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O'Hara RP, Lacy A, Prakosa A, Kholmovski EG, Maurizi N, Pruvot EJ, Teres C, Antiochos P, Masi A, Schwitter J, Trayanova NA. Cardiac MRI Oversampling in Heart Digital Twins Improves Preprocedure Ventricular Tachycardia Identification in Postinfarction Patients. JACC Clin Electrophysiol 2024; 10:2035-2048. [PMID: 38934970 DOI: 10.1016/j.jacep.2024.04.032] [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: 12/19/2023] [Revised: 04/19/2024] [Accepted: 04/27/2024] [Indexed: 06/28/2024]
Abstract
BACKGROUND Ventricular tachycardia (VT), which can lead to sudden cardiac death, occurs frequently in patients after myocardial infarction. Radiofrequency catheter ablation (RFA) is a modestly effective treatment of VT, but it has limitations and risks. Cardiac magnetic resonance (CMR)-based heart digital twins have emerged as a useful tool for identifying VT circuits for RFA treatment planning. However, the CMR resolution used to reconstruct these digital twins may impact VT circuit predictions, leading to incorrect RFA treatment planning. OBJECTIVES This study sought to predict RFA targets in the arrhythmogenic substrate using heart digital twins reconstructed from both clinical and high-resolution 2-dimensional CMR datasets and compare the predictions. METHODS High-resolution (1.35 × 1.35 × 3 mm), or oversampled resolution (Ov-Res), short-axis late gadolinium-enhanced CMR was acquired by combining 2 subsequent clinical resolution (Clin-Res) (1.35 × 1.35 × 6 mm) short-axis late gadolinium-enhanced CMR scans from 6 post-myocardial infarction patients undergoing VT ablation and used to reconstruct a total of 3 digital twins (1 Ov-Res, 2 Clin-Res) for each patient. Rapid pacing was used to assess VT circuits and identify the optimal ablation targets in each digital twin. VT circuits predicted by the digital twins were compared with intraprocedural electroanatomic mapping data and used to identify emergent VT. RESULTS The Ov-Res digital twins reduced partial volume effects and better predicted unique VT circuits compared with the Clin-Res digital twins (66.6% vs 54.5%; P < 0.01). Only the Ov-Res digital twin successfully identified emergent VT after a failed initial ablation. CONCLUSIONS Digital twin infarct geometry and VT circuit predictions depend on the magnetic resonance resolution. Ov-Res digital twins better predict VT circuits and emergent VT, which may improve RFA outcomes.
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Affiliation(s)
- Ryan P O'Hara
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Audrey Lacy
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Adityo Prakosa
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Eugene G Kholmovski
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Niccolo Maurizi
- Department of Cardiology, Lausanne University Hospital, Lausanne, Switzerland
| | - Etienne J Pruvot
- Department of Cardiology, Lausanne University Hospital, Lausanne, Switzerland
| | - Cheryl Teres
- Department of Cardiology, Lausanne University Hospital, Lausanne, Switzerland
| | | | - Ambra Masi
- Department of Cardiology, Lausanne University Hospital, Lausanne, Switzerland
| | - Juerg Schwitter
- Department of Cardiology, Lausanne University Hospital, Lausanne, Switzerland
| | - Natalia A Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA.
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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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Cao B, Zhang N, Fu Z, Dong R, Chen T, Zhang W, Tong L, Wang Z, Ma M, Song Z, Pan F, Bai J, Wu Y, Deng D, Xia L. Studying the Influence of Finite Element Mesh Size on the Accuracy of Ventricular Tachycardia Simulation. Rev Cardiovasc Med 2023; 24:351. [PMID: 39077071 PMCID: PMC11272846 DOI: 10.31083/j.rcm2412351] [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: 03/21/2023] [Revised: 07/12/2023] [Accepted: 07/17/2023] [Indexed: 07/31/2024] Open
Abstract
Background Ventricular tachycardia (VT) is a life-threatening heart condition commonly seen in patients with myocardial infarction (MI). Although personalized computational modeling has been used to understand VT and its treatment noninvasively, this approach can be computationally intensive and time consuming. Therefore, finding a balance between mesh size and computational efficiency is important. This study aimed to find an optimal mesh resolution that minimizes the need for computational resources while maintaining numerical accuracy and to investigate the effect of mesh resolution variation on the simulation results. Methods We constructed ventricular models from contrast-enhanced magnetic resonance imaging data from six patients with MI. We created seven different models for each patient, with average edge lengths ranging from 315 to 645 µm using commercial software, Mimics. Programmed electrical stimulation was used to assess VT inducibility from 19 sites in each heart model. Results The simulation results in the slab model with adaptive tetrahedral mesh (same as in the patient-specific model) showed that the absolute and relative differences in conduction velocity (CV) were 6.1 cm/s and 7.8% between average mesh sizes of 142 and 600 µm, respectively. However, the simulation results in the six patient-specific models showed that average mesh sizes with 350 µm yielded over 85% accuracy for clinically relevant VT. Although average mesh sizes of 417 and 478 µm could also achieve approximately 80% accuracy for clinically relevant VT, the percentage of incorrectly predicted VTs increases. When conductivity was modified to match the CV in the model with the finest mesh size, the overall ratio of positively predicted VT increased. Conclusions The proposed personalized heart model could achieve an optimal balance between simulation time and VT prediction accuracy when discretized with adaptive tetrahedral meshes with an average edge length about 350 µm.
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Affiliation(s)
- Boyang Cao
- College of Biomedical Engineering & Instrument Science, Zhejiang University, 310058 Hangzhou, Zhejiang, China
- School of Biomedical Engineering, Dalian University of Technology, 116024 Dalian, Liaoning, China
| | - Nan Zhang
- Department of Radiology, Beijing Anzhen Hospital Affiliated to Capital Medical University, 100029 Beijing, China
| | - Zhenyin Fu
- College of Biomedical Engineering & Instrument Science, Zhejiang University, 310058 Hangzhou, Zhejiang, China
| | - Ruiqing Dong
- Department of Radiology, Dushu Lake Hospital Affiliated to Soochow University, 215000 Suzhou, Jiangsu, China
| | - Tan Chen
- Department of Radiology, Dushu Lake Hospital Affiliated to Soochow University, 215000 Suzhou, Jiangsu, China
| | - Weiguo Zhang
- Department of Radiology, Dushu Lake Hospital Affiliated to Soochow University, 215000 Suzhou, Jiangsu, China
| | - Lv Tong
- School of Biomedical Engineering, Dalian University of Technology, 116024 Dalian, Liaoning, China
| | - Zefeng Wang
- Department of Cardiology, Beijing Anzhen Hospital Affiliated to Capital Medical University, 100029 Beijing, China
| | - Mingxia Ma
- Department of General Medicine, Liaoning Cancer Hospital of Dalian University of Technology, 116024 Dalian, Liaoning, China
| | - Zhanchun Song
- Department of Cardiology, Fushun Central Hospital, 113006 Fushun, Liaoning, China
| | - Fuzhi Pan
- Department of General Medicine, Liaoning Cancer Hospital of Dalian University of Technology, 116024 Dalian, Liaoning, China
| | - Jinghui Bai
- Department of General Medicine, Liaoning Cancer Hospital of Dalian University of Technology, 116024 Dalian, Liaoning, China
| | - Yongquan Wu
- Department of Cardiology, Beijing Anzhen Hospital Affiliated to Capital Medical University, 100029 Beijing, China
| | - Dongdong Deng
- School of Biomedical Engineering, Dalian University of Technology, 116024 Dalian, Liaoning, China
| | - Ling Xia
- College of Biomedical Engineering & Instrument Science, Zhejiang University, 310058 Hangzhou, Zhejiang, China
- Research Center for Healthcare Data Science, Zhejiang Lab, 310003 Hangzhou, Zhejiang, China
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4
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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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5
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Ochs AR, Boyle PM. Optogenetic Modulation of Arrhythmia Triggers: Proof-of-Concept from Computational Modeling. Cell Mol Bioeng 2023; 16:243-259. [PMID: 37810996 PMCID: PMC10550900 DOI: 10.1007/s12195-023-00781-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 08/14/2023] [Indexed: 10/10/2023] Open
Abstract
Introduction Early afterdepolarizations (EADs) are secondary voltage depolarizations associated with reduced repolarization reserve (RRR) that can trigger lethal arrhythmias. Relating EADs to triggered activity is difficult to study, so the ability to suppress or provoke EADs would be experimentally useful. Here, we use computational simulations to assess the feasibility of subthreshold optogenetic stimulation modulating the propensity for EADs (cell-scale) and EAD-associated ectopic beats (organ-scale). Methods We modified a ventricular ionic model by reducing rapid delayed rectifier potassium (0.25-0.1 × baseline) and increasing L-type calcium (1.0-3.5 × baseline) currents to create RRR conditions with varying severity. We ran simulations in models of single cardiomyocytes and left ventricles from post-myocardial infarction patient MRI scans. Optogenetic stimulation was simulated using either ChR2 (depolarizing) or GtACR1 (repolarizing) opsins. Results In cell-scale simulations without illumination, EADs were seen for 164 of 416 RRR conditions. Subthreshold stimulation of GtACR1 reduced EAD incidence by up to 84.8% (25/416 RRR conditions; 0.1 μW/mm2); in contrast, subthreshold ChR2 excitation increased EAD incidence by up to 136.6% (388/416 RRR conditions; 50 μW/mm2). At the organ scale, we assumed simultaneous, uniform illumination of the epicardial and endocardial surfaces. GtACR1-mediated suppression (10-50 μW/mm2) and ChR2-mediated unmasking (50-100 μW/mm2) of EAD-associated ectopic beats were feasible in three distinct ventricular models. Conclusions Our findings suggest that optogenetics could be used to silence or provoke both EADs and EAD-associated ectopic beats. Validation in animal models could lead to exciting new experimental regimes and potentially to novel anti-arrhythmia treatments. Supplementary Information The online version contains supplementary material available at 10.1007/s12195-023-00781-z.
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Affiliation(s)
- Alexander R. Ochs
- Department of Bioengineering, UW Bioengineering, University of Washington, 3720 15th Ave NE N107, UW Mailbox 355061, Seattle, WA 98195 USA
| | - Patrick M. Boyle
- Department of Bioengineering, UW Bioengineering, University of Washington, 3720 15th Ave NE N107, UW Mailbox 355061, Seattle, WA 98195 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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6
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Campos FO, Neic A, Mendonca Costa C, Whitaker J, O'Neill M, Razavi R, Rinaldi CA, DanielScherr, Niederer SA, Plank G, Bishop MJ. An automated near-real time computational method for induction and treatment of scar-related ventricular tachycardias. Med Image Anal 2022; 80:102483. [PMID: 35667328 PMCID: PMC10114098 DOI: 10.1016/j.media.2022.102483] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 04/22/2022] [Accepted: 05/20/2022] [Indexed: 02/05/2023]
Abstract
Catheter ablation is currently the only curative treatment for scar-related ventricular tachycardias (VTs). However, not only are ablation procedures long, with relatively high risk, but success rates are punitively low, with frequent VT recurrence. Personalized in-silico approaches have the opportunity to address these limitations. However, state-of-the-art reaction diffusion (R-D) simulations of VT induction and subsequent circuits used for in-silico ablation target identification require long execution times, along with vast computational resources, which are incompatible with the clinical workflow. Here, we present the Virtual Induction and Treatment of Arrhythmias (VITA), a novel, rapid and fully automated computational approach that uses reaction-Eikonal methodology to induce VT and identify subsequent ablation targets. The rationale for VITA is based on finding isosurfaces associated with an activation wavefront that splits in the ventricles due to the presence of an isolated isthmus of conduction within the scar; once identified, each isthmus may be assessed for their vulnerability to sustain a reentrant circuit, and the corresponding exit site automatically identified for potential ablation targeting. VITA was tested on a virtual cohort of 7 post-infarcted porcine hearts and the results compared to R-D simulations. Using only a standard desktop machine, VITA could detect all scar-related VTs, simulating activation time maps and ECGs (for clinical comparison) as well as computing ablation targets in 48 minutes. The comparable VTs probed by the R-D simulations took 68.5 hours on 256 cores of high-performance computing infrastructure. The set of lesions computed by VITA was shown to render the ventricular model VT-free. VITA could be used in near real-time as a complementary modality aiding in clinical decision-making in the treatment of post-infarction VTs.
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Affiliation(s)
- Fernando O Campos
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
| | | | - Caroline Mendonca Costa
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - John Whitaker
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Guy's and St. Thomas' NHS Foundation Trust, Cardiovascular Directorate
| | - Mark O'Neill
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Guy's and St. Thomas' NHS Foundation Trust, Cardiovascular Directorate
| | - Reza Razavi
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Christopher A Rinaldi
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Guy's and St. Thomas' NHS Foundation Trust, Cardiovascular Directorate
| | - DanielScherr
- Division of Cardiology, Department of Internal Medicine, Medical University of Graz, Austria
| | - Steven A Niederer
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Gernot Plank
- Gottfried Schatz Research Center Biophysics, Medical University of Graz, Graz, Austria; BioTechMed-Graz, Graz, Austria
| | - Martin J Bishop
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
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Jáuregui B, Calvo N, Olóriz T, López-Perales C, Asso A. Cardiac Magnetic Resonance and Ventricular Arrhythmia Risk Assessment in Chronic Ischemic Cardiomyopathy: An Unmet Need? Rev Cardiovasc Med 2022; 23:246. [PMID: 39076917 PMCID: PMC11266788 DOI: 10.31083/j.rcm2307246] [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: 04/01/2022] [Revised: 05/21/2022] [Accepted: 05/27/2022] [Indexed: 07/31/2024] Open
Abstract
Ischemic cardiomyopathy (ICM) constitutes a major public health issue, directly involved in the prevalence and incidence of heart failure, ventricular arrhythmias (VA) and sudden cardiac death (SCD). Severe impairment of left ventricular ejection fraction (LVEF) is considered a high-risk marker for SCD, conditioning the criteria that determine an implantable cardiac defibrillator (ICD) placement in primary prevention according to current clinical guidelines. However, its sensitivity and specificity values for the prediction of SCD in ICM may not be highest. Myocardial characterization using cardiac magnetic resonance with late gadolinium enhancement (CMR-LGE) sequences has made it possible to answer clinically relevant questions that are currently not assessable with LVEF alone. There is growing scientific evidence in favor of the relationship between fibrosis evaluated with CMR and the appearance of VA/SCD in patients with ICM. This evidence should make us contemplate a more realistic clinical value of LVEF in our daily clinical decision-making.
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Affiliation(s)
- Beatriz Jáuregui
- Arrhytmia Section, Cardiology Department, Miguel Servet University Hospital, 50009 Zaragoza, Spain
| | - Naiara Calvo
- Arrhytmia Section, Cardiology Department, Miguel Servet University Hospital, 50009 Zaragoza, Spain
| | - Teresa Olóriz
- Arrhytmia Section, Cardiology Department, Miguel Servet University Hospital, 50009 Zaragoza, Spain
| | - Carlos López-Perales
- Arrhytmia Section, Cardiology Department, Miguel Servet University Hospital, 50009 Zaragoza, Spain
| | - Antonio Asso
- Arrhytmia Section, Cardiology Department, Miguel Servet University Hospital, 50009 Zaragoza, Spain
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Dawkins JF, Ehdaie A, Rogers R, Soetkamp D, Valle J, Holm K, Sanchez L, Tremmel I, Nawaz A, Shehata M, Wang X, Prakosa A, Yu J, Van Eyk JE, Trayanova N, Marbán E, Cingolani E. Biological substrate modification suppresses ventricular arrhythmias in a porcine model of chronic ischaemic cardiomyopathy. Eur Heart J 2022; 43:2139-2156. [PMID: 35262692 PMCID: PMC9649918 DOI: 10.1093/eurheartj/ehac042] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 01/25/2022] [Accepted: 01/26/2022] [Indexed: 08/15/2023] Open
Abstract
AIMS Cardiomyopathy patients are prone to ventricular arrhythmias (VA) and sudden cardiac death. Current therapies to prevent VA include radiofrequency ablation to destroy slowly conducting pathways of viable myocardium which support re-entry. Here, we tested the reverse concept, namely that boosting local tissue viability in zones of slow conduction might eliminate slow conduction and suppress VA in ischaemic cardiomyopathy. METHODS AND RESULTS Exosomes are extracellular vesicles laden with bioactive cargo. Exosomes secreted by cardiosphere-derived cells (CDCEXO) reduce scar and improve heart function after intramyocardial delivery. In a VA-prone porcine model of ischaemic cardiomyopathy, we injected CDCEXO or vehicle into zones of delayed conduction defined by electroanatomic mapping. Up to 1-month post-injection, CDCEXO, but not the vehicle, decreased myocardial scar, suppressed slowly conducting electrical pathways, and inhibited VA induction by programmed electrical stimulation. In silico reconstruction of electrical activity based on magnetic resonance images accurately reproduced the suppression of VA inducibility by CDCEXO. Strong anti-fibrotic effects of CDCEXO, evident histologically and by proteomic analysis from pig hearts, were confirmed in a co-culture assay of cardiomyocytes and fibroblasts. CONCLUSION Biological substrate modification by exosome injection may be worth developing as a non-destructive alternative to conventional ablation for the prevention of recurrent ventricular tachyarrhythmias.
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Affiliation(s)
- James F. Dawkins
- Smidt Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA 90048, USA
| | - Ashkan Ehdaie
- Smidt Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA 90048, USA
| | - Russell Rogers
- Smidt Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA 90048, USA
| | - Daniel Soetkamp
- Smidt Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA 90048, USA
| | - Jackelyn Valle
- Smidt Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA 90048, USA
| | - Kevin Holm
- Smidt Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA 90048, USA
| | - Lizbeth Sanchez
- Smidt Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA 90048, USA
| | - Ileana Tremmel
- Smidt Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA 90048, USA
| | - Asma Nawaz
- Smidt Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA 90048, USA
| | - Michael Shehata
- Smidt Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA 90048, USA
| | - Xunzhang Wang
- Smidt Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA 90048, USA
| | - Adityo Prakosa
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Joseph Yu
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jennifer E Van Eyk
- Smidt Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA 90048, USA
| | - Natalia Trayanova
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Eduardo Marbán
- Smidt Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA 90048, USA
| | - Eugenio Cingolani
- Smidt Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA 90048, USA
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9
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Liang C, Li Q, Wang K, Du Y, Wang W, Zhang H. Mechanisms of ventricular arrhythmias elicited by coexistence of multiple electrophysiological remodeling in ischemia: A simulation study. PLoS Comput Biol 2022; 18:e1009388. [PMID: 35476614 PMCID: PMC9045648 DOI: 10.1371/journal.pcbi.1009388] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 02/18/2022] [Indexed: 11/18/2022] Open
Abstract
Myocardial ischemia, injury and infarction (MI) are the three stages of acute coronary syndrome (ACS). In the past two decades, a great number of studies focused on myocardial ischemia and MI individually, and showed that the occurrence of reentrant arrhythmias is often associated with myocardial ischemia or MI. However, arrhythmogenic mechanisms in the tissue with various degrees of remodeling in the ischemic heart have not been fully understood. In this study, biophysical detailed single-cell models of ischemia 1a, 1b, and MI were developed to mimic the electrophysiological remodeling at different stages of ACS. 2D tissue models with different distributions of ischemia and MI areas were constructed to investigate the mechanisms of the initiation of reentrant waves during the progression of ischemia. Simulation results in 2D tissues showed that the vulnerable windows (VWs) in simultaneous presence of multiple ischemic conditions were associated with the dynamics of wave propagation in the tissues with each single pathological condition. In the tissue with multiple pathological conditions, reentrant waves were mainly induced by two different mechanisms: one is the heterogeneity along the excitation wavefront, especially the abrupt variation in conduction velocity (CV) across the border of ischemia 1b and MI, and the other is the decreased safe factor (SF) for conduction at the edge of the tissue in MI region which is attributed to the increased excitation threshold of MI region. Finally, the reentrant wave was observed in a 3D model with a scar reconstructed from MRI images of a MI patient. These comprehensive findings provide novel insights for understanding the arrhythmic risk during the progression of myocardial ischemia and highlight the importance of the multiple pathological stages in designing medical therapies for arrhythmias in ischemia.
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Affiliation(s)
- Cuiping Liang
- School of Computer Science and Technology, Harbin Institute of Technology (HIT), Harbin, China
| | - Qince Li
- School of Computer Science and Technology, Harbin Institute of Technology (HIT), Harbin, China
- Peng Cheng Laboratory, Shenzhen, China
- * E-mail:
| | - Kuanquan Wang
- School of Computer Science and Technology, Harbin Institute of Technology (HIT), Harbin, China
| | - Yimei Du
- Wuhan Union Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China
| | - Wei Wang
- School of Computer Science and Technology, Harbin Institute of Technology (HIT), Harbin, China
| | - Henggui Zhang
- Peng Cheng Laboratory, Shenzhen, China
- School of Physics and Astronomy, The University of Manchester, Manchester, United Kingdom
- Key Laboratory of Medical Electrophysiology of Ministry of Education and Medical Electrophysiological Key Laboratory of Sichuan Province, Institute of Cardiovascular Research, Southwest Medical University, Luzhou, China
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10
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Gionti V, Scacchi S, Colli Franzone P, Pavarino LF, Dore R, Storti C. Role of Scar and Border Zone Geometry on the Genesis and Maintenance of Re-Entrant Ventricular Tachycardia in Patients With Previous Myocardial Infarction. Front Physiol 2022; 13:834747. [PMID: 35399271 PMCID: PMC8989182 DOI: 10.3389/fphys.2022.834747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 02/21/2022] [Indexed: 11/23/2022] Open
Abstract
In patients with healed myocardial infarction, the left ventricular ejection fraction is characterized by low sensitivity and specificity in the prediction of future malignant arrhythmias. Thus, there is the need for new parameters in daily practice to perform arrhythmic risk stratification. The aim of this study is to identify some features of proarrhythmic geometric configurations of scars and border zones (BZ), by means of numerical simulations based on left ventricular models derived from post myocardial infarction patients. Two patients with similar clinical characteristics were included in this study. Both patients exhibited left ventricular scars characterized by subendo- and subepicardial BZ and a transmural BZ isthmus. The scar of patient #1 was significantly larger than that of patient #2, whereas the transmural BZ isthmus and the subdendo- and subepicardial BZs of patient #2 were thicker than those of patient #1. Patient #1 was positive at electrophysiologic testing, whereas patient #2 was negative. Based on the cardiac magnetic resonance (CMR) data, we developed a geometric model of the left ventricles of the two patients, taking into account the position, extent, and topological features of scars and BZ. The numerical simulations were based on the anisotropic monodomain model of electrocardiology. In the model of patient #1, sustained ventricular tachycardia (VT) was inducible by an S2 stimulus delivered at any of the six stimulation sites considered, while in the model of patient #2 we were not able to induce sustained VT. In the model of patient #1, making the subendo- and subepicardial BZs as thick as those of patient #2 did not affect the inducibility and maintenance of VT. On the other hand, in the model of patient #2, making the subendo- and subepicardial BZs as thin as those of patient #1 yielded sustained VT. In conclusion, the results show that the numerical simulations have an effective predictive capability in discriminating patients at high arrhythmic risk. The extent of the infarct scar and the presence of transmural BZ isthmuses and thin subendo- and subepicardial BZs promote sustained VT.
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Affiliation(s)
- Vincenzo Gionti
- Divisione di Cardiologia, Istituto di Cura Città di Pavia, Pavia, Italy
| | - Simone Scacchi
- Dipartimento di Matematica, Università degli Studi di Milano, Milan, Italy
- *Correspondence: Simone Scacchi
| | | | - Luca F. Pavarino
- Dipartimento di Matematica, Università degli Studi di Pavia, Pavia, Italy
| | - Roberto Dore
- Divisione di Cardiologia, Istituto di Cura Città di Pavia, Pavia, Italy
| | - Cesare Storti
- Divisione di Cardiologia, Istituto di Cura Città di Pavia, Pavia, Italy
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11
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Fu Z, Zhang J, Luo R, Sun Y, Deng D, Xia L. TF-Unet:An automatic cardiac MRI image segmentation method. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:5207-5222. [PMID: 35430861 DOI: 10.3934/mbe.2022244] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Personalized heart models are widely used to study the mechanisms of cardiac arrhythmias and have been used to guide clinical ablation of different types of arrhythmias in recent years. MRI images are now mostly used for model building. In cardiac modeling studies, the degree of segmentation of the heart image determines the success of subsequent 3D reconstructions. Therefore, a fully automated segmentation is needed. In this paper, we combine U-Net and Transformer as an alternative approach to perform powerful and fully automated segmentation of medical images. On the one hand, we use convolutional neural networks for feature extraction and spatial encoding of inputs to fully exploit the advantages of convolution in detail grasping; on the other hand, we use Transformer to add remote dependencies to high-level features and model features at different scales to fully exploit the advantages of Transformer. The results show that, the average dice coefficients for ACDC and Synapse datasets are 91.72 and 85.46%, respectively, and compared with Swin-Unet, the segmentation accuracy are improved by 1.72% for ACDC dataset and 6.33% for Synapse dataset.
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Affiliation(s)
- Zhenyin Fu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Institute of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China
| | - Jin Zhang
- Key Laboratory for Biomedical Engineering of Ministry of Education, Institute of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China
| | - Ruyi Luo
- Hangzhou Science and Technology Information Institute, Hangzhou 310026, China
| | - Yutong Sun
- School of Biomedical Engineering, Dalian University of Technology, Dalian 116024, China
| | - Dongdong Deng
- School of Biomedical Engineering, Dalian University of Technology, Dalian 116024, China
| | - Ling Xia
- Key Laboratory for Biomedical Engineering of Ministry of Education, Institute of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou 310026, China
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12
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Tong L, Zhao C, Fu Z, Dong R, Wu Z, Wang Z, Zhang N, Wang X, Cao B, Sun Y, Zheng D, Xia L, Deng D. Preliminary Study: Learning the Impact of Simulation Time on Reentry Location and Morphology Induced by Personalized Cardiac Modeling. Front Physiol 2021; 12:733500. [PMID: 35002750 PMCID: PMC8739986 DOI: 10.3389/fphys.2021.733500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 11/30/2021] [Indexed: 11/13/2022] Open
Abstract
Personalized cardiac modeling is widely used for studying the mechanisms of cardiac arrythmias. Due to the high demanding of computational resource of modeling, the arrhythmias induced in the models are usually simulated for just a few seconds. In clinic, it is common that arrhythmias last for more than several minutes and the morphologies of reentries are not always stable, so it is not clear that whether the simulation of arrythmias for just a few seconds is long enough to match the arrhythmias detected in patients. This study aimed to observe how long simulation of the induced arrhythmias in the personalized cardiac models is sufficient to match the arrhythmias detected in patients. A total of 5 contrast enhanced MRI datasets of patient hearts with myocardial infarction were used in this study. Then, a classification method based on Gaussian mixture model was used to detect the infarct tissue. For each reentry, 3 s and 10 s were simulated. The characteristics of each reentry simulated for different duration were studied. Reentries were induced in all 5 ventricular models and sustained reentries were induced at 39 stimulation sites in the model. By analyzing the simulation results, we found that 41% of the sustained reentries in the 3 s simulation group terminated in the longer simulation groups (10 s). The second finding in our simulation was that only 23.1% of the sustained reentries in the 3 s simulation did not change location and morphology in the extended 10 s simulation. The third finding was that 35.9% reentries were stable in the 3 s simulation and should be extended for the simulation time. The fourth finding was that the simulation results in 10 s simulation matched better with the clinical measurements than the 3 s simulation. It was shown that 10 s simulation was sufficient to make simulation results stable. The findings of this study not only improve the simulation accuracy, but also reduce the unnecessary simulation time to achieve the optimal use of computer resources to improve the simulation efficiency and shorten the simulation time to meet the time node requirements of clinical operation on patients.
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Affiliation(s)
- Lv Tong
- School of Biomedical Engineering, Dalian University of Technology, Dalian, China
| | - Caiming Zhao
- Department of Cardiology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Zhenyin Fu
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Ruiqing Dong
- Department of Cardiology, Dushu Lake Hospital Affiliated to Soochow University, Suzhou, China
| | - Zhenghong Wu
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Zefeng Wang
- Department of Cardiology, Beijing Anzhen Hospital Affiliated to Capital Medical University, Beijing, China
| | - Nan Zhang
- Department of Radiology, Beijing Anzhen Hospital Affiliated to Capital Medical University, Beijing, China
| | - Xinlu Wang
- Department of Cardiology, Beijing Anzhen Hospital Affiliated to Capital Medical University, Beijing, China
| | - Boyang Cao
- School of Biomedical Engineering, Dalian University of Technology, Dalian, China
| | - Yutong Sun
- School of Biomedical Engineering, Dalian University of Technology, Dalian, China
| | - Dingchang Zheng
- Research Centre for Intelligent Healthcare, Faculty of Health and Life Science, Coventry University, Coventry, United Kingdom
| | - Ling Xia
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Dongdong Deng
- School of Biomedical Engineering, Dalian University of Technology, Dalian, China
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13
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Ochs AR, Karathanos TV, Trayanova NA, Boyle PM. Optogenetic Stimulation Using Anion Channelrhodopsin (GtACR1) Facilitates Termination of Reentrant Arrhythmias With Low Light Energy Requirements: A Computational Study. Front Physiol 2021; 12:718622. [PMID: 34526912 PMCID: PMC8435849 DOI: 10.3389/fphys.2021.718622] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 07/23/2021] [Indexed: 12/24/2022] Open
Abstract
Optogenetic defibrillation of hearts expressing light-sensitive cation channels (e.g., ChR2) has been proposed as an alternative to conventional electrotherapy. Past modeling work has shown that ChR2 stimulation can depolarize enough myocardium to interrupt arrhythmia, but its efficacy is limited by light attenuation and high energy needs. These shortcomings may be mitigated by using new optogenetic proteins like Guillardia theta Anion Channelrhodopsin (GtACR1), which produces a repolarizing outward current upon illumination. Accordingly, we designed a study to assess the feasibility of GtACR1-based optogenetic arrhythmia termination in human hearts. We conducted electrophysiological simulations in MRI-based atrial or ventricular models (n = 3 each), with pathological remodeling from atrial fibrillation or ischemic cardiomyopathy, respectively. We simulated light sensitization via viral gene delivery of three different opsins (ChR2, red-shifted ChR2, GtACR1) and uniform endocardial illumination at the appropriate wavelengths (blue, red, or green light, respectively). To analyze consistency of arrhythmia termination, we varied pulse timing (three evenly spaced intervals spanning the reentrant cycle) and intensity (atrial: 0.001–1 mW/mm2; ventricular: 0.001–10 mW/mm2). In atrial models, GtACR1 stimulation with 0.005 mW/mm2 green light consistently terminated reentry; this was 10–100x weaker than the threshold levels for ChR2-mediated defibrillation. In ventricular models, defibrillation was observed in 2/3 models for GtACR1 stimulation at 0.005 mW/mm2 (100–200x weaker than ChR2 cases). In the third ventricular model, defibrillation failed in nearly all cases, suggesting that attenuation issues and patient-specific organ/scar geometry may thwart termination in some cases. Across all models, the mechanism of GtACR1-mediated defibrillation was voltage forcing of illuminated tissue toward the modeled channel reversal potential of −40 mV, which made propagation through affected regions impossible. Thus, our findings suggest GtACR1-based optogenetic defibrillation of the human heart may be feasible with ≈2–3 orders of magnitude less energy than ChR2.
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Affiliation(s)
- Alexander R Ochs
- Department of Bioengineering, University of Washington, Seattle, WA, United States
| | - Thomas V Karathanos
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Natalia A Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States.,Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, United States
| | - Patrick M Boyle
- Department of Bioengineering, University of Washington, Seattle, WA, United States.,Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, United States.,Center for Cardiovascular Biology, University of Washington, Seattle, WA, United States
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14
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Kruithof E, Amirrajab S, Cluitmans MJM, Lau KD, Breeuwer M. Influence of image artifacts on image-based computer simulations of the cardiac electrophysiology. Comput Biol Med 2021; 137:104773. [PMID: 34464852 DOI: 10.1016/j.compbiomed.2021.104773] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 08/12/2021] [Accepted: 08/13/2021] [Indexed: 11/17/2022]
Abstract
Myocardial infarct patients have an increased risk of scar-based ventricular tachycardia. Late gadolinium enhanced magnetic resonance (MR) imaging provides the geometric extent of myocardial infarct. Computational electrophysiological models based on such images can provide a personalized prediction of the patient's tachycardia risk. In this work, the effect of respiratory slice alignment image artifacts on image-based electrophysiological simulations is investigated in two series of models. For the first series, a clinical MR image is used in which slice translations are applied to artificially induce and correct for slice misalignment. For the second series, computer simulated MR images with and without slice misalignments are created using a mechanistic anatomical phantom of the torso. From those images, personalized models are created in which electrical stimuli are applied in an attempt to induce tachycardia. The response of slice-aligned and slice-misaligned models to different interval stimuli is used to assess tachycardia risk. The presented results indicate that slice misalignments affect image-based simulation outcomes. The extent to which the assessed risk is affected is found to depend upon the geometry of the infarct area. The number of unidirectional block tachycardias varied from 1 to 3 inducible patterns depending on slice misalignment severity and, along with it, the number of tachycardia inducing stimuli locations varied from 2 to 4 from 6 different locations. For tachycardias sustained by conducting channels through the scar core, no new patterns are induced by altering the slice alignment in the corresponding image. However, it affected the assessed risk as tachycardia inducing stimuli locations varied from 1 to 5 from the 6 stimuli locations. In addition, if the conducting channel is not maintained in the image due to slice misalignments, the channel-dependent tachycardia is not inducible anymore in the image-based model.
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Affiliation(s)
- E Kruithof
- Eindhoven University of Technology, the Netherlands.
| | - S Amirrajab
- Eindhoven University of Technology, the Netherlands
| | - M J M Cluitmans
- Philips Research Eindhoven, the Netherlands; Maastricht University Medical Center, the Netherlands
| | - K D Lau
- Philips Research Eindhoven, the Netherlands
| | - M Breeuwer
- Eindhoven University of Technology, the Netherlands; Philips Healthcare Best, the Netherlands
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15
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Boyle PM, Yu J, Klimas A, Williams JC, Trayanova NA, Entcheva E. OptoGap is an optogenetics-enabled assay for quantification of cell-cell coupling in multicellular cardiac tissue. Sci Rep 2021; 11:9310. [PMID: 33927252 PMCID: PMC8085001 DOI: 10.1038/s41598-021-88573-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 03/31/2021] [Indexed: 12/23/2022] Open
Abstract
Intercellular electrical coupling is an essential means of communication between cells. It is important to obtain quantitative knowledge of such coupling between cardiomyocytes and non-excitable cells when, for example, pathological electrical coupling between myofibroblasts and cardiomyocytes yields increased arrhythmia risk or during the integration of donor (e.g., cardiac progenitor) cells with native cardiomyocytes in cell-therapy approaches. Currently, there is no direct method for assessing heterocellular coupling within multicellular tissue. Here we demonstrate experimentally and computationally a new contactless assay for electrical coupling, OptoGap, based on selective illumination of inexcitable cells that express optogenetic actuators and optical sensing of the response of coupled excitable cells (e.g., cardiomyocytes) that are light-insensitive. Cell-cell coupling is quantified by the energy required to elicit an action potential via junctional current from the light-stimulated cell(s). The proposed technique is experimentally validated against the standard indirect approach, GapFRAP, using light-sensitive cardiac fibroblasts and non-transformed cardiomyocytes in a two-dimensional setting. Its potential applicability to the complex three-dimensional setting of the native heart is corroborated by computational modelling and proper calibration. Lastly, the sensitivity of OptoGap to intrinsic cell-scale excitability is robustly characterized via computational analysis.
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Affiliation(s)
- Patrick M Boyle
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
- 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
| | - Jinzhu Yu
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
| | - Aleksandra Klimas
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
- Department of Biomedical Engineering, George Washington University, 800 22nd Street NW, Suite 5000, Washington, DC, 20052, USA
| | - John C Williams
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
| | - Natalia A Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA
| | - Emilia Entcheva
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA.
- Department of Biomedical Engineering, George Washington University, 800 22nd Street NW, Suite 5000, Washington, DC, 20052, USA.
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16
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Integration of activation maps of epicardial veins in computational cardiac electrophysiology. Comput Biol Med 2020; 127:104047. [PMID: 33099220 DOI: 10.1016/j.compbiomed.2020.104047] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Revised: 10/06/2020] [Accepted: 10/06/2020] [Indexed: 12/16/2022]
Abstract
In this work we address the issue of validating the monodomain equation used in combination with the Bueno-Orovio ionic model for the prediction of the activation times in cardiac electro-physiology of the left ventricle. To this aim, we consider four patients who suffered from Left Bundle Branch Block (LBBB). We use activation maps performed at the septum as input data for the model and maps at the epicardial veins for the validation. In particular, a first set (half) of the latter are used to estimate the conductivities of the patient and a second set (the remaining half) to compute the errors of the numerical simulations. We find an excellent agreement between measures and numerical results. Our validated computational tool could be used to accurately predict activation times at the epicardial veins with a short mapping, i.e. by using only a part (the most proximal) of the standard acquisition points, thus reducing the invasive procedure and exposure to radiation.
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17
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Trayanova NA, Doshi AN, Prakosa A. How personalized heart modeling can help treatment of lethal arrhythmias: A focus on ventricular tachycardia ablation strategies in post-infarction patients. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2020; 12:e1477. [PMID: 31917524 DOI: 10.1002/wsbm.1477] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2019] [Revised: 12/16/2019] [Accepted: 12/17/2019] [Indexed: 12/18/2022]
Abstract
Precision Cardiology is a targeted strategy for cardiovascular disease prevention and treatment that accounts for individual variability. Computational heart modeling is one of the novel approaches that have been developed under the umbrella of Precision Cardiology. Personalized computational modeling of patient hearts has made strides in the development of models that incorporate the individual geometry and structure of the heart as well as other patient-specific information. Of these developments, one of the potentially most impactful is the research aimed at noninvasively predicting the targets of ablation of lethal arrhythmia, ventricular tachycardia (VT), using patient-specific models. The approach has been successfully applied to patients with ischemic cardiomyopathy in proof-of-concept studies. The goal of this paper is to review the strategies for computational VT ablation guidance in ischemic cardiomyopathy patients, from model developments to the intricacies of the actual clinical application. To provide context in describing the road these computational modeling applications have undertaken, we first review the state of the art in VT ablation in the clinic, emphasizing the benefits that personalized computational prediction of ablation targets could bring to the clinical electrophysiology practice. This article is characterized under: Analytical and Computational Methods > Computational Methods Models of Systems Properties and Processes > Organ, Tissue, and Physiological Models Translational, Genomic, and Systems Medicine > Translational Medicine.
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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
| | - Ashish N Doshi
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland
| | - Adityo Prakosa
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland
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18
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Liang C, Wang K, Li Q, Bai J, Zhang H. Influence of the distribution of fibrosis within an area of myocardial infarction on wave propagation in ventricular tissue. Sci Rep 2019; 9:14151. [PMID: 31578428 PMCID: PMC6775234 DOI: 10.1038/s41598-019-50478-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Accepted: 09/13/2019] [Indexed: 12/22/2022] Open
Abstract
The presence of fibrosis in heart tissue is strongly correlated with an incidence of arrhythmia, which is a leading cause of sudden cardiac death (SCD). However, it remains incompletely understood how different distributions, sizes and positions of fibrotic tissues contribute to arrhythmogenesis. In this study, we designed 4 different ventricular models mimicking wave propagation in cardiac tissues under normal, myocardial infarction (MI), MI with random fibrosis and MI with gradient fibrosis conditions. Simulation results of ideal square tissues indicate that vulnerable windows (VWs) of random and gradient fibrosis distributions are similar with low levels of fibrosis. However, with a high level of fibrosis, the VWs significantly increase in random fibrosis tissue but not in gradient fibrosis tissue. In addition, we systematically analyzed the effects of the size and position of fibrosis tissues on VWs. Simulation results show that it is more likely for a reentry wave to appear when the length of the infarcted area is greater than 25% of the perimeter of the ventricle, when the width is approximately half that of the ventricular wall, or when the infarcted area is attached to the inside or outside of the ventricular wall.
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Affiliation(s)
- Cuiping Liang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China
| | - Kuanquan Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China.
| | - Qince Li
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China.
| | - Jieyun Bai
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China
| | - Henggui Zhang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China.,School of Physics and Astronomy, The University of Manchester, Manchester, UK.,Space Institute of Southern China, Shenzhen, China.,Key Laboratory of Medical Electrophysiology, Ministry of Education, Collaborative Innovation Center for Prevention and Treatment of Cardiovascular Disease/Institute of Cardiovascular Research, Southwest Medical University, Luzhou, China
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19
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Pashakhanloo F, Herzka DA, Halperin H, McVeigh ER, Trayanova NA. Role of 3-Dimensional Architecture of Scar and Surviving Tissue in Ventricular Tachycardia: Insights From High-Resolution Ex Vivo Porcine Models. Circ Arrhythm Electrophysiol 2019; 11:e006131. [PMID: 29880529 DOI: 10.1161/circep.117.006131] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/23/2017] [Accepted: 04/05/2018] [Indexed: 11/16/2022]
Abstract
BACKGROUND An improved knowledge of the spatial organization of infarct structure and its contribution to ventricular tachycardia (VT) is important for designing optimal treatments. This study explores the relationship between the 3-dimensional structure of the healed infarct and the VT reentrant pathways in high-resolution models of infarcted porcine hearts. METHODS Structurally detailed models of infarcted ventricles were reconstructed from ex vivo late gadolinium enhancement and diffusion tensor magnetic resonance imaging data of 8 chronically infarcted porcine hearts at submillimeter resolution (0.25×0.25×0.5 mm3). To characterize the 3-dimensional structure of surviving tissue in the zone of infarct, a novel scar-mapped thickness metric was introduced. Further, using the ventricular models, electrophysiological simulations were conducted to determine and analyze the 3-dimensional VT pathways that were established in each of the complex infarct morphologies. RESULTS The scar-mapped thickness metric revealed the heterogeneous organization of infarct and enabled us to systematically characterize the distribution of surviving tissue thickness in 8 hearts. Simulation results demonstrated the involvement of a subendocardial tissue layer of varying thickness in the majority of VT pathways. Importantly, they revealed that VT pathways are most frequently established within thin surviving tissue structures of thickness ≤2.2 mm (90th percentile) surrounding the scar. CONCLUSIONS The combination of high-resolution imaging data and ventricular simulations revealed the 3-dimensional distribution of surviving tissue surrounding the scar and demonstrated its involvement in VT pathways. The new knowledge obtained in this study contributes toward a better understanding of infarct-related VT.
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Affiliation(s)
| | - Daniel A Herzka
- Department of Biomedical Engineering (F.P., D.A.H., E.R.M., N.A.T.)
| | | | - Elliot R McVeigh
- Department of Biomedical Engineering (F.P., D.A.H., E.R.M., N.A.T.).,Johns Hopkins University, Baltimore, MD. Departments of Bioengineering, Medicine, and Radiology, University of California, San Diego, La Jolla (E.R.M.)
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20
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Factors Promoting Conduction Slowing as Substrates for Block and Reentry in Infarcted Hearts. Biophys J 2019; 117:2361-2374. [PMID: 31521328 PMCID: PMC6990374 DOI: 10.1016/j.bpj.2019.08.008] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Revised: 07/03/2019] [Accepted: 08/05/2019] [Indexed: 01/11/2023] Open
Abstract
The development of effective and safe therapies for scar-related ventricular tachycardias requires a detailed understanding of the mechanisms underlying the conduction block that initiates electrical re-entries associated with these arrhythmias. Conduction block has been often associated with electrophysiological changes that prolong action potential duration (APD) within the border zone (BZ) of chronically infarcted hearts. However, experimental evidence suggests that remodeling processes promoting conduction slowing as opposed to APD prolongation mark the chronic phase. In this context, the substrate for the initial block at the mouth of an isthmus/diastolic channel leading to ventricular tachycardia is unclear. The goal of this study was to determine whether electrophysiological parameters associated with conduction slowing can cause block and re-entry in the BZ. In silico experiments were conducted on two-dimensional idealized infarct tissue as well as on a cohort of postinfarction porcine left ventricular models constructed from ex vivo magnetic resonance imaging scans. Functional conduction slowing in the BZ was modeled by reducing sodium current density, whereas structural conduction slowing was represented by decreasing tissue conductivity and including fibrosis. The arrhythmogenic potential of APD prolongation was also tested as a basis for comparison. Within all models, the combination of reduced sodium current with structural remodeling more often degenerated into re-entry and, if so, was more likely to be sustained for more cycles. Although re-entries were also detected in experiments with prolonged APD, they were often not sustained because of the subsequent block caused by long-lasting repolarization. Functional and structural conditions associated with slow conduction rather than APD prolongation form a potent substrate for arrhythmogenesis at the isthmus/BZ of chronically infarcted hearts. Reduced excitability led to block while slow conduction shortened the wavelength of propagation, facilitating the sustenance of re-entries. These findings provide important insights for models of patient-specific risk stratification and therapy planning.
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21
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Deng D, Prakosa A, Shade J, Nikolov P, Trayanova NA. Characterizing Conduction Channels in Postinfarction Patients Using a Personalized Virtual Heart. Biophys J 2019; 117:2287-2294. [PMID: 31447108 DOI: 10.1016/j.bpj.2019.07.024] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Revised: 06/25/2019] [Accepted: 07/10/2019] [Indexed: 01/22/2023] Open
Abstract
Patients with myocardial infarction have an abundance of conduction channels (CC); however, only a small subset of these CCs sustain ventricular tachycardia (VT). Identifying these critical CCs (CCCs) in the clinic so that they can be targeted by ablation remains a significant challenge. The objective of this study is to use a personalized virtual-heart approach to conduct a three-dimensional (3D) assessment of CCCs sustaining VTs of different morphologies in these patients, to investigate their 3D structural features, and to determine the optimal ablation strategy for each VT. To achieve these goals, ventricular models were constructed from contrast enhanced magnetic resonance imagings of six postinfarction patients. Rapid pacing induced VTs in each model. CCCs that sustained different VT morphologies were identified. CCCs' 3D structure and type and the resulting rotational electrical activity were examined. Ablation was performed at the optimal part of each CCC, aiming to terminate each VT with a minimal lesion size. Predicted ablation locations were compared to clinical. Analyzing the simulation results, we found that the observed VTs in each patient model were sustained by a limited number (2.7 ± 1.2) of CCCs. Further, we identified three types of CCCs sustaining VTs: I-type and T-type channels, with all channel branches bounded by scar, and functional reentry channels, which were fully or partially bounded by conduction block surfaces. The different types of CCCs accounted for 43.8, 18.8, and 37.4% of all CCCs, respectively. The mean narrowest width of CCCs or a branch of CCC was 9.7 ± 3.6 mm. Ablation of the narrowest part of each CCC was sufficient to terminate VT. Our results demonstrate that a personalized virtual-heart approach can determine the possible VT morphologies in each patient and identify the CCCs that sustain reentry. The approach can aid clinicians in identifying accurately the optimal VT ablation targets in postinfarction patients.
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Affiliation(s)
- Dongdong Deng
- School of Biomedical Engineering, Dalian University of Technology, Dalian, Liaoning, China; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Adityo Prakosa
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Julie Shade
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Plamen Nikolov
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Natalia A Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland.
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22
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A comprehensive, multiscale framework for evaluation of arrhythmias arising from cell therapy in the whole post-myocardial infarcted heart. Sci Rep 2019; 9:9238. [PMID: 31239508 PMCID: PMC6592890 DOI: 10.1038/s41598-019-45684-0] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Accepted: 06/12/2019] [Indexed: 12/19/2022] Open
Abstract
Direct remuscularization approaches to cell-based heart repair seek to restore ventricular contractility following myocardial infarction (MI) by introducing new cardiomyocytes (CMs) to replace lost or injured ones. However, despite promising improvements in cardiac function, high incidences of ventricular arrhythmias have been observed in animal models of MI injected with pluripotent stem cell-derived cardiomyocytes (PSC-CMs). The mechanisms of arrhythmogenesis remain unclear. Here, we present a comprehensive framework for computational modeling of direct remuscularization approaches to cell therapy. Our multiscale 3D whole-heart modeling framework integrates realistic representations of cell delivery and transdifferentiation therapy modalities as well as representation of spatial distributions of engrafted cells, enabling simulation of clinical therapy and the prediction of emergent electrophysiological behavior and arrhythmogenensis. We employ this framework to explore how varying parameters of cell delivery and transdifferentiation could result in three mechanisms of arrhythmogenesis: focal ectopy, heart block, and reentry.
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23
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Deng D, Prakosa A, Shade J, Nikolov P, Trayanova NA. Sensitivity of Ablation Targets Prediction to Electrophysiological Parameter Variability in Image-Based Computational Models of Ventricular Tachycardia in Post-infarction Patients. Front Physiol 2019; 10:628. [PMID: 31178758 PMCID: PMC6543853 DOI: 10.3389/fphys.2019.00628] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Accepted: 05/03/2019] [Indexed: 12/18/2022] Open
Abstract
Ventricular tachycardia (VT), which could lead to sudden cardiac death, occurs frequently in patients with myocardial infarction. Computational modeling has emerged as a powerful platform for the non-invasive investigation of lethal heart rhythm disorders in post-infarction patients and for guiding patient VT ablation. However, it remains unclear how VT dynamics and predicted ablation targets are influenced by inter-patient variability in action potential duration (APD) and conduction velocity (CV). The goal of this study was to systematically assess the effect of changes in the electrophysiological parameters on the induced VTs and predicted ablation targets in personalized models of post-infarction hearts. Simulations were conducted in 5 patient-specific left ventricular models reconstructed from late gadolinium-enhanced magnetic resonance imaging scans. We comprehensively characterized all possible pre-ablation and post-ablation VTs in simulations conducted with either an “average human VT”-based electrophysiological representation (i.e., EPavg) or with ±10% APD or CV (i.e., EPvar); additional simulations were also executed in some models for an extended range of these parameters. The results showed that: (1) a subset of reentries (76.2–100%, depending on EP parameter set) conducted with ±10% APD/CV was observed in approximately the same locations as reentries observed in EPavg cases; (2) emergent VTs could be induced sometimes after ablation in EPavg models, and these emergent VTs often corresponded to the pre-ablation reentries in simulations with EPvar parameter sets. These findings demonstrate that the VT ablation target uncertainty in patient-specific ventricular models with an average representation of VT-remodeled electrophysiology is relatively low and the ablation targets stable, as the localization of the induced VTs was primarily driven by the remodeled structural substrate. Thus, personalized ventricular modeling with an average representation of infarct-remodeled electrophysiology may uncover most targets for VT ablation.
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Affiliation(s)
- Dongdong Deng
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States.,School of Biomedical Engineering, Dalian University of Technology, Dalian, China
| | - Adityo Prakosa
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Julie Shade
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Plamen Nikolov
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Natalia A Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
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24
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Lopez-Perez A, Sebastian R, Izquierdo M, Ruiz R, Bishop M, Ferrero JM. Personalized Cardiac Computational Models: From Clinical Data to Simulation of Infarct-Related Ventricular Tachycardia. Front Physiol 2019; 10:580. [PMID: 31156460 PMCID: PMC6531915 DOI: 10.3389/fphys.2019.00580] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Accepted: 04/25/2019] [Indexed: 12/20/2022] Open
Abstract
In the chronic stage of myocardial infarction, a significant number of patients develop life-threatening ventricular tachycardias (VT) due to the arrhythmogenic nature of the remodeled myocardium. Radiofrequency ablation (RFA) is a common procedure to isolate reentry pathways across the infarct scar that are responsible for VT. Unfortunately, this strategy show relatively low success rates; up to 50% of patients experience recurrent VT after the procedure. In the last decade, intensive research in the field of computational cardiac electrophysiology (EP) has demonstrated the ability of three-dimensional (3D) cardiac computational models to perform in-silico EP studies. However, the personalization and modeling of certain key components remain challenging, particularly in the case of the infarct border zone (BZ). In this study, we used a clinical dataset from a patient with a history of infarct-related VT to build an image-based 3D ventricular model aimed at computational simulation of cardiac EP, including detailed patient-specific cardiac anatomy and infarct scar geometry. We modeled the BZ in eight different ways by combining the presence or absence of electrical remodeling with four different levels of image-based patchy fibrosis (0, 10, 20, and 30%). A 3D torso model was also constructed to compute the ECG. Patient-specific sinus activation patterns were simulated and validated against the patient's ECG. Subsequently, the pacing protocol used to induce reentrant VTs in the EP laboratory was reproduced in-silico. The clinical VT was induced with different versions of the model and from different pacing points, thus identifying the slow conducting channel responsible for such VT. Finally, the real patient's ECG recorded during VT episodes was used to validate our simulation results and to assess different strategies to model the BZ. Our study showed that reduced conduction velocities and heterogeneity in action potential duration in the BZ are the main factors in promoting reentrant activity. Either electrical remodeling or fibrosis in a degree of at least 30% in the BZ were required to initiate VT. Moreover, this proof-of-concept study confirms the feasibility of developing 3D computational models for cardiac EP able to reproduce cardiac activation in sinus rhythm and during VT, using exclusively non-invasive clinical data.
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Affiliation(s)
- Alejandro Lopez-Perez
- Center for Research and Innovation in Bioengineering (Ci2B), Universitat Politècnica de València, Valencia, Spain
| | - Rafael Sebastian
- Computational Multiscale Simulation Lab (CoMMLab), Universitat de València, Valencia, Spain
| | - M Izquierdo
- INCLIVA Health Research Institute, Valencia, Spain.,Arrhythmia Unit, Cardiology Department, Hospital Clínico Universitario de Valencia, Valencia, Spain
| | - Ricardo Ruiz
- INCLIVA Health Research Institute, Valencia, Spain.,Arrhythmia Unit, Cardiology Department, Hospital Clínico Universitario de Valencia, Valencia, Spain
| | - Martin Bishop
- Division of Imaging Sciences & Biomedical Engineering, Department of Biomedical Engineering, King's College London, London, United Kingdom
| | - Jose M Ferrero
- Center for Research and Innovation in Bioengineering (Ci2B), Universitat Politècnica de València, Valencia, Spain
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25
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Trayanova NA, Pashakhanloo F, Wu KC, Halperin HR. Imaging-Based Simulations for Predicting Sudden Death and Guiding Ventricular Tachycardia Ablation. Circ Arrhythm Electrophysiol 2019; 10:CIRCEP.117.004743. [PMID: 28696219 DOI: 10.1161/circep.117.004743] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2017] [Accepted: 06/08/2017] [Indexed: 11/16/2022]
Affiliation(s)
- Natalia A Trayanova
- From the Institute for Computational Medicine and Department of Biomedical Engineering (N.A.T., F.P.) and Departments of Radiology and Biomedical Engineering (H.R.H.), Johns Hopkins University, Baltimore, MD; and Division of Cardiology, Department of Medicine, Johns Hopkins Medical Institutions, Baltimore, MD (K.C.W., H.R.H.).
| | - Farhad Pashakhanloo
- From the Institute for Computational Medicine and Department of Biomedical Engineering (N.A.T., F.P.) and Departments of Radiology and Biomedical Engineering (H.R.H.), Johns Hopkins University, Baltimore, MD; and Division of Cardiology, Department of Medicine, Johns Hopkins Medical Institutions, Baltimore, MD (K.C.W., H.R.H.)
| | - Katherine C Wu
- From the Institute for Computational Medicine and Department of Biomedical Engineering (N.A.T., F.P.) and Departments of Radiology and Biomedical Engineering (H.R.H.), Johns Hopkins University, Baltimore, MD; and Division of Cardiology, Department of Medicine, Johns Hopkins Medical Institutions, Baltimore, MD (K.C.W., H.R.H.)
| | - Henry R Halperin
- From the Institute for Computational Medicine and Department of Biomedical Engineering (N.A.T., F.P.) and Departments of Radiology and Biomedical Engineering (H.R.H.), Johns Hopkins University, Baltimore, MD; and Division of Cardiology, Department of Medicine, Johns Hopkins Medical Institutions, Baltimore, MD (K.C.W., H.R.H.)
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26
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Doste R, Soto-Iglesias D, Bernardino G, Alcaine A, Sebastian R, Giffard-Roisin S, Sermesant M, Berruezo A, Sanchez-Quintana D, Camara O. A rule-based method to model myocardial fiber orientation in cardiac biventricular geometries with outflow tracts. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2019; 35:e3185. [PMID: 30721579 DOI: 10.1002/cnm.3185] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Revised: 10/23/2018] [Accepted: 01/05/2019] [Indexed: 06/09/2023]
Abstract
Rule-based methods are often used for assigning fiber orientation to cardiac anatomical models. However, existing methods have been developed using data mostly from the left ventricle. As a consequence, fiber information obtained from rule-based methods often does not match histological data in other areas of the heart such as the right ventricle, having a negative impact in cardiac simulations beyond the left ventricle. In this work, we present a rule-based method where fiber orientation is separately modeled in each ventricle following observations from histology. This allows to create detailed fiber orientation in specific regions such as the endocardium of the right ventricle, the interventricular septum, and the outflow tracts. We also carried out electrophysiological simulations involving these structures and with different fiber configurations. In particular, we built a modeling pipeline for creating patient-specific volumetric meshes of biventricular geometries, including the outflow tracts, and subsequently simulate the electrical wavefront propagation in outflow tract ventricular arrhythmias with different origins for the ectopic focus. The resulting simulations with the proposed rule-based method showed a very good agreement with clinical parameters such as the 10 ms isochrone ratio in a cohort of nine patients suffering from this type of arrhythmia. The developed modeling pipeline confirms its potential for an in silico identification of the site of origin in outflow tract ventricular arrhythmias before clinical intervention.
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Affiliation(s)
- Ruben Doste
- Physense, ETIC, Universitat Pompeu Fabra, Barcelona, Spain
| | | | | | | | - Rafael Sebastian
- Computational Multiscale Simulation Lab (CoMMLab), Department of Computer Science, Universitat de Valencia, Valencia, Spain
| | | | | | - Antonio Berruezo
- Arrhythmia Section, Cardiology Department, Thorax Institute, Hospital Clinic, Universitat de Barcelona, Barcelona, Spain
| | - Damian Sanchez-Quintana
- Department of Anatomy and Cell Biology, Faculty of Medicine, University of Extremadura, Badajoz, Spain
| | - Oscar Camara
- Physense, ETIC, Universitat Pompeu Fabra, Barcelona, Spain
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27
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Cartoski MJ, Nikolov PP, Prakosa A, Boyle PM, Spevak PJ, Trayanova NA. Computational Identification of Ventricular Arrhythmia Risk in Pediatric Myocarditis. Pediatr Cardiol 2019; 40:857-864. [PMID: 30840104 PMCID: PMC6451890 DOI: 10.1007/s00246-019-02082-7] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2018] [Accepted: 02/27/2019] [Indexed: 12/11/2022]
Abstract
Children with myocarditis have increased risk of ventricular tachycardia (VT) due to myocardial inflammation and remodeling. There is currently no accepted method for VT risk stratification in this population. We hypothesized that personalized models developed from cardiac late gadolinium enhancement magnetic resonance imaging (LGE-MRI) could determine VT risk in patients with myocarditis using a previously-validated protocol. Personalized three-dimensional computational cardiac models were reconstructed from LGE-MRI scans of 12 patients diagnosed with myocarditis. Four patients with clinical VT and eight patients without VT were included in this retrospective analysis. In each model, we incorporated a personalized spatial distribution of fibrosis and myocardial fiber orientations. Then, VT inducibility was assessed in each model by pacing rapidly from 26 sites distributed throughout both ventricles. Sustained reentrant VT was induced from multiple pacing sites in all patients with clinical VT. In the eight patients without clinical VT, we were unable to induce sustained reentry in our simulations using rapid ventricular pacing. Application of our non-invasive approach in children with myocarditis has the potential to correctly identify those at risk for developing VT.
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Affiliation(s)
- Mark J Cartoski
- Divison of Pediatric Cardiology, Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Plamen P Nikolov
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Adityo Prakosa
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Patrick M Boyle
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Philip J Spevak
- Divison of Pediatric Cardiology, Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Natalia A Trayanova
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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28
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Zabihollahy F, White JA, Ukwatta E. Convolutional neural network-based approach for segmentation of left ventricle myocardial scar from 3D late gadolinium enhancement MR images. Med Phys 2019; 46:1740-1751. [PMID: 30734937 DOI: 10.1002/mp.13436] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2018] [Revised: 01/10/2019] [Accepted: 01/31/2019] [Indexed: 02/04/2023] Open
Abstract
PURPOSE Accurate three-dimensional (3D) segmentation of myocardial replacement fibrosis (i.e., scar) is emerging as a potentially valuable tool for risk stratification and procedural planning in patients with ischemic cardiomyopathy. The main purpose of this study was to develop a semiautomated method using a 3D convolutional neural network (CNN)-based for the segmentation of left ventricle (LV) myocardial scar from 3D late gadolinium enhancement magnetic resonance (LGE-MR) images. METHODS Our proposed CNN is built upon several convolutional and pooling layers aimed at choosing appropriate features from LGE-MR images to distinguish between myocardial scar and healthy tissues of the left ventricle. In contrast to previous methods that consider image intensity as the sole feature, CNN-based algorithms have the potential to improve the accuracy of scar segmentation through the creation of unconventional features that separate scar from normal myocardium in the feature space. The first step of our pipeline was to manually delineate the left ventricular myocardium, which was used as the region of interest for scar segmentation. Our developed algorithm was trained using 265,220 volume patches extracted from ten 3D LGE-MR images, then was validated on 450,454 patches from a testing dataset of 24 3D LGE-MR images, all obtained from patients with chronic myocardial infarction. We evaluated our method in the context of several alternative methods by comparing algorithm-generated segmentations to manual delineations performed by experts. RESULTS Our CNN-based method reported an average Dice similarity coefficient (DSC) and Jaccard Index (JI) of 93.63% ± 2.6% and 88.13% ± 4.70%. In comparison to several previous methods, including K-nearest neighbor (KNN), hierarchical max flow (HMF), full width at half maximum (FWHM), and signal threshold to reference mean (STRM), the developed algorithm reported significantly higher accuracy for DSC with a P-value less than 0.0001. CONCLUSIONS Our experimental results demonstrated that our CNN-based proposed method yielded the highest accuracy of all contemporary LV myocardial scar segmentation methodologies, inclusive of the most widely used signal intensity-based methods, such as FWHM and STRM. To our knowledge, this is the first description of LV myocardial scar tissue segmentation from 3D LGE-MR images using a CNN-based method.
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Affiliation(s)
- Fatemeh Zabihollahy
- Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada
| | - James A White
- Stephenson Cardiac Imaging Centre, Libin Cardiovascular Institute of Alberta, University of Calgary, Calgary, AB, USA
| | - Eranga Ukwatta
- Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada
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29
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Deng D, Nikolov P, Arevalo HJ, Trayanova NA. Optimal contrast-enhanced MRI image thresholding for accurate prediction of ventricular tachycardia using ex-vivo high resolution models. Comput Biol Med 2018; 102:426-432. [PMID: 30301573 PMCID: PMC6218273 DOI: 10.1016/j.compbiomed.2018.09.031] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2018] [Revised: 09/12/2018] [Accepted: 09/30/2018] [Indexed: 11/23/2022]
Abstract
Patient specific models created from contrast-enhanced (i.e. late-gadolinium, LGE) MRI images can be used for prediction of reentry location and clinical ablation planning. However, there is still a need for direct and systematic comparison between characteristics of ventricular tachycardia (VT) morphologies predicted in computational models and those acquired in clinical or experimental protocols. In this study, we aimed to: 1) assess the differences in VT morphologies predicted by modeling and recorded in experiments in terms of patterns and location of reentries, earliest and latest activation sites, and cycle lengths; and 2) define the optimal range of infarct tissue threshold values which provide best match between simulation and experimental results. To achieve these goals, we utilized LGE-MRI images from 4 swine hearts with inducible monomorphic VT. The images were segmented to identify non-infarcted myocardium, semi viable gray zone (GZ), and core scar based on pixel intensity. Several models were reconstructed from each LGE-MRI scan, with voxels of intensity between that of non-infarcted myocardium and 20-50% of the maximum intensity (in 10% increments) in the infarct region classified as GZ. VT induction was simulated in each model. Our simulation results showed that using GZ intensity thresholds of 20% or 30% resulted in the best match of simulated propagation patterns and reentry locations with those from the experiment. Overall, we matched 70% (7/10) morphologies for all the hearts. Our simulation shows that MRI-based computational models of hearts with myocardial infarction can accurately reproduce the majority of experimentally recorded post-infarction VTs.
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Affiliation(s)
- Dongdong Deng
- Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Plamen Nikolov
- Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Hermenegild J Arevalo
- Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA; Cardiac Modelling Department, Simula Research Laboratory, Fornebu, Norway
| | - Natalia A Trayanova
- Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
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30
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Prakosa A, Arevalo HJ, Deng D, Boyle PM, Nikolov PP, Ashikaga H, Blauer JJE, Ghafoori E, Park CJ, Blake RC, Han FT, MacLeod RS, Halperin HR, Callans DJ, Ranjan R, Chrispin J, Nazarian S, Trayanova NA. Personalized virtual-heart technology for guiding the ablation of infarct-related ventricular tachycardia. Nat Biomed Eng 2018; 2:732-740. [PMID: 30847259 PMCID: PMC6400313 DOI: 10.1038/s41551-018-0282-2] [Citation(s) in RCA: 161] [Impact Index Per Article: 26.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2017] [Accepted: 07/27/2018] [Indexed: 11/08/2022]
Abstract
Ventricular tachycardia (VT), which can lead to sudden cardiac death, occurs frequently in patients with myocardial infarction. Catheter-based radiofrequency ablation of cardiac tissue has achieved only modest efficacy, owing to the inaccurate identification of ablation targets by current electrical mapping techniques, which can lead to extensive lesions and to a prolonged, poorly tolerated procedure. Here we show that personalized virtual-heart technology based on cardiac imaging and computational modelling can identify optimal infarct-related VT ablation targets in retrospective animal (5 swine) and human studies (21 patients) and in a prospective feasibility study (5 patients). We first assessed in retrospective studies (one of which included a proportion of clinical images with artifacts) the capability of the technology to determine the minimum-size ablation targets for eradicating all VTs. In the prospective study, VT sites predicted by the technology were targeted directly, without relying on prior electrical mapping. The approach could improve infarct-related VT ablation guidance, where accurate identification of patient-specific optimal targets could be achieved on a personalized virtual heart prior to the clinical procedure.
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Affiliation(s)
- Adityo Prakosa
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Hermenegild J Arevalo
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Cardiac Modelling Department, Simula Research Laboratory, Fornebu, Norway
| | - Dongdong Deng
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Patrick M Boyle
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Plamen P Nikolov
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Hiroshi Ashikaga
- Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Joshua J E Blauer
- Department of Bioengineering, University of Utah, Salt Lake City, UT, USA
| | - Elyar Ghafoori
- Department of Bioengineering, University of Utah, Salt Lake City, UT, USA
| | - Carolyn J Park
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Robert C Blake
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Frederick T Han
- University of Utah Health Sciences Center, Salt Lake City, UT, USA
| | - Rob S MacLeod
- Department of Bioengineering, University of Utah, Salt Lake City, UT, USA
| | - Henry R Halperin
- Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - David J Callans
- Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ravi Ranjan
- Department of Bioengineering, University of Utah, Salt Lake City, UT, USA
| | - Jonathan Chrispin
- Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Saman Nazarian
- Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Natalia A Trayanova
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
- Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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31
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Mendonca Costa C, Plank G, Rinaldi CA, Niederer SA, Bishop MJ. Modeling the Electrophysiological Properties of the Infarct Border Zone. Front Physiol 2018; 9:356. [PMID: 29686626 PMCID: PMC5900020 DOI: 10.3389/fphys.2018.00356] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2017] [Accepted: 03/22/2018] [Indexed: 12/28/2022] Open
Abstract
Ventricular arrhythmias (VA) in patients with myocardial infarction (MI) are thought to be associated with structural and electrophysiological remodeling within the infarct border zone (BZ). Personalized computational models have been used to investigate the potential role of the infarct BZ in arrhythmogenesis, which still remains incompletely understood. Most recent models have relied on experimental data to assign BZ properties. However, experimental measurements vary significantly resulting in different computational representations of this region. Here, we review experimental data available in the literature to determine the most prominent properties of the infarct BZ. Computational models are then used to investigate the effect of different representations of the BZ on activation and repolarization properties, which may be associated with VA. Experimental data obtained from several animal species and patients with infarct show that BZ properties vary significantly depending on disease's stage, with the early disease stage dominated by ionic remodeling and the chronic stage by structural remodeling. In addition, our simulations show that ionic remodeling in the BZ leads to large repolarization gradients in the vicinity of the scar, which may have a significant impact on arrhythmia simulations, while structural remodeling plays a secondary role. We conclude that it is imperative to faithfully represent the properties of regions of infarction within computational models specific to the disease stage under investigation in order to conduct in silico mechanistic investigations.
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Affiliation(s)
- Caroline Mendonca Costa
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Gernot Plank
- Department of Biophysics, Medical University of Graz, Graz, Austria
| | | | - Steven A Niederer
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Martin J Bishop
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
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32
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Trayanova NA, Boyle PM, Nikolov PP. Personalized Imaging and Modeling Strategies for Arrhythmia Prevention and Therapy. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2018; 5:21-28. [PMID: 29546250 PMCID: PMC5847279 DOI: 10.1016/j.cobme.2017.11.007] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The goal of this article is to review advances in computational modeling of the heart, with a focus on recent non-invasive clinical imaging- and simulation-based strategies aimed at improving the diagnosis and treatment of patients with arrhythmias and structural heart disease. Following a brief overview of the field of computational cardiology, we present recent applications of the personalized virtual-heart approach in predicting the optimal targets for infarct-related ventricular tachycardia and atrial fibrillation ablation, and in determining risk of sudden cardiac death in myocardial infarction patients. The hope is that with such models at the patient bedside, therapies could be improved, invasiveness of diagnostic procedures minimized, and health-care costs reduced.
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Affiliation(s)
- Natalia A Trayanova
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD
| | - Patrick M Boyle
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD
| | - Plamen P Nikolov
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD
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Hutchinson MD, Garza HHK. Contemporary Tools and Techniques for Substrate Ablation of Ventricular Tachycardia in Structural Heart Disease. CURRENT TREATMENT OPTIONS IN CARDIOVASCULAR MEDICINE 2018; 20:16. [PMID: 29478118 DOI: 10.1007/s11936-018-0610-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
As we have witnessed in other arenas of catheter-based therapeutics, ventricular tachycardia (VT) ablation has become increasingly anatomical in its execution. Multi-modality imaging provides anatomical detail in substrate characterization, which is often complex in nonischemic cardiomyopathy patients. Patients with intramural, intraseptal, and epicardial substrates provide challenges in delivering effective ablation to the critical arrhythmia substrate due to the depth of origin or the presence of adjacent critical structures. Novel ablation techniques such as simultaneous unipolar or bipolar ablation can be useful to achieve greater lesion depth, though at the expense of increasing collateral damage. Disruptive technologies like stereotactic radioablation may provide a tailored approach to these complex patients while minimizing procedural risk. Substrate ablation is a cornerstone of the contemporary VT ablation procedure, and recent data suggest that it is as effective and more efficient that conventional activation guided ablation. A number of specific targets and techniques for substrate ablation have been described, and all have shown a fairly high success in achieving their acute procedural endpoint. Substrate ablation also provides a novel and reproducible procedural endpoint, which may add predictive value for VT recurrence beyond conventional programmed stimulation. Extrapolation of outcome data to nonischemic phenotypes requires caution given both the variability in substrate nonischemic distribution and the underrepresentation of these patients in previous trials.
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Affiliation(s)
- Mathew D Hutchinson
- Division of Cardiovascular Medicine, Department of Medicine, University of Arizona College of Medicine, Tucson, AZ, USA. .,Sarver Heart Center, University of Arizona, 1501 N. Campbell Avenue, 4142B, Tucson, AZ, 85724, USA.
| | - Hyon-He K Garza
- Division of Cardiovascular Medicine, Department of Medicine, University of Arizona College of Medicine, Tucson, AZ, USA
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Pathmanathan P, Gray RA. Validation and Trustworthiness of Multiscale Models of Cardiac Electrophysiology. Front Physiol 2018; 9:106. [PMID: 29497385 PMCID: PMC5818422 DOI: 10.3389/fphys.2018.00106] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2017] [Accepted: 01/31/2018] [Indexed: 02/06/2023] Open
Abstract
Computational models of cardiac electrophysiology have a long history in basic science applications and device design and evaluation, but have significant potential for clinical applications in all areas of cardiovascular medicine, including functional imaging and mapping, drug safety evaluation, disease diagnosis, patient selection, and therapy optimisation or personalisation. For all stakeholders to be confident in model-based clinical decisions, cardiac electrophysiological (CEP) models must be demonstrated to be trustworthy and reliable. Credibility, that is, the belief in the predictive capability, of a computational model is primarily established by performing validation, in which model predictions are compared to experimental or clinical data. However, there are numerous challenges to performing validation for highly complex multi-scale physiological models such as CEP models. As a result, credibility of CEP model predictions is usually founded upon a wide range of distinct factors, including various types of validation results, underlying theory, evidence supporting model assumptions, evidence from model calibration, all at a variety of scales from ion channel to cell to organ. Consequently, it is often unclear, or a matter for debate, the extent to which a CEP model can be trusted for a given application. The aim of this article is to clarify potential rationale for the trustworthiness of CEP models by reviewing evidence that has been (or could be) presented to support their credibility. We specifically address the complexity and multi-scale nature of CEP models which makes traditional model evaluation difficult. In addition, we make explicit some of the credibility justification that we believe is implicitly embedded in the CEP modeling literature. Overall, we provide a fresh perspective to CEP model credibility, and build a depiction and categorisation of the wide-ranging body of credibility evidence for CEP models. This paper also represents a step toward the extension of model evaluation methodologies that are currently being developed by the medical device community, to physiological models.
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Affiliation(s)
- Pras Pathmanathan
- Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, United States
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Macatangay C, Viles-Gonzalez JF, Goldberger JJ. Role of Cardiac Imaging in Evaluating Risk for Sudden Cardiac Death. Card Electrophysiol Clin 2017; 9:639-650. [PMID: 29173407 DOI: 10.1016/j.ccep.2017.08.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Sudden cardiac death (SCD) is a major cause of death from cardiovascular disease. Our ability to predict patients at the highest risk of developing lethal ventricular arrhythmias remains limited. Despite recent studies evaluating risk stratification tools, there is no optimal strategy. Cardiac imaging provides the opportunity to assess left ventricular ejection fraction, strain, fibrosis, and sympathetic innervation, all of which are pathophysiologically related to SCD risk. These modalities may play a role in the identification of vulnerable anatomic substrates that provide the pathophysiologic basis for SCD. Further studies are required to identify optimal imaging platform for risk assessment.
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Affiliation(s)
- Constancia Macatangay
- Cardiovascular Division, Department of Medicine, Miller School of Medicine, University of Miami, 1120 NW 14th Street, Miami, FL 33136, USA
| | - Juan F Viles-Gonzalez
- Cardiovascular Division, Department of Medicine, Miller School of Medicine, University of Miami, 1120 NW 14th Street, Miami, FL 33136, USA
| | - Jeffrey J Goldberger
- Cardiovascular Division, Department of Medicine, Miller School of Medicine, University of Miami, 1120 NW 14th Street, Miami, FL 33136, USA.
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Shinbane JS, Saxon LA. Virtual medicine: Utilization of the advanced cardiac imaging patient avatar for procedural planning and facilitation. J Cardiovasc Comput Tomogr 2017; 12:16-27. [PMID: 29198733 DOI: 10.1016/j.jcct.2017.11.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2017] [Revised: 11/08/2017] [Accepted: 11/12/2017] [Indexed: 01/17/2023]
Abstract
Advances in imaging technology have led to a paradigm shift from planning of cardiovascular procedures and surgeries requiring the actual patient in a "brick and mortar" hospital to utilization of the digitalized patient in the virtual hospital. Cardiovascular computed tomographic angiography (CCTA) and cardiovascular magnetic resonance (CMR) digitalized 3-D patient representation of individual patient anatomy and physiology serves as an avatar allowing for virtual delineation of the most optimal approaches to cardiovascular procedures and surgeries prior to actual hospitalization. Pre-hospitalization reconstruction and analysis of anatomy and pathophysiology previously only accessible during the actual procedure could potentially limit the intrinsic risks related to time in the operating room, cardiac procedural laboratory and overall hospital environment. Although applications are specific to areas of cardiovascular specialty focus, there are unifying themes related to the utilization of technologies. The virtual patient avatar computer can also be used for procedural planning, computational modeling of anatomy, simulation of predicted therapeutic result, printing of 3-D models, and augmentation of real time procedural performance. Examples of the above techniques are at various stages of development for application to the spectrum of cardiovascular disease processes, including percutaneous, surgical and hybrid minimally invasive interventions. A multidisciplinary approach within medicine and engineering is necessary for creation of robust algorithms for maximal utilization of the virtual patient avatar in the digital medical center. Utilization of the virtual advanced cardiac imaging patient avatar will play an important role in the virtual health care system. Although there has been a rapid proliferation of early data, advanced imaging applications require further assessment and validation of accuracy, reproducibility, standardization, safety, efficacy, quality, cost effectiveness, and overall value to medical care.
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Affiliation(s)
- Jerold S Shinbane
- Division of Cardiovascular Medicine/USC Center for Body Computing, Keck School of Medicine of the University of Southern California, Los Angeles, CA, United States.
| | - Leslie A Saxon
- Division of Cardiovascular Medicine/USC Center for Body Computing, Keck School of Medicine of the University of Southern California, Los Angeles, CA, United States
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Deng D, Arevalo HJ, Prakosa A, Callans DJ, Trayanova NA. A feasibility study of arrhythmia risk prediction in patients with myocardial infarction and preserved ejection fraction. Europace 2017; 18:iv60-iv66. [PMID: 28011832 DOI: 10.1093/europace/euw351] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2016] [Accepted: 08/17/2016] [Indexed: 12/27/2022] Open
Abstract
AIM To predict arrhythmia susceptibility in myocardial infarction (MI) patients with left ventricular ejection fraction (LVEF) >35% using a personalized virtual heart simulation approach. METHODS AND RESULTS A total of four contrast enhanced magnetic resonance imaging (MRI) datasets of patient hearts with MI and average LVEF of 44.0 ± 2.6% were used in this study. Because of the preserved LVEF, the patients were not indicated for implantable cardioverter defibrillator (ICD) insertion. One patient had spontaneous ventricular tachycardia (VT) prior to the MRI scan; the others had no arrhythmic events. Simulations of arrhythmia susceptibility were blind to clinical outcome. Models were constructed from patient MRI images segmented to identify myocardium, grey zone, and scar based on pixel intensity. Grey zone was modelled as having altered electrophysiology. Programmed electrical stimulation (PES) was performed to assess VT inducibility from 19 bi-ventricular sites in each heart model. Simulations successfully predicted arrhythmia risk in all four patients. For the patient with arrhythmic event, in-silico PES resulted in VT induction. Simulations correctly predicted that VT was non-inducible for the three patients with no recorded VT events. CONCLUSIONS Results demonstrate that the personalized virtual heart simulation approach may provide a novel risk stratification modality to non-invasively and effectively identify patients with LVEF >35% who could benefit from ICD implantation.
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Affiliation(s)
- Dongdong Deng
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, 3400 North Charles Street, Hackerman 216, Baltimore, MD 21218, USA
| | - Hermenegild J Arevalo
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, 3400 North Charles Street, Hackerman 216, Baltimore, MD 21218, USA
| | - Adityo Prakosa
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, 3400 North Charles Street, Hackerman 216, Baltimore, MD 21218, USA
| | - David J Callans
- Division of Cardiovascular Medicine, Electrophysiology Section, University of Pennsylvania, 3400 Spruce St, 9 Founders Pavillion, Philadelphia, PA 19104
| | - Natalia A Trayanova
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, 3400 North Charles Street, Hackerman 216, Baltimore, MD 21218, USA
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Pashakhanloo F, Herzka DA, Mori S, Zviman M, Halperin H, Gai N, Bluemke DA, Trayanova NA, McVeigh ER. Submillimeter diffusion tensor imaging and late gadolinium enhancement cardiovascular magnetic resonance of chronic myocardial infarction. J Cardiovasc Magn Reson 2017; 19:9. [PMID: 28122618 PMCID: PMC5264305 DOI: 10.1186/s12968-016-0317-3] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2016] [Accepted: 12/20/2016] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Knowledge of the three-dimensional (3D) infarct structure and fiber orientation remodeling is essential for complete understanding of infarct pathophysiology and post-infarction electromechanical functioning of the heart. Accurate imaging of infarct microstructure necessitates imaging techniques that produce high image spatial resolution and high signal-to-noise ratio (SNR). The aim of this study is to provide detailed reconstruction of 3D chronic infarcts in order to characterize the infarct microstructural remodeling in porcine and human hearts. METHODS We employed a customized diffusion tensor imaging (DTI) technique in conjunction with late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) on a 3T clinical scanner to image, at submillimeter resolution, myofiber orientation and scar structure in eight chronically infarcted porcine hearts ex vivo. Systematic quantification of local microstructure was performed and the chronic infarct remodeling was characterized at different levels of wall thickness and scar transmurality. Further, a human heart with myocardial infarction was imaged using the same DTI sequence. RESULTS The SNR of non-diffusion-weighted images was >100 in the infarcted and control hearts. Mean diffusivity and fractional anisotropy (FA) demonstrated a 43% increase, and a 35% decrease respectively, inside the scar tissue. Despite this, the majority of the scar showed anisotropic structure with FA higher than an isotropic liquid. The analysis revealed that the primary eigenvector orientation at the infarcted wall on average followed the pattern of original fiber orientation (imbrication angle mean: 1.96 ± 11.03° vs. 0.84 ± 1.47°, p = 0.61, and inclination angle range: 111.0 ± 10.7° vs. 112.5 ± 6.8°, p = 0.61, infarcted/control wall), but at a higher transmural gradient of inclination angle that increased with scar transmurality (r = 0.36) and the inverse of wall thickness (r = 0.59). Further, the infarcted wall exhibited a significant increase in both the proportion of left-handed epicardial eigenvectors, and in the angle incoherency. The infarcted human heart demonstrated preservation of primary eigenvector orientation at the thinned region of infarct, consistent with the findings in the porcine hearts. CONCLUSIONS The application of high-resolution DTI and LGE-CMR revealed the detailed organization of anisotropic infarct structure at a chronic state. This information enhances our understanding of chronic post-infarction remodeling in large animal and human hearts.
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Affiliation(s)
- Farhad Pashakhanloo
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD USA
| | - Daniel A. Herzka
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD USA
| | - Susumu Mori
- Department of Radiology, Johns Hopkins University, Baltimore, MD USA
| | - Muz Zviman
- Department of Medicine, Johns Hopkins University, Baltimore, MD USA
| | - Henry Halperin
- Department of Medicine, Johns Hopkins University, Baltimore, MD USA
| | - Neville Gai
- Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD USA
| | - David A. Bluemke
- Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD USA
| | - Natalia A. Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD USA
| | - Elliot R. McVeigh
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD USA
- Department of Medicine, Johns Hopkins University, Baltimore, MD USA
- Departments of Bioengineering, Medicine, Radiology, University of California, 9500 Gilman Drive-MC0412,La Jolla, San Diego, 92093-0412 CA USA
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Bruegmann T, Boyle PM, Vogt CC, Karathanos TV, Arevalo HJ, Fleischmann BK, Trayanova NA, Sasse P. Optogenetic defibrillation terminates ventricular arrhythmia in mouse hearts and human simulations. J Clin Invest 2016; 126:3894-3904. [PMID: 27617859 DOI: 10.1172/jci88950] [Citation(s) in RCA: 115] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2016] [Accepted: 08/04/2016] [Indexed: 11/17/2022] Open
Abstract
Ventricular arrhythmias are among the most severe complications of heart disease and can result in sudden cardiac death. Patients at risk currently receive implantable defibrillators that deliver electrical shocks to terminate arrhythmias on demand. However, strong electrical shocks can damage the heart and cause severe pain. Therefore, we have tested optogenetic defibrillation using expression of the light-sensitive channel channelrhodopsin-2 (ChR2) in cardiac tissue. Epicardial illumination effectively terminated ventricular arrhythmias in hearts from transgenic mice and from WT mice after adeno-associated virus-based gene transfer of ChR2. We also explored optogenetic defibrillation for human hearts, taking advantage of a recently developed, clinically validated in silico approach for simulating infarct-related ventricular tachycardia (VT). Our analysis revealed that illumination with red light effectively terminates VT in diseased, ChR2-expressing human hearts. Mechanistically, we determined that the observed VT termination is due to ChR2-mediated transmural depolarization of the myocardium, which causes a block of voltage-dependent Na+ channels throughout the myocardial wall and interrupts wavefront propagation into illuminated tissue. Thus, our results demonstrate that optogenetic defibrillation is highly effective in the mouse heart and could potentially be translated into humans to achieve nondamaging and pain-free termination of ventricular arrhythmia.
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Connolly AJ, Bishop MJ. Computational Representations of Myocardial Infarct Scars and Implications for Arrhythmogenesis. CLINICAL MEDICINE INSIGHTS-CARDIOLOGY 2016; 10:27-40. [PMID: 27486348 PMCID: PMC4962962 DOI: 10.4137/cmc.s39708] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/19/2016] [Revised: 05/17/2016] [Accepted: 05/27/2016] [Indexed: 11/30/2022]
Abstract
Image-based computational modeling is becoming an increasingly used clinical tool to provide insight into the mechanisms of reentrant arrhythmias. In the context of ischemic heart disease, faithful representation of the electrophysiological properties of the infarct region within models is essential, due to the scars known for arrhythmic properties. Here, we review the different computational representations of the infarcted region, summarizing the experimental measurements upon which they are based. We then focus on the two most common representations of the scar core (complete insulator or electrically passive tissue) and perform simulations of electrical propagation around idealized infarct geometries. Our simulations highlight significant differences in action potential duration and focal effective refractory period (ERP) around the scar, driven by differences in electrotonic loading, depending on the choice of scar representation. Finally, a novel mechanism for arrhythmia induction, following a focal ectopic beat, is demonstrated, which relies on localized gradients in ERP directly caused by the electrotonic sink effects of the neighboring passive scar.
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Affiliation(s)
- Adam J Connolly
- Department of Imaging Sciences and Bioengineering, King's College London, St Thomas' Hospital, London, UK
| | - Martin J Bishop
- Department of Imaging Sciences and Bioengineering, King's College London, St Thomas' Hospital, London, UK
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The Fault Is in Our Scars: LGE and Ventricular Arrhythmia Risk in LV Dysfunction. JACC Cardiovasc Imaging 2016; 9:1056-1058. [PMID: 27450872 DOI: 10.1016/j.jcmg.2015.12.025] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2015] [Revised: 12/03/2015] [Accepted: 12/10/2015] [Indexed: 11/23/2022]
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Hill AP, Perry MD, Abi-Gerges N, Couderc JP, Fermini B, Hancox JC, Knollmann BC, Mirams GR, Skinner J, Zareba W, Vandenberg JI. Computational cardiology and risk stratification for sudden cardiac death: one of the grand challenges for cardiology in the 21st century. J Physiol 2016; 594:6893-6908. [PMID: 27060987 PMCID: PMC5134408 DOI: 10.1113/jp272015] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2015] [Accepted: 03/16/2016] [Indexed: 12/25/2022] Open
Abstract
Risk stratification in the context of sudden cardiac death has been acknowledged as one of the major challenges facing cardiology for the past four decades. In recent years, the advent of high performance computing has facilitated organ-level simulation of the heart, meaning we can now examine the causes, mechanisms and impact of cardiac dysfunction in silico. As a result, computational cardiology, largely driven by the Physiome project, now stands at the threshold of clinical utility in regards to risk stratification and treatment of patients at risk of sudden cardiac death. In this white paper, we outline a roadmap of what needs to be done to make this translational step, using the relatively well-developed case of acquired or drug-induced long QT syndrome as an exemplar case.
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Affiliation(s)
- Adam P Hill
- Victor Chang Cardiac Research Institute, 405 Liverpool Street, Darlinghurst, NSW, 2010, Australia.,St. Vincent's Clinical School, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Matthew D Perry
- Victor Chang Cardiac Research Institute, 405 Liverpool Street, Darlinghurst, NSW, 2010, Australia.,St. Vincent's Clinical School, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Najah Abi-Gerges
- AnaBios Corporation, 3030 Bunker Hill St., San Diego, CA, 92109, USA
| | | | - Bernard Fermini
- Global Safety Pharmacology, Pfizer Inc, MS8274-1347 Eastern Point Road, Groton, CT, 06340, USA
| | - Jules C Hancox
- School of Physiology, Pharmacology and Neuroscience, University of Bristol, Bristol, UK
| | - Bjorn C Knollmann
- Vanderbilt University School of Medicine, 1285 Medical Research Building IV, Nashville, Tennessee, 37232, USA
| | - Gary R Mirams
- Computational Biology, Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | - Jon Skinner
- Cardiac Inherited Disease Group, Starship Hospital, Auckland, New Zealand
| | - Wojciech Zareba
- University of Rochester Medical Center, Rochester, NY, 14642, USA
| | - Jamie I Vandenberg
- Victor Chang Cardiac Research Institute, 405 Liverpool Street, Darlinghurst, NSW, 2010, Australia.,St. Vincent's Clinical School, University of New South Wales, Sydney, NSW, 2052, Australia
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Chen Z, Cabrera-Lozoya R, Relan J, Sohal M, Shetty A, Karim R, Delingette H, Gill J, Rhode K, Ayache N, Taggart P, Rinaldi CA, Sermesant M, Razavi R. Biophysical Modeling Predicts Ventricular Tachycardia Inducibility and Circuit Morphology: A Combined Clinical Validation and Computer Modeling Approach. J Cardiovasc Electrophysiol 2016; 27:851-60. [PMID: 27094470 DOI: 10.1111/jce.12991] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/25/2015] [Accepted: 04/11/2016] [Indexed: 11/30/2022]
Abstract
INTRODUCTION Computational modeling of cardiac arrhythmogenesis and arrhythmia maintenance has made a significant contribution to the understanding of the underlying mechanisms of arrhythmia. We hypothesized that a cardiac model using personalized electro-anatomical parameters could define the underlying ventricular tachycardia (VT) substrate and predict reentrant VT circuits. We used a combined modeling and clinical approach in order to validate the concept. METHODS AND RESULTS Non-contact electroanatomic mapping studies were performed in 7 patients (5 ischemics, 2 non-ischemics). Three ischemic cardiomyopathy patients underwent a clinical VT stimulation study. Anatomical information was obtained from cardiac magnetic resonance imaging (CMR) including high-resolution scar imaging. A simplified biophysical mono-domain action potential model personalized with the patients' anatomical and electrical information was used to perform in silico VT stimulation studies for comparison. The personalized in silico VT stimulations were able to predict VT inducibility as well as the macroscopic characteristics of the VT circuits in patients who had clinical VT stimulation studies. The patients with positive clinical VT stimulation studies had wider distribution of action potential duration restitution curve (APD-RC) slopes and APDs than the patient with a negative VT stimulation study. The exit points of reentrant VT circuits encompassed a higher percentage of the maximum APD-RC slope compared to the scar and non-scar areas, 32%, 4%, and 0.2%, respectively. CONCLUSIONS VT stimulation studies can be simulated in silico using a personalized biophysical cardiac model. Myocardial spatial heterogeneity of APD restitution properties and conductivity may help predict the location of crucial entry/exit points of reentrant VT circuits.
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Affiliation(s)
- Zhong Chen
- Kings College London, London, UK.,Guy's and St. Thomas' Hospital, London, UK
| | | | - Jatin Relan
- Inria, Asclepios Team, Sophia Antipolis, France
| | - Manav Sohal
- Kings College London, London, UK.,Guy's and St. Thomas' Hospital, London, UK
| | - Anoop Shetty
- Kings College London, London, UK.,Guy's and St. Thomas' Hospital, London, UK
| | | | | | - Jaswinder Gill
- Kings College London, London, UK.,Guy's and St. Thomas' Hospital, London, UK
| | | | | | | | | | | | - Reza Razavi
- Kings College London, London, UK.,Guy's and St. Thomas' Hospital, London, UK
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Arrhythmia risk stratification of patients after myocardial infarction using personalized heart models. Nat Commun 2016; 7:11437. [PMID: 27164184 PMCID: PMC4866040 DOI: 10.1038/ncomms11437] [Citation(s) in RCA: 225] [Impact Index Per Article: 28.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2016] [Accepted: 03/24/2016] [Indexed: 12/13/2022] Open
Abstract
Sudden cardiac death (SCD) from arrhythmias is a leading cause of mortality. For patients at high SCD risk, prophylactic insertion of implantable cardioverter defibrillators (ICDs) reduces mortality. Current approaches to identify patients at risk for arrhythmia are, however, of low sensitivity and specificity, which results in a low rate of appropriate ICD therapy. Here, we develop a personalized approach to assess SCD risk in post-infarction patients based on cardiac imaging and computational modelling. We construct personalized three-dimensional computer models of post-infarction hearts from patients' clinical magnetic resonance imaging data and assess the propensity of each model to develop arrhythmia. In a proof-of-concept retrospective study, the virtual heart test significantly outperformed several existing clinical metrics in predicting future arrhythmic events. The robust and non-invasive personalized virtual heart risk assessment may have the potential to prevent SCD and avoid unnecessary ICD implantations.
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Arevalo HJ, Vadakkumpadan F, Guallar E, Jebb A, Malamas P, Wu KC, Trayanova NA. Arrhythmia risk stratification of patients after myocardial infarction using personalized heart models. Nat Commun 2016. [PMID: 27164184 DOI: 10.1038/ncommsll437] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2023] Open
Abstract
Sudden cardiac death (SCD) from arrhythmias is a leading cause of mortality. For patients at high SCD risk, prophylactic insertion of implantable cardioverter defibrillators (ICDs) reduces mortality. Current approaches to identify patients at risk for arrhythmia are, however, of low sensitivity and specificity, which results in a low rate of appropriate ICD therapy. Here, we develop a personalized approach to assess SCD risk in post-infarction patients based on cardiac imaging and computational modelling. We construct personalized three-dimensional computer models of post-infarction hearts from patients' clinical magnetic resonance imaging data and assess the propensity of each model to develop arrhythmia. In a proof-of-concept retrospective study, the virtual heart test significantly outperformed several existing clinical metrics in predicting future arrhythmic events. The robust and non-invasive personalized virtual heart risk assessment may have the potential to prevent SCD and avoid unnecessary ICD implantations.
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Affiliation(s)
- Hermenegild J Arevalo
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Fijoy Vadakkumpadan
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Eliseo Guallar
- Welch Center for Prevention, Epidemiology, and Clinical Research, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21287, USA
| | - Alexander Jebb
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Peter Malamas
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Katherine C Wu
- Division of Cardiology, Department of Medicine, Johns Hopkins Medical Institutions, Baltimore, Maryland 21287, USA
| | - Natalia A Trayanova
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
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Trayanova NA, Chang KC. How computer simulations of the human heart can improve anti-arrhythmia therapy. J Physiol 2016; 594:2483-502. [PMID: 26621489 DOI: 10.1113/jp270532] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2015] [Accepted: 11/25/2015] [Indexed: 01/26/2023] Open
Abstract
Over the last decade, the state-of-the-art in cardiac computational modelling has progressed rapidly. The electrophysiological function of the heart can now be simulated with a high degree of detail and accuracy, opening the doors for simulation-guided approaches to anti-arrhythmic drug development and patient-specific therapeutic interventions. In this review, we outline the basic methodology for cardiac modelling, which has been developed and validated over decades of research. In addition, we present several recent examples of how computational models of the human heart have been used to address current clinical problems in cardiac electrophysiology. We will explore the use of simulations to improve anti-arrhythmic pacing and defibrillation interventions; to predict optimal sites for clinical ablation procedures; and to aid in the understanding and selection of arrhythmia risk markers. Together, these studies illustrate how the tremendous advances in cardiac modelling are poised to revolutionize medical treatment and prevention of arrhythmia.
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Affiliation(s)
- Natalia A Trayanova
- Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA.,Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Kelly C Chang
- Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
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