND RESULTS Pre- and post-ablation individualized left atrial models were constructed from 12 AF patients who underwent pre- and post-PVI LGE-MRI, in six of whom PVI failed. Pre-ablation AF sustained by RDs was induced in 10 models. RDs in the post-ablation models were classified as either preserved or emergent. Pre-ablation models derived from patients for whom the procedure failed exhibited a higher number of RDs and larger areas defined as promoting RD formation when compared with atrial models from patients who had successful ablation, 2.6 ± 0.9 vs. 1.8 ± 0.2 and 18.9 ± 1.6% vs. 13.8 ± 1.5%, respectively. In cases of successful ablation, PVI eliminated completely the RDs sustaining AF. Preserved RDs unaffected by ablation were documented only in post-ablation models of patients who experienced recurrent AF (2/5 models); all of these models had also one or more emergent RDs at locations distinct from those of pre-ablation RDs. Emergent RDs occurred in regions that had the same characteristics of the fibrosis spatial distribution (entropy and density) as regions that harboured RDs in pre-ablation models. CONCLUSION Recurrent AF after PVI in the fibrotic atria may be attributable to both preserved RDs that sustain AF pre- and post-ablation, and the emergence of new RDs following ablation. The same levels of fibrosis entropy and density underlie the pro-RD propensity in both pre- and post-ablation substrates.
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Affiliation(s)
- Rheeda L Ali
- Institute for Computational Medicine, Johns Hopkins University, 3400 N Charles Street, 208 Hackerman, Baltimore, MD, USA
| | - Joe B Hakim
- Institute for Computational Medicine, Johns Hopkins University, 3400 N Charles Street, 208 Hackerman, Baltimore, MD, USA
| | - Patrick M Boyle
- Institute for Computational Medicine, Johns Hopkins University, 3400 N Charles Street, 208 Hackerman, Baltimore, MD, USA.,Department of Biomedical Engineering, Johns Hopkins University, 3400 N Charles Street, 208 Hackerman, Baltimore, MD, USA.,Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Sohail Zahid
- Department of Biomedical Engineering, Johns Hopkins University, 3400 N Charles Street, 208 Hackerman, Baltimore, MD, USA
| | - Bhradeev Sivasambu
- Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Joseph E Marine
- Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Hugh Calkins
- Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Natalia A Trayanova
- Institute for Computational Medicine, Johns Hopkins University, 3400 N Charles Street, 208 Hackerman, Baltimore, MD, USA.,Department of Biomedical Engineering, Johns Hopkins University, 3400 N Charles Street, 208 Hackerman, Baltimore, MD, USA.,Department of Medicine, Johns Hopkins University School of Medicine, USA
| | - David D Spragg
- Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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26
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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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27
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Abstract
Atrial anisotropy affects electrical propagation patterns, anchor locations of atrial reentrant drivers, and atrial mechanics. However, patient-specific atrial fibre fields and anisotropy measurements are not currently available, and consequently assigning fibre fields to atrial models is challenging. We aimed to construct an atrial fibre atlas from a high-resolution DTMRI dataset that optimally reproduces electrophysiology simulation predictions corresponding to patient-specific fibre fields, and to develop a methodology for automatically assigning fibres to patient-specific anatomies. We extended an atrial coordinate system to map the pulmonary veins, vena cava and appendages to standardised positions in the coordinate system corresponding to the average location across the anatomies. We then expressed each fibre field in this atrial coordinate system and calculated an average fibre field. To assess the effects of fibre field on patient-specific modelling predictions, we calculated paced activation time maps and electrical driver locations during AF. In total, 756 activation time maps were calculated (7 anatomies with 9 fibre maps and 2 pacing locations, for the endocardial, epicardial and bilayer surface models of the LA and RA). Patient-specific fibre fields had a relatively small effect on average paced activation maps (range of mean local activation time difference for LA fields: 2.67-3.60 ms, and for RA fields: 2.29-3.44 ms), but had a larger effect on maximum LAT differences (range for LA 12.7-16.6%; range for RA 11.9-15.0%). A total of 126 phase singularity density maps were calculated (7 anatomies with 9 fibre maps for the LA and RA bilayer models). The fibre field corresponding to anatomy 1 had the highest median PS density map correlation coefficient for LA bilayer simulations (0.44 compared to the other correlations, ranging from 0.14 to 0.39), while the average fibre field had the highest correlation for the RA bilayer simulations (0.61 compared to the other correlations, ranging from 0.37 to 0.56). For sinus rhythm simulations, average activation time is robust to fibre field direction; however, maximum differences can still be significant. Patient specific fibres are more important for arrhythmia simulations, particularly in the left atrium. We propose using the fibre field corresponding to DTMRI dataset 1 for LA simulations, and the average fibre field for RA simulations as these optimally predicted arrhythmia properties.
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28
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Zaman J, Baykaner T, Narayan SM. Mapping and Ablation of Rotational and Focal Drivers in Atrial Fibrillation. Card Electrophysiol Clin 2020; 11:583-595. [PMID: 31706467 DOI: 10.1016/j.ccep.2019.08.010] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Drivers are increasingly studied ablation targets for atrial fibrillation (AF). However, results from ablation remain controversial. First, outcomes vary between centers and patients. Second, it is unclear how best to perform driver ablation. Third, there is a lack of practical guidance on how to identify critical from secondary sites using different AF mapping methods. This article addresses each of these issues.
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Affiliation(s)
- Junaid Zaman
- Stanford University, 780 Welch Road, Suite CJ250F, Stanford, CA 94305, USA; Imperial College London, London, UK
| | - Tina Baykaner
- Department of Medicine/Cardiovascular Medicine, Stanford University, 780 Welch Road, Suite CJ250F, Stanford, CA 94305, USA
| | - Sanjiv M Narayan
- Department of Medicine/Cardiovascular Medicine and Cardiovascular Institute, Stanford University, 780 Welch Road, Suite CJ250F, MC 5773, Stanford, CA 94305, USA.
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29
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Li J, Chen R, Wu J. Structural Analysis of Complex Atrial Intramural Microstructure from A Multi-layer Model Based on Siamese Network. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:294-297. [PMID: 31945899 DOI: 10.1109/embc.2019.8857276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Quantitative analysis of complex atrial intramural microstructure is a crucial step towards understanding the mechanism behind atrial fibrillation (AF) maintenance. Siamese network was adopted to extract features from computationally simulated multi-layer fibrosis structure. Through analysis of the features produced by the feature extractor, the difference between Non-sustained and Sustained simulations was comprehended intuitively and electrophysiologically. Complex conduction pathway marked by the feature extractor might be an indicator for AF radio-frequency ablation clinically.
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30
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Abstract
The treatment of individual patients in cardiology practice increasingly relies on advanced imaging, genetic screening and devices. As the amount of imaging and other diagnostic data increases, paralleled by the greater capacity to personalize treatment, the difficulty of using the full array of measurements of a patient to determine an optimal treatment seems also to be paradoxically increasing. Computational models are progressively addressing this issue by providing a common framework for integrating multiple data sets from individual patients. These models, which are based on physiology and physics rather than on population statistics, enable computational simulations to reveal diagnostic information that would have otherwise remained concealed and to predict treatment outcomes for individual patients. The inherent need for patient-specific models in cardiology is clear and is driving the rapid development of tools and techniques for creating personalized methods to guide pharmaceutical therapy, deployment of devices and surgical interventions.
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31
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Ye T, Zhang C, Wu G, Wan W, Liang J, Liu X, Liu D, Yang B. Pinocembrin attenuates autonomic dysfunction and atrial fibrillation susceptibility via inhibition of the NF-κB/TNF-α pathway in a rat model of myocardial infarction. Int Immunopharmacol 2019; 77:105926. [PMID: 31704291 DOI: 10.1016/j.intimp.2019.105926] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2019] [Revised: 09/05/2019] [Accepted: 09/19/2019] [Indexed: 12/27/2022]
Abstract
Previous studies indicate that myocardial infarction (MI) may contribute to atrial fibrillation (AF). Emerging evidence has shown that pinocembrin protects myocardial ischemic injury (I/R)-induced cardiac fibrosis and arrhythmias in animals via its anti-inflammatory or antioxidant activities. However, the effects of pinocembrin on MI-induced atrial arrhythmias remain unknown. Thus, this study aimed to investigate the effects of pinocembrin on autonomic dysfunction and AF susceptibility in MI rats and the possible mechanism. In a standard experimental MI model, Sprague-Dawley rats received permanent ligation of the left anterior descending (LAD) coronary artery and were treated with pinocembrin or saline for 6 days. Our results demonstrated that pinocembrin treatment significantly decreased sympathetic activity, augmented parasympathetic activity, improved heart rate variability (HRV), prolonged the atrial effective refractory period (ERP) and action potential duration (APD), shortened activation latency (AL), lowered the indicibility rate of AF, attenuated atrial fibrosis, and decreased concentrations of norepinephrine (NE), tumor necrosis factor-α (TNF-α), interleukin (IL)-1β and IL-6 in the serum and the left atrial (LA). Furthermore, pinocembrin treatment significantly increased the expression levels of Cx43 and Cav1.2 and suppressed the phosphorylation of inhibitor-κBα (IκBα) and the activation of nuclear factor-kappa B (NF-κB)subunit p65. In conclusion, the findings indicate that pinocembrin treatment decreases autonomic remodeling, lowers atrial fibrosis, ameliorates atrial electrical remodeling, and suppresses MI-induced inflammatory responses, which suggests a potential novel strategy for atrial arrhythmias.
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Affiliation(s)
- Tianxin Ye
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan 430060, PR China; Cardiovascular Research Institute, Wuhan University, Wuhan 430060, PR China; Hubei Key Laboratory of Cardiology, Wuhan 430060, PR China
| | - Cui Zhang
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan 430060, PR China; Cardiovascular Research Institute, Wuhan University, Wuhan 430060, PR China; Hubei Key Laboratory of Cardiology, Wuhan 430060, PR China
| | - Gang Wu
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan 430060, PR China; Cardiovascular Research Institute, Wuhan University, Wuhan 430060, PR China; Hubei Key Laboratory of Cardiology, Wuhan 430060, PR China
| | - Weiguo Wan
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan 430060, PR China; Cardiovascular Research Institute, Wuhan University, Wuhan 430060, PR China; Hubei Key Laboratory of Cardiology, Wuhan 430060, PR China
| | - Jinjun Liang
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan 430060, PR China; Cardiovascular Research Institute, Wuhan University, Wuhan 430060, PR China; Hubei Key Laboratory of Cardiology, Wuhan 430060, PR China
| | - Xin Liu
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan 430060, PR China; Cardiovascular Research Institute, Wuhan University, Wuhan 430060, PR China; Hubei Key Laboratory of Cardiology, Wuhan 430060, PR China
| | - Dishiwen Liu
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan 430060, PR China; Cardiovascular Research Institute, Wuhan University, Wuhan 430060, PR China; Hubei Key Laboratory of Cardiology, Wuhan 430060, PR China
| | - Bo Yang
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan 430060, PR China; Cardiovascular Research Institute, Wuhan University, Wuhan 430060, PR China; Hubei Key Laboratory of Cardiology, Wuhan 430060, PR China.
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Bayer JD, Boukens BJ, Krul SPJ, Roney CH, Driessen AHG, Berger WR, van den Berg NWE, Verkerk AO, Vigmond EJ, Coronel R, de Groot JR. Acetylcholine Delays Atrial Activation to Facilitate Atrial Fibrillation. Front Physiol 2019; 10:1105. [PMID: 31551802 PMCID: PMC6737394 DOI: 10.3389/fphys.2019.01105] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Accepted: 08/09/2019] [Indexed: 11/13/2022] Open
Abstract
Background Acetylcholine (ACh) shortens action potential duration (APD) in human atria. APD shortening facilitates atrial fibrillation (AF) by reducing the wavelength for reentry. However, the influence of ACh on electrical conduction in human atria and its contribution to AF are unclear, particularly when combined with impaired conduction from interstitial fibrosis. Objective To investigate the effect of ACh on human atrial conduction and its role in AF with computational, experimental, and clinical approaches. Methods S1S2 pacing (S1 = 600 ms and S2 = variable cycle lengths) was applied to the following human AF computer models: a left atrial appendage (LAA) myocyte to quantify the effects of ACh on APD, maximum upstroke velocity (V max ), and resting membrane potential (RMP); a monolayer of LAA myocytes to quantify the effects of ACh on conduction; and 3) an intact left atrium (LA) to determine the effects of ACh on arrhythmogenicity. Heterogeneous ACh and interstitial fibrosis were applied to the monolayer and LA models. To corroborate the simulations, APD and RMP from isolated human atrial myocytes were recorded before and after 0.1 μM ACh. At the tissue level, LAAs from AF patients were optically mapped ex vivo using Di-4-ANEPPS. The difference in total activation time (AT) was determined between AT initially recorded with S1 pacing, and AT recorded during subsequent S1 pacing without (n = 6) or with (n = 7) 100 μM ACh. Results In LAA myocyte simulations, S1 pacing with 0.1 μM ACh shortened APD by 41 ms, hyperpolarized RMP by 7 mV, and increased V max by 27 mV/ms. In human atrial myocytes, 0.1 μM ACh shortened APD by 48 ms, hyperpolarized RMP by 3 mV, and increased V max by 6 mV/ms. In LAA monolayer simulations, S1 pacing with ACh hyperpolarized RMP to delay total AT by 32 ms without and 35 ms with fibrosis. This led to unidirectional conduction block and sustained reentry in fibrotic LA with heterogeneous ACh during S2 pacing. In AF patient LAAs, S1 pacing with ACh increased total AT from 39.3 ± 26 ms to 71.4 ± 31.2 ms (p = 0.036) compared to no change without ACh (56.7 ± 29.3 ms to 50.0 ± 21.9 ms, p = 0.140). Conclusion In fibrotic atria with heterogeneous parasympathetic activation, ACh facilitates AF by shortening APD and slowing conduction to promote unidirectional conduction block and reentry.
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Affiliation(s)
- Jason D Bayer
- Electrophysiology and Heart Modeling Institute (IHU-LIRYC), Bordeaux University Foundation, Bordeaux, France.,Institute of Mathematics of Bordeaux (U5251), University of Bordeaux, Bordeaux, France
| | - Bastiaan J Boukens
- Department of Medical Biology, Academic Medical Center, Amsterdam, Netherlands
| | - Sébastien P J Krul
- Department of Cardiology, Academic Medical Center, Amsterdam, Netherlands
| | - Caroline H Roney
- Division of Imaging Sciences and Bioengineering, King's College London, London, United Kingdom
| | | | - Wouter R Berger
- Department of Cardiology, Academic Medical Center, Amsterdam, Netherlands.,Department of Cardiology, Heart Center, OLVG, Amsterdam, Netherlands
| | | | - Arie O Verkerk
- Department of Medical Biology, Academic Medical Center, Amsterdam, Netherlands.,Department of Experimental Cardiology, Academic Medical Center, Amsterdam, Netherlands
| | - Edward J Vigmond
- Electrophysiology and Heart Modeling Institute (IHU-LIRYC), Bordeaux University Foundation, Bordeaux, France.,Institute of Mathematics of Bordeaux (U5251), University of Bordeaux, Bordeaux, France
| | - Ruben Coronel
- Electrophysiology and Heart Modeling Institute (IHU-LIRYC), Bordeaux University Foundation, Bordeaux, France.,Department of Experimental Cardiology, Academic Medical Center, Amsterdam, Netherlands
| | - Joris R de Groot
- Department of Cardiology, Academic Medical Center, Amsterdam, Netherlands
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Hakim JB, Murphy MJ, Trayanova NA, Boyle PM. Arrhythmia dynamics in computational models of the atria following virtual ablation of re-entrant drivers. Europace 2019; 20:iii45-iii54. [PMID: 30476053 DOI: 10.1093/europace/euy234] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2018] [Accepted: 09/18/2018] [Indexed: 12/19/2022] Open
Abstract
Aims Efforts to improve ablation success rates in persistent atrial fibrillation (AF) patients by targeting re-entrant driver (RD) sites have been hindered by weak mechanistic understanding regarding emergent RDs localization following initial fibrotic substrate modification. This study aimed to systematically assess arrhythmia dynamics after virtual ablation of RD sites in computational models. Methods and results Simulations were conducted in 12 patient-specific atrial models reconstructed from pre-procedure late gadolinium-enhanced magnetic resonance imaging scans. In a previous study involving these same models, we comprehensively characterized pre-ablation RDs in simulations conducted with either 'average human AF'-based electrophysiology (i.e. EPavg) or ±10% action potential duration or conduction velocity (i.e. EPvar). Re-entrant drivers seen under the EPavg condition were virtually ablated and the AF initiation protocol was re-applied. Twenty-one emergent RDs were observed in 9/12 atrial models (1.75 ± 1.35 emergent RDs per model); these dynamically localized to boundary regions between fibrotic and non-fibrotic tissue. Most emergent RD locations (15/21, 71.4%) were within 0.1 cm of sites where RDs were seen pre-ablation in simulations under EPvar conditions. Importantly, this suggests that the level of uncertainty in our models' ability to predict patient-specific ablation targets can be substantially mitigated by running additional simulations that include virtual ablation of RDs. In 7/12 atrial models, at least one episode of macro-reentry around ablation lesion(s) was observed. Conclusion Arrhythmia episodes after virtual RD ablation are perpetuated by both emergent RDs and by macro-reentrant circuits formed around lesions. Custom-tailoring of ablation procedures based on models should take steps to mitigate these sources of AF recurrence.
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Affiliation(s)
- Joe B Hakim
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, 3400 N. Charles St, 208 Hackerman Hall, Baltimore, MD, USA
| | - Michael J Murphy
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, 3400 N. Charles St, 208 Hackerman Hall, Baltimore, MD, USA
| | - Natalia A Trayanova
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, 3400 N. Charles St, 208 Hackerman Hall, Baltimore, MD, USA
| | - Patrick M Boyle
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, 3400 N. Charles St, 208 Hackerman Hall, Baltimore, MD, USA.,Department of Bioengineering, University of Washington, N310H Foege, Box 355061, Seattle WA, USA
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34
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Computationally guided personalized targeted ablation of persistent atrial fibrillation. Nat Biomed Eng 2019; 3:870-879. [PMID: 31427780 PMCID: PMC6842421 DOI: 10.1038/s41551-019-0437-9] [Citation(s) in RCA: 153] [Impact Index Per Article: 30.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Accepted: 07/03/2019] [Indexed: 12/12/2022]
Abstract
Atrial fibrillation (AF) — the most common arrhythmia — significantly increases the risk of stroke and heart failure. Although catheter ablation can restore normal heart rhythms, patients with persistent AF who develop atrial fibrosis often undergo multiple failed ablations and thus increased procedural risks. Here, we present personalized computational modelling for the reliable predetermination of ablation targets, which are then used to guide the ablation procedure in patients with persistent AF and atrial fibrosis. We first show that a computational model of the atria of patients identifies fibrotic tissue that if ablated will not sustain AF. We then integrated the target-ablation sites in a clinical-mapping system, and tested its feasibility in 10 patients with persistent AF. The computational prediction of ablation targets avoids lengthy electrical mapping and could improve the accuracy and efficacy of targeted AF ablation in patients whilst eliminating the need for repeat procedures.
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35
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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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36
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Ni H, Zhang H, Grandi E, Narayan SM, Giles WR. Transient outward K + current can strongly modulate action potential duration and initiate alternans in the human atrium. Am J Physiol Heart Circ Physiol 2019; 316:H527-H542. [PMID: 30576220 PMCID: PMC6415821 DOI: 10.1152/ajpheart.00251.2018] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/19/2018] [Revised: 07/27/2018] [Accepted: 08/15/2018] [Indexed: 01/14/2023]
Abstract
Efforts to identify the mechanisms for the initiation and maintenance of human atrial fibrillation (AF) often focus on changes in specific elements of the atrial "substrate," i.e., its electrophysiological properties and/or structural components. We used experimentally validated mathematical models of the human atrial myocyte action potential (AP), both at baseline in sinus rhythm (SR) and in the setting of chronic AF, to identify significant contributions of the Ca2+-independent transient outward K+ current ( Ito) to electrophysiological instability and arrhythmia initiation. First, we explored whether changes in the recovery or restitution of the AP duration (APD) and/or its dynamic stability (alternans) can be modulated by Ito. Recent reports have identified disease-dependent spatial differences in expression levels of the specific K+ channel α-subunits that underlie Ito in the left atrium. Therefore, we studied the functional consequences of this by deletion of 50% of native Ito (Kv4.3) and its replacement with Kv1.4. Interestingly, significant changes in the short-term stability of the human atrial AP waveform were revealed. Specifically, this K+ channel isoform switch produced discontinuities in the initial slope of the APD restitution curve and appearance of APD alternans. This pattern of in silico results resembles some of the changes observed in high-resolution clinical electrophysiological recordings. Important insights into mechanisms for these changes emerged from known biophysical properties (reactivation kinetics) of Kv1.4 versus those of Kv4.3. These results suggest new approaches for pharmacological management of AF, based on molecular properties of specific K+ isoforms and their changed expression during progressive disease. NEW & NOTEWORTHY Clinical studies identify oscillations (alternans) in action potential (AP) duration as a predictor for atrial fibrillation (AF). The abbreviated AP in AF also involves changes in K+ currents and early repolarization of the AP. Our simulations illustrate how substitution of Kv1.4 for the native current, Kv4.3, alters the AP waveform and enhances alternans. Knowledge of this "isoform switch" and related dynamics in the AF substrate may guide new approaches for detection and management of AF.
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Affiliation(s)
- Haibo Ni
- Biological Physics Group, School of Physics and Astronomy, University of Manchester , Manchester , United Kingdom
- Department of Pharmacology, University of California , Davis, California
| | - Henggui Zhang
- Biological Physics Group, School of Physics and Astronomy, University of Manchester , Manchester , United Kingdom
| | - Eleonora Grandi
- Department of Pharmacology, University of California , Davis, California
| | - Sanjiv M Narayan
- Division of Cardiology, Cardiovascular Institute, Stanford University , Stanford, California
| | - Wayne R Giles
- Faculties of Kinesiology and Medicine, University of Calgary , Calgary, Alberta , Canada
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37
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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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38
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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: 34] [Impact Index Per Article: 5.7] [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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39
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Boyle PM, Hakim JB, Zahid S, Franceschi WH, Murphy MJ, Prakosa A, Aronis KN, Zghaib T, Balouch M, Ipek EG, Chrispin J, Berger RD, Ashikaga H, Marine JE, Calkins H, Nazarian S, Spragg DD, Trayanova NA. The Fibrotic Substrate in Persistent Atrial Fibrillation Patients: Comparison Between Predictions From Computational Modeling and Measurements From Focal Impulse and Rotor Mapping. Front Physiol 2018; 9:1151. [PMID: 30210356 PMCID: PMC6123380 DOI: 10.3389/fphys.2018.01151] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Accepted: 07/31/2018] [Indexed: 12/19/2022] Open
Abstract
Focal impulse and rotor mapping (FIRM) involves intracardiac detection and catheter ablation of re-entrant drivers (RDs), some of which may contribute to arrhythmia perpetuation in persistent atrial fibrillation (PsAF). Patient-specific computational models derived from late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) has the potential to non-invasively identify all areas of the fibrotic substrate where RDs could potentially be sustained, including locations where RDs may not manifest during mapped AF episodes. The objective of this study was to carry out multi-modal assessment of the arrhythmogenic propensity of the fibrotic substrate in PsAF patients by comparing locations of RD-harboring regions found in simulations and detected by FIRM (RDsim and RDFIRM) and analyze implications for ablation strategies predicated on targeting RDs. For 11 PsAF patients who underwent pre-procedure LGE-MRI and FIRM-guided ablation, we retrospectively simulated AF in individualized atrial models, with geometry and fibrosis distribution reconstructed from pre-ablation LGE-MRI scans, and identified RDsim sites. Regions harboring RDsim and RDFIRM were compared. RDsim were found in 38 atrial regions (median [inter-quartile range (IQR)] = 4 [3; 4] per model). RDFIRM were identified and subsequently ablated in 24 atrial regions (2 [1; 3] per patient), which was significantly fewer than the number of RDsim-harboring regions in corresponding models (p < 0.05). Computational modeling predicted RDsim in 20 of 24 (83%) atrial regions identified as RDFIRM-harboring during clinical mapping. In a large number of cases, we uncovered RDsim-harboring regions in which RDFIRM were never observed (18/22 regions that differed between the two modalities; 82%); we termed such cases “latent” RDsim sites. During follow-up (230 [180; 326] days), AF recurrence occurred in 7/11 (64%) individuals. Interestingly, latent RDsim sites were observed in all seven computational models corresponding to patients who experienced recurrent AF (2 [2; 2] per patient); in contrast, latent RDsim sites were only discovered in two of four patients who were free from AF during follow-up (0.5 [0; 1.5] per patient; p < 0.05 vs. patients with AF recurrence). We conclude that substrate-based ablation based on computational modeling could improve outcomes.
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Affiliation(s)
- Patrick M Boyle
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Joe B Hakim
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Sohail Zahid
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - William H Franceschi
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Michael J Murphy
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Adityo Prakosa
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
| | | | - Tarek Zghaib
- Department of Cardiology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Muhammed Balouch
- Department of Cardiology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Esra G Ipek
- Department of Cardiology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Jonathan Chrispin
- Department of Cardiology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Ronald D Berger
- Department of Cardiology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Hiroshi Ashikaga
- Department of Cardiology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Joseph E Marine
- Department of Cardiology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Hugh Calkins
- Department of Cardiology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Saman Nazarian
- Penn Heart & Vascular Center, University of Pennsylvania, Philadelphia, PA, United States
| | - David D Spragg
- Department of Cardiology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Natalia A Trayanova
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
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40
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Roney CH, Bayer JD, Cochet H, Meo M, Dubois R, Jaïs P, Vigmond EJ. Variability in pulmonary vein electrophysiology and fibrosis determines arrhythmia susceptibility and dynamics. PLoS Comput Biol 2018; 14:e1006166. [PMID: 29795549 PMCID: PMC5997352 DOI: 10.1371/journal.pcbi.1006166] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Revised: 06/12/2018] [Accepted: 04/30/2018] [Indexed: 11/28/2022] Open
Abstract
Success rates for catheter ablation of persistent atrial fibrillation patients are currently low; however, there is a subset of patients for whom electrical isolation of the pulmonary veins alone is a successful treatment strategy. It is difficult to identify these patients because there are a multitude of factors affecting arrhythmia susceptibility and maintenance, and the individual contributions of these factors are difficult to determine clinically. We hypothesised that the combination of pulmonary vein (PV) electrophysiology and atrial body fibrosis determine driver location and effectiveness of pulmonary vein isolation (PVI). We used bilayer biatrial computer models based on patient geometries to investigate the effects of PV properties and atrial fibrosis on arrhythmia inducibility, maintenance mechanisms, and the outcome of PVI. Short PV action potential duration (APD) increased arrhythmia susceptibility, while longer PV APD was found to be protective. Arrhythmia inducibility increased with slower conduction velocity (CV) at the LA/PV junction, but not for cases with homogeneous CV changes or slower CV at the distal PV. Phase singularity (PS) density in the PV region for cases with PV fibrosis was increased. Arrhythmia dynamics depend on both PV properties and fibrosis distribution, varying from meandering rotors to PV reentry (in cases with baseline or long APD), to stable rotors at regions of high fibrosis density. Measurement of fibrosis and PV properties may indicate patient specific susceptibility to AF initiation and maintenance. PV PS density before PVI was higher for cases in which AF terminated or converted to a macroreentry; thus, high PV PS density may indicate likelihood of PVI success. Atrial fibrillation is the most commonly encountered cardiac arrhythmia, affecting a significant portion of the population. Currently, ablation is the most effective treatment but success rates are less than optimal, being 70% one-year post-treatment. There is a large effort to find better ablation strategies to permanently cure the condition. Pulmonary vein isolation by ablation is more or less the standard of care, but many questions remain since pulmonary vein ectopy by itself does not explain all of the clinical successes or failures. We used computer simulations to investigate how electrophysiological properties of the pulmonary veins can affect rotor formation and maintenance in patients suffering from atrial fibrillation. We used complex, biophysical representations of cellular electrophysiology in highly detailed geometries constructed from patient scans. We heterogeneously varied electrophysiological and structural properties to see their effects on rotor initiation and maintenance. Our study suggests a metric for indicating the likelihood of success of pulmonary vein isolation. Thus either measuring this clinically, or running patient-specific simulations to estimate this metric may suggest whether ablation in addition to pulmonary vein isolation should be performed. Our study provides motivation for a retrospective clinical study or experimental study into this metric.
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Affiliation(s)
- Caroline H. Roney
- IHU Liryc, Electrophysiology and Heart Modeling Institute, foundation Bordeaux Université, F-33600 Pessac- Bordeaux, France
- Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, United Kingdom
| | - Jason D. Bayer
- IHU Liryc, Electrophysiology and Heart Modeling Institute, foundation Bordeaux Université, F-33600 Pessac- Bordeaux, France
- Univ. Bordeaux, IMB UMR 5251, CNRS, F-33400 Talence, France
| | - Hubert Cochet
- IHU Liryc, Electrophysiology and Heart Modeling Institute, foundation Bordeaux Université, F-33600 Pessac- Bordeaux, France
- Hôpital Cardiologique du Haut-L’évêque, Université de Bordeaux, LIRYC Institute: IHU LIRYC ANR-10-IAHU-04 and Equipex MUSIC ANR-11-EQPX-0030, Bordeaux, France
| | - Marianna Meo
- IHU Liryc, Electrophysiology and Heart Modeling Institute, foundation Bordeaux Université, F-33600 Pessac- Bordeaux, France
| | - Rémi Dubois
- IHU Liryc, Electrophysiology and Heart Modeling Institute, foundation Bordeaux Université, F-33600 Pessac- Bordeaux, France
| | - Pierre Jaïs
- IHU Liryc, Electrophysiology and Heart Modeling Institute, foundation Bordeaux Université, F-33600 Pessac- Bordeaux, France
- Hôpital Cardiologique du Haut-L’évêque, Université de Bordeaux, LIRYC Institute: IHU LIRYC ANR-10-IAHU-04 and Equipex MUSIC ANR-11-EQPX-0030, Bordeaux, France
| | - Edward J. Vigmond
- IHU Liryc, Electrophysiology and Heart Modeling Institute, foundation Bordeaux Université, F-33600 Pessac- Bordeaux, France
- Univ. Bordeaux, IMB UMR 5251, CNRS, F-33400 Talence, France
- * E-mail:
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41
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Boyle PM, Hakim JB, Zahid S, Franceschi WH, Murphy MJ, Vigmond EJ, Dubois R, Haïssaguerre M, Hocini M, Jaïs P, Trayanova NA, Cochet H. Comparing Reentrant Drivers Predicted by Image-Based Computational Modeling and Mapped by Electrocardiographic Imaging in Persistent Atrial Fibrillation. Front Physiol 2018; 9:414. [PMID: 29725307 PMCID: PMC5917348 DOI: 10.3389/fphys.2018.00414] [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: 11/19/2017] [Accepted: 04/04/2018] [Indexed: 02/06/2023] Open
Abstract
Electrocardiographic mapping (ECGI) detects reentrant drivers (RDs) that perpetuate arrhythmia in persistent AF (PsAF). Patient-specific computational models derived from late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) identify all latent sites in the fibrotic substrate that could potentially sustain RDs, not just those manifested during mapped AF. The objective of this study was to compare RDs from simulations and ECGI (RDsim/RDECGI) and analyze implications for ablation. We considered 12 PsAF patients who underwent RDECGI ablation. For the same cohort, we simulated AF and identified RDsim sites in patient-specific models with geometry and fibrosis distribution from pre-ablation LGE-MRI. RDsim- and RDECGI-harboring regions were compared, and the extent of agreement between macroscopic locations of RDs identified by simulations and ECGI was assessed. Effects of ablating RDECGI/RDsim were analyzed. RDsim were predicted in 28 atrial regions (median [inter-quartile range (IQR)] = 3.0 [1.0; 3.0] per model). ECGI detected 42 RDECGI-harboring regions (4.0 [2.0; 5.0] per patient). The number of regions with RDsim and RDECGI per individual was not significantly correlated (R = 0.46, P = ns). The overall rate of regional agreement was fair (modified Cohen's κ0 statistic = 0.11), as expected, based on the different mechanistic underpinning of RDsim- and RDECGI. nineteen regions were found to harbor both RDsim and RDECGI, suggesting that a subset of clinically observed RDs was fibrosis-mediated. The most frequent source of differences (23/32 regions) between the two modalities was the presence of RDECGI perpetuated by mechanisms other than the fibrotic substrate. In 6/12 patients, there was at least one region where a latent RD was observed in simulations but was not manifested during clinical mapping. Ablation of fibrosis-mediated RDECGI (i.e., targets in regions that also harbored RDsim) trended toward a higher rate of positive response compared to ablation of other RDECGI targets (57 vs. 41%, P = ns). Our analysis suggests that RDs in human PsAF are at least partially fibrosis-mediated. Substrate-based ablation combining simulations with ECGI could improve outcomes.
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Affiliation(s)
- Patrick M Boyle
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Joe B Hakim
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Sohail Zahid
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - William H Franceschi
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Michael J Murphy
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Edward J Vigmond
- L'Institut de RYthmologie et Modélisation Cardiaque (IHU-LIRYC), Pessac-Bordeaux, France
| | - Rémi Dubois
- L'Institut de RYthmologie et Modélisation Cardiaque (IHU-LIRYC), Pessac-Bordeaux, France
| | - Michel Haïssaguerre
- L'Institut de RYthmologie et Modélisation Cardiaque (IHU-LIRYC), Pessac-Bordeaux, France.,Centre Hospitalier Universitaire de Bordeaux, Pessac-Bordeaux, France
| | - Mélèze Hocini
- L'Institut de RYthmologie et Modélisation Cardiaque (IHU-LIRYC), Pessac-Bordeaux, France.,Centre Hospitalier Universitaire de Bordeaux, Pessac-Bordeaux, France
| | - Pierre Jaïs
- L'Institut de RYthmologie et Modélisation Cardiaque (IHU-LIRYC), Pessac-Bordeaux, France.,Centre Hospitalier Universitaire de Bordeaux, Pessac-Bordeaux, France
| | - Natalia A Trayanova
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Hubert Cochet
- L'Institut de RYthmologie et Modélisation Cardiaque (IHU-LIRYC), Pessac-Bordeaux, France.,Centre Hospitalier Universitaire de Bordeaux, Pessac-Bordeaux, France
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42
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Barichello S, Roberts JD, Backx P, Boyle PM, Laksman Z. Personalizing therapy for atrial fibrillation: the role of stem cell and in silico disease models. Cardiovasc Res 2018; 114:931-943. [DOI: 10.1093/cvr/cvy090] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/26/2017] [Accepted: 04/06/2018] [Indexed: 11/12/2022] Open
Affiliation(s)
- Scott Barichello
- University of British Columbia, 2329 West Mall, Vancouver, BC V6T 1Z4, Canada
| | - Jason D Roberts
- Section of Cardiac Electrophysiology, Division of Cardiology, Department of Medicine, Western University, London, ON, Canada
| | | | - Patrick M Boyle
- Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University
| | - Zachary Laksman
- Division of Cardiology, University of British Columbia, 211-1033 Davie Street Vancouver, BC V6E 1M7, Canada
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43
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Trayanova NA, Boyle PM, Nikolov PP. Personalized Imaging and Modeling Strategies for Arrhythmia Prevention and Therapy. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2018; 5:21-28. [PMID: 29546250 PMCID: PMC5847279 DOI: 10.1016/j.cobme.2017.11.007] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The goal of this article is to review advances in computational modeling of the heart, with a focus on recent non-invasive clinical imaging- and simulation-based strategies aimed at improving the diagnosis and treatment of patients with arrhythmias and structural heart disease. Following a brief overview of the field of computational cardiology, we present recent applications of the personalized virtual-heart approach in predicting the optimal targets for infarct-related ventricular tachycardia and atrial fibrillation ablation, and in determining risk of sudden cardiac death in myocardial infarction patients. The hope is that with such models at the patient bedside, therapies could be improved, invasiveness of diagnostic procedures minimized, and health-care costs reduced.
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Affiliation(s)
- Natalia A Trayanova
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD
| | - Patrick M Boyle
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD
| | - Plamen P Nikolov
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD
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44
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Boyle PM, Murphy MJ, Karathanos TV, Zahid S, Blake RC, Trayanova NA. Termination of re-entrant atrial tachycardia via optogenetic stimulation with optimized spatial targeting: insights from computational models. J Physiol 2017; 596:181-196. [PMID: 29193078 DOI: 10.1113/jp275264] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2017] [Accepted: 11/22/2017] [Indexed: 11/08/2022] Open
Abstract
KEY POINTS Optogenetics has emerged as a potential alternative to electrotherapy for treating heart rhythm disorders, but its applicability for terminating atrial arrhythmias remains largely unexplored. We used computational models reconstructed from clinical MRI scans of fibrotic patient atria to explore the feasibility of optogenetic termination of atrial tachycardia (AT), comparing two different illumination strategies: distributed vs. targeted. We show that targeted optogenetic stimulation based on automated, non-invasive flow-network analysis of patient-specific re-entry morphology may be a reliable approach for identifying the optimal illumination target in each individual (i.e. the critical AT isthmus). The above-described approach yields very high success rates (up to 100%) and requires dramatically less input power than distributed illumination We conclude that simulations in patient-specific models show that targeted light pulses lasting longer than the AT cycle length can efficiently and reliably terminate AT if the human atria can be successfully light-sensitized via gene delivery of ChR2. ABSTRACT Optogenetics has emerged as a potential alternative to electrotherapy for treating arrhythmia, but feasibility studies have been limited to ventricular defibrillation via epicardial light application. Here, we assess the efficacy of optogenetic atrial tachycardia (AT) termination in human hearts using a strategy that targets for illumination specific regions identified in an automated manner. In three patient-specific models reconstructed from late gadolinium-enhanced MRI scans, we simulated channelrhodopsin-2 (ChR2) expression via gene delivery. In all three models, we attempted to terminate re-entrant AT (induced via rapid pacing) via optogenetic stimulation. We compared two strategies: (1) distributed illumination of the endocardium by multi-optrode grids (number of optrodes, Nopt = 64, 128, 256) and (2) targeted illumination of the critical isthmus, which was identified via analysis of simulated activation patterns using an algorithm based on flow networks. The illuminated area and input power were smaller for the targeted approach (19-57.8 mm2 ; 0.6-1.8 W) compared to the sparsest distributed arrays (Nopt = 64; 124.9 ± 6.3 mm2 ; 3.9 ± 0.2 W). AT termination rates for distributed illumination were low, ranging from <5% for short pulses (1/10 ms long) to ∼20% for longer stimuli (100/1000 ms). When we attempted to terminate the same AT episodes with targeted illumination, outcomes were similar for short pulses (1/10 ms long: 0% success) but improved for longer stimuli (100 ms: 54% success; 1000 ms: 90% success). We conclude that simulations in patient-specific models show that light pulses lasting longer than the AT cycle length can efficiently and reliably terminate AT in atria light-sensitized via gene delivery. We show that targeted optogenetic stimulation based on analysis of AT morphology may be a reliable approach for defibrillation and requires less power than distributed illumination.
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Affiliation(s)
- Patrick M Boyle
- Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Michael J Murphy
- Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Thomas V Karathanos
- Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Sohail Zahid
- Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Robert C Blake
- Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Natalia A Trayanova
- Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA.,Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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45
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Lin YT, Chang ETY, Eatock J, Galla T, Clayton RH. Mechanisms of stochastic onset and termination of atrial fibrillation studied with a cellular automaton model. J R Soc Interface 2017; 14:rsif.2016.0968. [PMID: 28356539 PMCID: PMC5378131 DOI: 10.1098/rsif.2016.0968] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2016] [Accepted: 03/02/2017] [Indexed: 01/23/2023] Open
Abstract
Mathematical models of cardiac electrical excitation are increasingly complex, with multiscale models seeking to represent and bridge physiological behaviours across temporal and spatial scales. The increasing complexity of these models makes it computationally expensive to both evaluate long term (more than 60 s) behaviour and determine sensitivity of model outputs to inputs. This is particularly relevant in models of atrial fibrillation (AF), where individual episodes last from seconds to days, and interepisode waiting times can be minutes to months. Potential mechanisms of transition between sinus rhythm and AF have been identified but are not well understood, and it is difficult to simulate AF for long periods of time using state-of-the-art models. In this study, we implemented a Moe-type cellular automaton on a novel, topologically equivalent surface geometry of the left atrium. We used the model to simulate stochastic initiation and spontaneous termination of AF, arising from bursts of spontaneous activation near pulmonary veins. The simplified representation of atrial electrical activity reduced computational cost, and so permitted us to investigate AF mechanisms in a probabilistic setting. We computed large numbers (approx. 105) of sample paths of the model, to infer stochastic initiation and termination rates of AF episodes using different model parameters. By generating statistical distributions of model outputs, we demonstrated how to propagate uncertainties of inputs within our microscopic level model up to a macroscopic level. Lastly, we investigated spontaneous termination in the model and found a complex dependence on its past AF trajectory, the mechanism of which merits future investigation.
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Affiliation(s)
- Yen Ting Lin
- Theoretical Physics Division, School of Physics and Astronomy, University of Manchester, Manchester, UK
| | - Eugene T Y Chang
- Department of Computer Science and INSIGNEO Institute for in silico Medicine, University of Sheffield, Sheffield, UK
| | - Julie Eatock
- Department of Computer Science, Brunel University London, Uxbridge UB8 3PH, UK
| | - Tobias Galla
- Theoretical Physics Division, School of Physics and Astronomy, University of Manchester, Manchester, UK
| | - Richard H Clayton
- Department of Computer Science and INSIGNEO Institute for in silico Medicine, University of Sheffield, Sheffield, UK
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46
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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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47
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Deng D, Murphy MJ, Hakim JB, Franceschi WH, Zahid S, Pashakhanloo F, Trayanova NA, Boyle PM. Sensitivity of reentrant driver localization to electrophysiological parameter variability in image-based computational models of persistent atrial fibrillation sustained by a fibrotic substrate. CHAOS (WOODBURY, N.Y.) 2017; 27:093932. [PMID: 28964164 PMCID: PMC5605332 DOI: 10.1063/1.5003340] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2017] [Accepted: 09/04/2017] [Indexed: 05/30/2023]
Abstract
Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia, causing morbidity and mortality in millions worldwide. The atria of patients with persistent AF (PsAF) are characterized by the presence of extensive and distributed atrial fibrosis, which facilitates the formation of persistent reentrant drivers (RDs, i.e., spiral waves), which promote fibrillatory activity. Targeted catheter ablation of RD-harboring tissues has shown promise as a clinical treatment for PsAF, but the outcomes remain sub-par. Personalized computational modeling has been proposed as a means of non-invasively predicting optimal ablation targets in individual PsAF patients, but it remains unclear how RD localization dynamics are influenced by inter-patient variability in the spatial distribution of atrial fibrosis, action potential duration (APD), and conduction velocity (CV). Here, we conduct simulations in computational models of fibrotic atria derived from the clinical imaging of PsAF patients to characterize the sensitivity of RD locations to these three factors. We show that RDs consistently anchor to boundaries between fibrotic and non-fibrotic tissues, as delineated by late gadolinium-enhanced magnetic resonance imaging, but those changes in APD/CV can enhance or attenuate the likelihood that an RD will anchor to a specific site. These findings show that the level of uncertainty present in patient-specific atrial models reconstructed without any invasive measurements (i.e., incorporating each individual's unique distribution of fibrotic tissue from medical imaging alongside an average representation of AF-remodeled electrophysiology) is sufficiently high that a personalized ablation strategy based on targeting simulation-predicted RD trajectories alone may not produce the desired result.
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Affiliation(s)
- Dongdong Deng
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Michael J Murphy
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Joe B Hakim
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - William H Franceschi
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Sohail Zahid
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Farhad Pashakhanloo
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Natalia A Trayanova
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Patrick M Boyle
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
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48
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Boyle PM, Zahid S, Trayanova NA. Towards personalized computational modelling of the fibrotic substrate for atrial arrhythmia. Europace 2017; 18:iv136-iv145. [PMID: 28011841 DOI: 10.1093/europace/euw358] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2016] [Accepted: 07/28/2016] [Indexed: 11/13/2022] Open
Abstract
: Atrial arrhythmias involving a fibrotic substrate are an important cause of morbidity and mortality. In many cases, effective treatment of such rhythm disorders is severely hindered by a lack of mechanistic understanding relating features of fibrotic remodelling to dynamics of re-entrant arrhythmia. With the advent of clinical imaging modalities capable of resolving the unique fibrosis spatial pattern present in the atria of each individual patient, a promising new research trajectory has emerged in which personalized computational models are used to analyse mechanistic underpinnings of arrhythmia dynamics based on the distribution of fibrotic tissue. In this review, we first present findings that have yielded a robust and detailed biophysical representation of fibrotic substrate electrophysiological properties. Then, we summarize the results of several recent investigations seeking to use organ-scale models of the fibrotic human atria to derive new insights on mechanisms of arrhythmia perpetuation and to develop novel strategies for model-assisted individualized planning of catheter ablation procedures for atrial arrhythmias.
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Affiliation(s)
- Patrick M Boyle
- Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, 3400 N Charles St, 208 Hackerman Hall, Baltimore, MD 21218, USA
| | - Sohail Zahid
- Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, 3400 N Charles St, 208 Hackerman Hall, Baltimore, MD 21218, USA
| | - Natalia A Trayanova
- Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, 3400 N Charles St, 208 Hackerman Hall, Baltimore, MD 21218, USA
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49
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Roney CH, Bayer JD, Zahid S, Meo M, Boyle PMJ, Trayanova NA, Haïssaguerre M, Dubois R, Cochet H, Vigmond EJ. Modelling methodology of atrial fibrosis affects rotor dynamics and electrograms. Europace 2017; 18:iv146-iv155. [PMID: 28011842 DOI: 10.1093/europace/euw365] [Citation(s) in RCA: 75] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2016] [Accepted: 06/28/2016] [Indexed: 11/14/2022] Open
Abstract
AIMS Catheter ablation is an effective technique for terminating atrial arrhythmia. However, given a high atrial fibrillation (AF) recurrence rate, optimal ablation strategies have yet to be defined. Computer modelling can be a powerful aid but modelling of fibrosis, a major factor associated with AF, is an open question. Several groups have proposed methodologies based on imaging data, but no comparison to determine which methodology best corroborates clinically observed reentrant behaviour has been performed. We examined several methodologies to determine the best method for capturing fibrillation dynamics. METHODS AND RESULTS Patient late gadolinium-enhanced magnetic resonance imaging data were transferred onto a bilayer atrial computer model and used to assign fibrosis distributions. Fibrosis was modelled as conduction disturbances (lower conductivity, edge splitting, or percolation), transforming growth factor-β1 ionic channel effects, myocyte-fibroblast coupling, and combinations of the preceding. Reentry was induced through pulmonary vein ectopy and the ensuing rotor dynamics characterized. Non-invasive electrocardiographic imaging data of the patients in AF was used for comparison. Electrograms were computed and the fractionation durations measured over the surface. Edge splitting produced more phase singularities from wavebreaks than the other representations. The number of phase singularities seen with percolation was closer to the clinical values. Addition of fibroblast coupling had an organizing effect on rotor dynamics. Simple tissue conductivity changes with ionic changes localized rotors over fibrosis which was not observed with clinical data. CONCLUSION The specific representation of fibrosis has a large effect on rotor dynamics and needs to be carefully considered for patient specific modelling.
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Affiliation(s)
- Caroline H Roney
- IHU Liryc, Electrophysiology and Heart Modeling Institute, foundation Bordeaux Université, F-33600 Pessac- Bordeaux, France.,Univ. Bordeaux, IMB UMR 5251, CNRS, F-33400 Talence, France
| | - Jason D Bayer
- IHU Liryc, Electrophysiology and Heart Modeling Institute, foundation Bordeaux Université, F-33600 Pessac- Bordeaux, France.,Univ. Bordeaux, IMB UMR 5251, CNRS, F-33400 Talence, France
| | - Sohail Zahid
- Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Marianna Meo
- IHU Liryc, Electrophysiology and Heart Modeling Institute, foundation Bordeaux Université, F-33600 Pessac- Bordeaux, France.,Univ. Bordeaux, Centre de recherche Cardio-Thoracique de Bordeaux, U1045, F- 33000, Bordeaux, France
| | - Patrick M J Boyle
- Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Natalia A Trayanova
- Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Michel Haïssaguerre
- IHU Liryc, Electrophysiology and Heart Modeling Institute, foundation Bordeaux Université, F-33600 Pessac- Bordeaux, France.,Univ. Bordeaux, Centre de recherche Cardio-Thoracique de Bordeaux, U1045, F- 33000, Bordeaux, France.,Bordeaux University Hospital (CHU), Electrophysiology and Ablation Unit, F-33600 Pessac, France
| | - Rémi Dubois
- IHU Liryc, Electrophysiology and Heart Modeling Institute, foundation Bordeaux Université, F-33600 Pessac- Bordeaux, France.,Univ. Bordeaux, Centre de recherche Cardio-Thoracique de Bordeaux, U1045, F- 33000, Bordeaux, France
| | - Hubert Cochet
- IHU Liryc, Electrophysiology and Heart Modeling Institute, foundation Bordeaux Université, F-33600 Pessac- Bordeaux, France.,Univ. Bordeaux, Centre de recherche Cardio-Thoracique de Bordeaux, U1045, F- 33000, Bordeaux, France.,Department of Cardiac Imaging, Bordeaux University Hospital, Bordeaux, France
| | - Edward J Vigmond
- IHU Liryc, Electrophysiology and Heart Modeling Institute, foundation Bordeaux Université, F-33600 Pessac- Bordeaux, France.,Univ. Bordeaux, IMB UMR 5251, CNRS, F-33400 Talence, France
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50
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Baykaner T, Zografos TA, Zaman JAB, Pantos I, Alhusseini M, Navara R, Krummen DE, Narayan SM, Katritsis DG. Spatial relationship of organized rotational and focal sources in human atrial fibrillation to autonomic ganglionated plexi. Int J Cardiol 2017; 240:234-239. [PMID: 28433558 PMCID: PMC5856175 DOI: 10.1016/j.ijcard.2017.02.152] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2017] [Accepted: 02/28/2017] [Indexed: 12/21/2022]
Abstract
BACKGROUND One approach to improve ablation for atrial fibrillation (AF) is to focus on physiological targets including focal or rotational sources or ganglionic plexi (GP). However, the spatial relationship between these potential mechanisms has never been studied. We tested the hypothesis that rotors and focal sources for AF may co-localize with ganglionated plexi (GP). METHODS We prospectively identified locations of AF rotors and focal sources, and correlated these to GP sites in 97 consecutive patients (age 59.9±11.4, 73% persistent AF). AF was recorded with 64-pole catheters with activation/phase mapping, and related to anatomic GP sites on electroanatomic maps. RESULTS AF sources arose in 96/97 (99%) patients for 2.6±1.4 sources per patient (left atrium: 1.7±0.9 right atrium: 1.1±0.8), each with an area of 2-3cm2. On area analyses, the probability of an AF source randomly overlapping a GP area was 26%. Left atrial sources were seen in 94 (97%) patients, in whom ≥1 source co-localized with GP in 75 patients (80%; p<0.05). AF sources were more likely to colocalize with left vs right GPs (p<0.05), and colocalization was more likely in patients with higher CHADS2VASc scores (age>65, diabetes; p<0.05). CONCLUSIONS This is the first study to demonstrate that clinically detected AF focal and rotational sources in the left atrium often colocalize with regions of autonomic innervation. Studies should define if the role of AF sources differs by their anatomical location.
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Affiliation(s)
- Tina Baykaner
- Division of Cardiology, Stanford University, Stanford, CA, USA.
| | | | | | - Ioannis Pantos
- Department of Cardiology, Athens Euroclinic, Athens, Greece
| | | | - Rachita Navara
- Division of Cardiology, Stanford University, Stanford, CA, USA
| | | | | | - Demosthenes G Katritsis
- Department of Cardiology, Athens Euroclinic, Athens, Greece; Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
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