Imaging and biophysical modelling of thrombogenic mechanisms in atrial fibrillation and stroke
Atrial fibrillation (AF) underlies almost one third of all ischaemic strokes, with the left atrial appendage (LAA) identified as the primary thromboembolic source. Current stroke risk stratification approaches, such as the CHA2DS2-VASc score, rely mostly on clinical comorbidities, rather than thromb...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-01-01
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| Series: | Frontiers in Cardiovascular Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fcvm.2022.1074562/full |
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| author | Ahmed Qureshi Gregory Y. H. Lip David A. Nordsletten David A. Nordsletten Steven E. Williams Steven E. Williams Oleg Aslanidi Adelaide de Vecchi |
| author_facet | Ahmed Qureshi Gregory Y. H. Lip David A. Nordsletten David A. Nordsletten Steven E. Williams Steven E. Williams Oleg Aslanidi Adelaide de Vecchi |
| author_sort | Ahmed Qureshi |
| collection | DOAJ |
| description | Atrial fibrillation (AF) underlies almost one third of all ischaemic strokes, with the left atrial appendage (LAA) identified as the primary thromboembolic source. Current stroke risk stratification approaches, such as the CHA2DS2-VASc score, rely mostly on clinical comorbidities, rather than thrombogenic mechanisms such as blood stasis, hypercoagulability and endothelial dysfunction—known as Virchow’s triad. While detection of AF-related thrombi is possible using established cardiac imaging techniques, such as transoesophageal echocardiography, there is a growing need to reliably assess AF-patient thrombogenicity prior to thrombus formation. Over the past decade, cardiac imaging and image-based biophysical modelling have emerged as powerful tools for reproducing the mechanisms of thrombogenesis. Clinical imaging modalities such as cardiac computed tomography, magnetic resonance and echocardiographic techniques can measure blood flow velocities and identify LA fibrosis (an indicator of endothelial dysfunction), but imaging remains limited in its ability to assess blood coagulation dynamics. In-silico cardiac modelling tools—such as computational fluid dynamics for blood flow, reaction-diffusion-convection equations to mimic the coagulation cascade, and surrogate flow metrics associated with endothelial damage—have grown in prevalence and advanced mechanistic understanding of thrombogenesis. However, neither technique alone can fully elucidate thrombogenicity in AF. In future, combining cardiac imaging with in-silico modelling and integrating machine learning approaches for rapid results directly from imaging data will require development under a rigorous framework of verification and clinical validation, but may pave the way towards enhanced personalised stroke risk stratification in the growing population of AF patients. This Review will focus on the significant progress in these fields. |
| first_indexed | 2024-04-10T22:23:11Z |
| format | Article |
| id | doaj.art-78c4429bd34d4eb6a5ecc103bf594a03 |
| institution | Directory Open Access Journal |
| issn | 2297-055X |
| language | English |
| last_indexed | 2024-04-10T22:23:11Z |
| publishDate | 2023-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Cardiovascular Medicine |
| spelling | doaj.art-78c4429bd34d4eb6a5ecc103bf594a032023-01-17T13:12:50ZengFrontiers Media S.A.Frontiers in Cardiovascular Medicine2297-055X2023-01-01910.3389/fcvm.2022.10745621074562Imaging and biophysical modelling of thrombogenic mechanisms in atrial fibrillation and strokeAhmed Qureshi0Gregory Y. H. Lip1David A. Nordsletten2David A. Nordsletten3Steven E. Williams4Steven E. Williams5Oleg Aslanidi6Adelaide de Vecchi7School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United KingdomLiverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool, United KingdomSchool of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United KingdomBiomedical Engineering, University of Michigan, Ann Arbor, MI, United StatesSchool of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United KingdomCentre for Cardiovascular Science, The University of Edinburgh, Edinburgh, United KingdomSchool of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United KingdomSchool of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United KingdomAtrial fibrillation (AF) underlies almost one third of all ischaemic strokes, with the left atrial appendage (LAA) identified as the primary thromboembolic source. Current stroke risk stratification approaches, such as the CHA2DS2-VASc score, rely mostly on clinical comorbidities, rather than thrombogenic mechanisms such as blood stasis, hypercoagulability and endothelial dysfunction—known as Virchow’s triad. While detection of AF-related thrombi is possible using established cardiac imaging techniques, such as transoesophageal echocardiography, there is a growing need to reliably assess AF-patient thrombogenicity prior to thrombus formation. Over the past decade, cardiac imaging and image-based biophysical modelling have emerged as powerful tools for reproducing the mechanisms of thrombogenesis. Clinical imaging modalities such as cardiac computed tomography, magnetic resonance and echocardiographic techniques can measure blood flow velocities and identify LA fibrosis (an indicator of endothelial dysfunction), but imaging remains limited in its ability to assess blood coagulation dynamics. In-silico cardiac modelling tools—such as computational fluid dynamics for blood flow, reaction-diffusion-convection equations to mimic the coagulation cascade, and surrogate flow metrics associated with endothelial damage—have grown in prevalence and advanced mechanistic understanding of thrombogenesis. However, neither technique alone can fully elucidate thrombogenicity in AF. In future, combining cardiac imaging with in-silico modelling and integrating machine learning approaches for rapid results directly from imaging data will require development under a rigorous framework of verification and clinical validation, but may pave the way towards enhanced personalised stroke risk stratification in the growing population of AF patients. This Review will focus on the significant progress in these fields.https://www.frontiersin.org/articles/10.3389/fcvm.2022.1074562/fullatrial fibrillationstrokecomputational cardiologyleft atrial appendagemedical imagingVirchow’s triad |
| spellingShingle | Ahmed Qureshi Gregory Y. H. Lip David A. Nordsletten David A. Nordsletten Steven E. Williams Steven E. Williams Oleg Aslanidi Adelaide de Vecchi Imaging and biophysical modelling of thrombogenic mechanisms in atrial fibrillation and stroke Frontiers in Cardiovascular Medicine atrial fibrillation stroke computational cardiology left atrial appendage medical imaging Virchow’s triad |
| title | Imaging and biophysical modelling of thrombogenic mechanisms in atrial fibrillation and stroke |
| title_full | Imaging and biophysical modelling of thrombogenic mechanisms in atrial fibrillation and stroke |
| title_fullStr | Imaging and biophysical modelling of thrombogenic mechanisms in atrial fibrillation and stroke |
| title_full_unstemmed | Imaging and biophysical modelling of thrombogenic mechanisms in atrial fibrillation and stroke |
| title_short | Imaging and biophysical modelling of thrombogenic mechanisms in atrial fibrillation and stroke |
| title_sort | imaging and biophysical modelling of thrombogenic mechanisms in atrial fibrillation and stroke |
| topic | atrial fibrillation stroke computational cardiology left atrial appendage medical imaging Virchow’s triad |
| url | https://www.frontiersin.org/articles/10.3389/fcvm.2022.1074562/full |
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