Mechanistic modelling of Virchows triad to assess thrombogenicity and stroke risk in atrial fibrillation patients

BACKGROUND: Atrial fibrillation (AF) is responsible for almost one third of all strokes, with the left atrial appendage (LAA) being the primary thromboembolic source due to localised stimulation of prothrombotic mechanisms; blood stasis, hypercoagulability and endothelial damage, known as Virchow's triad. AIM: We propose an in-silico modelling pipeline that leverages clinical imaging data to mechanistically assess patient thrombogenicity for all aspects of Virchow's triad to improve the prediction and prevention of AF-related stroke. METHODS: Two AF patients undergoing Cine magnetic resonance imaging (sinus rhythm (SR) N=1 or AF N=1 during imaging) were selected for 3D left atrial (LA) modelling with patient-specific myocardial deformation prescribed from image-derived wall motion. Blood stasis was quantified by computational fluid dynamics (CFD) simulations of 5 cardiac cycles [1]. Generation of three key coagulation proteins; thrombin, fibrinogen and fibrin, were modelled to represent thrombus growth and hypercoagulability [2]. Regions prone to thrombogenesis by endothelial damage were identified by the oscillatory shear index (OSI), time averaged wall shear stress (TAWSS) and endothelial cell activation potential (ECAP) metrics in the LAA [3]. RESULTS: Patient-specific LA simulations enabled the assessment of differences between SR and AF conditions, quantified as numerical characteristics of each aspect of Virchow's triad. In SR, blood flow velocities were in the range 0–2.6 m/s with mean of 0.85 m/s in the LA cavity, while AF had a range between 0–1.6 m/s with mean of 0.55 m/s. The peak and mean LAA velocities in SR were 0.85 m/s and 0.14 m/s, while AF had a peak LAA velocity of 0.32 m/s and mean of 0.09 m/s, showing a 38% decrease during AF. The thrombin concentration reached its steady state at 1.26 mmol/m(3) in the AF case after 4.7 seconds, while thrombin was washed away from the initial injury site in SR. After 5 cardiac cycles of thrombus growth dynamics, the peak fibrin concentration in the LAA was 1.3 mmol/m(3) in SR and 3.8 mmol/m(3) in AF, with the thrombus area in AF being 40% larger. Fibrinogen concentration decreased at a rate equal to fibrin generation in both SR and AF solely in the area of thrombus formation. ECAP in the LAA had peak values of 2.9 in SR and 3.7 in AF, with the location at highest risk of thrombogenesis above the LAA entrance. LAA OSI had an average value of 0.45 in AF versus 0.36 in SR, showing a 26% increase. Similarly, the TAWSS was 3.5x10(–3) Pa on average over the LAA in AF compared to 1.4x10(–3) Pa in SR. CONCLUSIONS: Patient-specific LA models combining these three quantitative characteristics can be used to predict the higher thrombogenic risk in AF. After further validation, this novel approach for quantitative assessment of AF patient thrombogenicity based on modelling all factors in Virchow's triad can personalise and improve management of AF patients with a risk of stroke. FUNDING ACKNOWLEDGEMENT: Type of funding sources: Public grant(s) – National budget only. Main funding source(s): UK Engineering and Physical Sciences Research Council