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Yamamoto C, Trayanova NA. Atrial fibrillation: Insights from animal models, computational modeling, and clinical studies. EBioMedicine 2022; 85:104310. [PMID: 36309006 PMCID: PMC9619190 DOI: 10.1016/j.ebiom.2022.104310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 09/25/2022] [Accepted: 10/04/2022] [Indexed: 11/11/2022] Open
Abstract
Atrial fibrillation (AF) is the most common human arrhythmia, affecting millions of patients worldwide. A combination of risk factors and comorbidities results in complex atrial remodeling, which increases AF vulnerability and persistence. Insights from animal models, clinical studies, and computational modeling have advanced the understanding of the mechanisms and pathophysiology of AF. Areas of heterogeneous pathological remodeling, as well as altered electrophysiological properties, serve as a substrate for AF drivers and spontaneous activations. The complex and individualized presentation of this arrhythmia suggests that mechanisms-based personalized approaches will likely be needed to overcome current challenges in AF management. In this paper, we review the insights on the mechanisms of AF obtained from animal models and clinical studies and how computational models integrate this knowledge to advance AF clinical management. We also assess the challenges that need to be overcome to implement these mechanistic models in clinical practice.
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Affiliation(s)
- Carolyna Yamamoto
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Natalia A. Trayanova
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA,Alliance for Cardiovascular Diagnostic and Treatment Innovation (ADVANCE), Johns Hopkins University, Baltimore, MD, USA,Corresponding author. Johns Hopkins, Johns Hopkins University, United States.
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2
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Ding Y, Xie W, Wong KKL, Liao Z. Classification of myocardial fibrosis in DE-MRI based on semi-supervised semantic segmentation and dual attention mechanism. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 225:107041. [PMID: 35994871 DOI: 10.1016/j.cmpb.2022.107041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 07/24/2022] [Accepted: 07/25/2022] [Indexed: 06/15/2023]
Abstract
OBJECTIVE It is essential to utilize cardiac delayed-enhanced magnetic resonance imaging (DE-MRI) to diagnose cardiovascular disease. By segmenting myocardium DE-MRI images, it provides critical information for the evaluation and treatment of myocardial infarction. As a consequence, it is vital to investigate the segmentation and classification technique of myocardial DE-MRI. METHODS Firstly, an end-to-end minimally supervised and semi-supervised semantic DE-MRI myocardial fibrosis segmentation framework is proposed, which combines image classification and semantic segmentation branches based on the self-attention mechanism. Following that, a residual hole network fused with the dual attention mechanism was built, and a double attention metabolic pathway classification method for cardiac fibrosis in DE-MRI images was developed. RESULTS By adding pixel-level labels to an extra 40 training images, the segmentation model may enhance semantic segmentation performance by 2.6 percent (from 61.2 percent to 63.8 percent). When the number of pixel-level labels is increased to 80, semi-supervised feature extraction increases by 4.7 percent when compared to weakly guided semantic segmentation. Adding an attention mechanism to the critical network DRN (Deep Residual Network) can increase the classifier's performance by a small amount. Experiments revealed that the models worked effectively. CONCLUSION This paper investigates the segmentation and classification of cardiac fibrosis in DE-MRI data using a semi-supervised semantic segmentation and dual attention mechanism, dealing with the issue that existing segmentation algorithms have difficulty segmenting myocardial fibrosis tissue. In the future, we can consider optimizing the design of the attention module to reduce the module computation.
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Affiliation(s)
- Yuhan Ding
- School of Computer Science and Engineering, Central South University, Changsha 410000, China
| | - Weifang Xie
- School of Computer Science and Engineering, Central South University, Changsha 410000, China
| | - Kelvin K L Wong
- School of Computer Science and Engineering, Central South University, Changsha 410000, China.
| | - Zhifang Liao
- School of Computer Science and Engineering, Central South University, Changsha 410000, China.
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3
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ZHU HONGLEI, JIN LIAN, ZHANG JIAYU, WU XIAOMEI. OPTIMIZATION OF RABBIT VENTRICULAR ELECTROPHYSIOLOGICAL MODEL AND SIMULATION OF SYNTHETIC ELECTROCARDIOGRAM. J MECH MED BIOL 2021. [DOI: 10.1142/s0219519421500019] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
This study aimed to use computer simulation method to study the mechanism of cardiac electrical activities. We optimized an electrophysiological rabbit ventricular model, including myocardial segmentation, heterogeneity and a realistic His-Purkinje network. Simulations of normal state, several types of ventricular premature contractions (VPC), conduction system pacing and right ventricular apical pacing were performed and the detailed cardiac electrical activities were studied from cell level to electrocardiogram (ECG) level. A detailed multiscale optimized ventricular model was obtained. The model effectively simulated various types of electrical activities. The synthetic ECG results were very similar to the real clinical ECG. The duration of QRS of typical VPC is 58[Formula: see text]ms, 71% longer than that of a normal-state synthetic QRS and the amplitude of the QRS is 35% larger, while the QRS duration and amplitude of the real clinical ECG of typical VPC are 69% longer and 36% larger than those of the real normal QRS. The duration of QRS of ventricular fusion beat is 31[Formula: see text]ms, 91% of that of a normal-state synthetic QRS and the amplitude of the QRS is 36% larger, while the QRS duration of the real clinical ECG of a ventricular fusion beat is 92% of the real normal QRS and the amplitude is 37% larger. Therefore, the results indicate that this model is effective and reliable in studying the detailed process of cardiac excitation and pacing.
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Affiliation(s)
- HONGLEI ZHU
- Department of Electronic Engineering, Fudan University, Shanghai 200433, P. R. China
| | - LIAN JIN
- Department of Electronic Engineering, Fudan University, Shanghai 200433, P. R. China
| | - JIAYU ZHANG
- Department of Electronic Engineering, Fudan University, Shanghai 200433, P. R. China
| | - XIAOMEI WU
- Department of Electronic Engineering, Fudan University, Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention (MICCAI) of Shanghai, Research Center of Assistive Devices, Shanghai, P. R. China
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4
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Zabihollahy F, Rajan S, Ukwatta E. Machine Learning-Based Segmentation of Left Ventricular Myocardial Fibrosis from Magnetic Resonance Imaging. Curr Cardiol Rep 2020; 22:65. [PMID: 32562100 DOI: 10.1007/s11886-020-01321-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
PURPOSE OF REVIEW Myocardial fibrosis (MF) arises due to myocardial infarction and numerous cardiac diseases. MF may lead to several heart disorders, such as heart failure, arrhythmias, and ischemia. Cardiac magnetic resonance (CMR) imaging techniques, such as late gadolinium enhancement (LGE) CMR, enable non-invasive assessment of MF in the left ventricle (LV). Manual assessment of MF on CMR is a tedious and time-consuming task that is subject to high observer variability. Automated segmentation and quantification of MF is important for risk stratification and treatment planning in patients with heart disorders. This article aims to review the machine learning (ML)-based methodologies developed for MF quantification in the LV using CMR images. RECENT FINDINGS With the availability of relatively large labeled datasets supervised learning methods based on both conventional ML and state-of-the-art deep learning (DL) methods have been successfully applied for automated segmentation of MF. The incorporation of ML algorithms into imaging techniques such as 3D LGE CMR permits fast characterization of MF on CMR imaging and may enhance the diagnosis and prognosis of patients with heart disorders. Concurrently, the studies using cine CMR images have revealed that accurate segmentation of MF on non-contrast CMR imaging might be possible. The application of ML/DL tools in CMR image interpretation is likely to result in accurate and efficient quantification of MF.
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Affiliation(s)
- Fatemeh Zabihollahy
- Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada.
| | - S Rajan
- Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada
| | - E Ukwatta
- School of Engineering, University of Guelph, Guelph, ON, Canada
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5
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Connexin43 expression in bone marrow derived cells contributes to the electrophysiological properties of cardiac scar tissue. Sci Rep 2020; 10:2617. [PMID: 32054938 PMCID: PMC7018966 DOI: 10.1038/s41598-020-59449-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2019] [Accepted: 01/29/2020] [Indexed: 11/26/2022] Open
Abstract
Cardiac pathologies associated with arrhythmic activity are often accompanied by inflammation. The contribution of inflammatory cells to the electrophysiological properties of injured myocardium is unknown. Myocardial scar cell types and intercellular contacts were analyzed using a three-dimensional reconstruction from serial blockface scanning electron microscopy data. Three distinct cell populations were identified: inflammatory, fibroblastic and endocardial cells. While individual fibroblastic cells interface with a greater number of cells, inflammatory cells have the largest contact area suggesting a role in establishing intercellular electrical connections in scar tissue. Optical mapping was used to study the electrophysiological properties of scars in fetal liver chimeric mice generated using connexin43 knockout donors (bmpKO). Voltage changes were elicited in response to applied current pulses. Isopotential maps showed a steeper pattern of decay with distance from the electrode in scars compared with uninjured regions, suggesting reduced electrical coupling. The tissue decay constant, defined as the distance voltage reaches 37% of the amplitude at the edge of the scar, was 0.48 ± 0.04 mm (n = 11) in the scar of the bmpCTL group and decreased 37.5% in the bmpKO group (n = 10). Together these data demonstrate inflammatory cells significantly contribute to scar electrophysiology through coupling mediated at least partially by connexin43 expression.
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Lopez-Perez A, Sebastian R, Izquierdo M, Ruiz R, Bishop M, Ferrero JM. Personalized Cardiac Computational Models: From Clinical Data to Simulation of Infarct-Related Ventricular Tachycardia. Front Physiol 2019; 10:580. [PMID: 31156460 PMCID: PMC6531915 DOI: 10.3389/fphys.2019.00580] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Accepted: 04/25/2019] [Indexed: 12/20/2022] Open
Abstract
In the chronic stage of myocardial infarction, a significant number of patients develop life-threatening ventricular tachycardias (VT) due to the arrhythmogenic nature of the remodeled myocardium. Radiofrequency ablation (RFA) is a common procedure to isolate reentry pathways across the infarct scar that are responsible for VT. Unfortunately, this strategy show relatively low success rates; up to 50% of patients experience recurrent VT after the procedure. In the last decade, intensive research in the field of computational cardiac electrophysiology (EP) has demonstrated the ability of three-dimensional (3D) cardiac computational models to perform in-silico EP studies. However, the personalization and modeling of certain key components remain challenging, particularly in the case of the infarct border zone (BZ). In this study, we used a clinical dataset from a patient with a history of infarct-related VT to build an image-based 3D ventricular model aimed at computational simulation of cardiac EP, including detailed patient-specific cardiac anatomy and infarct scar geometry. We modeled the BZ in eight different ways by combining the presence or absence of electrical remodeling with four different levels of image-based patchy fibrosis (0, 10, 20, and 30%). A 3D torso model was also constructed to compute the ECG. Patient-specific sinus activation patterns were simulated and validated against the patient's ECG. Subsequently, the pacing protocol used to induce reentrant VTs in the EP laboratory was reproduced in-silico. The clinical VT was induced with different versions of the model and from different pacing points, thus identifying the slow conducting channel responsible for such VT. Finally, the real patient's ECG recorded during VT episodes was used to validate our simulation results and to assess different strategies to model the BZ. Our study showed that reduced conduction velocities and heterogeneity in action potential duration in the BZ are the main factors in promoting reentrant activity. Either electrical remodeling or fibrosis in a degree of at least 30% in the BZ were required to initiate VT. Moreover, this proof-of-concept study confirms the feasibility of developing 3D computational models for cardiac EP able to reproduce cardiac activation in sinus rhythm and during VT, using exclusively non-invasive clinical data.
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Affiliation(s)
- Alejandro Lopez-Perez
- Center for Research and Innovation in Bioengineering (Ci2B), Universitat Politècnica de València, Valencia, Spain
| | - Rafael Sebastian
- Computational Multiscale Simulation Lab (CoMMLab), Universitat de València, Valencia, Spain
| | - M Izquierdo
- INCLIVA Health Research Institute, Valencia, Spain.,Arrhythmia Unit, Cardiology Department, Hospital Clínico Universitario de Valencia, Valencia, Spain
| | - Ricardo Ruiz
- INCLIVA Health Research Institute, Valencia, Spain.,Arrhythmia Unit, Cardiology Department, Hospital Clínico Universitario de Valencia, Valencia, Spain
| | - Martin Bishop
- Division of Imaging Sciences & Biomedical Engineering, Department of Biomedical Engineering, King's College London, London, United Kingdom
| | - Jose M Ferrero
- Center for Research and Innovation in Bioengineering (Ci2B), Universitat Politècnica de València, Valencia, Spain
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7
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Andlauer R, Seemann G, Baron L, Dössel O, Kohl P, Platonov P, Loewe A. Influence of left atrial size on P-wave morphology: differential effects of dilation and hypertrophy. Europace 2018; 20:iii36-iii44. [DOI: 10.1093/europace/euy231] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2018] [Accepted: 09/19/2018] [Indexed: 11/13/2022] Open
Affiliation(s)
- Robin Andlauer
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, Karlsruhe, Germany
| | - Gunnar Seemann
- Institute for Experimental Cardiovascular Medicine, University Heart Center Freiburg Bad Krozingen, Medical Center, University of Freiburg, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Lukas Baron
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, Karlsruhe, Germany
| | - Olaf Dössel
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, Karlsruhe, Germany
| | - Peter Kohl
- Institute for Experimental Cardiovascular Medicine, University Heart Center Freiburg Bad Krozingen, Medical Center, University of Freiburg, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Pyotr Platonov
- Department of Cardiology and The Center for Integrative Electrocardiology at Lund University (CIEL), Lund, Sweden
- Arrhythmia Clinic, Skåne University Hospital, Lund, Sweden
| | - Axel Loewe
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, Karlsruhe, Germany
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8
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Deng D, Nikolov P, Arevalo HJ, Trayanova NA. Optimal contrast-enhanced MRI image thresholding for accurate prediction of ventricular tachycardia using ex-vivo high resolution models. Comput Biol Med 2018; 102:426-432. [PMID: 30301573 PMCID: PMC6218273 DOI: 10.1016/j.compbiomed.2018.09.031] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2018] [Revised: 09/12/2018] [Accepted: 09/30/2018] [Indexed: 11/23/2022]
Abstract
Patient specific models created from contrast-enhanced (i.e. late-gadolinium, LGE) MRI images can be used for prediction of reentry location and clinical ablation planning. However, there is still a need for direct and systematic comparison between characteristics of ventricular tachycardia (VT) morphologies predicted in computational models and those acquired in clinical or experimental protocols. In this study, we aimed to: 1) assess the differences in VT morphologies predicted by modeling and recorded in experiments in terms of patterns and location of reentries, earliest and latest activation sites, and cycle lengths; and 2) define the optimal range of infarct tissue threshold values which provide best match between simulation and experimental results. To achieve these goals, we utilized LGE-MRI images from 4 swine hearts with inducible monomorphic VT. The images were segmented to identify non-infarcted myocardium, semi viable gray zone (GZ), and core scar based on pixel intensity. Several models were reconstructed from each LGE-MRI scan, with voxels of intensity between that of non-infarcted myocardium and 20-50% of the maximum intensity (in 10% increments) in the infarct region classified as GZ. VT induction was simulated in each model. Our simulation results showed that using GZ intensity thresholds of 20% or 30% resulted in the best match of simulated propagation patterns and reentry locations with those from the experiment. Overall, we matched 70% (7/10) morphologies for all the hearts. Our simulation shows that MRI-based computational models of hearts with myocardial infarction can accurately reproduce the majority of experimentally recorded post-infarction VTs.
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Affiliation(s)
- Dongdong Deng
- Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Plamen Nikolov
- Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Hermenegild J Arevalo
- Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA; Cardiac Modelling Department, Simula Research Laboratory, Fornebu, Norway
| | - Natalia A Trayanova
- Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
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9
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Rat atrial engineered heart tissue: a new in vitro model to study atrial biology. Basic Res Cardiol 2018; 113:41. [DOI: 10.1007/s00395-018-0701-2] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/19/2018] [Accepted: 08/27/2018] [Indexed: 10/28/2022]
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10
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Clayton RH. Dispersion of Recovery and Vulnerability to Re-entry in a Model of Human Atrial Tissue With Simulated Diffuse and Focal Patterns of Fibrosis. Front Physiol 2018; 9:1052. [PMID: 30131713 PMCID: PMC6090998 DOI: 10.3389/fphys.2018.01052] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2018] [Accepted: 07/16/2018] [Indexed: 12/03/2022] Open
Abstract
Fibrosis in atrial tissue can act as a substrate for persistent atrial fibrillation, and can be focal or diffuse. Regions of fibrosis are associated with slowed or blocked conduction, and several approaches have been used to model these effects. In this study a computational model of 2D atrial tissue was used to investigate how the spatial scale of regions of simulated fibrosis influenced the dispersion of action potential duration (APD) and vulnerability to re-entry in simulated normal human atrial tissue, and human tissue that has undergone remodeling as a result of persistent atrial fibrillation. Electrical activity was simulated in a 10 × 10 cm square 2D domain, with a spatially varying diffusion coefficient as described below. Cellular electrophysiology was represented by the Courtemanche model for human atrial cells, with the model parameters set for normal and remodeled cells. The effect of fibrosis was modeled with a smoothly varying diffusion coefficient, obtained from sampling a Gaussian random field (GRF) with length scales of between 1.25 and 10.0 mm. Twenty samples were drawn from each field, and used to allocate a value of diffusion coefficient between 0.05 and 0.2 mm2/ms. Dispersion of APD was assessed in each sample by pacing at a cycle length of 1,000 ms, followed by a premature beat with a coupling interval of 400 ms. Vulnerability to re-entry was assessed with an aggressive pacing protocol with pacing cycle lengths decreasing from 450 to 250 ms in 25 ms intervals for normal tissue and 300–150 ms for remodeled tissue. Simulated fibrosis at smaller spatial scales tended to lengthen APD, increase APD dispersion, and increase vulnerability to sustained re-entry relative to fibrosis at larger spatial scales. This study shows that when fibrosis is represented by smoothly varying tissue diffusion, the spatial scale of fibrosis has important effects on both dispersion of recovery and vulnerability to re-entry.
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Affiliation(s)
- Richard H Clayton
- Department of Computer Science, Insigneo Institute for in-silico Medicine, University of Sheffield, Sheffield, United Kingdom
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11
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Ugarte JP, Tobón C, Lopes AM, Machado JAT. Atrial Rotor Dynamics Under Complex Fractional Order Diffusion. Front Physiol 2018; 9:975. [PMID: 30087620 PMCID: PMC6066719 DOI: 10.3389/fphys.2018.00975] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2018] [Accepted: 07/03/2018] [Indexed: 12/27/2022] Open
Abstract
The mechanisms of atrial fibrillation (AF) are a challenging research topic. The rotor hypothesis states that the AF is sustained by a reentrant wave that propagates around an unexcited core. Cardiac tissue heterogeneities, both structural and cellular, play an important role during fibrillatory dynamics, so that the ionic characteristics of the currents, their spatial distribution and their structural heterogeneity determine the meandering of the rotor. Several studies about rotor dynamics implement the standard diffusion equation. However, this mathematical scheme carries some limitations. It assumes the myocardium as a continuous medium, ignoring, therefore, its discrete and heterogeneous aspects. A computational model integrating both, electrical and structural properties could complement experimental and clinical results. A new mathematical model of the action potential propagation, based on complex fractional order derivatives is presented. The complex derivative order appears of considering the myocardium as discrete-scale invariant fractal. The main aim is to study the role of a myocardial, with fractal characteristics, on atrial fibrillatory dynamics. For this purpose, the degree of structural heterogeneity is described through derivatives of complex order γ = α + jβ. A set of variations for γ is tested. The real part α takes values ranging from 1.1 to 2 and the imaginary part β from 0 to 0.28. Under this scheme, the standard diffusion is recovered when α = 2 and β = 0. The effect of γ on the action potential propagation over an atrial strand is investigated. Rotors are generated in a 2D model of atrial tissue under electrical remodeling due to chronic AF. The results show that the degree of structural heterogeneity, given by γ, modulates the electrophysiological properties and the dynamics of rotor-type reentrant mechanisms. The spatial stability of the rotor and the area of its unexcited core are modulated. As the real part decreases and the imaginary part increases, simulating a higher structural heterogeneity, the vulnerable window to reentrant is increased, as the total meandering of the rotor tip. This in silico study suggests that structural heterogeneity, described by means of complex order derivatives, modulates the stability of rotors and that a wide range of rotor dynamics can be generated.
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Affiliation(s)
- Juan P. Ugarte
- Grupo de Investigación en Modelamiento y Simulación Computacional, Facultad de Ingenierías, Universidad de San Buenaventura, Medellín, Colombia
| | | | - António M. Lopes
- UISPA-LAETA/INEGI, Faculty of Engineering, University of Porto, Porto, Portugal
| | - J. A. Tenreiro Machado
- Department of Electrical Engineering, Institute of Engineering, Polytechnic of Porto, Porto, Portugal
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12
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De Coster T, Claus P, Kazbanov IV, Haemers P, Willems R, Sipido KR, Panfilov AV. Arrhythmogenicity of fibro-fatty infiltrations. Sci Rep 2018; 8:2050. [PMID: 29391548 PMCID: PMC5795000 DOI: 10.1038/s41598-018-20450-w] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2017] [Accepted: 01/18/2018] [Indexed: 12/14/2022] Open
Abstract
The onset of cardiac arrhythmias depends on electrophysiological and structural properties of cardiac tissue. One of the most important changes leading to arrhythmias is characterised by the presence of a large number of non-excitable cells in the heart, of which the most well-known example is fibrosis. Recently, adipose tissue was put forward as another similar factor contributing to cardiac arrhythmias. Adipocytes infiltrate into cardiac tissue and produce in-excitable obstacles that interfere with myocardial conduction. However, adipose infiltrates have a different spatial texture than fibrosis. Over the course of time, adipose tissue also remodels into fibrotic tissue. In this paper we investigate the arrhythmogenic mechanisms resulting from the presence of adipose tissue in the heart using computer modelling. We use the TP06 model for human ventricular cells and study how the size and percentage of adipose infiltrates affects basic properties of wave propagation and the onset of arrhythmias under high frequency pacing in a 2D model for cardiac tissue. We show that although presence of adipose infiltrates can result in the onset of cardiac arrhythmias, its impact is less than that of fibrosis. We quantify this process and discuss how the remodelling of adipose infiltrates affects arrhythmia onset.
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Affiliation(s)
- Tim De Coster
- Department of Physics and Astronomy, Ghent University, Gent, 9000, Belgium. .,Department of Cardiovascular Sciences, KU Leuven, Leuven, 3000, Belgium.
| | - Piet Claus
- Department of Cardiovascular Sciences, KU Leuven, Leuven, 3000, Belgium
| | - Ivan V Kazbanov
- Department of Physics and Astronomy, Ghent University, Gent, 9000, Belgium
| | - Peter Haemers
- Department of Cardiovascular Sciences, KU Leuven, Leuven, 3000, Belgium
| | - Rik Willems
- Department of Cardiovascular Sciences, KU Leuven, Leuven, 3000, Belgium
| | - Karin R Sipido
- Department of Cardiovascular Sciences, KU Leuven, Leuven, 3000, Belgium
| | - Alexander V Panfilov
- Department of Physics and Astronomy, Ghent University, Gent, 9000, Belgium. .,Moscow Institute of Physics and Technology, (State University), Dolgoprudny, Moscow Region, 141701, Russia. .,Laboratory of Experimental Cardiology, Department of Cardiology, Heart Lung Centre Leiden, Leiden University Medical Center, Leiden, 2333ZA, The Netherlands.
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13
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Kudryashova N, Tsvelaya V, Agladze K, Panfilov A. Virtual cardiac monolayers for electrical wave propagation. Sci Rep 2017; 7:7887. [PMID: 28801548 PMCID: PMC5554264 DOI: 10.1038/s41598-017-07653-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2017] [Accepted: 06/28/2017] [Indexed: 11/08/2022] Open
Abstract
The complex structure of cardiac tissue is considered to be one of the main determinants of an arrhythmogenic substrate. This study is aimed at developing the first mathematical model to describe the formation of cardiac tissue, using a joint in silico-in vitro approach. First, we performed experiments under various conditions to carefully characterise the morphology of cardiac tissue in a culture of neonatal rat ventricular cells. We considered two cell types, namely, cardiomyocytes and fibroblasts. Next, we proposed a mathematical model, based on the Glazier-Graner-Hogeweg model, which is widely used in tissue growth studies. The resultant tissue morphology was coupled to the detailed electrophysiological Korhonen-Majumder model for neonatal rat ventricular cardiomyocytes, in order to study wave propagation. The simulated waves had the same anisotropy ratio and wavefront complexity as those in the experiment. Thus, we conclude that our approach allows us to reproduce the morphological and physiological properties of cardiac tissue.
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Affiliation(s)
- Nina Kudryashova
- Department of Physics and Astronomy, Gent University, Gent, 9000, Belgium
- Laboratory of Biophysics of Excitable Systems, Moscow Institute of Physics and Technology, Dolgoprudny, 141701, Moscow Region, Russia
| | - Valeriya Tsvelaya
- Laboratory of Biophysics of Excitable Systems, Moscow Institute of Physics and Technology, Dolgoprudny, 141701, Moscow Region, Russia
| | - Konstantin Agladze
- Laboratory of Biophysics of Excitable Systems, Moscow Institute of Physics and Technology, Dolgoprudny, 141701, Moscow Region, Russia.
| | - Alexander Panfilov
- Department of Physics and Astronomy, Gent University, Gent, 9000, Belgium.
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14
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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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15
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Fibrosis and Atrial Fibrillation: Computerized and Optical Mapping; A View into the Human Atria at Submillimeter Resolution. JACC Clin Electrophysiol 2017; 3:531-546. [PMID: 29159313 DOI: 10.1016/j.jacep.2017.05.002] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Recent studies strongly suggest that the majority of atrial fibrillation (AF) patients with diagnosed or subclinical cardiac diseases have established or even pre-existing fibrotic structural remodeling, which may lead to conduction abnormalities and reentrant activity that sustain AF. As conventional treatments fail to treat AF in far too many cases, an urgent need exists to identify specific structural arrhythmogenic fibrosis patterns, which may maintain AF, in order to identify effective ablation targets for AF treatment. However, the existing challenge is to define what exact structural remodeling within the complex 3D human atrial wall is arrhythmogenic, as well as linking arrhythmogenic fibrosis to an underlying mechanism of AF maintenance in the clinical setting. This review is focused on the role of 3D fibrosis architecture in the mechanisms of AF maintenance revealed by submillimeter, high-resolution ex-vivo imaging modalities directly of human atria, as well as from in-silico 3D computational techniques that can be able to overcome in-vivo clinical limitations. The systematic integration of functional and structural imaging ex-vivo may inform the necessary integration of electrode and structural mapping in-vivo. A holistic view of AF driver mechanisms may begin to identify the defining characteristics or "fingerprints" of reentrant AF drivers, such as 3D fibrotic architecture, in order to design optimal patient-specific ablation strategies.
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16
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Disertori M, Masè M, Ravelli F. Myocardial fibrosis predicts ventricular tachyarrhythmias. Trends Cardiovasc Med 2017; 27:363-372. [PMID: 28262437 DOI: 10.1016/j.tcm.2017.01.011] [Citation(s) in RCA: 75] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2016] [Revised: 01/31/2017] [Accepted: 01/31/2017] [Indexed: 12/12/2022]
Abstract
Myocardial fibrosis is a common pattern in the setting of different heart diseases, and promotes ventricular tachyarrhythmias by creating a vulnerable substrate for reentrant activity and by favoring the emergence of triggers. Currently, late gadolinium enhancement (LGE) cardiac magnetic resonance is considered the reference method for the noninvasive assessment of ventricular fibrosis. Several studies and meta-analyses have shown that ventricular fibrosis detected by LGE is a powerful predictor of ventricular tachyarrhythmic events in ischemic, non-ischemic dilated cardiomyopathy and hypertrophic cardiomyopathy patients. Both the presence and extension of ventricular fibrosis were shown to correlate with the occurrence of ventricular arrhythmias and sudden cardiac death, irrespective of the grade of left ventricular dysfunction. Based on these results, the assessment of ventricular fibrosis has been suggested as a candidate marker to improve the decision making for implantable cardioverter-defibrillator therapy in patients with left ventricular dysfunction. These points will be discussed in the review.
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Affiliation(s)
- Marcello Disertori
- Healthcare Research and Innovation Program, PAT-FBK, Trento, Italy; Department of Cardiology, Santa Chiara Hospital, Trento, Italy.
| | - Michela Masè
- Department of Physics, University of Trento, Povo, Trento, Italy
| | - Flavia Ravelli
- Department of Physics, University of Trento, Povo, Trento, Italy
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17
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Karathanos TV, Boyle PM, Trayanova NA. Light-based Approaches to Cardiac Arrhythmia Research: From Basic Science to Translational Applications. CLINICAL MEDICINE INSIGHTS-CARDIOLOGY 2016; 10:47-60. [PMID: 27840581 PMCID: PMC5094582 DOI: 10.4137/cmc.s39711] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2016] [Revised: 09/27/2016] [Accepted: 10/09/2016] [Indexed: 02/06/2023]
Abstract
Light has long been used to image the heart, but now it can be used to modulate its electrophysiological function. Imaging modalities and techniques have long constituted an indispensable part of arrhythmia research and treatment. Recently, advances in the fields of optogenetics and photodynamic therapy have provided scientists with more effective approaches for probing, studying and potentially devising new treatments for cardiac arrhythmias. This article is a review of research toward the application of these techniques. It contains (a) an overview of advancements in technology and research that have contributed to light-based cardiac applications and (b) a summary of current and potential future applications of light-based control of cardiac cells, including modulation of heart rhythm, manipulation of cardiac action potential morphology, quantitative analysis of arrhythmias, defibrillation and cardiac ablation.
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Affiliation(s)
- Thomas V. Karathanos
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Patrick M. Boyle
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Natalia A. Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
- Department of Medicine, Johns Hopkins University, Baltimore, MD, USA
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18
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Ukwatta E, Arevalo H, Li K, Yuan J, Qiu W, Malamas P, Wu KC, Trayanova NA, Vadakkumpadan F. Myocardial Infarct Segmentation From Magnetic Resonance Images for Personalized Modeling of Cardiac Electrophysiology. IEEE TRANSACTIONS ON MEDICAL IMAGING 2016; 35:1408-1419. [PMID: 26731693 PMCID: PMC4891256 DOI: 10.1109/tmi.2015.2512711] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Accurate representation of myocardial infarct geometry is crucial to patient-specific computational modeling of the heart in ischemic cardiomyopathy. We have developed a methodology for segmentation of left ventricular (LV) infarct from clinically acquired, two-dimensional (2D), late-gadolinium enhanced cardiac magnetic resonance (LGE-CMR) images, for personalized modeling of ventricular electrophysiology. The infarct segmentation was expressed as a continuous min-cut optimization problem, which was solved using its dual formulation, the continuous max-flow (CMF). The optimization objective comprised of a smoothness term, and a data term that quantified the similarity between image intensity histograms of segmented regions and those of a set of training images. A manual segmentation of the LV myocardium was used to initialize and constrain the developed method. The three-dimensional geometry of infarct was reconstructed from its segmentation using an implicit, shape-based interpolation method. The proposed methodology was extensively evaluated using metrics based on geometry, and outcomes of individualized electrophysiological simulations of cardiac dys(function). Several existing LV infarct segmentation approaches were implemented, and compared with the proposed method. Our results demonstrated that the CMF method was more accurate than the existing approaches in reproducing expert manual LV infarct segmentations, and in electrophysiological simulations. The infarct segmentation method we have developed and comprehensively evaluated in this study constitutes an important step in advancing clinical applications of personalized simulations of cardiac electrophysiology.
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Affiliation(s)
- Eranga Ukwatta
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
- Correspondent author:
| | - Hermenegild Arevalo
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Kristina Li
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Jing Yuan
- Robarts Research Institute, Western University, London, ON, Canada
| | - Wu Qiu
- Robarts Research Institute, Western University, London, ON, Canada
| | - Peter Malamas
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Katherine C. Wu
- Division of Cardiology, Department of Medicine, Johns Hopkins Medical Institutions, Baltimore, MD, USA
| | - Natalia A. Trayanova
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Fijoy Vadakkumpadan
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
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19
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Multi-scale Modeling of the Cardiovascular System: Disease Development, Progression, and Clinical Intervention. Ann Biomed Eng 2016; 44:2642-60. [PMID: 27138523 DOI: 10.1007/s10439-016-1628-0] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2015] [Accepted: 04/22/2016] [Indexed: 12/19/2022]
Abstract
Cardiovascular diseases (CVDs) are the leading cause of death in the western world. With the current development of clinical diagnostics to more accurately measure the extent and specifics of CVDs, a laudable goal is a better understanding of the structure-function relation in the cardiovascular system. Much of this fundamental understanding comes from the development and study of models that integrate biology, medicine, imaging, and biomechanics. Information from these models provides guidance for developing diagnostics, and implementation of these diagnostics to the clinical setting, in turn, provides data for refining the models. In this review, we introduce multi-scale and multi-physical models for understanding disease development, progression, and designing clinical interventions. We begin with multi-scale models of cardiac electrophysiology and mechanics for diagnosis, clinical decision support, personalized and precision medicine in cardiology with examples in arrhythmia and heart failure. We then introduce computational models of vasculature mechanics and associated mechanical forces for understanding vascular disease progression, designing clinical interventions, and elucidating mechanisms that underlie diverse vascular conditions. We conclude with a discussion of barriers that must be overcome to provide enhanced insights, predictions, and decisions in pre-clinical and clinical applications.
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20
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Effects of Heterogeneous Diffuse Fibrosis on Arrhythmia Dynamics and Mechanism. Sci Rep 2016; 6:20835. [PMID: 26861111 PMCID: PMC4748409 DOI: 10.1038/srep20835] [Citation(s) in RCA: 71] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2015] [Accepted: 12/30/2015] [Indexed: 12/19/2022] Open
Abstract
Myocardial fibrosis is an important risk factor for cardiac arrhythmias. Previous experimental and numerical studies have shown that the texture and spatial distribution of fibrosis may play an important role in arrhythmia onset. Here, we investigate how spatial heterogeneity of fibrosis affects arrhythmia onset using numerical methods. We generate various tissue textures that differ by the mean amount of fibrosis, the degree of heterogeneity and the characteristic size of heterogeneity. We study the onset of arrhythmias using a burst pacing protocol. We confirm that spatial heterogeneity of fibrosis increases the probability of arrhythmia induction. This effect is more pronounced with the increase of both the spatial size and the degree of heterogeneity. The induced arrhythmias have a regular structure with the period being mostly determined by the maximal local fibrosis level. We perform ablations of the induced fibrillatory patterns to classify their type. We show that in fibrotic tissue fibrillation is usually of the mother rotor type but becomes of the multiple wavelet type with increase in tissue size. Overall, we conclude that the most important factor determining the formation and dynamics of arrhythmia in heterogeneous fibrotic tissue is the value of maximal local fibrosis.
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21
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Lerma C, Glass L. Predicting the risk of sudden cardiac death. J Physiol 2016; 594:2445-58. [PMID: 26660287 DOI: 10.1113/jp270535] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2015] [Accepted: 12/07/2015] [Indexed: 12/18/2022] Open
Abstract
Sudden cardiac death (SCD) is the result of a change of cardiac activity from normal (typically sinus) rhythm to a rhythm that does not pump adequate blood to the brain. The most common rhythms leading to SCD are ventricular tachycardia (VT) or ventricular fibrillation (VF). These result from an accelerated ventricular pacemaker or ventricular reentrant waves. Despite significant efforts to develop accurate predictors for the risk of SCD, current methods for risk stratification still need to be improved. In this article we briefly review current approaches to risk stratification. Then we discuss the mathematical basis for dynamical transitions (called bifurcations) that may lead to VT and VF. One mechanism for transition to VT or VF involves a perturbation by a premature ventricular complex (PVC) during sinus rhythm. We describe the main mechanisms of PVCs (reentry, independent pacemakers and abnormal depolarizations). An emerging approach to risk stratification for SCD involves the development of individualized dynamical models of a patient based on measured anatomy and physiology. Careful analysis and modelling of dynamics of ventricular arrhythmia on an individual basis will be essential in order to improve risk stratification for SCD and to lay a foundation for personalized (precision) medicine in cardiology.
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Affiliation(s)
- Claudia Lerma
- Departamento de Instrumentación Electromecánica, Instituto Nacional de Cardiología Ignacio Chávez, México, Distrito Federal, México, 14080
| | - Leon Glass
- Department of Physiology, McGill University, Montreal, Quebec, Canada, H3G 1Y6
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22
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Trayanova NA, Chang KC. How computer simulations of the human heart can improve anti-arrhythmia therapy. J Physiol 2016; 594:2483-502. [PMID: 26621489 DOI: 10.1113/jp270532] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2015] [Accepted: 11/25/2015] [Indexed: 01/26/2023] Open
Abstract
Over the last decade, the state-of-the-art in cardiac computational modelling has progressed rapidly. The electrophysiological function of the heart can now be simulated with a high degree of detail and accuracy, opening the doors for simulation-guided approaches to anti-arrhythmic drug development and patient-specific therapeutic interventions. In this review, we outline the basic methodology for cardiac modelling, which has been developed and validated over decades of research. In addition, we present several recent examples of how computational models of the human heart have been used to address current clinical problems in cardiac electrophysiology. We will explore the use of simulations to improve anti-arrhythmic pacing and defibrillation interventions; to predict optimal sites for clinical ablation procedures; and to aid in the understanding and selection of arrhythmia risk markers. Together, these studies illustrate how the tremendous advances in cardiac modelling are poised to revolutionize medical treatment and prevention of arrhythmia.
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Affiliation(s)
- Natalia A Trayanova
- Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA.,Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Kelly C Chang
- Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
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23
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Billman GE, Del Rio CL. Editorial: Cardiac electronic remodeling and susceptibility to arrhythmias: an introduction and brief historical overview. Front Physiol 2015. [PMID: 26217235 PMCID: PMC4491600 DOI: 10.3389/fphys.2015.00196] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Affiliation(s)
- George E Billman
- Department of Physiology and Cell Biology, The Ohio State University Columbus, OH, USA
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24
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Optimization of catheter ablation of atrial fibrillation: insights gained from clinically-derived computer models. Int J Mol Sci 2015; 16:10834-54. [PMID: 25984605 PMCID: PMC4463678 DOI: 10.3390/ijms160510834] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2015] [Revised: 05/03/2015] [Accepted: 05/06/2015] [Indexed: 12/04/2022] Open
Abstract
Atrial fibrillation (AF) is the most common heart rhythm disturbance, and its treatment is an increasing economic burden on the health care system. Despite recent intense clinical, experimental and basic research activity, the treatment of AF with current antiarrhythmic drugs and catheter/surgical therapies remains limited. Radiofrequency catheter ablation (RFCA) is widely used to treat patients with AF. Current clinical ablation strategies are largely based on atrial anatomy and/or substrate detected using different approaches, and they vary from one clinical center to another. The nature of clinical ablation leads to ambiguity regarding the optimal patient personalization of the therapy partly due to the fact that each empirical configuration of ablation lines made in a patient is irreversible during one ablation procedure. To investigate optimized ablation lesion line sets, in silico experimentation is an ideal solution. 3D computer models give us a unique advantage to plan and assess the effectiveness of different ablation strategies before and during RFCA. Reliability of in silico assessment is ensured by inclusion of accurate 3D atrial geometry, realistic fiber orientation, accurate fibrosis distribution and cellular kinetics; however, most of this detailed information in the current computer models is extrapolated from animal models and not from the human heart. The predictive power of computer models will increase as they are validated with human experimental and clinical data. To make the most from a computer model, one needs to develop 3D computer models based on the same functionally and structurally mapped intact human atria with high spatial resolution. The purpose of this review paper is to summarize recent developments in clinically-derived computer models and the clinical insights they provide for catheter ablation.
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