1
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Sung E, Kyranakis S, Daimee UA, Engels M, Prakosa A, Zhou S, Nazarian S, Zimmerman SL, Chrispin J, Trayanova NA. Evaluation of a deep learning-enabled automated computational heart modelling workflow for personalized assessment of ventricular arrhythmias. J Physiol 2024; 602:4625-4644. [PMID: 37060278 DOI: 10.1113/jp284125] [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: 11/17/2022] [Accepted: 04/12/2023] [Indexed: 04/16/2023] Open
Abstract
Personalized, image-based computational heart modelling is a powerful technology that can be used to improve patient-specific arrhythmia risk stratification and ventricular tachycardia (VT) ablation targeting. However, most state-of-the-art methods still require manual interactions by expert users. The goal of this study is to evaluate the feasibility of an automated, deep learning-based workflow for reconstructing personalized computational electrophysiological heart models to guide patient-specific treatment of VT. Contrast-enhanced computed tomography (CE-CT) images with expert ventricular myocardium segmentations were acquired from 111 patients across five cohorts from three different institutions. A deep convolutional neural network (CNN) for segmenting left ventricular myocardium from CE-CT was developed, trained and evaluated. From both CNN-based and expert segmentations in a subset of patients, personalized electrophysiological heart models were reconstructed and rapid pacing was used to induce VTs. CNN-based and expert segmentations were more concordant in the middle myocardium than in the heart's base or apex. Wavefront propagation during pacing was similar between CNN-based and original heart models. Between most sets of heart models, VT inducibility was the same, the number of induced VTs was strongly correlated, and VT circuits co-localized. Our results demonstrate that personalized computational heart models reconstructed from deep learning-based segmentations even with a small training set size can predict similar VT inducibility and circuit locations as those from expertly-derived heart models. Hence, a user-independent, automated framework for simulating arrhythmias in personalized heart models could feasibly be used in clinical settings to aid VT risk stratification and guide VT ablation therapy. KEY POINTS: Personalized electrophysiological heart modelling can aid in patient-specific ventricular tachycardia (VT) risk stratification and VT ablation targeting. Current state-of-the-art, image-based heart models for VT prediction require expert-dependent, manual interactions that may not be accessible across clinical settings. In this study, we develop an automated, deep learning-based workflow for reconstructing personalized heart models capable of simulating arrhythmias and compare its predictions with that of expert-generated heart models. The number and location of VTs was similar between heart models generated from the deep learning-based workflow and expert-generated heart models. These results demonstrate the feasibility of using an automated computational heart modelling workflow to aid in VT therapeutics and has implications for generalizing personalized computational heart technology to a broad range of clinical centres.
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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
| | - Stephen Kyranakis
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA
| | - Usama A Daimee
- Division of Cardiology, Department of Medicine, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Marc Engels
- Division of Cardiology, Department of Medicine, Johns Hopkins Hospital, 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
| | - Saman Nazarian
- Division of Cardiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Stefan L Zimmerman
- Department of Radiology and Radiological Sciences, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Jonathan Chrispin
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Division of Cardiology, Department of Medicine, Johns Hopkins Hospital, Baltimore, MD, USA
| | - 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
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2
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Amini N. The emergence of deep learning as the current state of art for classification and risk assessment of ventricular arrhythmias. J Physiol 2024; 602:4645-4647. [PMID: 39252427 DOI: 10.1113/jp287420] [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: 07/31/2024] [Accepted: 07/31/2024] [Indexed: 09/11/2024] Open
Affiliation(s)
- Negin Amini
- Physiology Department, McGill University, Canada
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3
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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 PMCID: PMC11381036 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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4
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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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5
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Torre M, Morganti S, Pasqualini FS, Reali A. Current progress toward isogeometric modeling of the heart biophysics. BIOPHYSICS REVIEWS 2023; 4:041301. [PMID: 38510845 PMCID: PMC10903424 DOI: 10.1063/5.0152690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 10/24/2023] [Indexed: 03/22/2024]
Abstract
In this paper, we review a powerful methodology to solve complex numerical simulations, known as isogeometric analysis, with a focus on applications to the biophysical modeling of the heart. We focus on the hemodynamics, modeling of the valves, cardiac tissue mechanics, and on the simulation of medical devices and treatments. For every topic, we provide an overview of the methods employed to solve the specific numerical issue entailed by the simulation. We try to cover the complete process, starting from the creation of the geometrical model up to the analysis and post-processing, highlighting the advantages and disadvantages of the methodology.
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Affiliation(s)
- Michele Torre
- Department of Civil Engineering and Architecture, University of Pavia, Via Ferrata 3, 27100 Pavia, Italy
| | - Simone Morganti
- Department of Electrical, Computer, and Biomedical Engineering, University of Pavia, Via Ferrata 5, 27100 Pavia, Italy
| | - Francesco S. Pasqualini
- Department of Civil Engineering and Architecture, University of Pavia, Via Ferrata 3, 27100 Pavia, Italy
| | - Alessandro Reali
- Department of Civil Engineering and Architecture, University of Pavia, Via Ferrata 3, 27100 Pavia, Italy
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6
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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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7
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Jæger KH, Tveito A. The simplified Kirchhoff network model (SKNM): a cell-based reaction-diffusion model of excitable tissue. Sci Rep 2023; 13:16434. [PMID: 37777588 PMCID: PMC10542379 DOI: 10.1038/s41598-023-43444-9] [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: 06/02/2023] [Accepted: 09/24/2023] [Indexed: 10/02/2023] Open
Abstract
Cell-based models of excitable tissues offer the advantage of cell-level precision, which cannot be achieved using traditional homogenized electrophysiological models. However, this enhanced accuracy comes at the cost of increased computational demands, necessitating the development of efficient cell-based models. The widely-accepted bidomain model serves as the standard in computational cardiac electrophysiology, and under certain anisotropy ratio conditions, it is well known that it can be reduced to the simpler monodomain model. Recently, the Kirchhoff Network Model (KNM) was developed as a cell-based counterpart to the bidomain model. In this paper, we aim to demonstrate that KNM can be simplified using the same steps employed to derive the monodomain model from the bidomain model. We present the cell-based Simplified Kirchhoff Network Model (SKNM), which produces results closely aligned with those of KNM while requiring significantly less computational resources.
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8
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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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9
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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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10
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Serra D, Franco P, Romero P, Romitti G, Garcia-Fernandez I, Lozano M, Liberos A, Penela D, Berruezo A, Camara O, Rodrigo M, Sebastian R. Assessment of Risk for Ventricular Tachycardia based on Extensive Electrophysiology Simulations. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083190 DOI: 10.1109/embc40787.2023.10340169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Patients that have suffered a myocardial infarction are at high risk of developing ventricular tachycardia. Patient stratification is often determined by characterization of the underlying myocardial substrate by cardiac imaging methods. In this study, we show that computer modeling of cardiac electrophysiology based on personalized fast 3D simulations can help to assess patient risk to arrhythmia. We perform a large simulation study on 21 patient digital twins and reproduce successfully the clinical outcomes. In addition, we provide the sites which are prone to sustain ventricular tachycardias, i.e, onset sites around the scar region, and validate if they colocalize with exit sites from slow conduction channels.Clinical relevance- Fast electrophysiological simulations can provide advanced patient stratification indices and predict arrhythmic susceptibility to suffer from ventricular tachycardia in patients that have suffered a myocardial infarction.
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11
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Jæger KH, Tveito A. Efficient, cell-based simulations of cardiac electrophysiology; The Kirchhoff Network Model (KNM). NPJ Syst Biol Appl 2023; 9:25. [PMID: 37316522 DOI: 10.1038/s41540-023-00288-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 06/05/2023] [Indexed: 06/16/2023] Open
Abstract
Mathematical models based on homogenized representation of cardiac tissue have greatly improved our understanding of cardiac electrophysiology. However, these models are too coarse to investigate the dynamics at the level of the myocytes since the cells are not present in homogenized models. Recently, fine scale models have been proposed to allow for cell-level resolution of the dynamics, but these models are too computationally expensive to be used in applications like whole heart simulations of large animals. To address this issue, we propose a model that balances computational demands and physiological accuracy. The model is founded on Kirchhoff's current law, and represents every myocyte in the tissue. This allows specific properties to be assigned to individual cardiomyocytes, and other cell types like fibroblasts can be added to the model in an accurate manner while keeping the computing efforts reasonable.
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12
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Viola F, Del Corso G, De Paulis R, Verzicco R. GPU accelerated digital twins of the human heart open new routes for cardiovascular research. Sci Rep 2023; 13:8230. [PMID: 37217483 DOI: 10.1038/s41598-023-34098-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 04/24/2023] [Indexed: 05/24/2023] Open
Abstract
The recruitment of patients for rare or complex cardiovascular diseases is a bottleneck for clinical trials and digital twins of the human heart have recently been proposed as a viable alternative. In this paper we present an unprecedented cardiovascular computer model which, relying on the latest GPU-acceleration technologies, replicates the full multi-physics dynamics of the human heart within a few hours per heartbeat. This opens the way to extensive simulation campaigns to study the response of synthetic cohorts of patients to cardiovascular disorders, novel prosthetic devices or surgical procedures. As a proof-of-concept we show the results obtained for left bundle branch block disorder and the subsequent cardiac resynchronization obtained by pacemaker implantation. The in-silico results closely match those obtained in clinical practice, confirming the reliability of the method. This innovative approach makes possible a systematic use of digital twins in cardiovascular research, thus reducing the need of real patients with their economical and ethical implications. This study is a major step towards in-silico clinical trials in the era of digital medicine.
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Affiliation(s)
- Francesco Viola
- Gran Sasso Science Institute (GSSI), L'Aquila, Italy
- INFN-Laboratori Nazionali del Gran Sasso, Assergi (AQ), Italy
| | - Giulio Del Corso
- Gran Sasso Science Institute (GSSI), L'Aquila, Italy
- Institute of Information Science and Technologies A. Faedo, CNR, Pisa, Italy
| | - Ruggero De Paulis
- European Hospital, Rome, Italy
- UniCamillus International University of Health Sciences, Rome, Italy
| | - Roberto Verzicco
- Gran Sasso Science Institute (GSSI), L'Aquila, Italy.
- University of Rome Tor Vergata, Rome, Italy.
- POF Group, University of Twente, Enschede, The Netherlands.
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13
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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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14
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Ezzeddine FM, Ward RC, Asirvatham SJ, DeSimone CV. Mapping and ablation of ventricular fibrillation substrate. J Interv Card Electrophysiol 2023:10.1007/s10840-022-01454-z. [PMID: 36598715 DOI: 10.1007/s10840-022-01454-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 12/09/2022] [Indexed: 01/05/2023]
Abstract
Ventricular fibrillation (VF) is a life-threatening arrhythmia and a common cause of sudden cardiac death (SCD). A basic understanding of its mechanistic underpinning is crucial for enhancing our knowledge to develop innovative mapping and ablation techniques for this lethal rhythm. Significant advances in our understanding of VF have been made especially in the basic science and pre-clinical experimental realms. However, these studies have not yet translated into a robust clinical approach to identify and successfully ablate both the structural and functional substrate of VF. In this review, we aim to (1) provide a conceptual framework of VF and an overview of the data supporting the spatiotemporal dynamics of VF, (2) review experimental approaches to mapping VF to elucidate drivers and substrate for maintenance with a focus on the His-Purkinje system, (3) discuss current approaches using catheter ablation to treat VF, and (4) highlight current unknowns and gaps in the field where future work is necessary to transform the clinical landscape.
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Affiliation(s)
- Fatima M Ezzeddine
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
| | - Robert Charles Ward
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
| | - Samuel J Asirvatham
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
| | - Christopher V DeSimone
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.
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15
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Calzone L, Noël V, Barillot E, Kroemer G, Stoll G. Modeling signaling pathways in biology with MaBoSS: From one single cell to a dynamic population of heterogeneous interacting cells. Comput Struct Biotechnol J 2022; 20:5661-5671. [PMID: 36284705 PMCID: PMC9582792 DOI: 10.1016/j.csbj.2022.10.003] [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: 07/05/2022] [Revised: 09/30/2022] [Accepted: 10/02/2022] [Indexed: 11/24/2022] Open
Abstract
As a result of the development of experimental technologies and the accumulation of data, biological and molecular processes can be described as complex networks of signaling pathways. These networks are often directed and signed, where nodes represent entities (genes/proteins) and arrows interactions. They are translated into mathematical models by adding a dynamic layer onto them. Such mathematical models help to understand and interpret non-intuitive experimental observations and to anticipate the response to external interventions such as drug effects on phenotypes. Several frameworks for modeling signaling pathways exist. The choice of the appropriate framework is often driven by the experimental context. In this review, we present MaBoSS, a tool based on Boolean modeling using a continuous time approach, which predicts time-dependent probabilities of entities in different biological contexts. MaBoSS was initially built to model the intracellular signaling in non-interacting homogeneous cell populations. MaBoSS was then adapted to model heterogeneous cell populations (EnsembleMaBoSS) by considering families of models rather than a unique model. To account for more complex questions, MaBoSS was extended to simulate dynamical interacting populations (UPMaBoSS), with a precise spatial distribution (PhysiBoSS). To illustrate all these levels of description, we show how each of these tools can be used with a running example of a simple model of cell fate decisions. Finally, we present practical applications to cancer biology and studies of the immune response.
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Affiliation(s)
- Laurence Calzone
- Institut Curie, PSL Research University, F-75005 Paris, France
- INSERM, U900, F-75005 Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France
| | - Vincent Noël
- Institut Curie, PSL Research University, F-75005 Paris, France
- INSERM, U900, F-75005 Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France
| | - Emmanuel Barillot
- Institut Curie, PSL Research University, F-75005 Paris, France
- INSERM, U900, F-75005 Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France
| | - Guido Kroemer
- Centre de Recherche des Cordeliers, Equipe labellisé par la Ligue contre le cancer, Université de Paris Cité, Sorbonne Université, Inserm U1138, Institut Universitaire de France, Paris, France
- Metabolomics and Cell Biology Platforms, Institut Gustave Roussy, Villejuif, France
- Institut du Cancer Paris CARPEM, Department of Biology, Hôpital Europén Georges Pompidou, AP-HP, Paris, France
| | - Gautier Stoll
- Centre de Recherche des Cordeliers, Equipe labellisé par la Ligue contre le cancer, Université de Paris Cité, Sorbonne Université, Inserm U1138, Institut Universitaire de France, Paris, France
- Metabolomics and Cell Biology Platforms, Institut Gustave Roussy, Villejuif, France
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16
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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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17
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Xie E, Sung E, Saad E, Trayanova N, Wu KC, Chrispin J. Advanced imaging for risk stratification for ventricular arrhythmias and sudden cardiac death. Front Cardiovasc Med 2022; 9:884767. [PMID: 36072882 PMCID: PMC9441865 DOI: 10.3389/fcvm.2022.884767] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 08/02/2022] [Indexed: 11/13/2022] Open
Abstract
Sudden cardiac death (SCD) is a leading cause of mortality, comprising approximately half of all deaths from cardiovascular disease. In the US, the majority of SCD (85%) occurs in patients with ischemic cardiomyopathy (ICM) and a subset in patients with non-ischemic cardiomyopathy (NICM), who tend to be younger and whose risk of mortality is less clearly delineated than in ischemic cardiomyopathies. The conventional means of SCD risk stratification has been the determination of the ejection fraction (EF), typically via echocardiography, which is currently a means of determining candidacy for primary prevention in the form of implantable cardiac defibrillators (ICDs). Advanced cardiac imaging methods such as cardiac magnetic resonance imaging (CMR), single-photon emission computerized tomography (SPECT) and positron emission tomography (PET), and computed tomography (CT) have emerged as promising and non-invasive means of risk stratification for sudden death through their characterization of the underlying myocardial substrate that predisposes to SCD. Late gadolinium enhancement (LGE) on CMR detects myocardial scar, which can inform ICD decision-making. Overall scar burden, region-specific scar burden, and scar heterogeneity have all been studied in risk stratification. PET and SPECT are nuclear methods that determine myocardial viability and innervation, as well as inflammation. CT can be used for assessment of myocardial fat and its association with reentrant circuits. Emerging methodologies include the development of "virtual hearts" using complex electrophysiologic modeling derived from CMR to attempt to predict arrhythmic susceptibility. Recent developments have paired novel machine learning (ML) algorithms with established imaging techniques to improve predictive performance. The use of advanced imaging to augment risk stratification for sudden death is increasingly well-established and may soon have an expanded role in clinical decision-making. ML could help shift this paradigm further by advancing variable discovery and data analysis.
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Affiliation(s)
- Eric Xie
- Division of Cardiology, Department of Medicine, Section of Cardiac Electrophysiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Eric Sung
- Division of Cardiology, Department of Medicine, Section of Cardiac Electrophysiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Elie Saad
- Division of Cardiology, Department of Medicine, Section of Cardiac Electrophysiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Natalia Trayanova
- Division of Cardiology, Department of Medicine, Section of Cardiac Electrophysiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Katherine C. Wu
- Division of Cardiology, Department of Medicine, Section of Cardiac Electrophysiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Jonathan Chrispin
- Division of Cardiology, Department of Medicine, Section of Cardiac Electrophysiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
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18
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Molinari L, Zaltieri M, Massaroni C, Filippi S, Gizzi A, Schena E. Multiscale and Multiphysics Modeling of Anisotropic Cardiac RFCA: Experimental-Based Model Calibration via Multi-Point Temperature Measurements. Front Physiol 2022; 13:845896. [PMID: 35514332 PMCID: PMC9062295 DOI: 10.3389/fphys.2022.845896] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 03/22/2022] [Indexed: 11/13/2022] Open
Abstract
Radiofrequency catheter ablation (RFCA) is the mainstream treatment for drug-refractory cardiac fibrillation. Multiple studies demonstrated that incorrect dosage of radiofrequency energy to the myocardium could lead to uncontrolled tissue damage or treatment failure, with the consequent need for unplanned reoperations. Monitoring tissue temperature during thermal therapy and predicting the extent of lesions may improve treatment efficacy. Cardiac computational modeling represents a viable tool for identifying optimal RFCA settings, though predictability issues still limit a widespread usage of such a technology in clinical scenarios. We aim to fill this gap by assessing the influence of the intrinsic myocardial microstructure on the thermo-electric behavior at the tissue level. By performing multi-point temperature measurements on ex-vivo swine cardiac tissue samples, the experimental characterization of myocardial thermal anisotropy allowed us to assemble a fine-tuned thermo-electric material model of the cardiac tissue. We implemented a multiphysics and multiscale computational framework, encompassing thermo-electric anisotropic conduction, phase-lagging for heat transfer, and a three-state dynamical system for cellular death and lesion estimation. Our analysis resulted in a remarkable agreement between ex-vivo measurements and numerical results. Accordingly, we identified myocardium anisotropy as the driving effect on the outcomes of hyperthermic treatments. Furthermore, we characterized the complex nonlinear couplings regulating tissue behavior during RFCA, discussing model calibration, limitations, and perspectives.
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Affiliation(s)
- Leonardo Molinari
- Department of Mathematics and Computer Science, Emory University, Atlanta, GA, United States
| | - Martina Zaltieri
- Laboratory of Measurement and Biomedical Instrumentation, Department of Engineering, University of Rome Campus Bio-Medico, Rome, Italy
| | - Carlo Massaroni
- Laboratory of Measurement and Biomedical Instrumentation, Department of Engineering, University of Rome Campus Bio-Medico, Rome, Italy
| | - Simonetta Filippi
- Nonlinear Physics and Mathematical Modeling Lab, Department of Engineering, University of Rome Campus Bio-Medico, Rome, Italy
| | - Alessio Gizzi
- Nonlinear Physics and Mathematical Modeling Lab, Department of Engineering, University of Rome Campus Bio-Medico, Rome, Italy
| | - Emiliano Schena
- Laboratory of Measurement and Biomedical Instrumentation, Department of Engineering, University of Rome Campus Bio-Medico, Rome, Italy
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19
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Roth BJ. Bidomain modeling of electrical and mechanical properties of cardiac tissue. BIOPHYSICS REVIEWS 2021; 2:041301. [PMID: 38504719 PMCID: PMC10903405 DOI: 10.1063/5.0059358] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Accepted: 10/15/2021] [Indexed: 03/21/2024]
Abstract
Throughout the history of cardiac research, there has been a clear need to establish mathematical models to complement experimental studies. In an effort to create a more complete picture of cardiac phenomena, the bidomain model was established in the late 1970s to better understand pacing and defibrillation in the heart. This mathematical model has seen ongoing use in cardiac research, offering mechanistic insight that could not be obtained from experimental pursuits. Introduced from a historical perspective, the origins of the bidomain model are reviewed to provide a foundation for researchers new to the field and those conducting interdisciplinary research. The interplay of theory and experiment with the bidomain model is explored, and the contributions of this model to cardiac biophysics are critically evaluated. Also discussed is the mechanical bidomain model, which is employed to describe mechanotransduction. Current challenges and outstanding questions in the use of the bidomain model are addressed to give a forward-facing perspective of the model in future studies.
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Affiliation(s)
- Bradley J. Roth
- Department of Physics, Oakland University, Rochester, Michigan 48309, USA
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