Imaging and biophysical modelling of thrombogenic mechanisms in atrial fibrillation and stroke.

Qureshi, Ahmed, Lip, Gregory YH ORCID: 0000-0002-7566-1626, Nordsletten, David A ORCID: 0000-0002-5363-4715, Williams, Steven E, Aslanidi, Oleg and de Vecchi, Adelaide ORCID: 0000-0002-4378-0401 (2022) Imaging and biophysical modelling of thrombogenic mechanisms in atrial fibrillation and stroke. Frontiers in cardiovascular medicine, 9. 1074562-.

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Official URL: http://dx.doi.org/10.3389/fcvm.2022.1074562

Abstract

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 CHA<sub>2</sub>DS<sub>2</sub>-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.

Item Type:	Article
Uncontrolled Keywords:	Virchow’s triad, atrial fibrillation, computational cardiology, left atrial appendage, medical imaging, stroke, thrombus formation
Divisions:	Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Life Courses and Medical Sciences
Depositing User:	Symplectic Admin
Date Deposited:	05 Oct 2023 13:29
Last Modified:	18 Mar 2024 03:29
DOI:	10.3389/fcvm.2022.1074562
Open Access URL:	https://doi.org/10.3389/fcvm.2022.1074562
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3173465