51
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Sánchez J, Loewe A. A Review of Healthy and Fibrotic Myocardium Microstructure Modeling and Corresponding Intracardiac Electrograms. Front Physiol 2022; 13:908069. [PMID: 35620600 PMCID: PMC9127661 DOI: 10.3389/fphys.2022.908069] [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: 03/30/2022] [Accepted: 04/21/2022] [Indexed: 11/13/2022] Open
Abstract
Computational simulations of cardiac electrophysiology provide detailed information on the depolarization phenomena at different spatial and temporal scales. With the development of new hardware and software, in silico experiments have gained more importance in cardiac electrophysiology research. For plane waves in healthy tissue, in vivo and in silico electrograms at the surface of the tissue demonstrate symmetric morphology and high peak-to-peak amplitude. Simulations provided insight into the factors that alter the morphology and amplitude of the electrograms. The situation is more complex in remodeled tissue with fibrotic infiltrations. Clinically, different changes including fractionation of the signal, extended duration and reduced amplitude have been described. In silico, numerous approaches have been proposed to represent the pathological changes on different spatial and functional scales. Different modeling approaches can reproduce distinct subsets of the clinically observed electrogram phenomena. This review provides an overview of how different modeling approaches to incorporate fibrotic and structural remodeling affect the electrogram and highlights open challenges to be addressed in future research.
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52
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Karabelas E, Gsell MA, Haase G, Plank G, Augustin CM. An accurate, robust, and efficient finite element framework with applications to anisotropic, nearly and fully incompressible elasticity. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2022; 394:114887. [PMID: 35432634 PMCID: PMC7612621 DOI: 10.1016/j.cma.2022.114887] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Fiber-reinforced soft biological tissues are typically modeled as hyperelastic, anisotropic, and nearly incompressible materials. To enforce incompressibility a multiplicative split of the deformation gradient into a volumetric and an isochoric part is a very common approach. However, the finite element analysis of such problems often suffers from severe volumetric locking effects and numerical instabilities. In this paper, we present novel methods to overcome volumetric locking phenomena for using stabilized P1-P1 elements. We introduce different stabilization techniques and demonstrate the high robustness and computational efficiency of the chosen methods. In two benchmark problems from the literature as well as an advanced application to cardiac electromechanics, we compare the approach to standard linear elements and show the accuracy and versatility of the methods to simulate anisotropic, nearly and fully incompressible materials. We demonstrate the potential of this numerical framework to accelerate accurate simulations of biological tissues to the extent of enabling patient-specific parameterization studies, where numerous forward simulations are required.
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Affiliation(s)
- Elias Karabelas
- Institute for Mathematics and Scientific Computing, Karl-Franzens-University Graz, Graz, Austria
- BioTechMed-Graz, Graz, Austria
| | - Matthias A.F. Gsell
- Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Gundolf Haase
- Institute for Mathematics and Scientific Computing, Karl-Franzens-University Graz, Graz, Austria
- BioTechMed-Graz, Graz, Austria
| | - Gernot Plank
- Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz, Graz, Austria
- BioTechMed-Graz, Graz, Austria
| | - Christoph M. Augustin
- Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz, Graz, Austria
- BioTechMed-Graz, Graz, Austria
- Correspondence to: Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz, Neue Stiftingtalstrasse 6/IV, Graz 8010, Austria. (C.M. Augustin)
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53
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An Automata-Based Cardiac Electrophysiology Simulator to Assess Arrhythmia Inducibility. MATHEMATICS 2022. [DOI: 10.3390/math10081293] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Personalized cardiac electrophysiology simulations have demonstrated great potential to study cardiac arrhythmias and help in therapy planning of radio-frequency ablation. Its application to analyze vulnerability to ventricular tachycardia and sudden cardiac death in infarcted patients has been recently explored. However, the detailed multi-scale biophysical simulations used in these studies are very demanding in terms of memory and computational resources, which prevents their clinical translation. In this work, we present a fast phenomenological system based on cellular automata (CA) to simulate personalized cardiac electrophysiology. The system is trained on biophysical simulations to reproduce cellular and tissue dynamics in healthy and pathological conditions, including action potential restitution, conduction velocity restitution and cell safety factor. We show that a full ventricular simulation can be performed in the order of seconds, emulate the results of a biophysical simulation and reproduce a patient’s ventricular tachycardia in a model that includes a heterogeneous scar region. The system could be used to study the risk of arrhythmia in infarcted patients for a large number of scenarios.
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54
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Ryzhii M, Ryzhii E. Pacemaking function of two simplified cell models. PLoS One 2022; 17:e0257935. [PMID: 35404982 PMCID: PMC9000119 DOI: 10.1371/journal.pone.0257935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 03/29/2022] [Indexed: 12/03/2022] Open
Abstract
Simplified nonlinear models of biological cells are widely used in computational electrophysiology. The models reproduce qualitatively many of the characteristics of various organs, such as the heart, brain, and intestine. In contrast to complex cellular ion-channel models, the simplified models usually contain a small number of variables and parameters, which facilitates nonlinear analysis and reduces computational load. In this paper, we consider pacemaking variants of the Aliev-Panfilov and Corrado two-variable excitable cell models. We conducted a numerical simulation study of these models and investigated the main nonlinear dynamic features of both isolated cells and 1D coupled pacemaker-excitable systems. Simulations of the 2D sinoatrial node and 3D intestine tissue as application examples of combined pacemaker-excitable systems demonstrated results similar to obtained previously. The uniform formulation for the conventional excitable cell models and proposed pacemaker models allows a convenient and easy implementation for the construction of personalized physiological models, inverse tissue modeling, and development of real-time simulation systems for various organs that contain both pacemaker and excitable cells.
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Affiliation(s)
- Maxim Ryzhii
- Complex Systems Modeling Laboratory, University of Aizu, Aizu-Wakamatsu, Japan
- * E-mail:
| | - Elena Ryzhii
- Department of Anatomy and Histology, Fukushima Medical University, Fukushima, Japan
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55
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Integrative Computational Modeling of Cardiomyocyte Calcium Handling and Cardiac Arrhythmias: Current Status and Future Challenges. Cells 2022; 11:cells11071090. [PMID: 35406654 PMCID: PMC8997666 DOI: 10.3390/cells11071090] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 03/22/2022] [Accepted: 03/22/2022] [Indexed: 12/26/2022] Open
Abstract
Cardiomyocyte calcium-handling is the key mediator of cardiac excitation-contraction coupling. In the healthy heart, calcium controls both electrical impulse propagation and myofilament cross-bridge cycling, providing synchronous and adequate contraction of cardiac muscles. However, calcium-handling abnormalities are increasingly implicated as a cause of cardiac arrhythmias. Due to the complex, dynamic and localized interactions between calcium and other molecules within a cardiomyocyte, it remains experimentally challenging to study the exact contributions of calcium-handling abnormalities to arrhythmogenesis. Therefore, multiscale computational modeling is increasingly being used together with laboratory experiments to unravel the exact mechanisms of calcium-mediated arrhythmogenesis. This article describes various examples of how integrative computational modeling makes it possible to unravel the arrhythmogenic consequences of alterations to cardiac calcium handling at subcellular, cellular and tissue levels, and discusses the future challenges on the integration and interpretation of such computational data.
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56
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Rodriguez Padilla J, Petras A, Magat J, Bayer J, Bihan-Poudec Y, El-Hamrani D, Ramlugun G, Neic A, Augustin C, Vaillant F, Constantin M, Benoist D, Pourtau L, Dubes V, Rogier J, Labrousse L, Bernus O, Quesson B, Haissaguerre M, Gsell M, Plank G, Ozenne V, Vigmond E. Impact of Intraventricular Septal Fiber Orientation on Cardiac Electromechanical Function. Am J Physiol Heart Circ Physiol 2022; 322:H936-H952. [PMID: 35302879 PMCID: PMC9109800 DOI: 10.1152/ajpheart.00050.2022] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Cardiac fiber direction is an important factor determining the propagation of electrical activity, as well as the development of mechanical force. In this article, we imaged the ventricles of several species with special attention to the intraventricular septum to determine the functional consequences of septal fiber organization. First, we identified a dual-layer organization of the fiber orientation in the intraventricular septum of ex vivo sheep hearts using diffusion tensor imaging at high field MRI. To expand the scope of the results, we investigated the presence of a similar fiber organization in five mammalian species (rat, canine, pig, sheep, and human) and highlighted the continuity of the layer with the moderator band in large mammalian species. We implemented the measured septal fiber fields in three-dimensional electromechanical computer models to assess the impact of the fiber orientation. The downward fibers produced a diamond activation pattern superficially in the right ventricle. Electromechanically, there was very little change in pressure volume loops although the stress distribution was altered. In conclusion, we clarified that the right ventricular septum has a downwardly directed superficial layer in larger mammalian species, which can have modest effects on stress distribution. NEW & NOTEWORTHY A dual-layer organization of the fiber orientation in the intraventricular septum was identified in ex vivo hearts of large mammals. The RV septum has a downwardly directed superficial layer that is continuous with the moderator band. Electrically, it produced a diamond activation pattern. Electromechanically, little change in pressure volume loops were noticed but stress distribution was altered. Fiber distribution derived from diffusion tensor imaging should be considered for an accurate strain and stress analysis.
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Affiliation(s)
| | - Argyrios Petras
- Johann Radon Institute for Computational and Applied Mathematics (RICAM), Austrian Academy of Sciences, Linz, Austria
| | - Julie Magat
- Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Pessac-Bordeaux, France.,Univ. Bordeaux, Centre de recherche Cardio-Thoracique de Bordeaux, Bordeaux, France.,INSERM, Centre de recherche Cardio-Thoracique de Bordeaux, Bordeaux, France
| | - Jason Bayer
- Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Pessac-Bordeaux, France.,Univ. Bordeaux, IMB, UMR 5251, Talence, France
| | - Yann Bihan-Poudec
- Centre de Neuroscience Cognitive, CNRS UMR 5229, Université Claude Bernard Lyon I, France
| | - Dounia El-Hamrani
- Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Pessac-Bordeaux, France.,Univ. Bordeaux, Centre de recherche Cardio-Thoracique de Bordeaux, Bordeaux, France.,INSERM, Centre de recherche Cardio-Thoracique de Bordeaux, Bordeaux, France
| | - Girish Ramlugun
- Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Pessac-Bordeaux, France.,Univ. Bordeaux, Centre de recherche Cardio-Thoracique de Bordeaux, Bordeaux, France.,INSERM, Centre de recherche Cardio-Thoracique de Bordeaux, Bordeaux, France
| | - Aurel Neic
- Gottfried Schatz Research Center, Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Christoph Augustin
- Gottfried Schatz Research Center, Division of Biophysics, Medical University of Graz, Graz, Austria.,BioTechMed-Graz, Graz, Austria
| | - Fanny Vaillant
- Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Pessac-Bordeaux, France.,Univ. Bordeaux, Centre de recherche Cardio-Thoracique de Bordeaux, Bordeaux, France.,INSERM, Centre de recherche Cardio-Thoracique de Bordeaux, Bordeaux, France
| | - Marion Constantin
- Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Pessac-Bordeaux, France.,Univ. Bordeaux, Centre de recherche Cardio-Thoracique de Bordeaux, Bordeaux, France.,INSERM, Centre de recherche Cardio-Thoracique de Bordeaux, Bordeaux, France
| | - David Benoist
- Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Pessac-Bordeaux, France.,Univ. Bordeaux, Centre de recherche Cardio-Thoracique de Bordeaux, Bordeaux, France.,INSERM, Centre de recherche Cardio-Thoracique de Bordeaux, Bordeaux, France
| | - Line Pourtau
- Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Pessac-Bordeaux, France.,Univ. Bordeaux, Centre de recherche Cardio-Thoracique de Bordeaux, Bordeaux, France.,INSERM, Centre de recherche Cardio-Thoracique de Bordeaux, Bordeaux, France
| | - Virginie Dubes
- Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Pessac-Bordeaux, France.,Univ. Bordeaux, Centre de recherche Cardio-Thoracique de Bordeaux, Bordeaux, France.,INSERM, Centre de recherche Cardio-Thoracique de Bordeaux, Bordeaux, France
| | | | | | - Olivier Bernus
- Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Pessac-Bordeaux, France.,Univ. Bordeaux, Centre de recherche Cardio-Thoracique de Bordeaux, Bordeaux, France.,INSERM, Centre de recherche Cardio-Thoracique de Bordeaux, Bordeaux, France
| | - Bruno Quesson
- Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Pessac-Bordeaux, France.,Univ. Bordeaux, Centre de recherche Cardio-Thoracique de Bordeaux, Bordeaux, France.,INSERM, Centre de recherche Cardio-Thoracique de Bordeaux, Bordeaux, France
| | | | - Matthias Gsell
- Gottfried Schatz Research Center, Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Gernot Plank
- Gottfried Schatz Research Center, Division of Biophysics, Medical University of Graz, Graz, Austria.,BioTechMed-Graz, Graz, Austria
| | - Valéry Ozenne
- Centre de Résonance Magnétique des Systèmes Biologiques, UMR 5536, CNRS/Université de Bordeaux, Bordeaux, France
| | - Edward Vigmond
- Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Pessac-Bordeaux, France.,Univ. Bordeaux, IMB, UMR 5251, Talence, France
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57
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Jung A, Gsell MAF, Augustin CM, Plank G. An Integrated Workflow for Building Digital Twins of Cardiac Electromechanics—A Multi-Fidelity Approach for Personalising Active Mechanics. MATHEMATICS 2022; 10:823. [PMID: 35295404 PMCID: PMC7612499 DOI: 10.3390/math10050823] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Personalised computer models of cardiac function, referred to as cardiac digital twins, are envisioned to play an important role in clinical precision therapies of cardiovascular diseases. A major obstacle hampering clinical translation involves the significant computational costs involved in the personalisation of biophysically detailed mechanistic models that require the identification of high-dimensional parameter vectors. An important aspect to identify in electromechanics (EM) models are active mechanics parameters that govern cardiac contraction and relaxation. In this study, we present a novel, fully automated, and efficient approach for personalising biophysically detailed active mechanics models using a two-step multi-fidelity solution. In the first step, active mechanical behaviour in a given 3D EM model is represented by a purely phenomenological, low-fidelity model, which is personalised at the organ scale by calibration to clinical cavity pressure data. Then, in the second step, median traces of nodal cellular active stress, intracellular calcium concentration, and fibre stretch are generated and utilised to personalise the desired high-fidelity model at the cellular scale using a 0D model of cardiac EM. Our novel approach was tested on a cohort of seven human left ventricular (LV) EM models, created from patients treated for aortic coarctation (CoA). Goodness of fit, computational cost, and robustness of the algorithm against uncertainty in the clinical data and variations of initial guesses were evaluated. We demonstrate that our multi-fidelity approach facilitates the personalisation of a biophysically detailed active stress model within only a few (2 to 4) expensive 3D organ-scale simulations—a computational effort compatible with clinical model applications.
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Affiliation(s)
- Alexander Jung
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging—Division of Biophysics, Medical University Graz, 8010 Graz, Austria
| | - Matthias A. F. Gsell
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging—Division of Biophysics, Medical University Graz, 8010 Graz, Austria
- NAWI Graz, Institute of Mathematics and Scientific Computing, University of Graz, 8010 Graz, Austria
| | - Christoph M. Augustin
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging—Division of Biophysics, Medical University Graz, 8010 Graz, Austria
- BioTechMed-Graz, 8010 Graz, Austria
- Correspondence:
| | - Gernot Plank
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging—Division of Biophysics, Medical University Graz, 8010 Graz, Austria
- BioTechMed-Graz, 8010 Graz, Austria
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58
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Blackwell DJ, Faggioni M, Wleklinski MJ, Gomez-Hurtado N, Venkataraman R, Gibbs CE, Baudenbacher FJ, Gong S, Fishman GI, Boyle PM, Pfeifer K, Knollmann BC. The Purkinje-myocardial junction is the anatomic origin of ventricular arrhythmia in CPVT. JCI Insight 2022; 7:e151893. [PMID: 34990403 PMCID: PMC8855823 DOI: 10.1172/jci.insight.151893] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 12/21/2021] [Indexed: 11/17/2022] Open
Abstract
Catecholaminergic polymorphic ventricular tachycardia (CPVT) is an arrhythmia syndrome caused by gene mutations that render RYR2 Ca release channels hyperactive, provoking spontaneous Ca release and delayed afterdepolarizations (DADs). What remains unknown is the cellular source of ventricular arrhythmia triggered by DADs: Purkinje cells in the conduction system or ventricular cardiomyocytes in the working myocardium. To answer this question, we used a genetic approach in mice to knock out cardiac calsequestrin either in Purkinje cells or in ventricular cardiomyocytes. Total loss of calsequestrin in the heart causes a severe CPVT phenotype in mice and humans. We found that loss of calsequestrin only in ventricular myocytes produced a full-blown CPVT phenotype, whereas mice with loss of calsequestrin only in Purkinje cells were comparable to WT mice. Subendocardial chemical ablation or restoration of calsequestrin expression in subendocardial cardiomyocytes neighboring Purkinje cells was sufficient to protect against catecholamine-induced arrhythmias. In silico modeling demonstrated that DADs in ventricular myocardium can trigger full action potentials in the Purkinje fiber, but not vice versa. Hence, ectopic beats in CPVT are likely generated at the Purkinje-myocardial junction via a heretofore unrecognized tissue mechanism, whereby DADs in the ventricular myocardium trigger full action potentials in adjacent Purkinje cells.
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Affiliation(s)
- Daniel J. Blackwell
- Vanderbilt Center for Arrhythmia Research and Therapeutics, Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - Michela Faggioni
- Vanderbilt Center for Arrhythmia Research and Therapeutics, Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - Matthew J. Wleklinski
- Vanderbilt Center for Arrhythmia Research and Therapeutics, Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
- Department of Pharmacology and
| | - Nieves Gomez-Hurtado
- Vanderbilt Center for Arrhythmia Research and Therapeutics, Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - Raghav Venkataraman
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
| | - Chelsea E. Gibbs
- Department of Bioengineering, University of Washington, Seattle, Washington, USA
| | - Franz J. Baudenbacher
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
| | - Shiaoching Gong
- Laboratory of Molecular Biology, Rockefeller University, New York, New York, USA
| | - Glenn I. Fishman
- Leon H. Charney Division of Cardiology, Department of Medicine, New York University School of Medicine, New York, New York, USA
| | - Patrick M. Boyle
- Department of Bioengineering, University of Washington, Seattle, Washington, USA
- Institute for Stem Cell and Regenerative Medicine and
- Center for Cardiovascular Biology, University of Washington, Seattle, Washington, USA
| | - Karl Pfeifer
- Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, Maryland, USA
| | - Bjorn C. Knollmann
- Vanderbilt Center for Arrhythmia Research and Therapeutics, Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
- Department of Pharmacology and
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59
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Tong L, Zhao C, Fu Z, Dong R, Wu Z, Wang Z, Zhang N, Wang X, Cao B, Sun Y, Zheng D, Xia L, Deng D. Preliminary Study: Learning the Impact of Simulation Time on Reentry Location and Morphology Induced by Personalized Cardiac Modeling. Front Physiol 2021; 12:733500. [PMID: 35002750 PMCID: PMC8739986 DOI: 10.3389/fphys.2021.733500] [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: 06/30/2021] [Accepted: 11/30/2021] [Indexed: 11/13/2022] Open
Abstract
Personalized cardiac modeling is widely used for studying the mechanisms of cardiac arrythmias. Due to the high demanding of computational resource of modeling, the arrhythmias induced in the models are usually simulated for just a few seconds. In clinic, it is common that arrhythmias last for more than several minutes and the morphologies of reentries are not always stable, so it is not clear that whether the simulation of arrythmias for just a few seconds is long enough to match the arrhythmias detected in patients. This study aimed to observe how long simulation of the induced arrhythmias in the personalized cardiac models is sufficient to match the arrhythmias detected in patients. A total of 5 contrast enhanced MRI datasets of patient hearts with myocardial infarction were used in this study. Then, a classification method based on Gaussian mixture model was used to detect the infarct tissue. For each reentry, 3 s and 10 s were simulated. The characteristics of each reentry simulated for different duration were studied. Reentries were induced in all 5 ventricular models and sustained reentries were induced at 39 stimulation sites in the model. By analyzing the simulation results, we found that 41% of the sustained reentries in the 3 s simulation group terminated in the longer simulation groups (10 s). The second finding in our simulation was that only 23.1% of the sustained reentries in the 3 s simulation did not change location and morphology in the extended 10 s simulation. The third finding was that 35.9% reentries were stable in the 3 s simulation and should be extended for the simulation time. The fourth finding was that the simulation results in 10 s simulation matched better with the clinical measurements than the 3 s simulation. It was shown that 10 s simulation was sufficient to make simulation results stable. The findings of this study not only improve the simulation accuracy, but also reduce the unnecessary simulation time to achieve the optimal use of computer resources to improve the simulation efficiency and shorten the simulation time to meet the time node requirements of clinical operation on patients.
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Affiliation(s)
- Lv Tong
- School of Biomedical Engineering, Dalian University of Technology, Dalian, China
| | - Caiming Zhao
- Department of Cardiology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Zhenyin Fu
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Ruiqing Dong
- Department of Cardiology, Dushu Lake Hospital Affiliated to Soochow University, Suzhou, China
| | - Zhenghong Wu
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Zefeng Wang
- Department of Cardiology, Beijing Anzhen Hospital Affiliated to Capital Medical University, Beijing, China
| | - Nan Zhang
- Department of Radiology, Beijing Anzhen Hospital Affiliated to Capital Medical University, Beijing, China
| | - Xinlu Wang
- Department of Cardiology, Beijing Anzhen Hospital Affiliated to Capital Medical University, Beijing, China
| | - Boyang Cao
- School of Biomedical Engineering, Dalian University of Technology, Dalian, China
| | - Yutong Sun
- School of Biomedical Engineering, Dalian University of Technology, Dalian, China
| | - Dingchang Zheng
- Research Centre for Intelligent Healthcare, Faculty of Health and Life Science, Coventry University, Coventry, United Kingdom
| | - Ling Xia
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Dongdong Deng
- School of Biomedical Engineering, Dalian University of Technology, Dalian, China
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60
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Maleckar MM, Myklebust L, Uv J, Florvaag PM, Strøm V, Glinge C, Jabbari R, Vejlstrup N, Engstrøm T, Ahtarovski K, Jespersen T, Tfelt-Hansen J, Naumova V, Arevalo H. Combined In-silico and Machine Learning Approaches Toward Predicting Arrhythmic Risk in Post-infarction Patients. Front Physiol 2021; 12:745349. [PMID: 34819872 PMCID: PMC8606551 DOI: 10.3389/fphys.2021.745349] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 10/06/2021] [Indexed: 11/29/2022] Open
Abstract
Background: Remodeling due to myocardial infarction (MI) significantly increases patient arrhythmic risk. Simulations using patient-specific models have shown promise in predicting personalized risk for arrhythmia. However, these are computationally- and time- intensive, hindering translation to clinical practice. Classical machine learning (ML) algorithms (such as K-nearest neighbors, Gaussian support vector machines, and decision trees) as well as neural network techniques, shown to increase prediction accuracy, can be used to predict occurrence of arrhythmia as predicted by simulations based solely on infarct and ventricular geometry. We present an initial combined image-based patient-specific in silico and machine learning methodology to assess risk for dangerous arrhythmia in post-infarct patients. Furthermore, we aim to demonstrate that simulation-supported data augmentation improves prediction models, combining patient data, computational simulation, and advanced statistical modeling, improving overall accuracy for arrhythmia risk assessment. Methods: MRI-based computational models were constructed from 30 patients 5 days post-MI (the “baseline” population). In order to assess the utility biophysical model-supported data augmentation for improving arrhythmia prediction, we augmented the virtual baseline patient population. Each patient ventricular and ischemic geometry in the baseline population was used to create a subfamily of geometric models, resulting in an expanded set of patient models (the “augmented” population). Arrhythmia induction was attempted via programmed stimulation at 17 sites for each virtual patient corresponding to AHA LV segments and simulation outcome, “arrhythmia,” or “no-arrhythmia,” were used as ground truth for subsequent statistical prediction (machine learning, ML) models. For each patient geometric model, we measured and used choice data features: the myocardial volume and ischemic volume, as well as the segment-specific myocardial volume and ischemia percentage, as input to ML algorithms. For classical ML techniques (ML), we trained k-nearest neighbors, support vector machine, logistic regression, xgboost, and decision tree models to predict the simulation outcome from these geometric features alone. To explore neural network ML techniques, we trained both a three - and a four-hidden layer multilayer perceptron feed forward neural networks (NN), again predicting simulation outcomes from these geometric features alone. ML and NN models were trained on 70% of randomly selected segments and the remaining 30% was used for validation for both baseline and augmented populations. Results: Stimulation in the baseline population (30 patient models) resulted in reentry in 21.8% of sites tested; in the augmented population (129 total patient models) reentry occurred in 13.0% of sites tested. ML and NN models ranged in mean accuracy from 0.83 to 0.86 for the baseline population, improving to 0.88 to 0.89 in all cases. Conclusion: Machine learning techniques, combined with patient-specific, image-based computational simulations, can provide key clinical insights with high accuracy rapidly and efficiently. In the case of sparse or missing patient data, simulation-supported data augmentation can be employed to further improve predictive results for patient benefit. This work paves the way for using data-driven simulations for prediction of dangerous arrhythmia in MI patients.
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Affiliation(s)
- Mary M Maleckar
- Computational Physiology, Simula Research Laboratory, Oslo, Norway
| | - Lena Myklebust
- Computational Physiology, Simula Research Laboratory, Oslo, Norway
| | - Julie Uv
- Computational Physiology, Simula Research Laboratory, Oslo, Norway
| | | | - Vilde Strøm
- Computational Physiology, Simula Research Laboratory, Oslo, Norway
| | - Charlotte Glinge
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Reza Jabbari
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Niels Vejlstrup
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Thomas Engstrøm
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Kiril Ahtarovski
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Thomas Jespersen
- Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jacob Tfelt-Hansen
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark.,Department of Forensic Medicine, Faculty of Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Valeriya Naumova
- Computational Physiology, Simula Research Laboratory, Oslo, Norway
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61
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Sánchez J, Trenor B, Saiz J, Dössel O, Loewe A. Fibrotic Remodeling during Persistent Atrial Fibrillation: In Silico Investigation of the Role of Calcium for Human Atrial Myofibroblast Electrophysiology. Cells 2021; 10:cells10112852. [PMID: 34831076 PMCID: PMC8616446 DOI: 10.3390/cells10112852] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 10/08/2021] [Accepted: 10/19/2021] [Indexed: 12/20/2022] Open
Abstract
During atrial fibrillation, cardiac tissue undergoes different remodeling processes at different scales from the molecular level to the tissue level. One central player that contributes to both electrical and structural remodeling is the myofibroblast. Based on recent experimental evidence on myofibroblasts' ability to contract, we extended a biophysical myofibroblast model with Ca2+ handling components and studied the effect on cellular and tissue electrophysiology. Using genetic algorithms, we fitted the myofibroblast model parameters to the existing in vitro data. In silico experiments showed that Ca2+ currents can explain the experimentally observed variability regarding the myofibroblast resting membrane potential. The presence of an L-type Ca2+ current can trigger automaticity in the myofibroblast with a cycle length of 799.9 ms. Myocyte action potentials were prolonged when coupled to myofibroblasts with Ca2+ handling machinery. Different spatial myofibroblast distribution patterns increased the vulnerable window to induce arrhythmia from 12 ms in non-fibrotic tissue to 22 ± 2.5 ms and altered the reentry dynamics. Our findings suggest that Ca2+ handling can considerably affect myofibroblast electrophysiology and alter the electrical propagation in atrial tissue composed of myocytes coupled with myofibroblasts. These findings can inform experimental validation experiments to further elucidate the role of myofibroblast Ca2+ handling in atrial arrhythmogenesis.
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Affiliation(s)
- Jorge Sánchez
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany; (O.D.); (A.L.)
- Correspondence:
| | - Beatriz Trenor
- Centro de Investigación e Innovación en Bioingeniería (Ci2B), Universitàt Politècnica de València, 46022 Valencia, Spain; (B.T.); (J.S.)
| | - Javier Saiz
- Centro de Investigación e Innovación en Bioingeniería (Ci2B), Universitàt Politècnica de València, 46022 Valencia, Spain; (B.T.); (J.S.)
| | - Olaf Dössel
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany; (O.D.); (A.L.)
| | - Axel Loewe
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany; (O.D.); (A.L.)
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62
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Abstract
Computer modeling of the electrophysiology of the heart has undergone significant progress. A healthy heart can be modeled starting from the ion channels via the spread of a depolarization wave on a realistic geometry of the human heart up to the potentials on the body surface and the ECG. Research is advancing regarding modeling diseases of the heart. This article reviews progress in calculating and analyzing the corresponding electrocardiogram (ECG) from simulated depolarization and repolarization waves. First, we describe modeling of the P-wave, the QRS complex and the T-wave of a healthy heart. Then, both the modeling and the corresponding ECGs of several important diseases and arrhythmias are delineated: ischemia and infarction, ectopic beats and extrasystoles, ventricular tachycardia, bundle branch blocks, atrial tachycardia, flutter and fibrillation, genetic diseases and channelopathies, imbalance of electrolytes and drug-induced changes. Finally, we outline the potential impact of computer modeling on ECG interpretation. Computer modeling can contribute to a better comprehension of the relation between features in the ECG and the underlying cardiac condition and disease. It can pave the way for a quantitative analysis of the ECG and can support the cardiologist in identifying events or non-invasively localizing diseased areas. Finally, it can deliver very large databases of reliably labeled ECGs as training data for machine learning.
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63
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Coveney S, Corrado C, Oakley JE, Wilkinson RD, Niederer SA, Clayton RH. Bayesian Calibration of Electrophysiology Models Using Restitution Curve Emulators. Front Physiol 2021; 12:693015. [PMID: 34366883 PMCID: PMC8339909 DOI: 10.3389/fphys.2021.693015] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Accepted: 06/28/2021] [Indexed: 11/13/2022] Open
Abstract
Calibration of cardiac electrophysiology models is a fundamental aspect of model personalization for predicting the outcomes of cardiac therapies, simulation testing of device performance for a range of phenotypes, and for fundamental research into cardiac function. Restitution curves provide information on tissue function and can be measured using clinically feasible measurement protocols. We introduce novel "restitution curve emulators" as probabilistic models for performing model exploration, sensitivity analysis, and Bayesian calibration to noisy data. These emulators are built by decomposing restitution curves using principal component analysis and modeling the resulting coordinates with respect to model parameters using Gaussian processes. Restitution curve emulators can be used to study parameter identifiability via sensitivity analysis of restitution curve components and rapid inference of the posterior distribution of model parameters given noisy measurements. Posterior uncertainty about parameters is critical for making predictions from calibrated models, since many parameter settings can be consistent with measured data and yet produce very different model behaviors under conditions not effectively probed by the measurement protocols. Restitution curve emulators are therefore promising probabilistic tools for calibrating electrophysiology models.
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Affiliation(s)
- Sam Coveney
- Insigneo Institute for In-Silico Medicine and Department of Computer Science, University of Sheffield, Sheffield, United Kingdom
| | - Cesare Corrado
- Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom
| | - Jeremy E Oakley
- School of Mathematics and Statistics, University of Sheffield, Sheffield, United Kingdom
| | - Richard D Wilkinson
- School of Mathematical Sciences, University of Nottingham, Nottingham, United Kingdom
| | - Steven A Niederer
- Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom
| | - Richard H Clayton
- Insigneo Institute for In-Silico Medicine and Department of Computer Science, University of Sheffield, Sheffield, United Kingdom
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