Cargando…
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...
Autores principales: | , , , , , , , , |
---|---|
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 |
_version_ | 1784639728858955776 |
---|---|
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. |
format | Online Article Text |
id | pubmed-8789259 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT ohararyanp personalizedcomputationalheartmodelswitht1mappedfibroticremodelingpredictsuddendeathriskinpatientswithhypertrophiccardiomyopathy AT binkaedem personalizedcomputationalheartmodelswitht1mappedfibroticremodelingpredictsuddendeathriskinpatientswithhypertrophiccardiomyopathy AT prakosaadityo personalizedcomputationalheartmodelswitht1mappedfibroticremodelingpredictsuddendeathriskinpatientswithhypertrophiccardiomyopathy AT zimmermanstefanl personalizedcomputationalheartmodelswitht1mappedfibroticremodelingpredictsuddendeathriskinpatientswithhypertrophiccardiomyopathy AT cartoskimarkj personalizedcomputationalheartmodelswitht1mappedfibroticremodelingpredictsuddendeathriskinpatientswithhypertrophiccardiomyopathy AT abrahammroselle personalizedcomputationalheartmodelswitht1mappedfibroticremodelingpredictsuddendeathriskinpatientswithhypertrophiccardiomyopathy AT ludaiyin personalizedcomputationalheartmodelswitht1mappedfibroticremodelingpredictsuddendeathriskinpatientswithhypertrophiccardiomyopathy AT boylepatrickm personalizedcomputationalheartmodelswitht1mappedfibroticremodelingpredictsuddendeathriskinpatientswithhypertrophiccardiomyopathy AT trayanovanataliaa personalizedcomputationalheartmodelswitht1mappedfibroticremodelingpredictsuddendeathriskinpatientswithhypertrophiccardiomyopathy |