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Personalized computational heart models with T1-mapped fibrotic remodeling predict sudden death risk in patients with hypertrophic cardiomyopathy

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...

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Autores principales: O'Hara, Ryan P, Binka, Edem, Prakosa, Adityo, Zimmerman, Stefan L, Cartoski, Mark J, Abraham, M Roselle, Lu, Dai-Yin, Boyle, Patrick M, Trayanova, Natalia A
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8789259/
https://www.ncbi.nlm.nih.gov/pubmed/35076018
http://dx.doi.org/10.7554/eLife.73325
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author O'Hara, Ryan P
Binka, Edem
Prakosa, Adityo
Zimmerman, Stefan L
Cartoski, Mark J
Abraham, M Roselle
Lu, Dai-Yin
Boyle, Patrick M
Trayanova, Natalia A
author_facet O'Hara, Ryan P
Binka, Edem
Prakosa, Adityo
Zimmerman, Stefan L
Cartoski, Mark J
Abraham, M Roselle
Lu, Dai-Yin
Boyle, Patrick M
Trayanova, Natalia A
author_sort O'Hara, Ryan P
collection PubMed
description 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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spelling pubmed-87892592022-01-27 Personalized computational heart models with T1-mapped fibrotic remodeling predict sudden death risk in patients with hypertrophic cardiomyopathy O'Hara, Ryan P Binka, Edem Prakosa, Adityo Zimmerman, Stefan L Cartoski, Mark J Abraham, M Roselle Lu, Dai-Yin Boyle, Patrick M Trayanova, Natalia A eLife Computational and Systems Biology 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. eLife Sciences Publications, Ltd 2022-01-25 /pmc/articles/PMC8789259/ /pubmed/35076018 http://dx.doi.org/10.7554/eLife.73325 Text en © 2022, O'Hara et al https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited.
spellingShingle Computational and Systems Biology
O'Hara, Ryan P
Binka, Edem
Prakosa, Adityo
Zimmerman, Stefan L
Cartoski, Mark J
Abraham, M Roselle
Lu, Dai-Yin
Boyle, Patrick M
Trayanova, Natalia A
Personalized computational heart models with T1-mapped fibrotic remodeling predict sudden death risk in patients with hypertrophic cardiomyopathy
title Personalized computational heart models with T1-mapped fibrotic remodeling predict sudden death risk in patients with hypertrophic cardiomyopathy
title_full Personalized computational heart models with T1-mapped fibrotic remodeling predict sudden death risk in patients with hypertrophic cardiomyopathy
title_fullStr Personalized computational heart models with T1-mapped fibrotic remodeling predict sudden death risk in patients with hypertrophic cardiomyopathy
title_full_unstemmed Personalized computational heart models with T1-mapped fibrotic remodeling predict sudden death risk in patients with hypertrophic cardiomyopathy
title_short Personalized computational heart models with T1-mapped fibrotic remodeling predict sudden death risk in patients with hypertrophic cardiomyopathy
title_sort personalized computational heart models with t1-mapped fibrotic remodeling predict sudden death risk in patients with hypertrophic cardiomyopathy
topic Computational and Systems Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8789259/
https://www.ncbi.nlm.nih.gov/pubmed/35076018
http://dx.doi.org/10.7554/eLife.73325
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