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

TY - JOUR

T1 - Imaging and biophysical modelling of thrombogenic mechanisms in atrial fibrillation and stroke

AU - Qureshi, Ahmed

AU - Lip, Gregory YH

AU - Nordsletten, David

AU - Williams, Steven

AU - Aslanidi, Oleg

AU - De Vecchi, Adelaide

N1 - Funding Information: AQ was funded by the EPSRC Research Council, part of the EPSRC DTP (grant Ref: EP/R513064/1). SW acknowledges support from the British Heart Foundation (FS/20/26/34952 and PG/19/44/34368). This work was supported by the Wellcome/EPSRC Centre for Medical Engineering (WT203148/Z/16/Z) at King’s College London. The funders had no role in the preparation, review, or approval of the manuscript. Publisher Copyright: Copyright © 2023 Qureshi, Lip, Nordsletten, Williams, Aslanidi and de Vecchi.

PY - 2023/1/16

Y1 - 2023/1/16

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85147181179&partnerID=8YFLogxK

U2 - 10.3389/fcvm.2022.1074562

DO - 10.3389/fcvm.2022.1074562

M3 - Review article

SN - 2297-055X

VL - 9

JO - Frontiers in Cardiovascular Medicine

JF - Frontiers in Cardiovascular Medicine

M1 - 1074562

ER -