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Trayanova NA, Prakosa A. Up digital and personal: How heart digital twins can transform heart patient care. Heart Rhythm 2024; 21:89-99. [PMID: 37871809 PMCID: PMC10872898 DOI: 10.1016/j.hrthm.2023.10.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 10/12/2023] [Accepted: 10/15/2023] [Indexed: 10/25/2023]
Abstract
Precision medicine is the vision of health care where therapy is tailored to each patient. As part of this vision, digital twinning technology promises to deliver a digital representation of organs or even patients by using tools capable of simulating personal health conditions and predicting patient or disease trajectories on the basis of relationships learned both from data and from biophysics knowledge. Such virtual replicas would update themselves with data from monitoring devices and medical tests and assessments, reflecting dynamically the changes in our health conditions and the responses to treatment. In precision cardiology, the concepts and initial applications of heart digital twins have slowly been gaining popularity and the trust of the clinical community. In this article, we review the advancement in heart digital twinning and its initial translation to the management of heart rhythm disorders.
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Affiliation(s)
- Natalia A Trayanova
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland.
| | - Adityo Prakosa
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland
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2
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Zhang Y, Zhang K, Prakosa A, James C, Zimmerman SL, Carrick R, Sung E, Gasperetti A, Tichnell C, Murray B, Calkins H, Trayanova NA. Predicting ventricular tachycardia circuits in patients with arrhythmogenic right ventricular cardiomyopathy using genotype-specific heart digital twins. eLife 2023; 12:RP88865. [PMID: 37851708 PMCID: PMC10584370 DOI: 10.7554/elife.88865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2023] Open
Abstract
Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a genetic cardiac disease that leads to ventricular tachycardia (VT), a life-threatening heart rhythm disorder. Treating ARVC remains challenging due to the complex underlying arrhythmogenic mechanisms, which involve structural and electrophysiological (EP) remodeling. Here, we developed a novel genotype-specific heart digital twin (Geno-DT) approach to investigate the role of pathophysiological remodeling in sustaining VT reentrant circuits and to predict the VT circuits in ARVC patients of different genotypes. This approach integrates the patient's disease-induced structural remodeling reconstructed from contrast-enhanced magnetic-resonance imaging and genotype-specific cellular EP properties. In our retrospective study of 16 ARVC patients with two genotypes: plakophilin-2 (PKP2, n = 8) and gene-elusive (GE, n = 8), we found that Geno-DT accurately and non-invasively predicted the VT circuit locations for both genotypes (with 100%, 94%, 96% sensitivity, specificity, and accuracy for GE patient group, and 86%, 90%, 89% sensitivity, specificity, and accuracy for PKP2 patient group), when compared to VT circuit locations identified during clinical EP studies. Moreover, our results revealed that the underlying VT mechanisms differ among ARVC genotypes. We determined that in GE patients, fibrotic remodeling is the primary contributor to VT circuits, while in PKP2 patients, slowed conduction velocity and altered restitution properties of cardiac tissue, in addition to the structural substrate, are directly responsible for the formation of VT circuits. Our novel Geno-DT approach has the potential to augment therapeutic precision in the clinical setting and lead to more personalized treatment strategies in ARVC.
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Affiliation(s)
- Yingnan Zhang
- Department of Biomedical Engineering, Johns Hopkins UniversityBaltimoreUnited States
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins UniversityBaltimoreUnited States
| | - Kelly Zhang
- Department of Biomedical Engineering, Johns Hopkins UniversityBaltimoreUnited States
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins UniversityBaltimoreUnited States
| | - Adityo Prakosa
- Department of Biomedical Engineering, Johns Hopkins UniversityBaltimoreUnited States
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins UniversityBaltimoreUnited States
| | - Cynthia James
- Division of Cardiology, Department of Medicine, Johns Hopkins HospitalBaltimoreUnited States
| | | | - Richard Carrick
- Division of Cardiology, Department of Medicine, Johns Hopkins HospitalBaltimoreUnited States
| | - Eric Sung
- Department of Biomedical Engineering, Johns Hopkins UniversityBaltimoreUnited States
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins UniversityBaltimoreUnited States
| | - Alessio Gasperetti
- Division of Cardiology, Department of Medicine, Johns Hopkins HospitalBaltimoreUnited States
| | - Crystal Tichnell
- Division of Cardiology, Department of Medicine, Johns Hopkins HospitalBaltimoreUnited States
| | - Brittney Murray
- Division of Cardiology, Department of Medicine, Johns Hopkins HospitalBaltimoreUnited States
| | - Hugh Calkins
- Division of Cardiology, Department of Medicine, Johns Hopkins HospitalBaltimoreUnited States
| | - Natalia A Trayanova
- Department of Biomedical Engineering, Johns Hopkins UniversityBaltimoreUnited States
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins UniversityBaltimoreUnited States
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Zhang Y, Zhang K, Prakosa A, James C, Zimmerman SL, Carrick R, Sung E, Gasperetti A, Tichnell C, Murray B, Calkins H, Trayanova N. Predicting Ventricular Tachycardia Circuits in Patients with Arrhythmogenic Right Ventricular Cardiomyopathy using Genotype-specific Heart Digital Twins. medRxiv 2023:2023.05.31.23290587. [PMID: 37398074 PMCID: PMC10312861 DOI: 10.1101/2023.05.31.23290587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a genetic cardiac disease that leads to ventricular tachycardia (VT), a life-threatening heart rhythm disorder. Treating ARVC remains challenging due to the complex underlying arrhythmogenic mechanisms, which involve structural and electrophysiological (EP) remodeling. Here, we developed a novel genotype-specific heart digital twin (Geno-DT) approach to investigate the role of pathophysiological remodeling in sustaining VT reentrant circuits and to predict the VT circuits in ARVC patients of different genotypes. This approach integrates the patient's disease-induced structural remodeling reconstructed from contrast-enhanced magnetic-resonance imaging and genotype-specific cellular EP properties. In our retrospective study of 16 ARVC patients with two genotypes: plakophilin-2 (PKP2, n = 8) and gene-elusive (GE, n = 8), we found that Geno-DT accurately and non-invasively predicted the VT circuit locations for both genotypes (with 100%, 94%, 96% sensitivity, specificity, and accuracy for GE patient group, and 86%, 90%, 89% sensitivity, specificity, and accuracy for PKP2 patient group), when compared to VT circuit locations identified during clinical EP studies. Moreover, our results revealed that the underlying VT mechanisms differ among ARVC genotypes. We determined that in GE patients, fibrotic remodeling is the primary contributor to VT circuits, while in PKP2 patients, slowed conduction velocity and altered restitution properties of cardiac tissue, in addition to the structural substrate, are directly responsible for the formation of VT circuits. Our novel Geno-DT approach has the potential to augment therapeutic precision in the clinical setting and lead to more personalized treatment strategies in ARVC.
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Affiliation(s)
- Yingnan Zhang
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA
| | - Kelly Zhang
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA
| | - Adityo Prakosa
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA
| | - Cynthia James
- Division of Cardiology, Department of Medicine, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Stefan L Zimmerman
- Department of Radiology and Radiological Science, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Richard Carrick
- Division of Cardiology, Department of Medicine, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Eric Sung
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA
| | - Alessio Gasperetti
- Division of Cardiology, Department of Medicine, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Crystal Tichnell
- Division of Cardiology, Department of Medicine, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Brittney Murray
- Division of Cardiology, Department of Medicine, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Hugh Calkins
- Division of Cardiology, Department of Medicine, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Natalia Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA
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Lefebvre AL, Yamamoto CAP, Shade JK, Bradley RP, Yu RA, Ali RL, Popescu DM, Prakosa A, Kholmovski EG, Trayanova NA. LASSNet: A Four Steps Deep Neural Network for Left Atrial Segmentation and Scar Quantification. Left Atr Scar Quantif Segm (2022) 2023; 13586:1-15. [PMID: 37287952 PMCID: PMC10246435 DOI: 10.1007/978-3-031-31778-1_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Accurate quantification of left atrium (LA) scar in patients with atrial fibrillation is essential to guide successful ablation strategies. Prior to LA scar quantification, a proper LA cavity segmentation is required to ensure exact location of scar. Both tasks can be extremely time-consuming and are subject to inter-observer disagreements when done manually. We developed and validated a deep neural network to automatically segment the LA cavity and the LA scar. The global architecture uses a multi-network sequential approach in two stages which segment the LA cavity and the LA Scar. Each stage has two steps: a region of interest Neural Network and a refined segmentation network. We analysed the performances of our network according to different parameters and applied data triaging. 200+ late gadolinium enhancement magnetic resonance images were provided by the LAScarQS 2022 Challenge. Finally, we compared our performances for scar quantification to the literature and demonstrated improved performances.
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Affiliation(s)
- Arthur L Lefebvre
- Faculté polytechnique de Mons, UMONS, Mons, Belgium
- Alliance for Cardiovascular Diagnostic and Treatment Innovation (ADVANCE), Johns Hopkins University, Baltimore, MD, USA
| | - Carolyna A P Yamamoto
- Alliance for Cardiovascular Diagnostic and Treatment Innovation (ADVANCE), Johns Hopkins University, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Julie K Shade
- Alliance for Cardiovascular Diagnostic and Treatment Innovation (ADVANCE), Johns Hopkins University, Baltimore, MD, USA
| | - Ryan P Bradley
- Alliance for Cardiovascular Diagnostic and Treatment Innovation (ADVANCE), Johns Hopkins University, Baltimore, MD, USA
| | - Rebecca A Yu
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Rheeda L Ali
- Alliance for Cardiovascular Diagnostic and Treatment Innovation (ADVANCE), Johns Hopkins University, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Dan M Popescu
- Alliance for Cardiovascular Diagnostic and Treatment Innovation (ADVANCE), Johns Hopkins University, Baltimore, MD, USA
| | - Adityo Prakosa
- Alliance for Cardiovascular Diagnostic and Treatment Innovation (ADVANCE), Johns Hopkins University, Baltimore, MD, USA
| | - Eugene G Kholmovski
- Alliance for Cardiovascular Diagnostic and Treatment Innovation (ADVANCE), Johns Hopkins University, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Natalia A Trayanova
- Alliance for Cardiovascular Diagnostic and Treatment Innovation (ADVANCE), Johns Hopkins University, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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Sung E, Kyranakis S, Daimee UA, Engels M, Prakosa A, Zhou S, Nazarian S, Zimmerman SL, Chrispin J, Trayanova NA. Evaluation of a deep Learning-enabled automated computational heart modeling workflow for personalized assessment of ventricular arrhythmias. J Physiol 2023. [PMID: 37060278 DOI: 10.1113/jp284125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 04/12/2023] [Indexed: 04/16/2023] Open
Abstract
Personalized, image-based computational heart modeling is a powerful technology that can be used to improve patient-specific arrhythmia risk stratification and ventricular tachycardia (VT) ablation targeting. However, most state-of-the-art methods still require manual interactions by expert users. The goal of this study is to evaluate the feasibility of an automated, deep learning-based workflow for reconstructing personalized computational electrophysiological heart models to guide patient-specific treatment of VT. Contrast-enhanced computed tomography (CE-CT) images with expert ventricular myocardium segmentations were acquired from 111 patients across 5 cohorts from 3 different institutions. A deep convolutional neural network (CNN) for segmenting left ventricular myocardium from CE-CT was developed, trained, and evaluated. From both CNN-based and expert segmentations in a subset of patients, personalized electrophysiological heart models were reconstructed, and rapid pacing was used to induce VTs. CNN-based and expert segmentations were more concordant in the middle myocardium than in the heart's base or apex. Wavefront propagation during pacing was similar between CNN-based and original heart models. Between most sets of heart models, VT inducibility was the same, the number of induced VTs was strongly correlated, and VT circuits co-localized. Our results demonstrate that personalized computational heart models reconstructed from deep learning-based segmentations even with a small training set size can predict similar VT inducibility and circuit locations as those from expertly-derived heart models. Hence, a user-independent, automated framework for simulating arrhythmias in personalized heart models could feasibly be used in clinical settings to aid VT risk stratification and guide VT ablation therapy. KEY POINTS: Personalized electrophysiological heart modeling can aid in patient-specific ventricular tachycardia (VT) risk stratification and VT ablation targeting. Current state-of-the-art, image-based heart models for VT prediction require expert-dependent, manual interactions that may not be accessible across clinical settings. In this study, we develop an automated, deep learning-based workflow for reconstructing personalized heart models capable of simulating arrhythmias and compare its predictions with that of expert-generated heart models. The number and location of VTs was similar between heart models generated from the deep learning-based workflow and expert-generated heart models. These results demonstrate the feasibility of using an automated computational heart modeling workflow to aid in VT therapeutics and has implications for generalizing personalized computational heart technology to a broad range of clinical centers. Abstract figure legend In this study, we evaluate whether an automated, deep learning-based computational electrophysiological heart models can predict similar arrhythmias as those of expert, manually-derived heart models. First, we build a deep neural network to automatically segment contrast-enhanced CT scans and demonstrate that predicted imaging metrics are comparable to that of manual segmentations. Second, electrophysiological heart models reconstructed from these automated segmentations predict similar wavefront propagation and VT circuits as those of expert-reconstructed heart models. This work represents an advancement towards construction of a user-independent, computational framework to aid in VT risk stratification and guide VT ablation. CT: computed tomography, VT: ventricular tachycardia. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Eric Sung
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA
| | - Stephen Kyranakis
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA
| | - Usama A Daimee
- Division of Cardiology, Department of Medicine, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Marc Engels
- Division of Cardiology, Department of Medicine, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Adityo Prakosa
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA
| | - Shijie Zhou
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA
| | - Saman Nazarian
- Division of Cardiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Stefan L Zimmerman
- Department of Radiology and Radiological Sciences, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Jonathan Chrispin
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Division of Cardiology, Department of Medicine, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Natalia A Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA
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Sung E, Prakosa A, Kyranakis S, Berger RD, Chrispin J, Trayanova NA. Wavefront directionality and decremental stimuli synergistically improve identification of ventricular tachycardia substrate: insights from personalized computational heart models. Europace 2023; 25:223-235. [PMID: 36006658 PMCID: PMC10103576 DOI: 10.1093/europace/euac140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 07/16/2022] [Indexed: 11/14/2022] Open
Abstract
AIMS Multiple wavefront pacing (MWP) and decremental pacing (DP) are two electroanatomic mapping (EAM) strategies that have emerged to better characterize the ventricular tachycardia (VT) substrate. The aim of this study was to assess how well MWP, DP, and their combination improve identification of electrophysiological abnormalities on EAM that reflect infarct remodelling and critical VT sites. METHODS AND RESULTS Forty-eight personalized computational heart models were reconstructed using images from post-infarct patients undergoing VT ablation. Paced rhythms were simulated by delivering an initial (S1) and an extra-stimulus (S2) from one of 100 locations throughout each heart model. For each pacing, unipolar signals were computed along the myocardial surface to simulate substrate EAM. Six EAM features were extracted and compared with the infarct remodelling and critical VT sites. Concordance of S1 EAM features between different maps was lower in hearts with smaller amounts of remodelling. Incorporating S1 EAM features from multiple maps greatly improved the detection of remodelling, especially in hearts with less remodelling. Adding S2 EAM features from multiple maps decreased the number of maps required to achieve the same detection accuracy. S1 EAM features from multiple maps poorly identified critical VT sites. However, combining S1 and S2 EAM features from multiple maps paced near VT circuits greatly improved identification of critical VT sites. CONCLUSION Electroanatomic mapping with MWP is more advantageous for characterization of substrate in hearts with less remodelling. During substrate EAM, MWP and DP should be combined and delivered from locations proximal to a suspected VT circuit to optimize identification of the critical VT site.
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Affiliation(s)
- Eric Sung
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21287, USA
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD 21218, USA
| | - Adityo Prakosa
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21287, USA
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD 21218, USA
| | - Stephen Kyranakis
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Ronald D Berger
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD 21218, USA
- Section of Cardiac Electrophysiology, Division of Cardiology, Department of Medicine, Johns Hopkins Hospital, Baltimore, MD 21287, USA
| | - Jonathan Chrispin
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD 21218, USA
- Section of Cardiac Electrophysiology, Division of Cardiology, Department of Medicine, Johns Hopkins Hospital, Baltimore, MD 21287, USA
| | - Natalia A Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21287, USA
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD 21218, USA
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Sung E, Prakosa A, Zhou S, Berger RD, Chrispin J, Nazarian S, Trayanova NA. Fat infiltration in the infarcted heart as a paradigm for ventricular arrhythmias. Nat Cardiovasc Res 2022; 1:933-945. [PMID: 36589896 PMCID: PMC9802586 DOI: 10.1038/s44161-022-00133-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Infiltrating adipose tissue (inFAT) has been recently found to co-localize with scar in infarcted hearts and may contribute to ventricular arrhythmias (VAs), a life-threatening heart rhythm disorder. However, the contribution of inFAT to VA has not been well-established. We investigated the role of inFAT versus scar in VA through a combined prospective clinical and mechanistic computational study. Using personalized computational heart models and comparing the results from simulations of VA dynamics with measured electrophysiological abnormalities during the clinical procedure, we demonstrate that inFAT, rather than scar, is a primary driver of arrhythmogenic propensity and is frequently present in critical regions of the VA circuit. We determined that, within the VA circuitry, inFAT, as opposed to scar, is primarily responsible for conduction slowing in critical sites, mechanistically promoting VA. Our findings implicate inFAT as a dominant player in infarct-related VA, challenging existing paradigms and opening the door for unexplored anti-arrhythmic strategies.
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Affiliation(s)
- Eric Sung
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.,Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA
| | - Adityo Prakosa
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.,Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA
| | - Shijie Zhou
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.,Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA
| | - Ronald D. Berger
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA.,Department of Medicine, Division of Cardiology, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Jonathan Chrispin
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA.,Department of Medicine, Division of Cardiology, Johns Hopkins Hospital, Baltimore, MD, USA.,These authors jointly supervised this work: Jonathan Chrispin, Saman Nazarian, Natalia A. Trayanova
| | - Saman Nazarian
- Division of Cardiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,These authors jointly supervised this work: Jonathan Chrispin, Saman Nazarian, Natalia A. Trayanova
| | - Natalia A. Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.,Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA.,These authors jointly supervised this work: Jonathan Chrispin, Saman Nazarian, Natalia A. Trayanova.,Correspondence and requests for materials should be addressed to Natalia A. Trayanova.
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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: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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O'Hara RP, Prakosa A, zimmerman S, Trayanova NA. PO-691-08 ASSESSING THE MECHANISTIC ROLE OF DIFFUSE FIBROSIS TOWARDS ARRHYTHMOGENESIS IN HYPERTROPHIC CARDIOMYOPATHY. Heart Rhythm 2022. [DOI: 10.1016/j.hrthm.2022.03.622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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10
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Sung E, Prakosa A, Zhou S, Tandri H, Berger RD, Nazarian S, Chrispin J, Trayanova NA. PO-641-04 FUNCTIONAL MAPPING FOR ARRHYTHMOGENIC SUBSTRATE CHARACTERIZATION IS MORE EFFECTIVE IN HEARTS WITH LESS DISEASE REMODELING. Heart Rhythm 2022. [DOI: 10.1016/j.hrthm.2022.03.148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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O'Hara RP, Binka E, Prakosa A, Zimmerman SL, Cartoski MJ, Abraham MR, Lu DY, Boyle PM, Trayanova NA. Personalized computational heart models with T1-mapped fibrotic remodeling predict sudden death risk in patients with hypertrophic cardiomyopathy. eLife 2022; 11:73325. [PMID: 35076018 PMCID: PMC8789259 DOI: 10.7554/elife.73325] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 01/07/2022] [Indexed: 11/13/2022] Open
Abstract
Hypertrophic cardiomyopathy (HCM) is associated with risk of sudden cardiac death (SCD) due to ventricular arrhythmias (VAs) arising from the proliferation of fibrosis in the heart. Current clinical risk stratification criteria inadequately identify at-risk patients in need of primary prevention of VA. Here, we use mechanistic computational modeling of the heart to analyze how HCM-specific remodeling promotes arrhythmogenesis and to develop a personalized strategy to forecast risk of VAs in these patients. We combine contrast-enhanced cardiac magnetic resonance imaging and T1 mapping data to construct digital replicas of HCM patient hearts that represent the patient-specific distribution of focal and diffuse fibrosis and evaluate the substrate propensity to VA. Our analysis indicates that the presence of diffuse fibrosis, which is rarely assessed in these patients, increases arrhythmogenic propensity. In forecasting future VA events in HCM patients, the imaging-based computational heart approach achieved 84.6%, 76.9%, and 80.1% sensitivity, specificity, and accuracy, respectively, and significantly outperformed current clinical risk predictors. This novel VA risk assessment may have the potential to prevent SCD and help deploy primary prevention appropriately in HCM patients.
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Affiliation(s)
- Ryan P O'Hara
- Department of Biomedical Engineering, Johns Hopkins University
| | - Edem Binka
- Division of Pediatric Cardiology, Johns Hopkins University
| | - Adityo Prakosa
- Department of Biomedical Engineering, Johns Hopkins University
| | | | - Mark J Cartoski
- Division of Pediatric Cardiology, Alfred I. duPont Hospital for Children
| | | | - Dai-Yin Lu
- Division of Cardiology, University of California, San Francisco
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12
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O’Hara RP, Prakosa A, Binka E, Lacy A, Trayanova NA. Arrhythmia in hypertrophic cardiomyopathy: Risk prediction using contrast enhanced MRI, T1 mapping, and personalized virtual heart technology. J Electrocardiol 2022; 74:122-127. [PMID: 36183522 PMCID: PMC9729380 DOI: 10.1016/j.jelectrocard.2022.09.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 08/04/2022] [Accepted: 09/12/2022] [Indexed: 12/13/2022]
Abstract
BACKGROUND Hypertrophic cardiomyopathy (HCM), a disease with myocardial fibrosis manifestation, is a common cause of sudden cardiac death (SCD) due to ventricular arrhythmias (VA). Current clinical risk stratification criteria are inadequate in identifying patients who are at risk for VA and in need of an implantable cardioverter defibrillator (ICD) for primary prevention. OBJECTIVE We aimed to develop a risk prediction approach based on imaging biomarkers from the combination of late gadolinium contrast-enhanced (LGE) MRI and T1 mapping. We then aimed to compare the prediction to a virtual heart computational risk assessment approach based on LGE-T1 virtual heart models. METHODS The methodology involved combining short-axis LGE-MRI with post-contrast T1 maps to define personalized thresholds for diffuse and dense fibrosis. The combined LGE-T1 maps were used to evaluate imaging biomarkers for VA risk prediction. The risk prediction capability of the biomarkers was compared with that of the LGE-T1 virtual heart arrhythmia inducibility simulation. VA risk prediction performance from both approaches was compared to clinical outcome (presence of clinical VA). RESULTS Image-based biomarkers, including hypertrophy, signal intensity heterogeneity, and fibrotic border complexity, could not discriminate high vs low VA risk. LGE-T1 virtual heart technology outperformed all the image-based biomarker metrics and was statistically significant in predicting VA risk in HCM. CONCLUSIONS We combined two MR imaging techniques to analyze imaging biomarkers in HCM. Raw and processed image-based biomarkers cannot discriminate patients with VA from those without VA. Hybrid LGE-T1 virtual heart models could correctly predict VA risk for this cohort and may improve SCD risk stratification to better identify HCM patients for primary preventative ICD implantation.
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Affiliation(s)
- Ryan P. O’Hara
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, United States of America
| | - Adityo Prakosa
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, United States of America
| | - Edem Binka
- Division of Pediatric Cardiology, Department of Pediatrics, Johns Hopkins School of Medicine, Baltimore, MD 21205, United States of America
| | - Audrey Lacy
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, United States of America
| | - Natalia A. Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, United States of America,Corresponding author at: 3400 N Charles Street, Hackerman Hall 216, Baltimore, MD 21218, United States of America. (N.A. Trayanova)
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13
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Aronis KN, Okada DR, Xie E, Daimee UA, Prakosa A, Gilotra NA, Wu KC, Trayanova N, Chrispin J. Spatial dispersion analysis of LGE-CMR for prediction of ventricular arrhythmias in patients with cardiac sarcoidosis. Pacing Clin Electrophysiol 2021; 44:2067-2074. [PMID: 34766627 DOI: 10.1111/pace.14406] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 10/15/2021] [Accepted: 11/07/2021] [Indexed: 11/28/2022]
Abstract
BACKGROUND Patients with cardiac sarcoidosis (CS) are at increased risk of life-threatening ventricular arrhythmias (VA). Current approaches to risk stratification have limited predictive value. OBJECTIVES To assess the utility of spatial dispersion analysis of late gadolinium enhancement cardiac magnetic resonance (LGE-CMR), as a quantitative measure of myocardial tissue heterogeneity, in risk stratifying patients with CS for VA and death. METHODS Sixty two patients with CS underwent LGE-CMR. LGE images were segmented and dispersion maps of the left and right ventricles were generated as follows. Based on signal intensity (SI), each pixel was categorized as abnormal (SI ≥3SD above the mean), intermediate (SI 1-3 SD above the mean) or normal (SI <1SD above the mean); and each pixel was then assigned a value of 0 to 8 based on the number of adjacent pixels of a different category. Average dispersion score was calculated for each patient. The primary endpoint was VA during follow up. The composite of VA or death was assessed as a secondary endpoint. RESULTS During 4.7 ± 3.5 years of follow up, six patients had VA, and five without documented VA died. Average dispersion score was significantly higher in patients with VA versus those without (0.87 ± 0.08 vs. 0.71 ± 0.16; p = .002) and in patients with events versus those without (0.83 ± 0.08 vs. 0.70 ± 0.16; p = .003). Patients at higher tertiles of dispersion score had a higher incidence of VA (p = .03) and the composite of VA or death (p = .01). CONCLUSIONS Increased substrate heterogeneity, quantified by spatial dispersion analysis of LGE-CMR, may be helpful in risk-stratifying patients with CS for adverse events, including life-threatening arrhythmias.
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Affiliation(s)
- Konstantinos N Aronis
- Division of Cardiology, Department of Medicine, Johns Hopkins University, Baltimore, Maryland, USA.,Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland, USA
| | - David R Okada
- Division of Cardiology, Department of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Eric Xie
- Division of Cardiology, Department of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Usama A Daimee
- Division of Cardiology, Department of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Adityo Prakosa
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA.,Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland, USA
| | - Nisha A Gilotra
- Division of Cardiology, Department of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Katherine C Wu
- Division of Cardiology, Department of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Natalia Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA.,Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland, USA
| | - Jonathan Chrispin
- Division of Cardiology, Department of Medicine, Johns Hopkins University, Baltimore, Maryland, USA.,Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland, USA
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14
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Sung E, Prakosa A, Trayanova NA. Analyzing the Role of Repolarization Gradients in Post-infarct Ventricular Tachycardia Dynamics Using Patient-Specific Computational Heart Models. Front Physiol 2021; 12:740389. [PMID: 34658925 PMCID: PMC8514757 DOI: 10.3389/fphys.2021.740389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 09/06/2021] [Indexed: 11/20/2022] Open
Abstract
Aims: Disease-induced repolarization heterogeneity in infarcted myocardium contributes to VT arrhythmogenesis but how apicobasal and transmural (AB-TM) repolarization gradients additionally affect post-infarct VT dynamics is unknown. The goal of this study is to assess how AB-TM repolarization gradients impact post-infarct VT dynamics using patient-specific heart models. Method: 3D late gadolinium-enhanced cardiac magnetic resonance images were acquired from seven post-infarct patients. Models representing the patient-specific scar and infarct border zone distributions were reconstructed without (baseline) and with repolarization gradients along both the AB-TM axes. AB only and TM only models were created to assess the effects of each ventricular gradient on VT dynamics. VTs were induced in all models via rapid pacing. Results: Ten baseline VTs were induced. VT inducibility in AB-TM models was not significantly different from baseline (p>0.05). Reentry pathways in AB-TM models were different than baseline pathways due to alterations in the location of conduction block (p<0.05). VT exit sites in AB-TM models were different than baseline VT exit sites (p<0.05). VT inducibility of AB only and TM only models were not significantly different than that of baseline (p>0.05) or AB-TM models (p>0.05). Reentry pathways and VT exit sites in AB only and TM only models were different than in baseline (p<0.05). Lastly, repolarization gradients uncovered multiple VT morphologies with different reentrant pathways and exit sites within the same structural, conducting channels. Conclusion: VT inducibility was not impacted by the addition of AB-TM repolarization gradients, but the VT reentrant pathway and exit sites were greatly affected due to modulation of conduction block. Thus, during ablation procedures, physiological and pharmacological factors that impact the ventricular repolarization gradient might need to be considered when targeting the VTs.
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Affiliation(s)
- Eric Sung
- 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
| | - Adityo Prakosa
- 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
| | - 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
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15
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Sung E, Prakosa A, Zhou S, Tandri H, Berger RD, Nazarian S, Chrispin J, Trayanova NA. B-PO02-123 PATIENT-SPECIFIC DIGITAL HEART TWINS PREDICT SIMILAR ARRHYTHMOGENIC PROPENSITY BETWEEN POST-INFARCT SCAR AND INFILTRATING FAT. Heart Rhythm 2021. [DOI: 10.1016/j.hrthm.2021.06.377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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16
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Aronis KN, Okada DR, Xie E, Daimee UA, Prakosa A, Gilotra NA, Wu KC, Trayanova NA, Chrispin J. B-PO02-177 INCREASED SUBSTRATE HETEROGENEITY ASSESSED BY LGE-CMR IS ASSOCIATED WITH VENTRICULAR ARRHYTHMIAS AND MORTALITY IN PATIENTS WITH CARDIAC SARCOIDOSIS. Heart Rhythm 2021. [DOI: 10.1016/j.hrthm.2021.06.430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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17
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Salvador M, Fedele M, Africa PC, Sung E, Dede' L, Prakosa A, Chrispin J, Trayanova N, Quarteroni A. Electromechanical modeling of human ventricles with ischemic cardiomyopathy: numerical simulations in sinus rhythm and under arrhythmia. Comput Biol Med 2021; 136:104674. [PMID: 34340126 DOI: 10.1016/j.compbiomed.2021.104674] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 07/15/2021] [Accepted: 07/19/2021] [Indexed: 12/16/2022]
Abstract
We developed a novel patient-specific computational model for the numerical simulation of ventricular electromechanics in patients with ischemic cardiomyopathy (ICM). This model reproduces the activity both in sinus rhythm (SR) and in ventricular tachycardia (VT). The presence of scars, grey zones and non-remodeled regions of the myocardium is accounted for by the introduction of a spatially heterogeneous coefficient in the 3D electromechanics model. This 3D electromechanics model is firstly coupled with a 2-element Windkessel afterload model to fit the pressure-volume (PV) loop of a patient-specific left ventricle (LV) with ICM in SR. Then, we employ the coupling with a 0D closed-loop circulation model to analyze a VT circuit over multiple heartbeats on the same LV. We highlight similarities and differences on the solutions obtained by the electrophysiology model and those of the electromechanics model, while considering different scenarios for the circulatory system. We observe that very different parametrizations of the circulation model induce the same hemodynamical considerations for the patient at hand. Specifically, we classify this VT as unstable. We conclude by stressing the importance of combining electrophysiological, mechanical and hemodynamical models to provide relevant clinical indicators in how arrhythmias evolve and can potentially lead to sudden cardiac death.
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Affiliation(s)
- Matteo Salvador
- MOX-Dipartimento di Matematica, Politecnico di Milano, Milan, Italy.
| | - Marco Fedele
- MOX-Dipartimento di Matematica, Politecnico di Milano, Milan, Italy
| | | | - Eric Sung
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Luca Dede'
- MOX-Dipartimento di Matematica, Politecnico di Milano, Milan, Italy
| | - Adityo Prakosa
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | | | - Natalia Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Alfio Quarteroni
- MOX-Dipartimento di Matematica, Politecnico di Milano, Milan, Italy; École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
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18
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Aronis KN, Prakosa A, Bergamaschi T, Berger RD, Boyle PM, Chrispin J, Ju S, Marine JE, Sinha S, Tandri H, Ashikaga H, Trayanova NA. Characterization of the Electrophysiologic Remodeling of Patients With Ischemic Cardiomyopathy by Clinical Measurements and Computer Simulations Coupled With Machine Learning. Front Physiol 2021; 12:684149. [PMID: 34335294 PMCID: PMC8317643 DOI: 10.3389/fphys.2021.684149] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Accepted: 06/22/2021] [Indexed: 11/13/2022] Open
Abstract
Rationale Patients with ischemic cardiomyopathy (ICMP) are at high risk for malignant arrhythmias, largely due to electrophysiological remodeling of the non-infarcted myocardium. The electrophysiological properties of the non-infarcted myocardium of patients with ICMP remain largely unknown. Objectives To assess the pro-arrhythmic behavior of non-infarcted myocardium in ICMP patients and couple computational simulations with machine learning to establish a methodology for the development of disease-specific action potential models based on clinically measured action potential duration restitution (APDR) data. Methods and Results We enrolled 22 patients undergoing left-sided ablation (10 ICMP) and compared APDRs between ICMP and structurally normal left ventricles (SNLVs). APDRs were clinically assessed with a decremental pacing protocol. Using genetic algorithms (GAs), we constructed populations of action potential models that incorporate the cohort-specific APDRs. The variability in the populations of ICMP and SNLV models was captured by clustering models based on their similarity using unsupervised machine learning. The pro-arrhythmic potential of ICMP and SNLV models was assessed in cell- and tissue-level simulations. Clinical measurements established that ICMP patients have a steeper APDR slope compared to SNLV (by 38%, p < 0.01). In cell-level simulations, APD alternans were induced in ICMP models at a longer cycle length compared to SNLV models (385–400 vs 355 ms). In tissue-level simulations, ICMP models were more susceptible for sustained functional re-entry compared to SNLV models. Conclusion Myocardial remodeling in ICMP patients is manifested as a steeper APDR compared to SNLV, which underlies the greater arrhythmogenic propensity in these patients, as demonstrated by cell- and tissue-level simulations using action potential models developed by GAs from clinical measurements. The methodology presented here captures the uncertainty inherent to GAs model development and provides a blueprint for use in future studies aimed at evaluating electrophysiological remodeling resulting from other cardiac diseases.
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Affiliation(s)
- Konstantinos N Aronis
- Section of Electrophysiology, Division of Cardiology, Johns Hopkins Hospital, Baltimore, MD, United States.,Department of Biomedical Engineering, The Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Adityo Prakosa
- Department of Biomedical Engineering, The Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Teya Bergamaschi
- Department of Biomedical Engineering, The Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Ronald D Berger
- Section of Electrophysiology, Division of Cardiology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Patrick M Boyle
- Department of Biomedical Engineering, The Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Jonathan Chrispin
- Section of Electrophysiology, Division of Cardiology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Suyeon Ju
- Department of Biomedical Engineering, The Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Joseph E Marine
- Section of Electrophysiology, Division of Cardiology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Sunil Sinha
- Section of Electrophysiology, Division of Cardiology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Harikrishna Tandri
- Section of Electrophysiology, Division of Cardiology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Hiroshi Ashikaga
- Section of Electrophysiology, Division of Cardiology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Natalia A Trayanova
- Section of Electrophysiology, Division of Cardiology, Johns Hopkins Hospital, Baltimore, MD, United States.,Department of Biomedical Engineering, The Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
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19
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Shade JK, Prakosa A, Popescu DM, Yu R, Okada DR, Chrispin J, Trayanova NA. Predicting risk of sudden cardiac death in patients with cardiac sarcoidosis using multimodality imaging and personalized heart modeling in a multivariable classifier. Sci Adv 2021; 7:7/31/eabi8020. [PMID: 34321202 PMCID: PMC8318376 DOI: 10.1126/sciadv.abi8020] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 06/11/2021] [Indexed: 05/13/2023]
Abstract
Cardiac sarcoidosis (CS), an inflammatory disease characterized by formation of granulomas in the heart, is associated with high risk of sudden cardiac death (SCD) from ventricular arrhythmias. Current "one-size-fits-all" guidelines for SCD risk assessment in CS result in insufficient appropriate primary prevention. Here, we present a two-step precision risk prediction technology for patients with CS. First, a patient's arrhythmogenic propensity arising from heterogeneous CS-induced ventricular remodeling is assessed using a novel personalized magnetic-resonance imaging and positron-emission tomography fusion mechanistic model. The resulting simulations of arrhythmogenesis are fed, together with a set of imaging and clinical biomarkers, into a supervised classifier. In a retrospective study of 45 patients, the technology achieved testing results of 60% sensitivity [95% confidence interval (CI): 57-63%], 72% specificity [95% CI: 70-74%], and 0.754 area under the receiver operating characteristic curve [95% CI: 0.710-0.797]. It outperformed clinical metrics, highlighting its potential to transform CS risk stratification.
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Affiliation(s)
- Julie K Shade
- Department of Biomedical Engineering, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Whiting School of Engineering and School of Medicine, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA
| | - Adityo Prakosa
- Department of Biomedical Engineering, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Whiting School of Engineering and School of Medicine, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA
| | - Dan M Popescu
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Whiting School of Engineering and School of Medicine, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA
- Department of Applied Math and Statistics, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA
| | - Rebecca Yu
- Department of Biomedical Engineering, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Whiting School of Engineering and School of Medicine, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA
| | - David R Okada
- Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 733 North Broadway, Baltimore, MD 21205, USA
| | - Jonathan Chrispin
- Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 733 North Broadway, Baltimore, MD 21205, USA
| | - Natalia A Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA.
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Whiting School of Engineering and School of Medicine, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA
- Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 733 North Broadway, Baltimore, MD 21205, USA
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20
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Prakosa A, Southworth MK, Avari Silva JN, Silva JR, Trayanova NA. Impact of augmented-reality improvement in ablation catheter navigation as assessed by virtual-heart simulations of ventricular tachycardia ablation. Comput Biol Med 2021; 133:104366. [PMID: 33836448 PMCID: PMC8169616 DOI: 10.1016/j.compbiomed.2021.104366] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 03/29/2021] [Accepted: 03/29/2021] [Indexed: 10/21/2022]
Abstract
BACKGROUND Recently, an augmented reality (AR) solution allows the physician to place the ablation catheter at the designated lesion site more accurately during cardiac electrophysiology studies. The improvement in navigation accuracy may positively affect ventricular tachycardia (VT) ablation termination, however assessment of this in the clinic would be difficult. Novel personalized virtual heart technology enables non-invasive identification of optimal lesion targets for infarct-related VT. This study aims to evaluate the potential impact of such catheter navigation accuracy improvement in virtual VT ablations. METHODS 2 MRI-based virtual hearts with 2 in silico induced VTs (VT 1, VT 2) were included. VTs were terminated with virtual "ground truth" endocardial ablation lesions. 106 navigation error values that were previously assessed in a clinical study evaluating the improvement of ablation catheter navigation accuracy guided with AR (53 with, 53 without) were used to displace the "ground truth" ablation targets. The corresponding ablations were simulated based on these errors and VT termination for each simulation was assessed. RESULTS In 54 VT 1 ablation simulations, smaller error with AR significantly resulted in more VT termination (25) compared to the error without AR (16) (P < 0.01). In 52 VT 2 ablation simulations, no significant difference was observed from error with (11) and without AR (13) (P = 0.58). The substrate characteristic may impact the effect of improved accuracy to an improved VT termination. CONCLUSION Virtual heart shows that the increased catheter navigation accuracy provided by AR guidance can affect the VT termination.
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Affiliation(s)
- Adityo Prakosa
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA.
| | | | - Jennifer N Avari Silva
- Department of Pediatrics, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Jonathan R Silva
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Natalia A Trayanova
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
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21
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Zhou S, Sung E, Prakosa A, Aronis KN, Chrispin J, Tandri H, AbdelWahab A, Horáček BM, Sapp JL, Trayanova NA. Feasibility study shows concordance between image-based virtual-heart ablation targets and predicted ECG-based arrhythmia exit-sites. Pacing Clin Electrophysiol 2021; 44:432-441. [PMID: 33527422 DOI: 10.1111/pace.14181] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Revised: 01/05/2021] [Accepted: 01/24/2021] [Indexed: 12/20/2022]
Abstract
INTRODUCTION We recently developed two noninvasive methodologies to help guide VT ablation: population-derived automated VT exit localization (PAVEL) and virtual-heart arrhythmia ablation targeting (VAAT). We hypothesized that while very different in their nature, limitations, and type of ablation targets (substrate-based vs. clinical VT), the image-based VAAT and the ECG-based PAVEL technologies would be spatially concordant in their predictions. OBJECTIVE The objective is to test this hypothesis in ischemic cardiomyopathy patients in a retrospective feasibility study. METHODS Four post-infarct patients who underwent LV VT ablation and had pre-procedural LGE-CMRs were enrolled. Virtual hearts with patient-specific scar and border zone identified potential VTs and ablation targets. Patient-specific PAVEL based on a population-derived statistical method localized VT exit sites onto a patient-specific 238-triangle LV endocardial surface. RESULTS Ten induced VTs were analyzed and 9-exit sites were localized by PAVEL onto the patient-specific LV endocardial surface. All nine predicted VT exit sites were in the scar border zone defined by voltage mapping and spatially correlated with successful clinical lesions. There were 2.3 ± 1.9 VTs per patient in the models. All five VAAT lesions fell within regions ablated clinically. VAAT targets correlated well with 6 PAVEL-predicted VT exit sites. The distance between the center of the predicted VT-exit-site triangle and nearest corresponding VAAT ablation lesion was 10.7 ± 7.3 mm. CONCLUSIONS VAAT targets are concordant with the patient-specific PAVEL-predicted VT exit sites. These findings support investigation into combining these two complementary technologies as a noninvasive, clinical tool for targeting clinically induced VTs and regions likely to harbor potential VTs.
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Affiliation(s)
- Shijie Zhou
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA.,Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland, USA
| | - Eric Sung
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA.,Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland, USA
| | - Adityo Prakosa
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA.,Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland, USA
| | - Konstantinos N Aronis
- Section of Cardiac Electrophysiology, Division of Cardiology, Department of Medicine, Johns Hopkins Hospital, Baltimore, Maryland, USA.,Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland, USA
| | - Jonathan Chrispin
- Section of Cardiac Electrophysiology, Division of Cardiology, Department of Medicine, Johns Hopkins Hospital, Baltimore, Maryland, USA.,Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland, USA
| | - Harikrishna Tandri
- Section of Cardiac Electrophysiology, Division of Cardiology, Department of Medicine, Johns Hopkins Hospital, Baltimore, Maryland, USA.,Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland, USA
| | - Amir AbdelWahab
- Department of Medicine, Queen Elizabeth II Health Sciences Centre, Halifax, Nova Scotia, Canada
| | - B Milan Horáček
- School of Biomedical Engineering, Dalhousie University, Halifax, Nova Scotia, Canada
| | - John L Sapp
- Department of Medicine, Queen Elizabeth II Health Sciences Centre, Halifax, Nova Scotia, Canada
| | - Natalia A Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA.,Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland, USA
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22
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Sung E, Prakosa A, Aronis KN, Zhou S, Zimmerman SL, Tandri H, Nazarian S, Berger RD, Chrispin J, Trayanova NA. Personalized Digital-Heart Technology for Ventricular Tachycardia Ablation Targeting in Hearts With Infiltrating Adiposity. Circ Arrhythm Electrophysiol 2020; 13:e008912. [PMID: 33198484 DOI: 10.1161/circep.120.008912] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
BACKGROUND Infiltrating adipose tissue (inFAT) is a newly recognized proarrhythmic substrate for postinfarct ventricular tachycardias (VT) identifiable on contrast-enhanced computed tomography. This study presents novel digital-heart technology that incorporates inFAT from contrast-enhanced computed tomography to noninvasively predict VT ablation targets and assesses the capability of the technology by comparing its predictions with VT ablation procedure data from patients with ischemic cardiomyopathy. METHODS Digital-heart models reflecting patient-specific inFAT distributions were reconstructed from contrast-enhanced computed tomography. The digital-heart identification of fat-based ablation targeting (DIFAT) technology evaluated the rapid-pacing-induced VTs in each personalized inFAT-based substrate. DIFAT targets that render the inFAT substrate noninducible to VT, including VTs that arise postablation, were determined. DIFAT predictions were compared with corresponding clinical ablations to assess the capabilities of the technology. RESULTS DIFAT was developed and applied retrospectively to 29 ischemic cardiomyopathy patients with contrast-enhanced computed tomography. DIFAT ablation volumes were significantly less than the estimated clinical ablation volumes (1.87±0.35 versus 7.05±0.88 cm3, P<0.0005). DIFAT targets overlapped with clinical ablations in 79% of patients, mostly in the apex (72%) and inferior/inferolateral (74%). In 3 patients, DIFAT targets colocalized with redo ablations delivered years after the index procedure. CONCLUSIONS DIFAT is a novel digital-heart technology for individualized VT ablation guidance designed to eliminate VT inducibility following initial ablation. DIFAT predictions colocalized well with clinical ablation locations but provided significantly smaller lesions. DIFAT also predicted VTs targeted in redo procedures years later. As DIFAT uses widely accessible computed tomography, its integration into clinical workflows may augment therapeutic precision and reduce redo procedures.
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Affiliation(s)
- Eric Sung
- Department of Biomedical Engineering (E.S., A.P., S.Z., N.A.T.), Johns Hopkins University, Baltimore, MD.,Alliance for Cardiovascular Diagnostic and Treatment Innovation (E.S., A.P., K.N.A., S.Z., S.L.Z., H.T., R.D.B., J.C., N.A.T.), Johns Hopkins University, Baltimore, MD
| | - Adityo Prakosa
- Department of Biomedical Engineering (E.S., A.P., S.Z., N.A.T.), Johns Hopkins University, Baltimore, MD.,Alliance for Cardiovascular Diagnostic and Treatment Innovation (E.S., A.P., K.N.A., S.Z., S.L.Z., H.T., R.D.B., J.C., N.A.T.), Johns Hopkins University, Baltimore, MD
| | - Konstantinos N Aronis
- Alliance for Cardiovascular Diagnostic and Treatment Innovation (E.S., A.P., K.N.A., S.Z., S.L.Z., H.T., R.D.B., J.C., N.A.T.), Johns Hopkins University, Baltimore, MD.,Section of Cardiac Electrophysiology, Division of Cardiology, Department of Medicine (K.N.A., H.T., R.D.B., J.C.), Johns Hopkins Hospital, Baltimore, MD
| | - Shijie Zhou
- Department of Biomedical Engineering (E.S., A.P., S.Z., N.A.T.), Johns Hopkins University, Baltimore, MD.,Alliance for Cardiovascular Diagnostic and Treatment Innovation (E.S., A.P., K.N.A., S.Z., S.L.Z., H.T., R.D.B., J.C., N.A.T.), Johns Hopkins University, Baltimore, MD
| | - Stefan L Zimmerman
- Alliance for Cardiovascular Diagnostic and Treatment Innovation (E.S., A.P., K.N.A., S.Z., S.L.Z., H.T., R.D.B., J.C., N.A.T.), Johns Hopkins University, Baltimore, MD.,Department of Radiological Sciences (S.L.Z.), Johns Hopkins Hospital, Baltimore, MD
| | - Harikrishna Tandri
- Alliance for Cardiovascular Diagnostic and Treatment Innovation (E.S., A.P., K.N.A., S.Z., S.L.Z., H.T., R.D.B., J.C., N.A.T.), Johns Hopkins University, Baltimore, MD.,Section of Cardiac Electrophysiology, Division of Cardiology, Department of Medicine (K.N.A., H.T., R.D.B., J.C.), Johns Hopkins Hospital, Baltimore, MD
| | - Saman Nazarian
- Division of Cardiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia (S.N.)
| | - Ronald D Berger
- Alliance for Cardiovascular Diagnostic and Treatment Innovation (E.S., A.P., K.N.A., S.Z., S.L.Z., H.T., R.D.B., J.C., N.A.T.), Johns Hopkins University, Baltimore, MD.,Section of Cardiac Electrophysiology, Division of Cardiology, Department of Medicine (K.N.A., H.T., R.D.B., J.C.), Johns Hopkins Hospital, Baltimore, MD
| | - Jonathan Chrispin
- Alliance for Cardiovascular Diagnostic and Treatment Innovation (E.S., A.P., K.N.A., S.Z., S.L.Z., H.T., R.D.B., J.C., N.A.T.), Johns Hopkins University, Baltimore, MD.,Section of Cardiac Electrophysiology, Division of Cardiology, Department of Medicine (K.N.A., H.T., R.D.B., J.C.), Johns Hopkins Hospital, Baltimore, MD
| | - Natalia A Trayanova
- Department of Biomedical Engineering (E.S., A.P., S.Z., N.A.T.), Johns Hopkins University, Baltimore, MD.,Alliance for Cardiovascular Diagnostic and Treatment Innovation (E.S., A.P., K.N.A., S.Z., S.L.Z., H.T., R.D.B., J.C., N.A.T.), Johns Hopkins University, Baltimore, MD
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23
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Aronis KN, Ali RL, Prakosa A, Ashikaga H, Berger RD, Hakim JB, Liang J, Tandri H, Teng F, Chrispin J, Trayanova NA. Accurate Conduction Velocity Maps and Their Association With Scar Distribution on Magnetic Resonance Imaging in Patients With Postinfarction Ventricular Tachycardias. Circ Arrhythm Electrophysiol 2020; 13:e007792. [PMID: 32191131 DOI: 10.1161/circep.119.007792] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Characterizing myocardial conduction velocity (CV) in patients with ischemic cardiomyopathy (ICM) and ventricular tachycardia (VT) is important for understanding the patient-specific proarrhythmic substrate of VTs and therapeutic planning. The objective of this study is to accurately assess the relation between CV and myocardial fibrosis density on late gadolinium-enhanced cardiac magnetic resonance imaging (LGE-CMR) in patients with ICM. METHODS We enrolled 6 patients with ICM undergoing VT ablation and 5 with structurally normal left ventricles (controls) undergoing premature ventricular contraction or VT ablation. All patients underwent LGE-CMR and electroanatomic mapping (EAM) in sinus rhythm (2960 electroanatomic mapping points analyzed). We estimated CV from electroanatomic mapping local activation time using the triangulation method that provides an accurate estimate of CV as it accounts for the direction of wavefront propagation. We evaluated the association between LGE-CMR intensity and CV with multilevel linear mixed models. RESULTS Median CV in patients with ICM and controls was 0.41 m/s and 0.65 m/s, respectively. In patients with ICM, CV in areas with no visible fibrosis was 0.81 m/s (95% CI, 0.59-1.12 m/s). For each 25% increase in normalized LGE intensity, CV decreased by 1.34-fold (95% CI, 1.25-1.43). Dense scar areas have, on average, 1.97- to 2.66-fold slower CV compared with areas without dense scar. Ablation lesions that terminated VTs were localized in areas of slow conduction on CV maps. CONCLUSIONS CV is inversely associated with LGE-CMR fibrosis density in patients with ICM. Noninvasive derivation of CV maps from LGE-CMR is feasible. Integration of noninvasive CV maps with electroanatomic mapping during substrate mapping has the potential to improve procedural planning and outcomes. Visual Overview: A visual overview is available for this article.
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Affiliation(s)
- Konstantinos N Aronis
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University (K.N.A., R.L.A., A.P., J.B.H., J.L., F.T., N.A.T.).,Section of Electrophysiology, Division of Cardiology, Johns Hopkins Hospital, Baltimore, MD (K.N.A., H.A., R.D.B., H.T., J.C.)
| | - Rheeda L Ali
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University (K.N.A., R.L.A., A.P., J.B.H., J.L., F.T., N.A.T.)
| | - Adityo Prakosa
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University (K.N.A., R.L.A., A.P., J.B.H., J.L., F.T., N.A.T.)
| | - Hiroshi Ashikaga
- Section of Electrophysiology, Division of Cardiology, Johns Hopkins Hospital, Baltimore, MD (K.N.A., H.A., R.D.B., H.T., J.C.)
| | - Ronald D Berger
- Section of Electrophysiology, Division of Cardiology, Johns Hopkins Hospital, Baltimore, MD (K.N.A., H.A., R.D.B., H.T., J.C.)
| | - Joe B Hakim
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University (K.N.A., R.L.A., A.P., J.B.H., J.L., F.T., N.A.T.)
| | - Jialiu Liang
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University (K.N.A., R.L.A., A.P., J.B.H., J.L., F.T., N.A.T.)
| | - Harikrishna Tandri
- Section of Electrophysiology, Division of Cardiology, Johns Hopkins Hospital, Baltimore, MD (K.N.A., H.A., R.D.B., H.T., J.C.)
| | - Fei Teng
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University (K.N.A., R.L.A., A.P., J.B.H., J.L., F.T., N.A.T.)
| | - Jonathan Chrispin
- Section of Electrophysiology, Division of Cardiology, Johns Hopkins Hospital, Baltimore, MD (K.N.A., H.A., R.D.B., H.T., J.C.)
| | - Natalia A Trayanova
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University (K.N.A., R.L.A., A.P., J.B.H., J.L., F.T., N.A.T.)
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24
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Okada DR, Miller J, Chrispin J, Prakosa A, Trayanova N, Jones S, Maggioni M, Wu KC. Substrate Spatial Complexity Analysis for the Prediction of Ventricular Arrhythmias in Patients With Ischemic Cardiomyopathy. Circ Arrhythm Electrophysiol 2020; 13:e007975. [PMID: 32188287 PMCID: PMC7207018 DOI: 10.1161/circep.119.007975] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
BACKGROUND Transition zones between healthy myocardium and scar form a spatially complex substrate that may give rise to reentrant ventricular arrhythmias (VAs). We sought to assess the utility of a novel machine learning approach for quantifying 3-dimensional spatial complexity of grayscale patterns on late gadolinium enhanced cardiac magnetic resonance images to predict VAs in patients with ischemic cardiomyopathy. METHODS One hundred twenty-two consecutive ischemic cardiomyopathy patients with left ventricular ejection fraction ≤35% without prior history of VAs underwent late gadolinium enhanced cardiac magnetic resonance images. From raw grayscale data, we generated graphs encoding the 3-dimensional geometry of the left ventricle. A novel technique, adapted to these graphs, assessed global regularity of signal intensity patterns using Fourier-like analysis and generated a substrate spatial complexity profile for each patient. A machine learning statistical algorithm was employed to discern which substrate spatial complexity profiles correlated with VA events (appropriate implantable cardioverter-defibrillator firings and arrhythmic sudden cardiac death) at 5 years of follow-up. From the statistical machine learning results, a complexity score ranging from 0 to 1 was calculated for each patient and tested using multivariable Cox regression models. RESULTS At 5 years of follow-up, 40 patients had VA events. The machine learning algorithm classified with 81% overall accuracy and correctly classified 86% of those without VAs. Overall negative predictive value was 91%. Average complexity score was significantly higher in patients with VA events versus those without (0.5±0.5 versus 0.1±0.2; P<0.0001) and was independently associated with VA events in a multivariable model (hazard ratio, 1.5 [1.2-2.0]; P=0.002). CONCLUSIONS Substrate spatial complexity analysis of late gadolinium enhanced cardiac magnetic resonance images may be helpful in refining VA risk in patients with ischemic cardiomyopathy, particularly to identify low-risk patients who may not benefit from prophylactic implantable cardioverter-defibrillator therapy. Visual Overview: A visual overview is available for this article.
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Affiliation(s)
- David R Okada
- Division of Cardiology, Department of Medicine (D.R.O., J.C., S.J., K.C.W.)
| | | | - Jonathan Chrispin
- Division of Cardiology, Department of Medicine (D.R.O., J.C., S.J., K.C.W.)
| | | | | | - Steven Jones
- Division of Cardiology, Department of Medicine (D.R.O., J.C., S.J., K.C.W.)
| | - Mauro Maggioni
- Department of Applied Mathematics (J.A., M.M.).,Department of Mathematics, Johns Hopkins University, Baltimore, MD (M.M.)
| | - Katherine C Wu
- Division of Cardiology, Department of Medicine (D.R.O., J.C., S.J., K.C.W.)
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25
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Shade JK, Cartoski MJ, Nikolov P, Prakosa A, Doshi A, Binka E, Olivieri L, Boyle PM, Spevak PJ, Trayanova NA. Ventricular arrhythmia risk prediction in repaired Tetralogy of Fallot using personalized computational cardiac models. Heart Rhythm 2020; 17:408-414. [PMID: 31589989 PMCID: PMC7056519 DOI: 10.1016/j.hrthm.2019.10.002] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Indexed: 12/29/2022]
Abstract
BACKGROUND Adults with repaired tetralogy of Fallot (rTOF) are at increased risk for ventricular tachycardia (VT) due to fibrotic remodeling of the myocardium. However, the current clinical guidelines for VT risk stratification and subsequent implantable cardioverter-defibrillator deployment for primary prevention of sudden cardiac death in rTOF remain inadequate. OBJECTIVE The purpose of this study was to determine the feasibility of using an rTOF-specific virtual-heart approach to identify patients stratified incorrectly as being at low VT risk by current clinical criteria. METHODS This multicenter retrospective pilot study included 7 adult rTOF patients who were considered low risk for VT based on clinical criteria. Patient-specific computational heart models were generated from late gadolinium enhanced magnetic resonance imaging (LGE-MRI), incorporating the individual distribution of rTOF fibrotic remodeling in both ventricles. Simulations of rapid pacing determined VT inducibility. Model creation and simulations were performed by operators blinded to clinical outcome. RESULTS Two patients in the study experienced clinical VT. The virtual hearts constructed from LGE-MRI scans of 7 rTOF patients correctly predicted reentrant VT in the models from VT-positive patients and no arrhythmia in those from VT-negative patients. There were no statistically significant differences in clinical criteria commonly used to assess VT risk, including QRS duration and age, between patients who did and those who did not experience clinical VT. CONCLUSION This study demonstrates the feasibility of image-based virtual-heart modeling in patients with congenital heart disease and structurally abnormal hearts. It highlights the potential of the methodology to improve VT risk stratification in patients with rTOF.
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Affiliation(s)
- Julie K Shade
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland; Alliance for Cardiovascular Diagnostic and Treatment Innovation, Whiting School of Engineering and School of Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Mark J Cartoski
- Division of Pediatric Cardiology, Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Plamen Nikolov
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Adityo Prakosa
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland; Alliance for Cardiovascular Diagnostic and Treatment Innovation, Whiting School of Engineering and School of Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Ashish Doshi
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland; Alliance for Cardiovascular Diagnostic and Treatment Innovation, Whiting School of Engineering and School of Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Edem Binka
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Whiting School of Engineering and School of Medicine, Johns Hopkins University, Baltimore, Maryland; Division of Pediatric Cardiology, Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Laura Olivieri
- Division of Cardiology, Children's National Medical Center, Washington, DC
| | - Patrick M Boyle
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Philip J Spevak
- Division of Pediatric Cardiology, Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Natalia A Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland; Alliance for Cardiovascular Diagnostic and Treatment Innovation, Whiting School of Engineering and School of Medicine, Johns Hopkins University, Baltimore, Maryland; Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland.
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26
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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 Interdiscip Rev Syst Biol Med 2020; 12:e1477. [PMID: 31917524 DOI: 10.1002/wsbm.1477] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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27
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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28
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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29
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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: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [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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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: 148] [Impact Index Per Article: 24.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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Boyle PM, Hakim JB, Zahid S, Franceschi WH, Murphy MJ, Prakosa A, Aronis KN, Zghaib T, Balouch M, Ipek EG, Chrispin J, Berger RD, Ashikaga H, Marine JE, Calkins H, Nazarian S, Spragg DD, Trayanova NA. The Fibrotic Substrate in Persistent Atrial Fibrillation Patients: Comparison Between Predictions From Computational Modeling and Measurements From Focal Impulse and Rotor Mapping. Front Physiol 2018; 9:1151. [PMID: 30210356 PMCID: PMC6123380 DOI: 10.3389/fphys.2018.01151] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Accepted: 07/31/2018] [Indexed: 12/19/2022] Open
Abstract
Focal impulse and rotor mapping (FIRM) involves intracardiac detection and catheter ablation of re-entrant drivers (RDs), some of which may contribute to arrhythmia perpetuation in persistent atrial fibrillation (PsAF). Patient-specific computational models derived from late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) has the potential to non-invasively identify all areas of the fibrotic substrate where RDs could potentially be sustained, including locations where RDs may not manifest during mapped AF episodes. The objective of this study was to carry out multi-modal assessment of the arrhythmogenic propensity of the fibrotic substrate in PsAF patients by comparing locations of RD-harboring regions found in simulations and detected by FIRM (RDsim and RDFIRM) and analyze implications for ablation strategies predicated on targeting RDs. For 11 PsAF patients who underwent pre-procedure LGE-MRI and FIRM-guided ablation, we retrospectively simulated AF in individualized atrial models, with geometry and fibrosis distribution reconstructed from pre-ablation LGE-MRI scans, and identified RDsim sites. Regions harboring RDsim and RDFIRM were compared. RDsim were found in 38 atrial regions (median [inter-quartile range (IQR)] = 4 [3; 4] per model). RDFIRM were identified and subsequently ablated in 24 atrial regions (2 [1; 3] per patient), which was significantly fewer than the number of RDsim-harboring regions in corresponding models (p < 0.05). Computational modeling predicted RDsim in 20 of 24 (83%) atrial regions identified as RDFIRM-harboring during clinical mapping. In a large number of cases, we uncovered RDsim-harboring regions in which RDFIRM were never observed (18/22 regions that differed between the two modalities; 82%); we termed such cases “latent” RDsim sites. During follow-up (230 [180; 326] days), AF recurrence occurred in 7/11 (64%) individuals. Interestingly, latent RDsim sites were observed in all seven computational models corresponding to patients who experienced recurrent AF (2 [2; 2] per patient); in contrast, latent RDsim sites were only discovered in two of four patients who were free from AF during follow-up (0.5 [0; 1.5] per patient; p < 0.05 vs. patients with AF recurrence). We conclude that substrate-based ablation based on computational modeling could improve outcomes.
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Affiliation(s)
- Patrick M Boyle
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Joe B Hakim
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Sohail Zahid
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - William H Franceschi
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Michael J Murphy
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Adityo Prakosa
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
| | | | - Tarek Zghaib
- Department of Cardiology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Muhammed Balouch
- Department of Cardiology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Esra G Ipek
- Department of Cardiology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Jonathan Chrispin
- Department of Cardiology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Ronald D Berger
- Department of Cardiology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Hiroshi Ashikaga
- Department of Cardiology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Joseph E Marine
- Department of Cardiology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Hugh Calkins
- Department of Cardiology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Saman Nazarian
- Penn Heart & Vascular Center, University of Pennsylvania, Philadelphia, PA, United States
| | - David D Spragg
- Department of Cardiology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Natalia A Trayanova
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
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Misra S, Zahid S, Prakosa A, Saju N, Tandri H, Berger RD, Marine JE, Calkins H, Zipunnikov V, Trayanova N, Zimmerman SL, Nazarian S. Field of view of mapping catheters quantified by electrogram associations with radius of myocardial attenuation on contrast-enhanced cardiac computed tomography. Heart Rhythm 2018; 15:1617-1625. [PMID: 29870783 DOI: 10.1016/j.hrthm.2018.05.031] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/15/2017] [Indexed: 11/26/2022]
Abstract
BACKGROUND Contrast-enhanced cardiac computed tomography (CE-CT) provides useful substrate characterization in patients with ventricular tachycardia (VT). OBJECTIVE The purpose of this study was to describe the association between endocardial electrogram measurements and myocardial characteristics on CE-CT, in particular the field of view of electrogram features. METHODS Fifteen patients with postinfarct VT who underwent catheter ablation with preprocedural CE-CT were included. Electroanatomic maps were registered to CE-CT, and myocardial attenuation surrounding each endocardial point was measured at a radius of 5, 10, and 15 mm. The association between endocardial voltage and attenuation was assessed using a multilevel random effects linear regression model, clustered by patient, with best model fit defined by highest log likelihood. RESULTS A total of 4698 points were included. There was a significant association of bipolar and unipolar voltage with myocardial attenuation at all radii. For unipolar voltage, the best model fit was at an analysis radius of 15 mm regardless of the mapping catheter used. For bipolar voltage, the best model fit was at an analysis radius of 15 mm for points acquired with a conventional ablation catheter. In contrast, the best model fit for points acquired with a multipolar mapping catheter was at an analysis radius of 5 mm. CONCLUSION Myocardial attenuation on CE-CT indicates a smaller myocardial field of view of bipolar electrograms using multipolar catheters with smaller electrodes in comparison to standard ablation catheters despite similar interelectrode spacing. Smaller electrodes may provide improved spatial resolution for the definition of myocardial substrate for VT ablation.
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Affiliation(s)
- Satish Misra
- Department of Cardiology, Johns Hopkins University School of Medicine, Baltimore, Maryland.
| | - Sohail Zahid
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Adityo Prakosa
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Nissi Saju
- Department of Cardiology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Harikrishna Tandri
- Department of Cardiology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Ronald D Berger
- Department of Cardiology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Joseph E Marine
- Department of Cardiology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Hugh Calkins
- Department of Cardiology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Vadim Zipunnikov
- Department of Epidemiology, Johns Hopkins University School of Public Heatlh, Baltimore, Maryland
| | - Natalia Trayanova
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Stefan L Zimmerman
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Saman Nazarian
- Department of Cardiology, University of Pennsylvania, Philadelphia, Pennsylvania
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Ukwatta E, Arevalo H, Rajchl M, White J, Pashakhanloo F, Prakosa A, Herzka DA, McVeigh E, Lardo AC, Trayanova NA, Vadakkumpadan F. Image-based reconstruction of three-dimensional myocardial infarct geometry for patient-specific modeling of cardiac electrophysiology. Med Phys 2016; 42:4579-90. [PMID: 26233186 DOI: 10.1118/1.4926428] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
PURPOSE Accurate three-dimensional (3D) reconstruction of myocardial infarct geometry is crucial to patient-specific modeling of the heart aimed at providing therapeutic guidance in ischemic cardiomyopathy. However, myocardial infarct imaging is clinically performed using two-dimensional (2D) late-gadolinium enhanced cardiac magnetic resonance (LGE-CMR) techniques, and a method to build accurate 3D infarct reconstructions from the 2D LGE-CMR images has been lacking. The purpose of this study was to address this need. METHODS The authors developed a novel methodology to reconstruct 3D infarct geometry from segmented low-resolution (Lo-res) clinical LGE-CMR images. Their methodology employed the so-called logarithm of odds (LogOdds) function to implicitly represent the shape of the infarct in segmented image slices as LogOdds maps. These 2D maps were then interpolated into a 3D image, and the result transformed via the inverse of LogOdds to a binary image representing the 3D infarct geometry. To assess the efficacy of this method, the authors utilized 39 high-resolution (Hi-res) LGE-CMR images, including 36 in vivo acquisitions of human subjects with prior myocardial infarction and 3 ex vivo scans of canine hearts following coronary ligation to induce infarction. The infarct was manually segmented by trained experts in each slice of the Hi-res images, and the segmented data were downsampled to typical clinical resolution. The proposed method was then used to reconstruct 3D infarct geometry from the downsampled images, and the resulting reconstructions were compared with the manually segmented data. The method was extensively evaluated using metrics based on geometry as well as results of electrophysiological simulations of cardiac sinus rhythm and ventricular tachycardia in individual hearts. Several alternative reconstruction techniques were also implemented and compared with the proposed method. RESULTS The accuracy of the LogOdds method in reconstructing 3D infarct geometry, as measured by the Dice similarity coefficient, was 82.10% ± 6.58%, a significantly higher value than those of the alternative reconstruction methods. Among outcomes of electrophysiological simulations with infarct reconstructions generated by various methods, the simulation results corresponding to the LogOdds method showed the smallest deviation from those corresponding to the manual reconstructions, as measured by metrics based on both activation maps and pseudo-ECGs. CONCLUSIONS The authors have developed a novel method for reconstructing 3D infarct geometry from segmented slices of Lo-res clinical 2D LGE-CMR images. This method outperformed alternative approaches in reproducing expert manual 3D reconstructions and in electrophysiological simulations.
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Affiliation(s)
- Eranga Ukwatta
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland 21205 and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205
| | - Hermenegild Arevalo
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland 21205 and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205
| | - Martin Rajchl
- Department of Computing, Imperial College London, London SW7 2AZ, United Kingdom
| | - James White
- Stephenson Cardiovascular MR Centre, University of Calgary, Calgary, Alberta T2N 2T9, Canada
| | - Farhad Pashakhanloo
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland 21205 and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205
| | - Adityo Prakosa
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland 21205 and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205
| | - Daniel A Herzka
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205
| | - Elliot McVeigh
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205
| | - Albert C Lardo
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205 and Division of Cardiology, Johns Hopkins Institute of Medicine, Baltimore, Maryland 21224
| | - Natalia A Trayanova
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland 21205; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205; and Department of Biomedical Engineering, Johns Hopkins Institute of Medicine, Baltimore, Maryland 21205
| | - Fijoy Vadakkumpadan
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland 21205 and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205
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Zahid S, Whyte KN, Schwarz EL, Blake RC, Boyle PM, Chrispin J, Prakosa A, Ipek EG, Pashakhanloo F, Halperin HR, Calkins H, Berger RD, Nazarian S, Trayanova NA. Feasibility of using patient-specific models and the "minimum cut" algorithm to predict optimal ablation targets for left atrial flutter. Heart Rhythm 2016; 13:1687-98. [PMID: 27108938 DOI: 10.1016/j.hrthm.2016.04.009] [Citation(s) in RCA: 67] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2015] [Indexed: 11/19/2022]
Abstract
BACKGROUND Left atrial flutter (LAFL) occurs in patients after atrial fibrillation ablation. Identification of optimal ablation targets to terminate LAFL remains challenging. OBJECTIVE The purpose of this study was to use patient-specific models to simulate LAFL and predict optimal ablation targets using a novel approach based on flow network theory. METHODS Late gadolinium-enhanced cardiac magnetic resonance scans from 10 patients with LAFL were used to construct atrial models incorporating fibrosis by investigators blinded to procedural findings. Rapid pacing was applied in silico to induce LAFL. In each LAFL, we represented reentrant wave propagation as an electric flow network and identified the "minimum cut" (MC), which was the smallest amount of tissue that separated the flow into 2 discontinuous components. In silico ablation was applied at MCs, and targets were compared to those that terminated LAFL during catheter ablation. RESULTS Patient-specific atrial models were successfully generated from patient scans. LAFL was induced in 7 of 10 models. Ablation of MCs terminated LAFL in 4 models and produced new, slower LAFL morphologies in the other 3. For the latter cases, flow analysis was repeated to identify MCs of emergent LAFLs. Ablation of these MCs terminated emergent LAFLs. The MC-based ablation lesions in simulations were similar in length and location to ablation targets that terminated LAFL during catheter ablation for these 7 patients. CONCLUSION Personalized atrial simulations can predict ablation targets for LAFL. These simulations provide a powerful tool for planning ablation procedures and may reduce procedural times and complications.
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Affiliation(s)
- Sohail Zahid
- Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Kaitlyn N Whyte
- Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Erica L Schwarz
- Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Robert C Blake
- CardioSolv Ablation Technologies Inc, Baltimore, Maryland
| | - Patrick M Boyle
- Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Jonathan Chrispin
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Adityo Prakosa
- Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Esra G Ipek
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Farhad Pashakhanloo
- Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Henry R Halperin
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Hugh Calkins
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Ronald D Berger
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Saman Nazarian
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland; Department of Epidemiology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Natalia A Trayanova
- Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland; Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland.
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Chrispin J, Gucuk Ipek E, Zahid S, Prakosa A, Habibi M, Spragg D, Marine JE, Ashikaga H, Rickard J, Trayanova NA, Zimmerman SL, Zipunnikov V, Berger RD, Calkins H, Nazarian S. Lack of regional association between atrial late gadolinium enhancement on cardiac magnetic resonance and atrial fibrillation rotors. Heart Rhythm 2016; 13:654-60. [DOI: 10.1016/j.hrthm.2015.11.011] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2015] [Indexed: 10/22/2022]
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Deng D, Arevalo H, Pashakhanloo F, Prakosa A, Ashikaga H, McVeigh E, Halperin H, Trayanova N. Accuracy of prediction of infarct-related arrhythmic circuits from image-based models reconstructed from low and high resolution MRI. Front Physiol 2015; 6:282. [PMID: 26528188 PMCID: PMC4602125 DOI: 10.3389/fphys.2015.00282] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2015] [Accepted: 09/22/2015] [Indexed: 11/13/2022] Open
Abstract
Identification of optimal ablation sites in hearts with infarct-related ventricular tachycardia (VT) remains difficult to achieve with the current catheter-based mapping techniques. Limitations arise from the ambiguities in determining the reentrant pathways location(s). The goal of this study was to develop experimentally validated, individualized computer models of infarcted swine hearts, reconstructed from high-resolution ex-vivo MRI and to examine the accuracy of the reentrant circuit location prediction when models of the same hearts are instead reconstructed from low clinical-resolution MRI scans. To achieve this goal, we utilized retrospective data obtained from four pigs ~10 weeks post infarction that underwent VT induction via programmed stimulation and epicardial activation mapping via a multielectrode epicardial sock. After the experiment, high-resolution ex-vivo MRI with late gadolinium enhancement was acquired. The Hi-res images were downsampled into two lower resolutions (Med-res and Low-res) in order to replicate image quality obtainable in the clinic. The images were segmented and models were reconstructed from the three image stacks for each pig heart. VT induction similar to what was performed in the experiment was simulated. Results of the reconstructions showed that the geometry of the ventricles including the infarct could be accurately obtained from Med-res and Low-res images. Simulation results demonstrated that induced VTs in the Med-res and Low-res models were located close to those in Hi-res models. Importantly, all models, regardless of image resolution, accurately predicted the VT morphology and circuit location induced in the experiment. These results demonstrate that MRI-based computer models of hearts with ischemic cardiomyopathy could provide a unique opportunity to predict and analyze VT resulting for from specific infarct architecture, and thus may assist in clinical decisions to identify and ablate the reentrant circuit(s).
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Affiliation(s)
- Dongdong Deng
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University Baltimore, MD, USA
| | - Hermenegild Arevalo
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University Baltimore, MD, USA
| | - Farhad Pashakhanloo
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University Baltimore, MD, USA
| | - Adityo Prakosa
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University Baltimore, MD, USA
| | - Hiroshi Ashikaga
- Division of Cardiology, Department of Medicine, Johns Hopkins Medical Institute Baltimore, MD, USA ; Department of Biomedical Engineering, Johns Hopkins University Baltimore, MD, USA
| | - Elliot McVeigh
- Department of Biomedical Engineering, Johns Hopkins University Baltimore, MD, USA
| | - Henry Halperin
- Division of Cardiology, Department of Medicine, Johns Hopkins Medical Institute Baltimore, MD, USA
| | - Natalia Trayanova
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University Baltimore, MD, USA
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Prakosa A, Sermesant M, Allain P, Villain N, Rinaldi CA, Rhode K, Razavi R, Delingette H, Ayache N. Cardiac electrophysiological activation pattern estimation from images using a patient-specific database of synthetic image sequences. IEEE Trans Biomed Eng 2014; 61:235-45. [PMID: 24058008 DOI: 10.1109/tbme.2013.2281619] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
While abnormal patterns of cardiac electrophysiological activation are at the origin of important cardiovascular diseases (e.g., arrhythmia, asynchrony), the only clinically available method to observe detailed left ventricular endocardial surface activation pattern is through invasive catheter mapping. However, this electrophysiological activation controls the onset of the mechanical contraction; therefore, important information about the electrophysiology could be deduced from the detailed observation of the resulting motion patterns. In this paper, we present the study of this inverse cardiac electrokinematic relationship. The objective is to predict the activation pattern knowing the cardiac motion from the analysis of cardiac image sequences. To achieve this, we propose to create a rich patient-specific database of synthetic time series of the cardiac images using simulations of a personalized cardiac electromechanical model, in order to study this complex relationship between electrical activity and kinematic patterns in the context of this specific patient. We use this database to train a machine-learning algorithm which estimates the depolarization times of each cardiac segment from global and regional kinematic descriptors based on displacements or strains and their derivatives. Finally, we use this learning to estimate the patient’s electrical activation times using the acquired clinical images. Experiments on the inverse electrokinematic learning are demonstrated on synthetic sequences and are evaluated on clinical data with promising results. The error calculated between our prediction and the invasive intracardiac mapping ground truth is relatively small (around 10 ms for ischemic patients and 20 ms for nonischemic patient). This approach suggests the possibility of noninvasive electrophysiological pattern estimation using cardiac motion imaging.
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De Craene M, Marchesseau S, Heyde B, Gao H, Alessandrini M, Bernard O, Piella G, Porras AR, Tautz L, Hennemuth A, Prakosa A, Liebgott H, Somphone O, Allain P, Makram Ebeid S, Delingette H, Sermesant M, D'hooge J, Saloux E. 3D strain assessment in ultrasound (Straus): a synthetic comparison of five tracking methodologies. IEEE Trans Med Imaging 2013; 32:1632-1646. [PMID: 23674439 DOI: 10.1109/tmi.2013.2261823] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
This paper evaluates five 3D ultrasound tracking algorithms regarding their ability to quantify abnormal deformation in timing or amplitude. A synthetic database of B-mode image sequences modeling healthy, ischemic and dyssynchrony cases was generated for that purpose. This database is made publicly available to the community. It combines recent advances in electromechanical and ultrasound modeling. For modeling heart mechanics, the Bestel-Clement-Sorine electromechanical model was applied to a realistic geometry. For ultrasound modeling, we applied a fast simulation technique to produce realistic images on a set of scatterers moving according to the electromechanical simulation result. Tracking and strain accuracies were computed and compared for all evaluated algorithms. For tracking, all methods were estimating myocardial displacements with an error below 1 mm on the ischemic sequences. The introduction of a dilated geometry was found to have a significant impact on accuracy. Regarding strain, all methods were able to recover timing differences between segments, as well as low strain values. On all cases, radial strain was found to have a low accuracy in comparison to longitudinal and circumferential components.
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Tobon-Gomez C, De Craene M, McLeod K, Tautz L, Shi W, Hennemuth A, Prakosa A, Wang H, Carr-White G, Kapetanakis S, Lutz A, Rasche V, Schaeffter T, Butakoff C, Friman O, Mansi T, Sermesant M, Zhuang X, Ourselin S, Peitgen HO, Pennec X, Razavi R, Rueckert D, Frangi AF, Rhode KS. Benchmarking framework for myocardial tracking and deformation algorithms: an open access database. Med Image Anal 2013; 17:632-48. [PMID: 23708255 DOI: 10.1016/j.media.2013.03.008] [Citation(s) in RCA: 123] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2012] [Revised: 03/12/2013] [Accepted: 03/18/2013] [Indexed: 11/24/2022]
Abstract
In this paper we present a benchmarking framework for the validation of cardiac motion analysis algorithms. The reported methods are the response to an open challenge that was issued to the medical imaging community through a MICCAI workshop. The database included magnetic resonance (MR) and 3D ultrasound (3DUS) datasets from a dynamic phantom and 15 healthy volunteers. Participants processed 3D tagged MR datasets (3DTAG), cine steady state free precession MR datasets (SSFP) and 3DUS datasets, amounting to 1158 image volumes. Ground-truth for motion tracking was based on 12 landmarks (4 walls at 3 ventricular levels). They were manually tracked by two observers in the 3DTAG data over the whole cardiac cycle, using an in-house application with 4D visualization capabilities. The median of the inter-observer variability was computed for the phantom dataset (0.77 mm) and for the volunteer datasets (0.84 mm). The ground-truth was registered to 3DUS coordinates using a point based similarity transform. Four institutions responded to the challenge by providing motion estimates for the data: Fraunhofer MEVIS (MEVIS), Bremen, Germany; Imperial College London - University College London (IUCL), UK; Universitat Pompeu Fabra (UPF), Barcelona, Spain; Inria-Asclepios project (INRIA), France. Details on the implementation and evaluation of the four methodologies are presented in this manuscript. The manually tracked landmarks were used to evaluate tracking accuracy of all methodologies. For 3DTAG, median values were computed over all time frames for the phantom dataset (MEVIS=1.20mm, IUCL=0.73 mm, UPF=1.10mm, INRIA=1.09 mm) and for the volunteer datasets (MEVIS=1.33 mm, IUCL=1.52 mm, UPF=1.09 mm, INRIA=1.32 mm). For 3DUS, median values were computed at end diastole and end systole for the phantom dataset (MEVIS=4.40 mm, UPF=3.48 mm, INRIA=4.78 mm) and for the volunteer datasets (MEVIS=3.51 mm, UPF=3.71 mm, INRIA=4.07 mm). For SSFP, median values were computed at end diastole and end systole for the phantom dataset(UPF=6.18 mm, INRIA=3.93 mm) and for the volunteer datasets (UPF=3.09 mm, INRIA=4.78 mm). Finally, strain curves were generated and qualitatively compared. Good agreement was found between the different modalities and methodologies, except for radial strain that showed a high variability in cases of lower image quality.
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Prakosa A, Sermesant M, Delingette H, Marchesseau S, Saloux E, Allain P, Villain N, Ayache N. Generation of synthetic but visually realistic time series of cardiac images combining a biophysical model and clinical images. IEEE Trans Med Imaging 2013; 32:99-109. [PMID: 23014716 DOI: 10.1109/tmi.2012.2220375] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
We propose a new approach for the generation of synthetic but visually realistic time series of cardiac images based on an electromechanical model of the heart and real clinical 4-D image sequences. This is achieved by combining three steps. The first step is the simulation of a cardiac motion using an electromechanical model of the heart and the segmentation of the end diastolic image of a cardiac sequence. We use biophysical parameters related to the desired condition of the simulated subject. The second step extracts the cardiac motion from the real sequence using nonrigid image registration. Finally, a synthetic time series of cardiac images corresponding to the simulated motion is generated in the third step by combining the motion estimated by image registration and the simulated one. With this approach, image processing algorithms can be evaluated as we know the ground-truth motion underlying the image sequence. Moreover, databases of visually realistic images of controls and patients can be generated for which the underlying cardiac motion and some biophysical parameters are known. Such databases can open new avenues for machine learning approaches.
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Affiliation(s)
- Adityo Prakosa
- Asclepios Research Project, Inria Sophia Antipolis, 06902 Sophia Antipolis, France
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Prakosa A, Sermesant M, Delingette H, Saloux E, Allain P, Cathier P, Etyngier P, Villain N, Ayache N. Synthetic echocardiographic image sequences for cardiac inverse electro-kinematic learning. Med Image Comput Comput Assist Interv 2011; 14:500-507. [PMID: 22003655 DOI: 10.1007/978-3-642-23623-5_63] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
In this paper, we propose to create a rich database of synthetic time series of 3D echocardiography (US) images using simulations of a cardiac electromechanical model, in order to study the relationship between electrical disorders and kinematic patterns visible in medical images. From a real 4D sequence, a software pipeline is applied to create several synthetic sequences by combining various steps including motion tracking and segmentation. We use here this synthetic database to train a machine learning algorithm which estimates the depolarization times of each cardiac segment from invariant kinematic descriptors such as local displacements or strains. First experiments on the inverse electrokinematic learning are demonstrated on the synthetic 3D US database and are evaluated on clinical 3D US sequences from two patients with Left Bundle Branch Block.
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Affiliation(s)
- Adityo Prakosa
- Asclepios Research Project, INRIA Sophia-Antipolis, France
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