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Trayanova NA, Lyon A, Shade J, Heijman J. Computational modeling of cardiac electrophysiology and arrhythmogenesis: toward clinical translation. Physiol Rev 2024; 104:1265-1333. [PMID: 38153307 DOI: 10.1152/physrev.00017.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 12/19/2023] [Accepted: 12/21/2023] [Indexed: 12/29/2023] Open
Abstract
The complexity of cardiac electrophysiology, involving dynamic changes in numerous components across multiple spatial (from ion channel to organ) and temporal (from milliseconds to days) scales, makes an intuitive or empirical analysis of cardiac arrhythmogenesis challenging. Multiscale mechanistic computational models of cardiac electrophysiology provide precise control over individual parameters, and their reproducibility enables a thorough assessment of arrhythmia mechanisms. This review provides a comprehensive analysis of models of cardiac electrophysiology and arrhythmias, from the single cell to the organ level, and how they can be leveraged to better understand rhythm disorders in cardiac disease and to improve heart patient care. Key issues related to model development based on experimental data are discussed, and major families of human cardiomyocyte models and their applications are highlighted. An overview of organ-level computational modeling of cardiac electrophysiology and its clinical applications in personalized arrhythmia risk assessment and patient-specific therapy of atrial and ventricular arrhythmias is provided. The advancements presented here highlight how patient-specific computational models of the heart reconstructed from patient data have achieved success in predicting risk of sudden cardiac death and guiding optimal treatments of heart rhythm disorders. Finally, an outlook toward potential future advances, including the combination of mechanistic modeling and machine learning/artificial intelligence, is provided. As the field of cardiology is embarking on a journey toward precision medicine, personalized modeling of the heart is expected to become a key technology to guide pharmaceutical therapy, deployment of devices, and surgical interventions.
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Affiliation(s)
- Natalia A Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland, United States
| | - Aurore Lyon
- Department of Biomedical Engineering, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
- Division of Heart and Lungs, Department of Medical Physiology, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Julie Shade
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland, United States
| | - Jordi Heijman
- Department of Cardiology, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
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2
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Que W, Bian Y, Chen S, Zhao X, Ji Z, Hu P, Han C, Shi L. Efficient electrocardiogram generation based on cardiac electric vector simulation model. Comput Biol Med 2024; 177:108629. [PMID: 38820778 DOI: 10.1016/j.compbiomed.2024.108629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 04/27/2024] [Accepted: 05/18/2024] [Indexed: 06/02/2024]
Abstract
This study introduces a novel Cardiac Electric Vector Simulation Model (CEVSM) to address the computational inefficiencies and low fidelity of traditional electrophysiological models in generating electrocardiograms (ECGs). Our approach leverages CEVSM to efficiently produce reliable ECG samples, facilitating data augmentation essential for the computer-aided diagnosis of myocardial infarction (MI). Significantly, experimental results show that our model dramatically reduces computation time compared to conventional models, with the self-adapting regression transformation matrix method (SRTM) providing clear advantages. SRTM not only achieves high fidelity in ECG simulations but also ensures exceptional consistency with the gold standard method, greatly enhancing MI localization accuracy by data augmentation. These advancements highlight the potential of our model to generate dependable ECG training samples, making it highly suitable for data augmentation and significantly advancing the development and validation of intelligent MI diagnostic systems. Furthermore, this study demonstrates the feasibility of applying life system simulations in the training of medical big models.
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Affiliation(s)
- Wenge Que
- Department of Automation, Tsinghua University, Beijing, 100084, China.
| | - Yingnan Bian
- School of Logistics, Henan College of Transportation, Zhengzhou, 450000, China.
| | - Shengjie Chen
- Department of Automation, Tsinghua University, Beijing, 100084, China.
| | - Xiliang Zhao
- Center for Coronary Artery Disease, Division of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, 100029, China.
| | - Zehua Ji
- Department of Automation, Tsinghua University, Beijing, 100084, China.
| | - Pingge Hu
- Department of Automation, Tsinghua University, Beijing, 100084, China.
| | - Chuang Han
- School of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou, 450000, China.
| | - Li Shi
- Department of Automation, Tsinghua University, Beijing, 100084, China; Beijing National Research Center for Information Science and Technology, Beijing, 100084, China.
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3
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Narayanan A, Kong F, Shadden S. LinFlo-Net: A Two-Stage Deep Learning Method to Generate Simulation Ready Meshes of the Heart. J Biomech Eng 2024; 146:071005. [PMID: 38258957 DOI: 10.1115/1.4064527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 01/19/2024] [Indexed: 01/24/2024]
Abstract
We present a deep learning model to automatically generate computer models of the human heart from patient imaging data with an emphasis on its capability to generate thin-walled cardiac structures. Our method works by deforming a template mesh to fit the cardiac structures to the given image. Compared with prior deep learning methods that adopted this approach, our framework is designed to minimize mesh self-penetration, which typically arises when deforming surface meshes separated by small distances. We achieve this by using a two-stage diffeomorphic deformation process along with a novel loss function derived from the kinematics of motion that penalizes surface contact and interpenetration. Our model demonstrates comparable accuracy with state-of-the-art methods while additionally producing meshes free of self-intersections. The resultant meshes are readily usable in physics based simulation, minimizing the need for postprocessing and cleanup.
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Affiliation(s)
- Arjun Narayanan
- Department of Mechanical Engineering, University of California, Berkeley, Berkeley, CA 94709
| | - Fanwei Kong
- Department of Pediatrics, Stanford University, Stanford, CA 94305
- Stanford University
| | - Shawn Shadden
- Department of Mechanical Engineering, University of California, Berkeley, Berkeley, CA 94709
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4
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Salvador M, Kong F, Peirlinck M, Parker DW, Chubb H, Dubin AM, Marsden AL. Digital twinning of cardiac electrophysiology for congenital heart disease. J R Soc Interface 2024; 21:20230729. [PMID: 38835246 DOI: 10.1098/rsif.2023.0729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 03/15/2024] [Indexed: 06/06/2024] Open
Abstract
In recent years, blending mechanistic knowledge with machine learning has had a major impact in digital healthcare. In this work, we introduce a computational pipeline to build certified digital replicas of cardiac electrophysiology in paediatric patients with congenital heart disease. We construct the patient-specific geometry by means of semi-automatic segmentation and meshing tools. We generate a dataset of electrophysiology simulations covering cell-to-organ level model parameters and using rigorous mathematical models based on differential equations. We previously proposed Branched Latent Neural Maps (BLNMs) as an accurate and efficient means to recapitulate complex physical processes in a neural network. Here, we employ BLNMs to encode the parametrized temporal dynamics of in silico 12-lead electrocardiograms (ECGs). BLNMs act as a geometry-specific surrogate model of cardiac function for fast and robust parameter estimation to match clinical ECGs in paediatric patients. Identifiability and trustworthiness of calibrated model parameters are assessed by sensitivity analysis and uncertainty quantification.
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Affiliation(s)
- Matteo Salvador
- Institute for Computational and Mathematical Engineering, Stanford University , Stanford, CA, USA
- Cardiovascular Institute, Stanford University , Stanford, CA, USA
- Pediatric Cardiology, Stanford University , Stanford, CA, USA
| | - Fanwei Kong
- Institute for Computational and Mathematical Engineering, Stanford University , Stanford, CA, USA
- Cardiovascular Institute, Stanford University , Stanford, CA, USA
- Pediatric Cardiology, Stanford University , Stanford, CA, USA
| | - Mathias Peirlinck
- Department of Biomechanical Engineering, Delft University of Technology , Delft, The Netherlands
| | - David W Parker
- Stanford Research Computing Center, Stanford University , Stanford, CA, USA
| | - Henry Chubb
- Pediatric Cardiology, Stanford University , Stanford, CA, USA
| | - Anne M Dubin
- Pediatric Cardiology, Stanford University , Stanford, CA, USA
| | - Alison L Marsden
- Institute for Computational and Mathematical Engineering, Stanford University , Stanford, CA, USA
- Cardiovascular Institute, Stanford University , Stanford, CA, USA
- Pediatric Cardiology, Stanford University , Stanford, CA, USA
- Department of Bioengineering, Stanford University , Stanford, CA, USA
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5
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Gsell MAF, Neic A, Bishop MJ, Gillette K, Prassl AJ, Augustin CM, Vigmond EJ, Plank G. ForCEPSS-A framework for cardiac electrophysiology simulations standardization. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 251:108189. [PMID: 38728827 DOI: 10.1016/j.cmpb.2024.108189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 04/04/2024] [Accepted: 04/17/2024] [Indexed: 05/12/2024]
Abstract
BACKGROUND AND OBJECTIVE Simulation of cardiac electrophysiology (CEP) is an important research tool that is increasingly being adopted in industrial and clinical applications. Typical workflows for CEP simulation consist of a sequence of processing stages starting with building an anatomical model and then calibrating its electrophysiological properties to match observable data. While the calibration stages are common and generalizable, most CEP studies re-implement these steps in complex and highly variable workflows. This lack of standardization renders the execution of computational CEP studies in an efficient, robust, and reproducible manner a significant challenge. Here, we propose ForCEPSS as an efficient and robust, yet flexible, software framework for standardizing CEP simulation studies. METHODS AND RESULTS Key processing stages of CEP simulation studies are identified and implemented in a standardized workflow that builds on openCARP1 Plank et al. (2021) and the Python-based carputils2 framework. Stages include (i) the definition and initialization of action potential phenotypes, (ii) the tissue scale calibration of conduction properties, (iii) the functional initialization to approximate a limit cycle corresponding to the dynamic reference state according to an experimental protocol, and, (iv) the execution of the CEP study where the electrophysiological response to a perturbation of the limit cycle is probed. As an exemplar application, we employ ForCEPSS to prepare a CEP study according to the Virtual Arrhythmia Risk Prediction protocol used for investigating the arrhythmogenic risk of developing infarct-related ventricular tachycardia (VT) in ischemic cardiomyopathy patients. We demonstrate that ForCEPSS enables a fully automated execution of all stages of this complex protocol. CONCLUSION ForCEPSS offers a novel comprehensive, standardized, and automated CEP simulation workflow. The high degree of automation accelerates the execution of CEP simulation studies, reduces errors, improves robustness, and makes CEP studies reproducible. Verification of simulation studies within the CEP modeling community is thus possible. As such, ForCEPSS makes an important contribution towards increasing transparency, standardization, and reproducibility of in silico CEP experiments.
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Affiliation(s)
- Matthias A F Gsell
- Gottfried Schatz Research Center, Division of Medical Physics and Biophysics, Medical University of Graz, Graz, Austria
| | | | | | - Karli Gillette
- Gottfried Schatz Research Center, Division of Medical Physics and Biophysics, Medical University of Graz, Graz, Austria
| | - Anton J Prassl
- Gottfried Schatz Research Center, Division of Medical Physics and Biophysics, Medical University of Graz, Graz, Austria
| | - Christoph M Augustin
- Gottfried Schatz Research Center, Division of Medical Physics and Biophysics, Medical University of Graz, Graz, Austria; BioTechMed-Graz, Graz, Austria
| | - Edward J Vigmond
- Liryc Cardiac Modeling Institute, Fondation Bordeaux University, Bordeaux, France; CNRS, Bordeaux INP, IMB, University of Bordeaux, Bordeaux, France
| | - Gernot Plank
- Gottfried Schatz Research Center, Division of Medical Physics and Biophysics, Medical University of Graz, Graz, Austria; BioTechMed-Graz, Graz, Austria.
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6
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Cluitmans MJM, Plank G, Heijman J. Digital twins for cardiac electrophysiology: state of the art and future challenges. Herzschrittmacherther Elektrophysiol 2024; 35:118-123. [PMID: 38607554 PMCID: PMC11161534 DOI: 10.1007/s00399-024-01014-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Accepted: 03/04/2024] [Indexed: 04/13/2024]
Abstract
Cardiac arrhythmias remain a major cause of death and disability. Current antiarrhythmic therapies are effective to only a limited extent, likely in large part due to their mechanism-independent approach. Precision cardiology aims to deliver targeted therapy for an individual patient to maximize efficacy and minimize adverse effects. In-silico digital twins have emerged as a promising strategy to realize the vision of precision cardiology. While there is no uniform definition of a digital twin, it typically employs digital tools, including simulations of mechanistic computer models, based on patient-specific clinical data to understand arrhythmia mechanisms and/or make clinically relevant predictions. Digital twins have become part of routine clinical practice in the setting of interventional cardiology, where commercially available services use digital twins to non-invasively determine the severity of stenosis (computed tomography-based fractional flow reserve). Although routine clinical application has not been achieved for cardiac arrhythmia management, significant progress towards digital twins for cardiac electrophysiology has been made in recent years. At the same time, significant technical and clinical challenges remain. This article provides a short overview of the history of digital twins for cardiac electrophysiology, including recent applications for the prediction of sudden cardiac death risk and the tailoring of rhythm control in atrial fibrillation. The authors highlight the current challenges for routine clinical application and discuss how overcoming these challenges may allow digital twins to enable a significant precision medicine-based advancement in cardiac arrhythmia management.
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Affiliation(s)
- Matthijs J M Cluitmans
- Department of Cardiology, Cardiovascular Research Institute Maastricht, Faculty of Health, Medicine, and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Gernot Plank
- Gottfried Schatz Research Center, Division of Medical Physics & Biophysics, Medical University of Graz, Neue Stiftingtalstraße 6, 8010, Graz, Austria
| | - Jordi Heijman
- Department of Cardiology, Cardiovascular Research Institute Maastricht, Faculty of Health, Medicine, and Life Sciences, Maastricht University, Maastricht, The Netherlands.
- Gottfried Schatz Research Center, Division of Medical Physics & Biophysics, Medical University of Graz, Neue Stiftingtalstraße 6, 8010, Graz, Austria.
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7
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Jani VP, Ostovaneh M, Chamera E, Kato Y, Lima JAC, Ambale-Venkatesh B. Deep learning for automatic volumetric segmentation of left ventricular myocardium and ischaemic scar from multi-slice late gadolinium enhancement cardiovascular magnetic resonance. Eur Heart J Cardiovasc Imaging 2024; 25:829-838. [PMID: 38244222 DOI: 10.1093/ehjci/jeae022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 12/09/2023] [Accepted: 01/16/2024] [Indexed: 01/22/2024] Open
Abstract
AIMS This study details application of deep learning for automatic volumetric segmentation of left ventricular (LV) myocardium and scar and automated quantification of myocardial ischaemic scar burden from late gadolinium enhancement cardiovascular magnetic resonance (LGE-CMR). METHODS AND RESULTS We included 501 images and manual segmentations of short-axis LGE-CMR from over 20 multinational sites, from which 377 studies were used for training and 124 studies from unique participants for internal validation. A third test set of 52 images was used for external evaluation. Three models, U-Net, Cascaded U-Net, and U-Net++, were trained with a novel adaptive weighted categorical cross-entropy loss function. Model performance was evaluated using concordance correlation coefficients (CCCs) for LV mass and per cent myocardial scar burden. Cascaded U-Net was found to be the best model for the quantification of LV mass and scar percentage. The model exhibited a mean difference of -5 ± 23 g for LV mass, -0.4 ± 11.2 g for scar mass, and -0.8 ± 7% for per cent scar. CCC were 0.87, 0.77, and 0.78 for LV mass, scar mass, and per cent scar burden, respectively, in the internal validation set and 0.75, 0.71, and 0.69, respectively, in the external test set. For segmental scar mass, CCC was 0.74 for apical scar, 0.91 for mid-ventricular scar, and 0.73 for basal scar, demonstrating moderate to strong agreement. CONCLUSION We successfully trained a convolutional neural network for volumetric segmentation and analysis of LV scar burden from LGE-CMR images in a large, multinational cohort of participants with ischaemic scar.
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Affiliation(s)
- Vivek P Jani
- Division of Cardiology, Johns Hopkins University School of Medicine, 600 N Wolfe St, Blalock 524, Baltimore, MD 21297-0409, USA
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Mohammad Ostovaneh
- Division of Cardiology, Johns Hopkins University School of Medicine, 600 N Wolfe St, Blalock 524, Baltimore, MD 21297-0409, USA
| | - Elzbieta Chamera
- Division of Cardiology, Johns Hopkins University School of Medicine, 600 N Wolfe St, Blalock 524, Baltimore, MD 21297-0409, USA
| | - Yoko Kato
- Division of Cardiology, Johns Hopkins University School of Medicine, 600 N Wolfe St, Blalock 524, Baltimore, MD 21297-0409, USA
| | - Joao A C Lima
- Division of Cardiology, Johns Hopkins University School of Medicine, 600 N Wolfe St, Blalock 524, Baltimore, MD 21297-0409, USA
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Riaz Gondal MU, Atta Mehdi H, Khenhrani RR, Kumari N, Ali MF, Kumar S, Faraz M, Malik J. Role of Machine Learning and Artificial Intelligence in Arrhythmias and Electrophysiology. Cardiol Rev 2024:00045415-990000000-00270. [PMID: 38761137 DOI: 10.1097/crd.0000000000000715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/20/2024]
Abstract
Machine learning (ML), a subset of artificial intelligence (AI) centered on machines learning from extensive datasets, stands at the forefront of a technological revolution shaping various facets of society. Cardiovascular medicine has emerged as a key domain for ML applications, with considerable efforts to integrate these innovations into routine clinical practice. Within cardiac electrophysiology, ML applications, especially in the automated interpretation of electrocardiograms, have garnered substantial attention in existing literature. However, less recognized are the diverse applications of ML in cardiac electrophysiology and arrhythmias, spanning basic science research on arrhythmia mechanisms, both experimental and computational, as well as contributions to enhanced techniques for mapping cardiac electrical function and translational research related to arrhythmia management. This comprehensive review delves into various ML applications within the scope of this journal, organized into 3 parts. The first section provides a fundamental understanding of general ML principles and methodologies, serving as a foundational resource for readers interested in exploring ML applications in arrhythmia research. The second part offers an in-depth review of studies in arrhythmia and electrophysiology that leverage ML methodologies, showcasing the broad potential of ML approaches. Each subject is thoroughly outlined, accompanied by a review of notable ML research advancements. Finally, the review delves into the primary challenges and future perspectives surrounding ML-driven cardiac electrophysiology and arrhythmias research.
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Affiliation(s)
| | - Hassan Atta Mehdi
- Department of Medicine, Jinnah Postgraduate Medical Centre, Karachi, Pakistan
| | - Raja Ram Khenhrani
- Department of Medicine, Internal Medicine Fellow, Shaheed Mohtarma Benazir Bhutto Medical College and Lyari General Hospital, Karachi, Pakistan
| | - Neha Kumari
- Department of Medicine, Jinnah Postgraduate Medical Centre, Karachi, Pakistan
| | - Muhammad Faizan Ali
- Department of Medicine, Jinnah Postgraduate Medical Centre, Karachi, Pakistan
| | - Sooraj Kumar
- Department of Medicine, Jinnah Sindh Medical University, Karachi, Pakistan; and
| | - Maria Faraz
- Department of Cardiovascular Medicine, Cardiovascular Analytics Group, Rawalpindi, Pakistan
| | - Jahanzeb Malik
- Department of Cardiovascular Medicine, Cardiovascular Analytics Group, Rawalpindi, Pakistan
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Willems E, Janssens KLPM, Dekker LRC, van de Vosse FN, Cluitmans MJM, Bovendeerd PHM. Strain-controlled electrophysiological wave propagation alters in silico scar-based substrate for ventricular tachycardia. Front Physiol 2024; 15:1330157. [PMID: 38655031 PMCID: PMC11036413 DOI: 10.3389/fphys.2024.1330157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 03/21/2024] [Indexed: 04/26/2024] Open
Abstract
Introduction: Assessing a patient's risk of scar-based ventricular tachycardia (VT) after myocardial infarction is a challenging task. It can take months to years after infarction for VT to occur. Also, if selected for ablation therapy, success rates are low. Methods: Computational ventricular models have been presented previously to support VT risk assessment and to provide ablation guidance. In this study, an extension to such virtual-heart models is proposed to phenomenologically incorporate tissue remodeling driven by mechanical load. Strain amplitudes in the heart muscle are obtained from simulations of mechanics and are used to adjust the electrical conductivity. Results: The mechanics-driven adaptation of electrophysiology resulted in a more heterogeneous distribution of propagation velocities than that of standard models, which adapt electrophysiology in the structural substrate from medical images only. Moreover, conduction slowing was not only present in such a structural substrate, but extended in the adjacent functional border zone with impaired mechanics. This enlarged the volumes with high repolarization time gradients (≥10 ms/mm). However, maximum gradient values were not significantly affected. The enlarged volumes were localized along the structural substrate border, which lengthened the line of conduction block. The prolonged reentry pathways together with conduction slowing in functional regions increased VT cycle time, such that VT was easier to induce, and the number of recommended ablation sites increased from 3 to 5 locations. Discussion: Sensitivity testing showed an accurate model of strain-dependency to be critical for low ranges of conductivity. The model extension with mechanics-driven tissue remodeling is a potential approach to capture the evolution of the functional substrate and may offer insight into the progression of VT risk over time.
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Affiliation(s)
- Evianne Willems
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Koen L. P. M. Janssens
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Lukas R. C. Dekker
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
- Department of Cardiology, Catharina Hospital, Eindhoven, Netherlands
| | - Frans N. van de Vosse
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Matthijs J. M. Cluitmans
- Maastricht University Medical Center, Maastricht, Netherlands
- Philips Research Eindhoven, Eindhoven, Netherlands
| | - Peter H. M. Bovendeerd
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
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10
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Armoundas AA, Narayan SM, Arnett DK, Spector-Bagdady K, Bennett DA, Celi LA, Friedman PA, Gollob MH, Hall JL, Kwitek AE, Lett E, Menon BK, Sheehan KA, Al-Zaiti SS. Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association. Circulation 2024; 149:e1028-e1050. [PMID: 38415358 PMCID: PMC11042786 DOI: 10.1161/cir.0000000000001201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/29/2024]
Abstract
A major focus of academia, industry, and global governmental agencies is to develop and apply artificial intelligence and other advanced analytical tools to transform health care delivery. The American Heart Association supports the creation of tools and services that would further the science and practice of precision medicine by enabling more precise approaches to cardiovascular and stroke research, prevention, and care of individuals and populations. Nevertheless, several challenges exist, and few artificial intelligence tools have been shown to improve cardiovascular and stroke care sufficiently to be widely adopted. This scientific statement outlines the current state of the art on the use of artificial intelligence algorithms and data science in the diagnosis, classification, and treatment of cardiovascular disease. It also sets out to advance this mission, focusing on how digital tools and, in particular, artificial intelligence may provide clinical and mechanistic insights, address bias in clinical studies, and facilitate education and implementation science to improve cardiovascular and stroke outcomes. Last, a key objective of this scientific statement is to further the field by identifying best practices, gaps, and challenges for interested stakeholders.
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11
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Harnod Z, Lin C, Yang HW, Wang ZW, Huang HL, Lin TY, Huang CY, Lin LY, Young HWV, Lo MT. A transferable in-silico augmented ischemic model for virtual myocardial perfusion imaging and myocardial infarction detection. Med Image Anal 2024; 93:103087. [PMID: 38244290 DOI: 10.1016/j.media.2024.103087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 03/03/2023] [Accepted: 01/08/2024] [Indexed: 01/22/2024]
Abstract
This paper proposes an innovative approach to generate a generalized myocardial ischemia database by modeling the virtual electrophysiology of the heart and the 12-lead electrocardiography projected by the in-silico model can serve as a ready-to-use database for automatic myocardial infarction/ischemia (MI) localization and classification. Although the virtual heart can be created by an established technique combining the cell model with personalized heart geometry to observe the spatial propagation of depolarization and repolarization waves, we developed a strategy based on the clinical pathophysiology of MI to generate a heterogeneous database with a generic heart while maintaining clinical relevance and reduced computational complexity. First, the virtual heart is simplified into 11 regions that match the types and locations, which can be diagnosed by 12-lead ECG; the major arteries were divided into 3-5 segments from the upstream to the downstream based on the general anatomy. Second, the stenosis or infarction of the major or minor coronary artery branches can cause different perfusion drops and infarct sizes. We simulated the ischemic sites in different branches of the arteries by meandering the infarction location to elaborate on possible ECG representations, which alters the infraction's size and changes the transmembrane potential (TMP) of the myocytes associated with different levels of perfusion drop. A total of 8190 different case combinations of cardiac potentials with ischemia and MI were simulated, and the corresponding ECGs were generated by forward calculations. Finally, we trained and validated our in-silico database with a sparse representation classification (SRC) and tested the transferability of the model on the real-world Physikalisch Technische Bundesanstalt (PTB) database. The overall accuracies for localizing the MI region on the PTB data achieved 0.86, which is only 2% drop compared to that derived from the simulated database (0.88). In summary, we have shown a proof-of-concept for transferring an in-silico model to real-world database to compensate for insufficient data.
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Affiliation(s)
- Zeus Harnod
- Department of Biomedical Sciences and Engineering, National Central University, Taoyuan, Taiwan
| | - Chen Lin
- Department of Biomedical Sciences and Engineering, National Central University, Taoyuan, Taiwan
| | - Hui-Wen Yang
- Division of Sleep Medicine, Department of Medicine, Harvard Medical School, Boston, USA
| | - Zih-Wen Wang
- Department of Biomedical Sciences and Engineering, National Central University, Taoyuan, Taiwan
| | - Han-Luen Huang
- Department of Cardiology, Hsinchu Cathay General Hospital, Hsinchu, Taiwan
| | - Tse-Yu Lin
- Department of Biomedical Sciences and Engineering, National Central University, Taoyuan, Taiwan
| | - Chun-Yao Huang
- Department of Internal Medicine, Taipei Medical University Hospital, Taipei, Taiwan
| | - Lian-Yu Lin
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Hsu-Wen V Young
- Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan, Taiwan.
| | - Men-Tzung Lo
- Department of Biomedical Sciences and Engineering, National Central University, Taoyuan, Taiwan.
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12
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Laubenbacher R, Mehrad B, Shmulevich I, Trayanova N. Digital twins in medicine. NATURE COMPUTATIONAL SCIENCE 2024; 4:184-191. [PMID: 38532133 PMCID: PMC11102043 DOI: 10.1038/s43588-024-00607-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 02/12/2024] [Indexed: 03/28/2024]
Abstract
Medical digital twins, which are potentially vital for personalized medicine, have become a recent focus in medical research. Here we present an overview of the state of the art in medical digital twin development, especially in oncology and cardiology, where it is most advanced. We discuss major challenges, such as data integration and privacy, and provide an outlook on future advancements. Emphasizing the importance of this technology in healthcare, we highlight the potential for substantial improvements in patient-specific treatments and diagnostics.
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Affiliation(s)
- R Laubenbacher
- Department of Medicine, University of Florida, Gainesville, FL, USA.
| | - B Mehrad
- Department of Medicine, University of Florida, Gainesville, FL, USA
| | | | - N Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
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13
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Fumagalli I, Pagani S, Vergara C, Dede’ L, Adebo DA, Del Greco M, Frontera A, Luciani GB, Pontone G, Scrofani R, Quarteroni A. The role of computational methods in cardiovascular medicine: a narrative review. Transl Pediatr 2024; 13:146-163. [PMID: 38323181 PMCID: PMC10839285 DOI: 10.21037/tp-23-184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 12/13/2023] [Indexed: 02/08/2024] Open
Abstract
Background and Objective Computational models of the cardiovascular system allow for a detailed and quantitative investigation of both physiological and pathological conditions, thanks to their ability to combine clinical-possibly patient-specific-data with physical knowledge of the processes underlying the heart function. These models have been increasingly employed in clinical practice to understand pathological mechanisms and their progression, design medical devices, support clinicians in improving therapies. Hinging upon a long-year experience in cardiovascular modeling, we have recently constructed a computational multi-physics and multi-scale integrated model of the heart for the investigation of its physiological function, the analysis of pathological conditions, and to support clinicians in both diagnosis and treatment planning. This narrative review aims to systematically discuss the role that such model had in addressing specific clinical questions, and how further impact of computational models on clinical practice are envisaged. Methods We developed computational models of the physical processes encompassed by the heart function (electrophysiology, electrical activation, force generation, mechanics, blood flow dynamics, valve dynamics, myocardial perfusion) and of their inherently strong coupling. To solve the equations of such models, we devised advanced numerical methods, implemented in a flexible and highly efficient software library. We also developed computational procedures for clinical data post-processing-like the reconstruction of the heart geometry and motion from diagnostic images-and for their integration into computational models. Key Content and Findings Our integrated computational model of the heart function provides non-invasive measures of indicators characterizing the heart function and dysfunctions, and sheds light on its underlying processes and their coupling. Moreover, thanks to the close collaboration with several clinical partners, we addressed specific clinical questions on pathological conditions, such as arrhythmias, ventricular dyssynchrony, hypertrophic cardiomyopathy, degeneration of prosthetic valves, and the way coronavirus disease 2019 (COVID-19) infection may affect the cardiac function. In multiple cases, we were also able to provide quantitative indications for treatment. Conclusions Computational models provide a quantitative and detailed tool to support clinicians in patient care, which can enhance the assessment of cardiac diseases, the prediction of the development of pathological conditions, and the planning of treatments and follow-up tests.
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Affiliation(s)
- Ivan Fumagalli
- MOX Laboratory, Department of Mathematics, Politecnico di Milano, Milan, Italy
| | - Stefano Pagani
- MOX Laboratory, Department of Mathematics, Politecnico di Milano, Milan, Italy
| | - Christian Vergara
- Laboratory of Biological Structures Mechanics (LaBS), Department of Chemistry, Materials and Chemical Engineering “Giulio Natta”, Politecnico di Milano, Milan, Italy
| | - Luca Dede’
- MOX Laboratory, Department of Mathematics, Politecnico di Milano, Milan, Italy
| | - Dilachew A. Adebo
- Children’s Heart Institute, Hermann Children’s Hospital, University of Texas Health Science Center, McGovern Medical School, Houston, TX, USA
| | - Maurizio Del Greco
- Department of Cardiology, S. Maria del Carmine Hospital, Rovereto, Italy
| | - Antonio Frontera
- Electrophysiology Department, De Gasperis Cardio Center, ASST Great Metropolitan Hospital Niguarda, Milan, Italy
| | | | - Gianluca Pontone
- Department of Perioperative Cardiology and Cardiovascular Imaging, Centro Cardiologico Monzino IRCSS, Milan, Italy
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy
| | - Roberto Scrofani
- Cardiovascular Department, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Alfio Quarteroni
- MOX Laboratory, Department of Mathematics, Politecnico di Milano, Milan, Italy
- Institute of Mathematics, École Polytechnique Fédérale de Lausanne, Switzerland
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14
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Stanciulescu LA, Vatasescu R. Ventricular Tachycardia Catheter Ablation: Retrospective Analysis and Prospective Outlooks-A Comprehensive Review. Biomedicines 2024; 12:266. [PMID: 38397868 PMCID: PMC10886924 DOI: 10.3390/biomedicines12020266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 01/16/2024] [Accepted: 01/23/2024] [Indexed: 02/25/2024] Open
Abstract
Ventricular tachycardia is a potentially life-threatening arrhythmia associated with an overall high morbi-mortality, particularly in patients with structural heart disease. Despite their pivotal role in preventing sudden cardiac death, implantable cardioverter-defibrillators, although a guideline-based class I recommendation, are unable to prevent arrhythmic episodes and significantly alter the quality of life by delivering recurrent therapies. From open-heart surgical ablation to the currently widely used percutaneous approach, catheter ablation is a safe and effective procedure able to target the responsible re-entry myocardial circuit from both the endocardium and the epicardium. There are four main mapping strategies, activation, entrainment, pace, and substrate mapping, each of them with their own advantages and limitations. The contemporary guideline-based recommendations for VT ablation primarily apply to patients experiencing antiarrhythmic drug ineffectiveness or those intolerant to the pharmacological treatment. Although highly effective in most cases of scar-related VTs, the traditional approach may sometimes be insufficient, especially in patients with nonischemic cardiomyopathies, where circuits may be unmappable using the classic techniques. Alternative methods have been proposed, such as stereotactic arrhythmia radioablation or radiotherapy ablation, surgical ablation, needle ablation, transarterial coronary ethanol ablation, and retrograde coronary venous ethanol ablation, with promising results. Further studies are needed in order to prove the overall efficacy of these methods in comparison to standard radiofrequency delivery. Nevertheless, as the field of cardiac electrophysiology continues to evolve, it is important to acknowledge the role of artificial intelligence in both the pre-procedural planning and the intervention itself.
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Affiliation(s)
- Laura Adina Stanciulescu
- Cardio-Thoracic Department, "Carol Davila" University of Medicine and Pharmacy, 050474 Bucharest, Romania
- Cardiology Department, Clinical Emergency Hospital, 014461 Bucharest, Romania
| | - Radu Vatasescu
- Cardio-Thoracic Department, "Carol Davila" University of Medicine and Pharmacy, 050474 Bucharest, Romania
- Cardiology Department, Clinical Emergency Hospital, 014461 Bucharest, Romania
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15
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Qian S, Ugurlu D, Fairweather E, Strocchi M, Toso LD, Deng Y, Plank G, Vigmond E, Razavi R, Young A, Lamata P, Bishop M, Niederer S. Developing Cardiac Digital Twins at Scale: Insights from Personalised Myocardial Conduction Velocity. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.12.05.23299435. [PMID: 38106072 PMCID: PMC10723499 DOI: 10.1101/2023.12.05.23299435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Large-cohort studies using cardiovascular imaging and diagnostic datasets have assessed cardiac anatomy, function, and outcomes, but typically do not reveal underlying biological mechanisms. Cardiac digital twins (CDTs) provide personalized physics- and physiology-constrained in-silico representations, enabling inference of multi-scale properties tied to these mechanisms. We constructed 3464 anatomically-accurate CDTs using cardiac magnetic resonance images from UK biobank and personalised their myocardial conduction velocities (CVs) from electrocardiograms (ECG), through an automated framework. We found well-known sex-specific differences in QRS duration were fully explained by myocardial anatomy, as CV remained consistent across sexes. Conversely, significant associations of CV with ageing and increased BMI suggest myocardial tissue remodelling. Novel associations were observed with left ventricular ejection fraction and mental-health phenotypes, through a phenome-wide association study, and CV was also linked with adverse clinical outcomes. Our study highlights the utility of population-based CDTs in assessing intersubject variability and uncovering strong links with mental health.
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16
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Trayanova NA, Prakosa A. Up digital and personal: How heart digital twins can transform heart patient care. Heart Rhythm 2024; 21:89-99. [PMID: 37871809 PMCID: PMC10872898 DOI: 10.1016/j.hrthm.2023.10.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 10/12/2023] [Accepted: 10/15/2023] [Indexed: 10/25/2023]
Abstract
Precision medicine is the vision of health care where therapy is tailored to each patient. As part of this vision, digital twinning technology promises to deliver a digital representation of organs or even patients by using tools capable of simulating personal health conditions and predicting patient or disease trajectories on the basis of relationships learned both from data and from biophysics knowledge. Such virtual replicas would update themselves with data from monitoring devices and medical tests and assessments, reflecting dynamically the changes in our health conditions and the responses to treatment. In precision cardiology, the concepts and initial applications of heart digital twins have slowly been gaining popularity and the trust of the clinical community. In this article, we review the advancement in heart digital twinning and its initial translation to the management of heart rhythm disorders.
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Affiliation(s)
- Natalia A Trayanova
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland.
| | - Adityo Prakosa
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland
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17
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Chrispin J, Trayanova N. Computational Heart Modeling to Guide VT Ablation: Is Wall Thickness Enough? JACC Clin Electrophysiol 2023; 9:2520-2522. [PMID: 38151302 DOI: 10.1016/j.jacep.2023.10.031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 10/28/2023] [Indexed: 12/29/2023]
Affiliation(s)
- Jonathan Chrispin
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland, USA.
| | - Natalia Trayanova
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland, USA
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18
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Cedilnik N, Pop M, Duchateau J, Sacher F, Jaïs P, Cochet H, Sermesant M. Efficient Patient-Specific Simulations of Ventricular Tachycardia Based on Computed Tomography-Defined Wall Thickness Heterogeneity. JACC Clin Electrophysiol 2023; 9:2507-2519. [PMID: 37804259 DOI: 10.1016/j.jacep.2023.08.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 07/20/2023] [Accepted: 08/02/2023] [Indexed: 10/09/2023]
Abstract
BACKGROUND Electrophysiological mapping of ventricular tachycardia (VT) is tedious and poorly reproducible. Substrate analysis on imaging cannot explicitly display VT circuits. OBJECTIVES This study sought to introduce a computed tomography-based model personalization approach, allowing for the simulation of postinfarction VT in a clinically compatible time frame. METHODS In 10 patients (age 65 ± 11 years, 9 male) referred for post-VT ablation, computed tomography-derived wall thickness maps were registered to 25 electroanatomical maps (sinus rhythm, paced, and VT). The relationship between wall thickness and electrophysiological characteristics (activation-recovery interval) was analyzed. Wall thickness was then employed to parameterize a fast and tractable organ-scale wave propagation model. Pacing protocols were simulated from multiple sites to test VT induction in silico. In silico VTs were compared to VT circuits mapped clinically. RESULTS Clinically, 6 different VTs could be induced with detailed maps in 9 patients. The proposed model allowed for fast simulation (median: 6 min/pacing site). Simulations of steady pacing (600 milliseconds) from 100 different sites/patient never triggered any arrhythmia. Applying S1-S2 or S1-S2-S3 induction schemes allowed for the induction of in silico VTs in the 9 of 10 patients who were clinically inducible. The patient who was not inducible clinically was also noninducible in silico. A total of 42 different VTs were simulated (4.2 ± 2 per patient). Six in silico VTs matched a VT circuit mapped clinically. CONCLUSIONS The proposed framework allows for personalized simulations in a matter of hours. In 6 of 9 patients, simulations show re-entrant patterns matching intracardiac recordings.
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Affiliation(s)
- Nicolas Cedilnik
- Université Côte d'Azur, Epione, Inria, Sophia-Antipolis, France; Institut Hospitalo-Universitaire Liryc, Bordeaux, France.
| | - Mihaela Pop
- Université Côte d'Azur, Epione, Inria, Sophia-Antipolis, France
| | - Josselin Duchateau
- Institut Hospitalo-Universitaire Liryc, Bordeaux, France; Cardiac Pacing and Electrophysiology Department, Bordeaux University Hospital, Bordeaux, France
| | - Frédéric Sacher
- Institut Hospitalo-Universitaire Liryc, Bordeaux, France; Cardiac Pacing and Electrophysiology Department, Bordeaux University Hospital, Bordeaux, France
| | - Pierre Jaïs
- Institut Hospitalo-Universitaire Liryc, Bordeaux, France; Cardiac Pacing and Electrophysiology Department, Bordeaux University Hospital, Bordeaux, France
| | - Hubert Cochet
- Institut Hospitalo-Universitaire Liryc, Bordeaux, France; Radiology Department, Bordeaux University Hospital, Bordeaux, France
| | - Maxime Sermesant
- Université Côte d'Azur, Epione, Inria, Sophia-Antipolis, France; Institut Hospitalo-Universitaire Liryc, Bordeaux, France
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19
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Salvador M, Kong F, Peirlinck M, Parker DW, Chubb H, Dubin AM, Marsden AL. Digital twinning of cardiac electrophysiology for congenital heart disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.27.568942. [PMID: 38076810 PMCID: PMC10705388 DOI: 10.1101/2023.11.27.568942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
In recent years, blending mechanistic knowledge with machine learning has had a major impact in digital healthcare. In this work, we introduce a computational pipeline to build certified digital replicas of cardiac electrophysiology in pediatric patients with congenital heart disease. We construct the patient-specific geometry by means of semi-automatic segmentation and meshing tools. We generate a dataset of electrophysiology simulations covering cell-to-organ level model parameters and utilizing rigorous mathematical models based on differential equations. We previously proposed Branched Latent Neural Maps (BLNMs) as an accurate and efficient means to recapitulate complex physical processes in a neural network. Here, we employ BLNMs to encode the parametrized temporal dynamics of in silico 12-lead electrocardiograms (ECGs). BLNMs act as a geometry-specific surrogate model of cardiac function for fast and robust parameter estimation to match clinical ECGs in pediatric patients. Identifiability and trustworthiness of calibrated model parameters are assessed by sensitivity analysis and uncertainty quantification.
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Affiliation(s)
- Matteo Salvador
- Institute for Computational and Mathematical Engineering, Stanford University, California, USA
- Cardiovascular Institute, Stanford University, California, USA
- Pediatric Cardiology, Stanford University, California, USA
| | - Fanwei Kong
- Cardiovascular Institute, Stanford University, California, USA
- Pediatric Cardiology, Stanford University, California, USA
| | - Mathias Peirlinck
- Department of Biomechanical Engineering, Delft University of Technology, Delft, Netherlands
| | - David W Parker
- Stanford Research Computing Center, Stanford University, California, USA
| | - Henry Chubb
- Pediatric Cardiology, Stanford University, California, USA
| | - Anne M Dubin
- Pediatric Cardiology, Stanford University, California, USA
| | - Alison Lesley Marsden
- Department of Bioengineering, Stanford University, California, USA
- Institute for Computational and Mathematical Engineering, Stanford University, California, USA
- Cardiovascular Institute, Stanford University, California, USA
- Pediatric Cardiology, Stanford University, California, USA
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20
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Gray RA, Franz MR. Amiodarone prevents wave front-tail interactions in patients with heart failure: an in silico study. Am J Physiol Heart Circ Physiol 2023; 325:H952-H964. [PMID: 37656133 PMCID: PMC10907032 DOI: 10.1152/ajpheart.00227.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 08/25/2023] [Accepted: 08/25/2023] [Indexed: 09/02/2023]
Abstract
Amiodarone (AM) is an antiarrhythmic drug whose chronic use has proved effective in preventing ventricular arrhythmias in a variety of patient populations, including those with heart failure (HF). AM has both class III [i.e., it prolongs the action potential duration (APD) via blocking potassium channels) and class I (i.e., it affects the rapid sodium channel) properties; however, the specific mechanism(s) by which it prevents reentry formation in patients with HF remains unknown. We tested the hypothesis that AM prevents reentry induction in HF during programmed electrical stimulation (PES) via its ability to induce postrepolarization refractoriness (PRR) via its class I effects on sodium channels. Here we extend our previous human action potential model to represent the effects of both HF and AM separately by calibrating to human tissue and clinical PES data, respectively. We then combine these models (HF + AM) to test our hypothesis. Results from simulations in cells and cables suggest that AM acts to increase PRR and decrease the elevation of takeoff potential. The ability of AM to prevent reentry was studied in silico in two-dimensional sheets in which a variety of APD gradients (ΔAPD) were imposed. Reentrant activity was induced in all HF simulations but was prevented in 23 of 24 HF + AM models. Eliminating the AM-induced slowing of the recovery of inactivation of the sodium channel restored the ability to induce reentry. In conclusion, in silico testing suggests that chronic AM treatment prevents reentry induction in patients with HF during PES via its class I effect to induce PRR.NEW & NOTEWORTHY This work presents a new model of the action potential of the human, which reproduces the complex dynamics during premature stimulation in heart failure patients with and without amiodarone. A specific mechanism of the ability of amiodarone to prevent reentrant arrhythmias is presented.
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Affiliation(s)
- Richard A Gray
- Division of Biomedical Physics, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, Maryland, United States
| | - Michael R Franz
- Cardiology Division, Veteran Affairs Medical Center, Washington, District of Columbia, United States
- Department of Pharmacology, Georgetown University Medical Center, Washington, District of Columbia, United States
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21
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Zhang Y, Zhang K, Prakosa A, James C, Zimmerman SL, Carrick R, Sung E, Gasperetti A, Tichnell C, Murray B, Calkins H, Trayanova NA. Predicting ventricular tachycardia circuits in patients with arrhythmogenic right ventricular cardiomyopathy using genotype-specific heart digital twins. eLife 2023; 12:RP88865. [PMID: 37851708 PMCID: PMC10584370 DOI: 10.7554/elife.88865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2023] Open
Abstract
Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a genetic cardiac disease that leads to ventricular tachycardia (VT), a life-threatening heart rhythm disorder. Treating ARVC remains challenging due to the complex underlying arrhythmogenic mechanisms, which involve structural and electrophysiological (EP) remodeling. Here, we developed a novel genotype-specific heart digital twin (Geno-DT) approach to investigate the role of pathophysiological remodeling in sustaining VT reentrant circuits and to predict the VT circuits in ARVC patients of different genotypes. This approach integrates the patient's disease-induced structural remodeling reconstructed from contrast-enhanced magnetic-resonance imaging and genotype-specific cellular EP properties. In our retrospective study of 16 ARVC patients with two genotypes: plakophilin-2 (PKP2, n = 8) and gene-elusive (GE, n = 8), we found that Geno-DT accurately and non-invasively predicted the VT circuit locations for both genotypes (with 100%, 94%, 96% sensitivity, specificity, and accuracy for GE patient group, and 86%, 90%, 89% sensitivity, specificity, and accuracy for PKP2 patient group), when compared to VT circuit locations identified during clinical EP studies. Moreover, our results revealed that the underlying VT mechanisms differ among ARVC genotypes. We determined that in GE patients, fibrotic remodeling is the primary contributor to VT circuits, while in PKP2 patients, slowed conduction velocity and altered restitution properties of cardiac tissue, in addition to the structural substrate, are directly responsible for the formation of VT circuits. Our novel Geno-DT approach has the potential to augment therapeutic precision in the clinical setting and lead to more personalized treatment strategies in ARVC.
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Affiliation(s)
- Yingnan Zhang
- Department of Biomedical Engineering, Johns Hopkins UniversityBaltimoreUnited States
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins UniversityBaltimoreUnited States
| | - Kelly Zhang
- Department of Biomedical Engineering, Johns Hopkins UniversityBaltimoreUnited States
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins UniversityBaltimoreUnited States
| | - Adityo Prakosa
- Department of Biomedical Engineering, Johns Hopkins UniversityBaltimoreUnited States
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins UniversityBaltimoreUnited States
| | - Cynthia James
- Division of Cardiology, Department of Medicine, Johns Hopkins HospitalBaltimoreUnited States
| | | | - Richard Carrick
- Division of Cardiology, Department of Medicine, Johns Hopkins HospitalBaltimoreUnited States
| | - Eric Sung
- Department of Biomedical Engineering, Johns Hopkins UniversityBaltimoreUnited States
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins UniversityBaltimoreUnited States
| | - Alessio Gasperetti
- Division of Cardiology, Department of Medicine, Johns Hopkins HospitalBaltimoreUnited States
| | - Crystal Tichnell
- Division of Cardiology, Department of Medicine, Johns Hopkins HospitalBaltimoreUnited States
| | - Brittney Murray
- Division of Cardiology, Department of Medicine, Johns Hopkins HospitalBaltimoreUnited States
| | - Hugh Calkins
- Division of Cardiology, Department of Medicine, Johns Hopkins HospitalBaltimoreUnited States
| | - Natalia A Trayanova
- Department of Biomedical Engineering, Johns Hopkins UniversityBaltimoreUnited States
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins UniversityBaltimoreUnited States
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22
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Africa PC, Piersanti R, Regazzoni F, Bucelli M, Salvador M, Fedele M, Pagani S, Dede' L, Quarteroni A. lifex-ep: a robust and efficient software for cardiac electrophysiology simulations. BMC Bioinformatics 2023; 24:389. [PMID: 37828428 PMCID: PMC10571323 DOI: 10.1186/s12859-023-05513-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 10/02/2023] [Indexed: 10/14/2023] Open
Abstract
BACKGROUND Simulating the cardiac function requires the numerical solution of multi-physics and multi-scale mathematical models. This underscores the need for streamlined, accurate, and high-performance computational tools. Despite the dedicated endeavors of various research teams, comprehensive and user-friendly software programs for cardiac simulations, capable of accurately replicating both normal and pathological conditions, are still in the process of achieving full maturity within the scientific community. RESULTS This work introduces [Formula: see text]-ep, a publicly available software for numerical simulations of the electrophysiology activity of the cardiac muscle, under both normal and pathological conditions. [Formula: see text]-ep employs the monodomain equation to model the heart's electrical activity. It incorporates both phenomenological and second-generation ionic models. These models are discretized using the Finite Element method on tetrahedral or hexahedral meshes. Additionally, [Formula: see text]-ep integrates the generation of myocardial fibers based on Laplace-Dirichlet Rule-Based Methods, previously released in Africa et al., 2023, within [Formula: see text]-fiber. As an alternative, users can also choose to import myofibers from a file. This paper provides a concise overview of the mathematical models and numerical methods underlying [Formula: see text]-ep, along with comprehensive implementation details and instructions for users. [Formula: see text]-ep features exceptional parallel speedup, scaling efficiently when using up to thousands of cores, and its implementation has been verified against an established benchmark problem for computational electrophysiology. We showcase the key features of [Formula: see text]-ep through various idealized and realistic simulations conducted in both normal and pathological scenarios. Furthermore, the software offers a user-friendly and flexible interface, simplifying the setup of simulations using self-documenting parameter files. CONCLUSIONS [Formula: see text]-ep provides easy access to cardiac electrophysiology simulations for a wide user community. It offers a computational tool that integrates models and accurate methods for simulating cardiac electrophysiology within a high-performance framework, while maintaining a user-friendly interface. [Formula: see text]-ep represents a valuable tool for conducting in silico patient-specific simulations.
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Affiliation(s)
- Pasquale Claudio Africa
- MOX, Department of Mathematics, Politecnico di Milano, Milano, Italy
- mathLab, Mathematics Area, SISSA International School for Advanced Studies, Trieste, Italy
| | - Roberto Piersanti
- MOX, Department of Mathematics, Politecnico di Milano, Milano, Italy.
| | | | - Michele Bucelli
- MOX, Department of Mathematics, Politecnico di Milano, Milano, Italy
| | - Matteo Salvador
- MOX, Department of Mathematics, Politecnico di Milano, Milano, Italy
- Institute for Computational and Mathematical Engineering, Stanford University, Stanford, California, USA
| | - Marco Fedele
- MOX, Department of Mathematics, Politecnico di Milano, Milano, Italy
| | - Stefano Pagani
- MOX, Department of Mathematics, Politecnico di Milano, Milano, Italy
| | - Luca Dede'
- MOX, Department of Mathematics, Politecnico di Milano, Milano, Italy
| | - Alfio Quarteroni
- MOX, Department of Mathematics, Politecnico di Milano, Milano, Italy
- Institute of Mathematics, École Polytechnique Fédérale de Lausanne, Lausanne, Professor emeritus, Switzerland
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23
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Moinuddin A, Ali SY, Goel A, Sethi Y, Patel N, Kaka N, Satapathy P, Sah R, Barboza JJ, Suhail MK. The age of computational cardiology and future of long-term ablation target prediction for ventricular tachycardia. Front Cardiovasc Med 2023; 10:1233991. [PMID: 37817867 PMCID: PMC10561379 DOI: 10.3389/fcvm.2023.1233991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Accepted: 08/30/2023] [Indexed: 10/12/2023] Open
Abstract
Ventricular arrhythmias, particularly ventricular tachycardia, are ubiquitously linked to 300,000 deaths annually. However, the current interventional procedure-the cardiac ablation-predict only short-term responses to treatment as the heart constantly remodels itself post-arrhythmia. To assist in the design of computational methods which focuses on long-term arrhythmia prediction, this review postulates three interdependent prospectives. The main objective is to propose computational methods for predicting long-term heart response to interventions in ventricular tachycardia Following a general discussion on the importance of devising simulations predicting long-term heart response to interventions, each of the following is discussed: (i) application of "metabolic sink theory" to elucidate the "re-entry" mechanism of ventricular tachycardia; (ii) application of "growth laws" to explain "mechanical load" translation in ventricular tachycardia; (iii) derivation of partial differential equations (PDE) to establish a pipeline to predict long-term clinical outcomes in ventricular tachycardia.
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Affiliation(s)
- Arsalan Moinuddin
- School of Sport and Exercise, University of Gloucestershire, Gloucester, United Kingdom
| | - Syed Yusuf Ali
- Department of Biomedical Engineering, Johns Hopkins University, Balimore, MD, United States
| | - Ashish Goel
- Department of Medicine, Government Doon Medical College, Dehradun, India
| | - Yashendra Sethi
- Department of Medicine, Government Doon Medical College, Dehradun, India
- PearResearch, Dehradun, India
| | - Neil Patel
- PearResearch, Dehradun, India
- Department of Medicine, GMERS Medical College, Himmatnagar, India
| | - Nirja Kaka
- PearResearch, Dehradun, India
- Department of Medicine, GMERS Medical College, Himmatnagar, India
| | - Prakasini Satapathy
- Global Center for Evidence Synthesis, Chandigarh, India
- Center for Global Health Research, Saveetha Medical College and Hospital, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, India
| | - Ranjit Sah
- Department of Microbiology, Tribhuvan University Teaching Hospital, Institute of Medicine, Kathmandu, Nepal
- Department of Microbiology, Dr. D. Y. Patil Medical College, Hospital and Research Centre, Dr. D. Y. Patil Vidyapeeth, Pune, India
- Department of Public Health Dentistry, Dr. D.Y. Patil Dental College and Hospital, Dr. D.Y. Patil Vidyapeeth, Pune, India
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Colman MA, Benson AP. A simple approach for image-based modelling of the heart that enables robust simulation of highly heterogeneous electrical excitation. Sci Rep 2023; 13:15119. [PMID: 37704647 PMCID: PMC10499818 DOI: 10.1038/s41598-023-39244-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 07/21/2023] [Indexed: 09/15/2023] Open
Abstract
Remodelling of cardiac tissue structure, including intercellular electrical coupling, is a major determinant of the complex and heterogeneous excitation patterns associated with cardiac arrhythmias. Evaluation of the precise mechanisms by which local tissue structure determines global arrhythmic excitation patterns is a major challenge that may be critically important for the development of effective treatment strategies. Computational modelling is a key tool in the study of cardiac arrhythmias, yet the established approaches for organ-scale modelling are unsuitable to capture the impact of local conduction heterogeneities; a novel approach is required to provide this multi-scale mechanistic insight. We present a fundamentally simple yet powerful approach to simulate electrical excitation in highly heterogeneous whole-heart models that exploits the underlying discreteness of the myocardium. Preliminary simulations demonstrate that this approach can capture lower conduction velocities and reproduce wave breakdown and the development of re-entry in a range of conditions.
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Affiliation(s)
- Michael A Colman
- Faculty of Biological Sciences, School of Biomedical Sciences, University of Leeds, Leeds, UK.
| | - Alan P Benson
- Faculty of Biological Sciences, School of Biomedical Sciences, University of Leeds, Leeds, UK
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25
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Marashly Q, Najjar SN, Hahn J, Rector GJ, Khawaja M, Chelu MG. Innovations in ventricular tachycardia ablation. J Interv Card Electrophysiol 2023; 66:1499-1518. [PMID: 35879516 DOI: 10.1007/s10840-022-01311-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 07/18/2022] [Indexed: 11/30/2022]
Abstract
Catheter ablation of ventricular arrhythmias (VAs) has evolved significantly over the past decade and is currently a well-established therapeutic option. Technological advances and improved understanding of VA mechanisms have led to tremendous innovations in VA ablation. The purpose of this review article is to provide an overview of current innovations in VA ablation. Mapping techniques, such as ultra-high density mapping, isochronal late activation mapping, and ripple mapping, have provided improved arrhythmogenic substrate delineation and potential procedural success while limiting duration of ablation procedure and potential hemodynamic compromise. Besides, more advanced mapping and ablation techniques such as epicardial and intramyocardial ablation approaches have allowed operators to more precisely target arrhythmogenic substrate. Moreover, advances in alternate energy sources, such as electroporation, as well as stereotactic radiation therapy have been proposed to be effective and safe. New catheters, such as the lattice and the saline-enhanced radiofrequency catheters, have been designed to provide deeper and more durable tissue ablation lesions compared to conventional catheters. Contact force optimization and baseline impedance modulation are important tools to optimize VT radiofrequency ablation and improve procedural success. Furthermore, advances in cardiac imaging, specifically cardiac MRI, have great potential in identifying arrhythmogenic substrate and evaluating ablation success. Overall, VA ablation has undergone significant advances over the past years. Innovations in VA mapping techniques, alternate energy source, new catheters, and utilization of cardiac imaging have great potential to improve overall procedural safety, hemodynamic stability, and procedural success.
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Affiliation(s)
- Qussay Marashly
- Division of Cardiology, Department of Medicine, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York, USA
| | - Salim N Najjar
- Division of Cardiology, Baylor College of Medicine, 7200 Cambridge Suite A6.137, MS: BCM621, Houston, TX, 77030, USA
| | - Joshua Hahn
- Division of Cardiology, Baylor College of Medicine, 7200 Cambridge Suite A6.137, MS: BCM621, Houston, TX, 77030, USA
| | - Graham J Rector
- Division of Cardiology, Baylor College of Medicine, 7200 Cambridge Suite A6.137, MS: BCM621, Houston, TX, 77030, USA
| | - Muzamil Khawaja
- Division of Cardiology, Baylor College of Medicine, 7200 Cambridge Suite A6.137, MS: BCM621, Houston, TX, 77030, USA
| | - Mihail G Chelu
- Division of Cardiology, Baylor College of Medicine, 7200 Cambridge Suite A6.137, MS: BCM621, Houston, TX, 77030, USA.
- Baylor St. Luke's Medical Center, Houston, USA.
- Texas Heart Institute, Houston, USA.
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26
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Berruezo A, Penela D, Jáuregui B, de Asmundis C, Peretto G, Marrouche N, Trayanova N, de Chillou C. Twenty-five years of research in cardiac imaging in electrophysiology procedures for atrial and ventricular arrhythmias. Europace 2023; 25:euad183. [PMID: 37622578 PMCID: PMC10450789 DOI: 10.1093/europace/euad183] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 06/14/2023] [Indexed: 08/26/2023] Open
Abstract
Catheter ablation is nowadays considered the treatment of choice for numerous cardiac arrhythmias in different clinical scenarios. Fluoroscopy has traditionally been the primary imaging modality for catheter ablation, providing real-time visualization of catheter navigation. However, its limitations, such as inadequate soft tissue visualization and exposure to ionizing radiation, have prompted the integration of alternative imaging modalities. Over the years, advancements in imaging techniques have played a pivotal role in enhancing the safety, efficacy, and efficiency of catheter ablation procedures. This manuscript aims to explore the utility of imaging, including electroanatomical mapping, cardiac computed tomography, echocardiography, cardiac magnetic resonance, and nuclear cardiology exams, in helping electrophysiology procedures. These techniques enable accurate anatomical guidance, identification of critical structures and substrates, and real-time monitoring of complications, ultimately enhancing procedural safety and success rates. Incorporating advanced imaging technologies into routine clinical practice has the potential to further improve clinical outcomes of catheter ablation procedures and pave the way for more personalized and precise ablation therapies in the future.
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Affiliation(s)
- Antonio Berruezo
- Arrhythmia Unit, Teknon Medical Centre, Carrer de Vilana, 12, 08022 Barcelona, Spain
| | - Diego Penela
- Arrhythmia Unit, Humanitas Research Hospital, Via Alessandro Manzoni, 56, 20089 Rozzano Milan, Italy
| | - Beatriz Jáuregui
- Arrhythmia Unit - Miguel Servet University Hospital, P.º de Isabel la Católica, 1-3, 50009 Zaragoza, Spain
| | - Carlo de Asmundis
- Heart Rhythm Management Centre, Universitair Ziekenhuis Brussel-Vrije Universiteit Brussel, Blvd Géneral Jacques 137, 1050 Ixelles, Brussels, Belgium
| | - Giovanni Peretto
- Arrhythmia Unit, Ospedale San Raffaele Hospital, Via Olgettina, 60, 20132 Milan, Italy
| | - Nassir Marrouche
- Department of Cardiology, Tulane University School of Medicine, 1430 Tulane Ave, New Orleans, LA 70112, USA
| | - Natalia Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Applied Math and Statistics, Johns Hopkins University, Baltimore, MD 21218, USA
- Division of Cardiology, Department of Medicine, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Christian de Chillou
- INSERM IADI U1254, University Hospital Nancy, University of Lorraine, 29 Av. du Maréchal de Lattre de Tassigny, 54000 Nancy, France
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27
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Chrispin J, Merchant FM, Lakdawala NK, Wu KC, Tomaselli GF, Navara R, Torbey E, Ambardekar AV, Kabra R, Arbustini E, Narula J, Guglin M, Albert CM, Chugh SS, Trayanova N, Cheung JW. Risk of Arrhythmic Death in Patients With Nonischemic Cardiomyopathy: JACC Review Topic of the Week. J Am Coll Cardiol 2023; 82:735-747. [PMID: 37587585 DOI: 10.1016/j.jacc.2023.05.064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 04/21/2023] [Accepted: 05/30/2023] [Indexed: 08/18/2023]
Abstract
Nonischemic cardiomyopathy (NICM) is common and patients are at significant risk for early mortality secondary to ventricular arrhythmias. Current guidelines recommend implantable cardioverter-defibrillator (ICD) therapy to decrease sudden cardiac death (SCD) in patients with heart failure and reduced left ventricular ejection fraction. However, in randomized clinical trials comprised solely of patients with NICM, primary prevention ICDs did not confer significant mortality benefit. Moreover, left ventricular ejection fraction has limited sensitivity and specificity for predicting SCD. Therefore, precise risk stratification algorithms are needed to define those at the highest risk of SCD. This review examines mechanisms of sudden arrhythmic death in patients with NICM, discusses the role of ICD therapy and treatment of heart failure for prevention of SCD in patients with NICM, examines the role of cardiac magnetic resonance imaging and computational modeling for SCD risk stratification, and proposes new strategies to guide future clinical trials on SCD risk assessment in patients with NICM.
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Affiliation(s)
- Jonathan Chrispin
- Department of Medicine, Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
| | | | - Neal K Lakdawala
- Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Katherine C Wu
- Department of Medicine, Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Gordon F Tomaselli
- Department of Medicine, Albert Einstein College of Medicine, Bronx, New York, USA
| | - Rachita Navara
- Division of Cardiac Electrophysiology, University of California, San Fransisco, California, USA
| | - Estelle Torbey
- Division of Electrophysiology, Brown University, Providence, Rhode Island, USA
| | - Amrut V Ambardekar
- Department of Medicine, Division of Cardiology, University of Colorado, Aurora, Colorado, USA
| | - Rajesh Kabra
- Kansas City Heart Rhythm Institute, Overland Park, Kansas, USA
| | - Eloisa Arbustini
- Center for Inherited Cardiovascular Diseases, IRCCS Foundation Policlinico San Matteo, Pavia, Italy
| | - Jagat Narula
- McGovern Medical School at the University of Texas Health Science Center, Houston, Texas, USA
| | - Maya Guglin
- Advanced Heart Failure and Transplant, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Christine M Albert
- Cardiac Electrohysiology, Cedars Sinai Smidt Heart Institute, Los Angeles, California, USA
| | - Sumeet S Chugh
- Cardiac Electrohysiology, Cedars Sinai Smidt Heart Institute, Los Angeles, California, USA
| | - Natalia Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Jim W Cheung
- Division of Cardiology, Weill Cornell Medicine, New York, New York, USA
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Filgueiras-Rama D, Ramos-Prada A, Cluitmans MJM. Arrhythmogenic vulnerability of reentrant pathways in post-infarct ventricular tachycardia assessed by advanced computational modelling. Europace 2023; 25:euad258. [PMID: 37647101 PMCID: PMC10481246 DOI: 10.1093/europace/euad258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 08/24/2023] [Indexed: 09/01/2023] Open
Affiliation(s)
- David Filgueiras-Rama
- Centro Nacional de Investigaciones Cardiovasculares (CNIC), Novel Arrhythmogenic Mechanisms Program, Melchor Fernández Almagro, 3, 28029 Madrid, Spain
- Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC), Cardiovascular Institute, Profesor Martín Lagos s/n, 28040Madrid, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Monforte de Lemos 3-5, 28029 Madrid, Spain
| | - Alba Ramos-Prada
- Centro Nacional de Investigaciones Cardiovasculares (CNIC), Novel Arrhythmogenic Mechanisms Program, Melchor Fernández Almagro, 3, 28029 Madrid, Spain
- Fundación Interhospitalaria para la Investigación Cardiovascular (FIC), Madrid, Spain
| | - Matthijs J M Cluitmans
- Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Center, Department of Cardiology, Maastricht, The Netherlands
- Philips Research, Eindhoven, The Netherlands
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Bhagirath P, Campos FO, Postema PG, Kemme MJB, Wilde AAM, Prassl AJ, Neic A, Rinaldi CA, Götte MJW, Plank G, Bishop MJ. Arrhythmogenic vulnerability of re-entrant pathways in post-infarct ventricular tachycardia assessed by advanced computational modelling. Europace 2023; 25:euad198. [PMID: 37421339 PMCID: PMC10481251 DOI: 10.1093/europace/euad198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 04/26/2023] [Accepted: 06/21/2023] [Indexed: 07/10/2023] Open
Abstract
AIMS Substrate assessment of scar-mediated ventricular tachycardia (VT) is frequently performed using late gadolinium enhancement (LGE) images. Although this provides structural information about critical pathways through the scar, assessing the vulnerability of these pathways for sustaining VT is not possible with imaging alone.This study evaluated the performance of a novel automated re-entrant pathway finding algorithm to non-invasively predict VT circuit and inducibility. METHODS Twenty post-infarct VT-ablation patients were included for retrospective analysis. Commercially available software (ADAS3D left ventricular) was used to generate scar maps from 2D-LGE images using the default 40-60 pixel-signal-intensity (PSI) threshold. In addition, algorithm sensitivity for altered thresholds was explored using PSI 45-55, 35-65, and 30-70. Simulations were performed on the Virtual Induction and Treatment of Arrhythmias (VITA) framework to identify potential sites of block and assess their vulnerability depending on the automatically computed round-trip-time (RTT). Metrics, indicative of substrate complexity, were correlated with VT-recurrence during follow-up. RESULTS Total VTs (85 ± 43 vs. 42 ± 27) and unique VTs (9 ± 4 vs. 5 ± 4) were significantly higher in patients with- compared to patients without recurrence, and were predictive of recurrence with area under the curve of 0.820 and 0.770, respectively. VITA was robust to scar threshold variations with no significant impact on total and unique VTs, and mean RTT between the four models. Simulation metrics derived from PSI 45-55 model had the highest number of parameters predictive for post-ablation VT-recurrence. CONCLUSION Advanced computational metrics can non-invasively and robustly assess VT substrate complexity, which may aid personalized clinical planning and decision-making in the treatment of post-infarction VT.
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Affiliation(s)
- Pranav Bhagirath
- School of Biomedical Engineering and Imaging Sciences, King's College London, 4th Floor, Lambeth Wing, St. Thomas' Hospital, London SE1 7EH, UK
- Department of Cardiology, St Thomas' Hospital, London SE1 7EH, UK
| | - Fernando O Campos
- School of Biomedical Engineering and Imaging Sciences, King's College London, 4th Floor, Lambeth Wing, St. Thomas' Hospital, London SE1 7EH, UK
| | - Pieter G Postema
- Department of Cardiology, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Michiel J B Kemme
- Department of Cardiology, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Arthur A M Wilde
- Department of Cardiology, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Anton J Prassl
- Gottfried Schatz Research Center, Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Aurel Neic
- Gottfried Schatz Research Center, Division of Biophysics, Medical University of Graz, Graz, Austria
| | | | - Marco J W Götte
- Department of Cardiology, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Gernot Plank
- Gottfried Schatz Research Center, Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Martin J Bishop
- School of Biomedical Engineering and Imaging Sciences, King's College London, 4th Floor, Lambeth Wing, St. Thomas' Hospital, London SE1 7EH, UK
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Zhang Y, Zhang K, Prakosa A, James C, Zimmerman SL, Carrick R, Sung E, Gasperetti A, Tichnell C, Murray B, Calkins H, Trayanova N. Predicting Ventricular Tachycardia Circuits in Patients with Arrhythmogenic Right Ventricular Cardiomyopathy using Genotype-specific Heart Digital Twins. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.31.23290587. [PMID: 37398074 PMCID: PMC10312861 DOI: 10.1101/2023.05.31.23290587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a genetic cardiac disease that leads to ventricular tachycardia (VT), a life-threatening heart rhythm disorder. Treating ARVC remains challenging due to the complex underlying arrhythmogenic mechanisms, which involve structural and electrophysiological (EP) remodeling. Here, we developed a novel genotype-specific heart digital twin (Geno-DT) approach to investigate the role of pathophysiological remodeling in sustaining VT reentrant circuits and to predict the VT circuits in ARVC patients of different genotypes. This approach integrates the patient's disease-induced structural remodeling reconstructed from contrast-enhanced magnetic-resonance imaging and genotype-specific cellular EP properties. In our retrospective study of 16 ARVC patients with two genotypes: plakophilin-2 (PKP2, n = 8) and gene-elusive (GE, n = 8), we found that Geno-DT accurately and non-invasively predicted the VT circuit locations for both genotypes (with 100%, 94%, 96% sensitivity, specificity, and accuracy for GE patient group, and 86%, 90%, 89% sensitivity, specificity, and accuracy for PKP2 patient group), when compared to VT circuit locations identified during clinical EP studies. Moreover, our results revealed that the underlying VT mechanisms differ among ARVC genotypes. We determined that in GE patients, fibrotic remodeling is the primary contributor to VT circuits, while in PKP2 patients, slowed conduction velocity and altered restitution properties of cardiac tissue, in addition to the structural substrate, are directly responsible for the formation of VT circuits. Our novel Geno-DT approach has the potential to augment therapeutic precision in the clinical setting and lead to more personalized treatment strategies in ARVC.
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Affiliation(s)
- Yingnan Zhang
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA
| | - Kelly Zhang
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA
| | - Adityo Prakosa
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA
| | - Cynthia James
- Division of Cardiology, Department of Medicine, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Stefan L Zimmerman
- Department of Radiology and Radiological Science, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Richard Carrick
- Division of Cardiology, Department of Medicine, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Eric Sung
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA
| | - Alessio Gasperetti
- Division of Cardiology, Department of Medicine, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Crystal Tichnell
- Division of Cardiology, Department of Medicine, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Brittney Murray
- Division of Cardiology, Department of Medicine, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Hugh Calkins
- Division of Cardiology, Department of Medicine, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Natalia Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA
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31
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Holz D, Martonová D, Schaller E, Duong MT, Alkassar M, Weyand M, Leyendecker S. Transmural fibre orientations based on Laplace-Dirichlet-Rule-Based-Methods and their influence on human heart simulations. J Biomech 2023; 156:111643. [PMID: 37321157 DOI: 10.1016/j.jbiomech.2023.111643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 02/10/2023] [Accepted: 05/15/2023] [Indexed: 06/17/2023]
Abstract
It is well known that the orthotropic tissue structure decisively influences the mechanical and electrical properties of the heart. Numerous approaches to compute the orthotropic tissue structure in computational heart models have been developed in the past decades. In this study, we investigate to what extent different Laplace-Dirichlet-Rule-Based-Methods (LDRBMs) influence the local orthotropic tissue structure and thus the electromechanical behaviour of the subsequent cardiac simulation. In detail, we are utilising three Laplace-Dirichlet-Rule-Based-Methods and compare: (i) the local myofibre orientation; (ii) important global characteristics (ejection fraction, peak pressure, apex shortening, myocardial volume reduction, fractional wall thickening); (iii) local characteristics (active fibre stress, fibre strain). We observe that the orthotropic tissue structures for the three LDRBMs show significant differences in the local myofibre orientation. The global characteristics myocardial volume reduction and peak pressure are rather insensitive to a change in local myofibre orientation, while the ejection fraction is moderately influenced by the different LDRBMs. Moreover, the apical shortening and fractional wall thickening exhibit a sensitive behaviour to a change in the local myofibre orientation. The highest sensitivity can be observed for the local characteristics.
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Affiliation(s)
- David Holz
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Institute of Applied Dynamics, Immerwahrstraße 1, Erlangen, 91058, Germany.
| | - Denisa Martonová
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Institute of Applied Dynamics, Immerwahrstraße 1, Erlangen, 91058, Germany
| | - Emely Schaller
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Institute of Applied Dynamics, Immerwahrstraße 1, Erlangen, 91058, Germany
| | - Minh Tuan Duong
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Institute of Applied Dynamics, Immerwahrstraße 1, Erlangen, 91058, Germany; School of Mechanical Engineering, Hanoi University of Science and Technology, Hanoi, 1 DaiCoViet Road, Viet Nam
| | - Muhannad Alkassar
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Department of Cardiac Surgery, Krankenhausstraße 12, Erlangen, 91054, Germany
| | - Michael Weyand
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Department of Cardiac Surgery, Krankenhausstraße 12, Erlangen, 91054, Germany
| | - Sigrid Leyendecker
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Institute of Applied Dynamics, Immerwahrstraße 1, Erlangen, 91058, Germany
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O’Hara RP, Meijborg VM, Jelvehgaran P, van der Waal J, Boink GJ, Trayanova NA, Coronel R, Boukens BJ. Site-specific prolongation of repolarization prevents postmyocardial infarction tachycardia. Heart Rhythm O2 2023; 4:466-468. [PMID: 37520014 PMCID: PMC10373146 DOI: 10.1016/j.hroo.2023.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/01/2023] Open
Affiliation(s)
- Ryan P. O’Hara
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Veronique M.F. Meijborg
- Heart Center, Department of Clinical and Experimental Cardiology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Pouya Jelvehgaran
- Heart Center, Department of Clinical and Experimental Cardiology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Jeanne van der Waal
- Heart Center, Department of Clinical and Experimental Cardiology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Gerard J.J. Boink
- Heart Center, Department of Clinical and Experimental Cardiology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
- Department of Medical Biology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Natalia A. Trayanova
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Ruben Coronel
- Heart Center, Department of Clinical and Experimental Cardiology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
- Fondation Bordeaux Université, Inserm, U1045 and Université de Bordeaux, Bordeaux, France
| | - Bastiaan J. Boukens
- Department of Medical Biology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
- Department of Physiology, Cardiovascular Research Institute Maastricht, Maastricht University Medical Center, Maastricht, The Netherlands
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Gibbs CE, Marchianó S, Zhang K, Yang X, Murry CE, Boyle PM. Graft-host coupling changes can lead to engraftment arrhythmia: a computational study. J Physiol 2023; 601:2733-2749. [PMID: 37014103 PMCID: PMC10901678 DOI: 10.1113/jp284244] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 03/30/2023] [Indexed: 04/05/2023] Open
Abstract
After myocardial infarction (MI), a significant portion of heart muscle is replaced with scar tissue, progressively leading to heart failure. Human pluripotent stem cell-derived cardiomyocytes (hPSC-CM) offer a promising option for improving cardiac function after MI. However, hPSC-CM transplantation can lead to engraftment arrhythmia (EA). EA is a transient phenomenon arising shortly after transplantation then spontaneously resolving after a few weeks. The underlying mechanism of EA is unknown. We hypothesize that EA may be explained partially by time-varying, spatially heterogeneous, graft-host electrical coupling. Here, we created computational slice models derived from histological images that reflect different configuration of grafts in the infarcted ventricle. We ran simulations with varying degrees of connection imposed upon the graft-host perimeter to assess how heterogeneous electrical coupling affected EA with non-conductive scar, slow-conducting scar and scar replaced by host myocardium. We also quantified the effect of variation in intrinsic graft conductivity. Susceptibility to EA initially increased and subsequently decreased with increasing graft-host coupling, suggesting the waxing and waning of EA is regulated by progressive increases in graft-host coupling. Different spatial distributions of graft, host and scar yielded markedly different susceptibility curves. Computationally replacing non-conductive scar with host myocardium or slow-conducting scar, and increasing intrinsic graft conductivity both demonstrated potential means to blunt EA vulnerability. These data show how graft location, especially relative to scar, along with its dynamic electrical coupling to host, can influence EA burden; moreover, they offer a rational base for further studies aimed to define the optimal delivery of hPSC-CM injection. KEY POINTS: Human pluripotent stem cell-derived cardiomyocytes (hPSC-CM) hold great cardiac regenerative potential but can also cause engraftment arrhythmias (EA). Spatiotemporal evolution in the pattern of electrical coupling between injected hPSC-CMs and surrounding host myocardium may explain the dynamics of EA observed in large animal models. We conducted simulations in histology-derived 2D slice computational models to assess the effects of heterogeneous graft-host electrical coupling on EA propensity, with or without scar tissue. Our findings suggest spatiotemporally heterogeneous graft-host coupling can create an electrophysiological milieu that favours graft-initiated host excitation, a surrogate metric of EA susceptibility. Removing scar from our models reduced but did not abolish the propensity for this phenomenon. Conversely, reduced intra-graft electrical connectedness increased the incidence of graft-initiated host excitation. The computational framework created for this study can be used to generate new hypotheses, targeted delivery of hPSC-CMs.
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Affiliation(s)
- Chelsea E Gibbs
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Silvia Marchianó
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA
- Department of Laboratory Medicine & Pathology, University of Washington, Seattle, WA, USA
- Center for Cardiovascular Biology, University of Washington, Seattle, WA, USA
| | - Kelly Zhang
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Xiulan Yang
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA
- Department of Laboratory Medicine & Pathology, University of Washington, Seattle, WA, USA
- Center for Cardiovascular Biology, University of Washington, Seattle, WA, USA
| | - Charles E Murry
- Department of Bioengineering, University of Washington, Seattle, WA, USA
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA
- Department of Laboratory Medicine & Pathology, University of Washington, Seattle, WA, USA
- Center for Cardiovascular Biology, University of Washington, Seattle, WA, USA
- Division of Cardiology, University of Washington, Seattle, WA, USA
| | - Patrick M Boyle
- Department of Bioengineering, University of Washington, Seattle, WA, USA
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA
- Center for Cardiovascular Biology, University of Washington, Seattle, WA, USA
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Kowalewski C, Ascione C, Nuñez-Garcia M, Ly B, Sermesant M, Bustin A, Sridi S, Bouteiller X, Yokoyama M, Vlachos K, Monaco C, Bouyer B, Buliard S, Arnaud M, Tixier R, Chauvel R, Derval N, Pambrun T, Duchateau J, Bordachar P, Hocini M, Hindricks G, Haïssaguerre M, Sacher F, Jais P, Cochet H. Advanced Imaging Integration for Catheter Ablation of Ventricular Tachycardia. Curr Cardiol Rep 2023; 25:535-542. [PMID: 37115434 DOI: 10.1007/s11886-023-01872-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/23/2023] [Indexed: 04/29/2023]
Abstract
PURPOSE OF REVIEW Imaging plays a crucial role in the therapy of ventricular tachycardia (VT). We offer an overview of the different methods and provide information on their use in a clinical setting. RECENT FINDINGS The use of imaging in VT has progressed recently. Intracardiac echography facilitates catheter navigation and the targeting of moving intracardiac structures. Integration of pre-procedural CT or MRI allows for targeting the VT substrate, with major expected impact on VT ablation efficacy and efficiency. Advances in computational modeling may further enhance the performance of imaging, giving access to pre-operative simulation of VT. These advances in non-invasive diagnosis are increasingly being coupled with non-invasive approaches for therapy delivery. This review highlights the latest research on the use of imaging in VT procedures. Image-based strategies are progressively shifting from using images as an adjunct tool to electrophysiological techniques, to an integration of imaging as a central element of the treatment strategy.
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Affiliation(s)
- Christopher Kowalewski
- Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, L'Institut de RYthmologie et modélisation Cardiaque (LIRYC), Université Bordeaux, Bordeaux, France.
| | - Ciro Ascione
- Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, L'Institut de RYthmologie et modélisation Cardiaque (LIRYC), Université Bordeaux, Bordeaux, France
| | - Marta Nuñez-Garcia
- Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, L'Institut de RYthmologie et modélisation Cardiaque (LIRYC), Université Bordeaux, Bordeaux, France
| | - Buntheng Ly
- Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, L'Institut de RYthmologie et modélisation Cardiaque (LIRYC), Université Bordeaux, Bordeaux, France
| | - Maxime Sermesant
- Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, L'Institut de RYthmologie et modélisation Cardiaque (LIRYC), Université Bordeaux, Bordeaux, France
| | - Aurélien Bustin
- Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, L'Institut de RYthmologie et modélisation Cardiaque (LIRYC), Université Bordeaux, Bordeaux, France
| | - Soumaya Sridi
- Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, L'Institut de RYthmologie et modélisation Cardiaque (LIRYC), Université Bordeaux, Bordeaux, France
| | - Xavier Bouteiller
- Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, L'Institut de RYthmologie et modélisation Cardiaque (LIRYC), Université Bordeaux, Bordeaux, France
| | - Masaaki Yokoyama
- Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, L'Institut de RYthmologie et modélisation Cardiaque (LIRYC), Université Bordeaux, Bordeaux, France
| | - Konstantinos Vlachos
- Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, L'Institut de RYthmologie et modélisation Cardiaque (LIRYC), Université Bordeaux, Bordeaux, France
| | - Cinzia Monaco
- Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, L'Institut de RYthmologie et modélisation Cardiaque (LIRYC), Université Bordeaux, Bordeaux, France
| | - Benjamin Bouyer
- Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, L'Institut de RYthmologie et modélisation Cardiaque (LIRYC), Université Bordeaux, Bordeaux, France
| | - Samuel Buliard
- Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, L'Institut de RYthmologie et modélisation Cardiaque (LIRYC), Université Bordeaux, Bordeaux, France
| | - Marine Arnaud
- Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, L'Institut de RYthmologie et modélisation Cardiaque (LIRYC), Université Bordeaux, Bordeaux, France
| | - Romain Tixier
- Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, L'Institut de RYthmologie et modélisation Cardiaque (LIRYC), Université Bordeaux, Bordeaux, France
| | - Remi Chauvel
- Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, L'Institut de RYthmologie et modélisation Cardiaque (LIRYC), Université Bordeaux, Bordeaux, France
| | - Nicolas Derval
- Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, L'Institut de RYthmologie et modélisation Cardiaque (LIRYC), Université Bordeaux, Bordeaux, France
| | - Thomas Pambrun
- Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, L'Institut de RYthmologie et modélisation Cardiaque (LIRYC), Université Bordeaux, Bordeaux, France
| | - Josselin Duchateau
- Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, L'Institut de RYthmologie et modélisation Cardiaque (LIRYC), Université Bordeaux, Bordeaux, France
| | - Pierre Bordachar
- Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, L'Institut de RYthmologie et modélisation Cardiaque (LIRYC), Université Bordeaux, Bordeaux, France
| | - Mélèze Hocini
- Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, L'Institut de RYthmologie et modélisation Cardiaque (LIRYC), Université Bordeaux, Bordeaux, France
| | - Gerhard Hindricks
- Department of Cardiology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Michel Haïssaguerre
- Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, L'Institut de RYthmologie et modélisation Cardiaque (LIRYC), Université Bordeaux, Bordeaux, France
| | - Frédéric Sacher
- Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, L'Institut de RYthmologie et modélisation Cardiaque (LIRYC), Université Bordeaux, Bordeaux, France
| | - Pierre Jais
- Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, L'Institut de RYthmologie et modélisation Cardiaque (LIRYC), Université Bordeaux, Bordeaux, France
| | - Hubert Cochet
- Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, L'Institut de RYthmologie et modélisation Cardiaque (LIRYC), Université Bordeaux, Bordeaux, France
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He J, Pertsov AM, Cherry EM, Fenton FH, Roney CH, Niederer SA, Zang Z, Mangharam R. Fiber Organization has Little Effect on Electrical Activation Patterns during Focal Arrhythmias in the Left Atrium. ARXIV 2023:arXiv:2210.16497v3. [PMID: 36776816 PMCID: PMC9915751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/21/2023]
Abstract
Over the past two decades there has been a steady trend towards the development of realistic models of cardiac conduction with increasing levels of detail. However, making models more realistic complicates their personalization and use in clinical practice due to limited availability of tissue and cellular scale data. One such limitation is obtaining information about myocardial fiber organization in the clinical setting. In this study, we investigated a chimeric model of the left atrium utilizing clinically derived patient-specific atrial geometry and a realistic, yet foreign for a given patient fiber organization. We discovered that even significant variability of fiber organization had a relatively small effect on the spatio-temporal activation pattern during regular pacing. For a given pacing site, the activation maps were very similar across all fiber organizations tested.
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Affiliation(s)
- Jiyue He
- Department of Electrical and Systems Engineering, University of Pennsylvania, USA
| | | | - Elizabeth M Cherry
- School of Computational Science and Engineering, Georgia Institute of Technology, USA
| | | | - Caroline H Roney
- School of Engineering and Materials Science, Queen Mary University of London, UK
| | - Steven A Niederer
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK
| | - Zirui Zang
- Department of Electrical and Systems Engineering, University of Pennsylvania, USA
| | - Rahul Mangharam
- Department of Electrical and Systems Engineering, University of Pennsylvania, USA
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Tejman-Yarden S, Freidin D, Nagar N, Parmet Y, Abed M, Vazhgovsky O, Yogev D, Ganchrow BID D, Mazor-Drey E, Chatterji S, Beinart R, Barbash I, Guetta V, Goitein O. Virtual reality utilization for left atrial appendage occluder device size prediction. Heliyon 2023; 9:e14790. [PMID: 37089380 PMCID: PMC10114146 DOI: 10.1016/j.heliyon.2023.e14790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 03/07/2023] [Accepted: 03/16/2023] [Indexed: 04/07/2023] Open
Abstract
Aim To explore the feasibility and accuracy of virtual reality (VR) derived from cardiac computed angiography (CCTA) data to predict left atrial appendage occlusion (LAAO) device size. Method Retrospective data of patients who underwent LAAO according to clinical indication were reviewed; all patients underwent a pre-procedural CCTA. Measurements of the left atrial appendage (LAA) orifice diameters by CCTA, VR, and transesophageal echocardiography (TEE) (acquired during the procedure) were compared to the implanted device size. The LAA perimeter was calculated using the Ramanujan approximation. Statistical analyses included Lin's Concordance Correlation Coefficient (ρ c ), the mean difference, and the mean square error (MSE). Results The sample was composed of 20 patients (mean age 75.7 ± 7.5 years, 60% males) who underwent successful LAAO insertion (ACP™ N = 8, Watchman™ N = 12). The CCTA, VR, and TEE maximal diameter ρ c was 0.52, 0.78 and 0.60, respectively with mean differences of +0.92 ± 4.0 mm, -1.12 ± 2.3 mm, and -3.45 ± 2.69 mm, respectively. The CCTA, VR, and TEE perimeter calculations ρ c were 0.49, 0.54, and 0.39 respectively with mean differences of +4.69 ± 11.5 mm, -9.88 ± 8.0 mm, and -16.79 ± 7.8 respectively. Discussion A VR visualization of the LAA ostium in different perspectives allows for a better understanding of its funnel-shaped structure. VR measurement of the maximal ostium diameter had the strongest correlation with the diameter of the inserted device. VR may thus provide new imaging possibilities for the evaluation of complex pre-procedural structures such as the LAA.
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Qiu J, Liu XJ, You BA, Ren N, Liu H. Application of Nanomaterials in Stem Cell-Based Therapeutics for Cardiac Repair and Regeneration. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2023; 19:e2206487. [PMID: 36642861 DOI: 10.1002/smll.202206487] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 12/16/2022] [Indexed: 06/17/2023]
Abstract
Cardiovascular disease is a leading cause of disability and death worldwide. Although the survival rate of patients with heart diseases can be improved with contemporary pharmacological treatments and surgical procedures, none of these therapies provide a significant improvement in cardiac repair and regeneration. Stem cell-based therapies are a promising approach for functional recovery of damaged myocardium. However, the available stem cells are difficult to differentiate into cardiomyocytes, which result in the extremely low transplantation efficiency. Nanomaterials are widely used to regulate the myocardial differentiation of stem cells, and play a very important role in cardiac tissue engineering. This study discusses the current status and limitations of stem cells and cell-derived exosomes/micro RNAs based cardiac therapy, describes the cardiac repair mechanism of nanomaterials, summarizes the recent advances in nanomaterials used in cardiac repair and regeneration, and evaluates the advantages and disadvantages of the relevant nanomaterials. Besides discussing the potential clinical applications of nanomaterials in cardiac therapy, the perspectives and challenges of nanomaterials used in stem cell-based cardiac repair and regeneration are also considered. Finally, new research directions in this field are proposed, and future research trends are highlighted.
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Affiliation(s)
- Jie Qiu
- Medical Research Institute, Jinan Nanjiao Hospital, Jinan, 250002, P. R. China
| | - Xiang-Ju Liu
- Department of Geriatric Medicine, Qilu Hospital of Shandong University, Jinan, 250012, P. R. China
| | - Bei-An You
- Department of Cardiovascular Center, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Jinan, 266035, P. R. China
| | - Na Ren
- Collaborative Innovation Center of Technology and Equipment for Biological Diagnosis and Therapy in Universities of Shandong, Institute for Advanced Interdisciplinary Research (iAIR), School of Chemistry and Chemical Engineering, University of Jinan, Jinan, 250022, P. R. China
| | - Hong Liu
- Collaborative Innovation Center of Technology and Equipment for Biological Diagnosis and Therapy in Universities of Shandong, Institute for Advanced Interdisciplinary Research (iAIR), School of Chemistry and Chemical Engineering, University of Jinan, Jinan, 250022, P. R. China
- State Key Laboratory of Crystal Materials, Shandong University, Jinan, 250100, P. R. China
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Sung E, Prakosa A, Kyranakis S, Berger RD, Chrispin J, Trayanova NA. Wavefront directionality and decremental stimuli synergistically improve identification of ventricular tachycardia substrate: insights from personalized computational heart models. Europace 2023; 25:223-235. [PMID: 36006658 PMCID: PMC10103576 DOI: 10.1093/europace/euac140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 07/16/2022] [Indexed: 11/14/2022] Open
Abstract
AIMS Multiple wavefront pacing (MWP) and decremental pacing (DP) are two electroanatomic mapping (EAM) strategies that have emerged to better characterize the ventricular tachycardia (VT) substrate. The aim of this study was to assess how well MWP, DP, and their combination improve identification of electrophysiological abnormalities on EAM that reflect infarct remodelling and critical VT sites. METHODS AND RESULTS Forty-eight personalized computational heart models were reconstructed using images from post-infarct patients undergoing VT ablation. Paced rhythms were simulated by delivering an initial (S1) and an extra-stimulus (S2) from one of 100 locations throughout each heart model. For each pacing, unipolar signals were computed along the myocardial surface to simulate substrate EAM. Six EAM features were extracted and compared with the infarct remodelling and critical VT sites. Concordance of S1 EAM features between different maps was lower in hearts with smaller amounts of remodelling. Incorporating S1 EAM features from multiple maps greatly improved the detection of remodelling, especially in hearts with less remodelling. Adding S2 EAM features from multiple maps decreased the number of maps required to achieve the same detection accuracy. S1 EAM features from multiple maps poorly identified critical VT sites. However, combining S1 and S2 EAM features from multiple maps paced near VT circuits greatly improved identification of critical VT sites. CONCLUSION Electroanatomic mapping with MWP is more advantageous for characterization of substrate in hearts with less remodelling. During substrate EAM, MWP and DP should be combined and delivered from locations proximal to a suspected VT circuit to optimize identification of the critical VT site.
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Affiliation(s)
- Eric Sung
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21287, USA
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD 21218, USA
| | - Adityo Prakosa
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21287, USA
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD 21218, USA
| | - Stephen Kyranakis
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Ronald D Berger
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD 21218, USA
- Section of Cardiac Electrophysiology, Division of Cardiology, Department of Medicine, Johns Hopkins Hospital, Baltimore, MD 21287, USA
| | - Jonathan Chrispin
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD 21218, USA
- Section of Cardiac Electrophysiology, Division of Cardiology, Department of Medicine, Johns Hopkins Hospital, Baltimore, MD 21287, USA
| | - Natalia A Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21287, USA
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD 21218, USA
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Kong F, Shadden SC. Learning Whole Heart Mesh Generation From Patient Images for Computational Simulations. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:533-545. [PMID: 36327186 DOI: 10.1109/tmi.2022.3219284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Patient-specific cardiac modeling combines geometries of the heart derived from medical images and biophysical simulations to predict various aspects of cardiac function. However, generating simulation-suitable models of the heart from patient image data often requires complicated procedures and significant human effort. We present a fast and automated deep-learning method to construct simulation-suitable models of the heart from medical images. The approach constructs meshes from 3D patient images by learning to deform a small set of deformation handles on a whole heart template. For both 3D CT and MR data, this method achieves promising accuracy for whole heart reconstruction, consistently outperforming prior methods in constructing simulation-suitable meshes of the heart. When evaluated on time-series CT data, this method produced more anatomically and temporally consistent geometries than prior methods, and was able to produce geometries that better satisfy modeling requirements for cardiac flow simulations. Our source code and pretrained networks are available at https://github.com/fkong7/HeartDeformNets.
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Zenger B, Bergquist JA, Busatto A, Good WW, Rupp LC, Sharma V, MacLeod RS. Tipping the scales of understanding: An engineering approach to design and implement whole-body cardiac electrophysiology experimental models. Front Physiol 2023; 14:1100471. [PMID: 36744034 PMCID: PMC9893785 DOI: 10.3389/fphys.2023.1100471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 01/02/2023] [Indexed: 01/21/2023] Open
Abstract
The study of cardiac electrophysiology is built on experimental models that span all scales, from ion channels to whole-body preparations. Novel discoveries made at each scale have contributed to our fundamental understanding of human cardiac electrophysiology, which informs clinicians as they detect, diagnose, and treat complex cardiac pathologies. This expert review describes an engineering approach to developing experimental models that is applicable across scales. The review also outlines how we applied the approach to create a set of multiscale whole-body experimental models of cardiac electrophysiology, models that are driving new insights into the response of the myocardium to acute ischemia. Specifically, we propose that researchers must address three critical requirements to develop an effective experimental model: 1) how the experimental model replicates and maintains human physiological conditions, 2) how the interventions possible with the experimental model capture human pathophysiology, and 3) what signals need to be measured, at which levels of resolution and fidelity, and what are the resulting requirements of the measurement system and the access to the organs of interest. We will discuss these requirements in the context of two examples of whole-body experimental models, a closed chest in situ model of cardiac ischemia and an isolated-heart, torso-tank preparation, both of which we have developed over decades and used to gather valuable insights from hundreds of experiments.
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Affiliation(s)
- Brian Zenger
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, United States,Nora Eccles Harrison Cardiovascular Research and Training Institute, The University of Utah, Salt Lake City, UT, United States,Spencer Eccles School of Medicine, University of Utah, Salt Lake City, UT, United States,*Correspondence: Brian Zenger,
| | - Jake A. Bergquist
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, United States,Nora Eccles Harrison Cardiovascular Research and Training Institute, The University of Utah, Salt Lake City, UT, United States,Department of Biomedical Engineering, College of Engineering, University of Utah, Salt Lake City, UT, United States
| | - Anna Busatto
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, United States,Nora Eccles Harrison Cardiovascular Research and Training Institute, The University of Utah, Salt Lake City, UT, United States,Department of Biomedical Engineering, College of Engineering, University of Utah, Salt Lake City, UT, United States
| | | | - Lindsay C. Rupp
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, United States,Nora Eccles Harrison Cardiovascular Research and Training Institute, The University of Utah, Salt Lake City, UT, United States,Department of Biomedical Engineering, College of Engineering, University of Utah, Salt Lake City, UT, United States
| | - Vikas Sharma
- Spencer Eccles School of Medicine, University of Utah, Salt Lake City, UT, United States
| | - Rob S. MacLeod
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, United States,Nora Eccles Harrison Cardiovascular Research and Training Institute, The University of Utah, Salt Lake City, UT, United States,Department of Biomedical Engineering, College of Engineering, University of Utah, Salt Lake City, UT, United States
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Sun T, He X, Li Z. Digital twin in healthcare: Recent updates and challenges. Digit Health 2023; 9:20552076221149651. [PMID: 36636729 PMCID: PMC9830576 DOI: 10.1177/20552076221149651] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 12/14/2022] [Indexed: 01/05/2023] Open
Abstract
As simulation is playing an increasingly important role in medicine, providing the individual patient with a customised diagnosis and treatment is envisaged as part of future precision medicine. Such customisation will become possible through the emergence of digital twin (DT) technology. The objective of this article is to review the progress of prominent research on DT technology in medicine and discuss the potential applications and future opportunities as well as several challenges remaining in digital healthcare. A review of the literature was conducted using PubMed, Web of Science, Google Scholar, Scopus and related bibliographic resources, in which the following terms and their derivatives were considered during the search: DT, medicine and digital health virtual healthcare. Finally, analyses of the literature yielded 465 pertinent articles, of which we selected 22 for detailed review. We summarised the application examples of DT in medicine and analysed the applications in many fields of medicine. It revealed encouraging results that DT is being increasing applied in medicine. Results from this literature review indicated that DT healthcare, as a key fusion approach of future medicine, will bring the advantages of precision diagnose and personalised treatment into reality.
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Affiliation(s)
- Tianze Sun
- Department of Orthopedics, First Affiliated Hospital of Dalian Medical University, Dalian, People's Republic of China,Key Laboratory of Molecular Mechanism for Repair and Remodeling of Orthopedic Diseases, Dalian, People's Republic of China
| | - Xiwang He
- School of Mechanical Engineering, Dalian University of Technology, Dalian, People's Republic of China
| | - Zhonghai Li
- Department of Orthopedics, First Affiliated Hospital of Dalian Medical University, Dalian, People's Republic of China,Key Laboratory of Molecular Mechanism for Repair and Remodeling of Orthopedic Diseases, Dalian, People's Republic of China,Zhonghai Li, Department of Orthopedics, First Affiliated Hospital of Dalian Medical University, Dalian 116000, People's Republic of China.
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Stoks J, Hermans BJM, Boukens BJD, Holtackers RJ, Gommers S, Kaya YS, Vernooy K, Cluitmans MJM, Volders PGA, Ter Bekke RMA. High-resolution structural-functional substrate-trigger characterization: Future roadmap for catheter ablation of ventricular tachycardia. Front Cardiovasc Med 2023; 10:1112980. [PMID: 36873402 PMCID: PMC9978225 DOI: 10.3389/fcvm.2023.1112980] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 02/03/2023] [Indexed: 02/18/2023] Open
Abstract
Introduction Patients with ventricular tachyarrhythmias (VT) are at high risk of sudden cardiac death. When appropriate, catheter ablation is modestly effective, with relatively high VT recurrence and complication rates. Personalized models that incorporate imaging and computational approaches have advanced VT management. However, 3D patient-specific functional electrical information is typically not considered. We hypothesize that incorporating non-invasive 3D electrical and structural characterization in a patient-specific model improves VT-substrate recognition and ablation targeting. Materials and methods In a 53-year-old male with ischemic cardiomyopathy and recurrent monomorphic VT, we built a structural-functional model based on high-resolution 3D late-gadolinium enhancement (LGE) cardiac magnetic resonance imaging (3D-LGE CMR), multi-detector computed tomography (CT), and electrocardiographic imaging (ECGI). Invasive data from high-density contact and pace mapping obtained during endocardial VT-substrate modification were also incorporated. The integrated 3D electro-anatomic model was analyzed off-line. Results Merging the invasive voltage maps and 3D-LGE CMR endocardial geometry led to a mean Euclidean node-to-node distance of 5 ± 2 mm. Inferolateral and apical areas of low bipolar voltage (<1.5 mV) were associated with high 3D-LGE CMR signal intensity (>0.4) and with higher transmurality of fibrosis. Areas of functional conduction delay or block (evoked delayed potentials, EDPs) were in close proximity to 3D-LGE CMR-derived heterogeneous tissue corridors. ECGI pinpointed the epicardial VT exit at ∼10 mm from the endocardial site of origin, both juxtaposed to the distal ends of two heterogeneous tissue corridors in the inferobasal left ventricle. Radiofrequency ablation at the entrances of these corridors, eliminating all EDPs, and at the VT site of origin rendered the patient non-inducible and arrhythmia-free until the present day (20 months follow-up). Off-line analysis in our model uncovered dynamic electrical instability of the LV inferolateral heterogeneous scar region which set the stage for an evolving VT circuit. Discussion and conclusion We developed a personalized 3D model that integrates high-resolution structural and electrical information and allows the investigation of their dynamic interaction during arrhythmia formation. This model enhances our mechanistic understanding of scar-related VT and provides an advanced, non-invasive roadmap for catheter ablation.
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Affiliation(s)
- Job Stoks
- Department of Cardiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Center+, Maastricht, Netherlands.,Department of Advanced Computing Sciences, Maastricht University, Maastricht, Netherlands.,Biomedical Research Institute, Hasselt University, Diepenbeek, Belgium
| | - Ben J M Hermans
- Department of Physiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, Netherlands
| | - Bas J D Boukens
- Department of Physiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, Netherlands.,Department of Medical Biology, Amsterdam University Medical Center (UMC), Amsterdam Medical Center (AMC), Amsterdam, Netherlands
| | - Robert J Holtackers
- Department of Radiology and Nuclear Medicine, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Center+, Maastricht, Netherlands
| | - Suzanne Gommers
- Department of Radiology and Nuclear Medicine, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Center+, Maastricht, Netherlands
| | - Yesim S Kaya
- Department of Cardiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Center+, Maastricht, Netherlands
| | - Kevin Vernooy
- Department of Cardiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Center+, Maastricht, Netherlands
| | - Matthijs J M Cluitmans
- Department of Cardiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Center+, Maastricht, Netherlands.,Philips Research, Eindhoven, Netherlands
| | - Paul G A Volders
- Department of Cardiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Center+, Maastricht, Netherlands
| | - Rachel M A Ter Bekke
- Department of Cardiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Center+, Maastricht, Netherlands
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Laumer F, Amrani M, Manduchi L, Beuret A, Rubi L, Dubatovka A, Matter CM, Buhmann JM. Weakly supervised inference of personalized heart meshes based on echocardiography videos. Med Image Anal 2023; 83:102653. [PMID: 36327655 DOI: 10.1016/j.media.2022.102653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 08/27/2022] [Accepted: 10/08/2022] [Indexed: 12/12/2022]
Abstract
Echocardiography provides recordings of the heart chamber size and function and is a central tool for non-invasive diagnosis of heart diseases. It produces high-dimensional video data with substantial stochasticity in the measurements, which frequently prove difficult to interpret. To address this challenge, we propose an automated framework to enable the inference of a high resolution personalized 4D (3D plus time) surface mesh of the cardiac structures from 2D echocardiography video data. Inferring such shape models arises as a key step towards accurate personalized simulation that enables an automated assessment of the cardiac chamber morphology and function. The proposed method is trained using only unpaired echocardiography and heart mesh videos to find a mapping between these distinct visual domains in a self-supervised manner. The resulting model produces personalized 4D heart meshes, which exhibit a high correspondence with the input echocardiography videos. Furthermore, the 4D heart meshes enable the automatic extraction of echocardiographic variables, such as ejection fraction, myocardial muscle mass, and volumetric changes of chamber volumes over time with high temporal resolution.
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Affiliation(s)
- Fabian Laumer
- Institute for Machine Learning at ETH Zürich, Zürich, Switzerland.
| | - Mounir Amrani
- Institute for Machine Learning at ETH Zürich, Zürich, Switzerland
| | - Laura Manduchi
- Institute for Machine Learning at ETH Zürich, Zürich, Switzerland
| | - Ami Beuret
- Institute for Machine Learning at ETH Zürich, Zürich, Switzerland
| | - Lena Rubi
- Institute for Machine Learning at ETH Zürich, Zürich, Switzerland
| | - Alina Dubatovka
- Institute for Machine Learning at ETH Zürich, Zürich, Switzerland
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Lin WH, Zhu Z, Ravikumar V, Sharma V, Tolkacheva EG, McAlpine MC, Ogle BM. A Bionic Testbed for Cardiac Ablation Tools. Int J Mol Sci 2022; 23:ijms232214444. [PMID: 36430922 PMCID: PMC9692733 DOI: 10.3390/ijms232214444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 11/03/2022] [Accepted: 11/09/2022] [Indexed: 11/22/2022] Open
Abstract
Bionic-engineered tissues have been proposed for testing the performance of cardiovascular medical devices and predicting clinical outcomes ex vivo. Progress has been made in the development of compliant electronics that are capable of monitoring treatment parameters and being coupled to engineered tissues; however, the scale of most engineered tissues is too small to accommodate the size of clinical-grade medical devices. Here, we show substantial progress toward bionic tissues for evaluating cardiac ablation tools by generating a centimeter-scale human cardiac disk and coupling it to a hydrogel-based soft-pressure sensor. The cardiac tissue with contiguous electromechanical function was made possible by our recently established method to 3D bioprint human pluripotent stem cells in an extracellular matrix-based bioink that allows for in situ cell expansion prior to cardiac differentiation. The pressure sensor described here utilized electrical impedance tomography to enable the real-time spatiotemporal mapping of pressure distribution. A cryoablation tip catheter was applied to the composite bionic tissues with varied pressure. We found a close correlation between the cell response to ablation and the applied pressure. Under some conditions, cardiomyocytes could survive in the ablated region with more rounded morphology compared to the unablated controls, and connectivity was disrupted. This is the first known functional characterization of living human cardiomyocytes following an ablation procedure that suggests several mechanisms by which arrhythmia might redevelop following an ablation. Thus, bionic-engineered testbeds of this type can be indicators of tissue health and function and provide unique insight into human cell responses to ablative interventions.
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Affiliation(s)
- Wei-Han Lin
- Department of Biomedical Engineering, University of Minnesota—Twin Cities, Minneapolis, MN 55455, USA
- Stem Cell Institute, University of Minnesota—Twin Cities, Minneapolis, MN 55455, USA
| | - Zhijie Zhu
- Department of Mechanical Engineering, University of Minnesota—Twin Cities, Minneapolis, MN 55455, USA
| | - Vasanth Ravikumar
- Department of Electrical Engineering, University of Minnesota—Twin Cities, Minneapolis, MN 55455, USA
| | - Vinod Sharma
- Cardiac Rhythm and Heart Failure Division, Medtronic Inc., Minneapolis, MN 55432, USA
| | - Elena G. Tolkacheva
- Department of Biomedical Engineering, University of Minnesota—Twin Cities, Minneapolis, MN 55455, USA
- Lillehei Heart Institute, University of Minnesota—Twin Cities, Minneapolis, MN 55455, USA
- Institute for Engineering in Medicine, University of Minnesota—Twin Cities, Minneapolis, MN 55455, USA
| | - Michael C. McAlpine
- Department of Mechanical Engineering, University of Minnesota—Twin Cities, Minneapolis, MN 55455, USA
- Institute for Engineering in Medicine, University of Minnesota—Twin Cities, Minneapolis, MN 55455, USA
- Correspondence: (M.C.M.); (B.M.O.)
| | - Brenda M. Ogle
- Department of Biomedical Engineering, University of Minnesota—Twin Cities, Minneapolis, MN 55455, USA
- Stem Cell Institute, University of Minnesota—Twin Cities, Minneapolis, MN 55455, USA
- Lillehei Heart Institute, University of Minnesota—Twin Cities, Minneapolis, MN 55455, USA
- Institute for Engineering in Medicine, University of Minnesota—Twin Cities, Minneapolis, MN 55455, USA
- Masonic Cancer Center, University of Minnesota—Twin Cities, Minneapolis, MN 55455, USA
- Correspondence: (M.C.M.); (B.M.O.)
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Computational Re-Entry Vulnerability Index Mapping to Guide Ablation in Patients With Post-Myocardial Infarction Ventricular Tachycardia. JACC Clin Electrophysiol 2022; 9:301-310. [PMID: 36752477 DOI: 10.1016/j.jacep.2022.10.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 09/07/2022] [Accepted: 10/03/2022] [Indexed: 12/02/2022]
Abstract
BACKGROUND Ventricular tachycardias (VTs) in patients with myocardial infarction (MI) are often treated with catheter ablation. However, the VT induction during this procedure does not always identify all of the relevant activation pathways or may not be possible or tolerated. The re-entry vulnerability index (RVI) quantifies regional activation-repolarization differences and can detect multiple regions susceptible to re-entry without the need to induce the arrhythmia. OBJECTIVES This study aimed to further develop and validate the RVI mapping in patient-specific computational models of post-MI VTs. METHODS Cardiac magnetic resonance imaging data from 4 patients with post-MI VTs were used to induce VTs in a computational electrophysiological model by pacing. The RVI map of a premature beat in each patient model was used to guide virtual ablations. We compared our results with those of clinical ablation in the same patients. RESULTS Single-site virtual RVI-guided ablation prevented VT induction in 3 of 9 cases. Multisite virtual ablations guided by RVI mapping successfully prevented re-entry in all cases (9 of 9). Overall, virtual ablation required 15-fold fewer ablation sites (235.5 ± 97.4 vs 17 ± 6.8) and 2-fold less ablation volume (5.34 ± 1.79 mL vs 2.11 ± 0.65 mL) than the clinical ablation. CONCLUSIONS RVI mapping allows localization of multiple regions susceptible to re-entry and may help guide VT ablation. RVI mapping does not require the induction of arrhythmia and may result in less ablated myocardial volumes with fewer ablation sites.
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Galappaththige S, Gray RA, Costa CM, Niederer S, Pathmanathan P. Credibility assessment of patient-specific computational modeling using patient-specific cardiac modeling as an exemplar. PLoS Comput Biol 2022; 18:e1010541. [PMID: 36215228 PMCID: PMC9550052 DOI: 10.1371/journal.pcbi.1010541] [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: 04/21/2022] [Accepted: 09/02/2022] [Indexed: 11/07/2022] Open
Abstract
Reliable and robust simulation of individual patients using patient-specific models (PSMs) is one of the next frontiers for modeling and simulation (M&S) in healthcare. PSMs, which form the basis of digital twins, can be employed as clinical tools to, for example, assess disease state, predict response to therapy, or optimize therapy. They may also be used to construct virtual cohorts of patients, for in silico evaluation of medical product safety and/or performance. Methods and frameworks have recently been proposed for evaluating the credibility of M&S in healthcare applications. However, such efforts have generally been motivated by models of medical devices or generic patient models; how best to evaluate the credibility of PSMs has largely been unexplored. The aim of this paper is to understand and demonstrate the credibility assessment process for PSMs using patient-specific cardiac electrophysiological (EP) modeling as an exemplar. We first review approaches used to generate cardiac PSMs and consider how verification, validation, and uncertainty quantification (VVUQ) apply to cardiac PSMs. Next, we execute two simulation studies using a publicly available virtual cohort of 24 patient-specific ventricular models, the first a multi-patient verification study, the second investigating the impact of uncertainty in personalized and non-personalized inputs in a virtual cohort. We then use the findings from our analyses to identify how important characteristics of PSMs can be considered when assessing credibility with the approach of the ASME V&V40 Standard, accounting for PSM concepts such as inter- and intra-user variability, multi-patient and “every-patient” error estimation, uncertainty quantification in personalized vs non-personalized inputs, clinical validation, and others. The results of this paper will be useful to developers of cardiac and other medical image based PSMs, when assessing PSM credibility. Patient-specific models are computational models that have been personalized using data from a patient. After decades of research, recent computational, data science and healthcare advances have opened the door to the fulfilment of the enormous potential of such models, from truly personalized medicine to efficient and cost-effective testing of new medical products. However, reliability (credibility) of patient-specific models is key to their success, and there are currently no general guidelines for evaluating credibility of patient-specific models. Here, we consider how frameworks and model evaluation activities that have been developed for generic (not patient-specific) computational models, can be extended to patient specific models. We achieve this through a detailed analysis of the activities required to evaluate cardiac electrophysiological models, chosen as an exemplar field due to its maturity and the complexity of such models. This is the first paper on the topic of reliability of patient-specific models and will help pave the way to reliable and trusted patient-specific modeling across healthcare applications.
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Affiliation(s)
- Suran Galappaththige
- Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland, United States of America
| | - Richard A. Gray
- Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland, United States of America
| | - Caroline Mendonca Costa
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Steven Niederer
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Pras Pathmanathan
- Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland, United States of America
- * E-mail:
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Calibrating cardiac electrophysiology models using latent Gaussian processes on atrial manifolds. Sci Rep 2022; 12:16572. [PMID: 36195766 PMCID: PMC9532401 DOI: 10.1038/s41598-022-20745-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 09/19/2022] [Indexed: 11/24/2022] Open
Abstract
Models of electrical excitation and recovery in the heart have become increasingly detailed, but have yet to be used routinely in the clinical setting to guide personalized intervention in patients. One of the main challenges is calibrating models from the limited measurements that can be made in a patient during a standard clinical procedure. In this work, we propose a novel framework for the probabilistic calibration of electrophysiology parameters on the left atrium of the heart using local measurements of cardiac excitability. Parameter fields are represented as Gaussian processes on manifolds and are linked to measurements via surrogate functions that map from local parameter values to measurements. The posterior distribution of parameter fields is then obtained. We show that our method can recover parameter fields used to generate localised synthetic measurements of effective refractory period. Our methodology is applicable to other measurement types collected with clinical protocols, and more generally for calibration where model parameters vary over a manifold.
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48
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Sung E, Prakosa A, Zhou S, Berger RD, Chrispin J, Nazarian S, Trayanova NA. Fat infiltration in the infarcted heart as a paradigm for ventricular arrhythmias. NATURE CARDIOVASCULAR RESEARCH 2022; 1:933-945. [PMID: 36589896 PMCID: PMC9802586 DOI: 10.1038/s44161-022-00133-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Infiltrating adipose tissue (inFAT) has been recently found to co-localize with scar in infarcted hearts and may contribute to ventricular arrhythmias (VAs), a life-threatening heart rhythm disorder. However, the contribution of inFAT to VA has not been well-established. We investigated the role of inFAT versus scar in VA through a combined prospective clinical and mechanistic computational study. Using personalized computational heart models and comparing the results from simulations of VA dynamics with measured electrophysiological abnormalities during the clinical procedure, we demonstrate that inFAT, rather than scar, is a primary driver of arrhythmogenic propensity and is frequently present in critical regions of the VA circuit. We determined that, within the VA circuitry, inFAT, as opposed to scar, is primarily responsible for conduction slowing in critical sites, mechanistically promoting VA. Our findings implicate inFAT as a dominant player in infarct-related VA, challenging existing paradigms and opening the door for unexplored anti-arrhythmic strategies.
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Affiliation(s)
- Eric Sung
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.,Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA
| | - Adityo Prakosa
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.,Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA
| | - Shijie Zhou
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.,Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA
| | - Ronald D. Berger
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA.,Department of Medicine, Division of Cardiology, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Jonathan Chrispin
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA.,Department of Medicine, Division of Cardiology, Johns Hopkins Hospital, Baltimore, MD, USA.,These authors jointly supervised this work: Jonathan Chrispin, Saman Nazarian, Natalia A. Trayanova
| | - Saman Nazarian
- Division of Cardiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,These authors jointly supervised this work: Jonathan Chrispin, Saman Nazarian, Natalia A. Trayanova
| | - Natalia A. Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.,Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA.,These authors jointly supervised this work: Jonathan Chrispin, Saman Nazarian, Natalia A. Trayanova.,Correspondence and requests for materials should be addressed to Natalia A. Trayanova.
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Abstract
Artificial intelligence (AI) and machine learning (ML) have significantly impacted the field of cardiovascular medicine, especially cardiac electrophysiology (EP), on multiple fronts. The goal of this review is to familiarize readers with the field of AI and ML and their emerging role in EP. The current review is divided into 3 sections. In the first section, we discuss the definitions and basics of AI, ML, and big data. In the second section, we discuss their application to EP in the context of detection, prediction, and management of arrhythmias. Finally, we discuss the regulatory issues, challenges, and future directions of AI in EP.
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50
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Abstract
The global burden caused by cardiovascular disease is substantial, with heart disease representing the most common cause of death around the world. There remains a need to develop better mechanistic models of cardiac function in order to combat this health concern. Heart rhythm disorders, or arrhythmias, are one particular type of disease which has been amenable to quantitative investigation. Here we review the application of quantitative methodologies to explore dynamical questions pertaining to arrhythmias. We begin by describing single-cell models of cardiac myocytes, from which two and three dimensional models can be constructed. Special focus is placed on results relating to pattern formation across these spatially-distributed systems, especially the formation of spiral waves of activation. Next, we discuss mechanisms which can lead to the initiation of arrhythmias, focusing on the dynamical state of spatially discordant alternans, and outline proposed mechanisms perpetuating arrhythmias such as fibrillation. We then review experimental and clinical results related to the spatio-temporal mapping of heart rhythm disorders. Finally, we describe treatment options for heart rhythm disorders and demonstrate how statistical physics tools can provide insights into the dynamics of heart rhythm disorders.
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Affiliation(s)
- Wouter-Jan Rappel
- Department of Physics, University of California San Diego, La Jolla, CA 92037
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