Blood coagulation modelling to inform stroke risk stratification and intervention planning in COVID-19 patients with atrial fibrillation

Project reference:  SIE_03_21

Co-supervisor: Adelaide De Vecchi
Co-supervisor: Oleg Aslanidi 

Start date:   October 2022

Project summary:  A 7.5-fold increase in the odds of stroke has been linked to blood hypercoagulation in hospitalised COVID-19 patients. Underlying atrial fibrillation (AF), a widespread heart condition that also promotes blood clotting, can significantly increase risks of a thromboembolic event after hospital discharge. Treatment options include anticoagulation therapy and surgical occlusion of the left atrial appendage, the anatomical region more prone to thrombus formation. However, the ongoing pandemic has exposed shortcomings in stroke risk stratification for COVID-19/AF patients and the urgent need for better-informed treatment strategies based on in-depth understanding of the interplay between COVID-19 and AF. To address this issue, this study will develop and apply novel computational models of blood flow and coagulation to identify crucial prognostic factors for thromboembolism in COVID-19/AF patients. The project aims to elucidate the diseases’ interplay for an earlier prediction of thrombus formation and to test new biomarkers to inform the choice of treatment. 


Project description: 

Links between AF and stroke are well documented, but COVID-19-related cardiovascular pathophysiology and prognosis are unclear. We hypothesise that stroke risks are particularly high in AF patients with COVID-19, where blood clotting is driven by the simultaneous presence of superimposed risk factors, such as blood stagnation, hyperviscosity and endothelial dysfunction. For this reason, stroke risk stratification, and subsequently treatment strategies, are suboptimal in COVID-19/AF patients. As more than 90% of thrombi in non-valvular AF are localised in the narrow region of the left atrial appendage (LAA), current treatment options include surgical intervention to occlude the LAA and prevent blood from entering this critical area, and anticoagulant drug therapy with heparin. However, it is uncertain whether anticoagulation therapy alone is sufficient to prevent thrombus formation in the LAA or if surgical occlusion is a necessary preventative step in these high-risk patients. Due to the overwhelming nature of the coronavirus disease, complete imaging datasets with blood samples in COVID-19/AF patients are lacking. This hinders a mechanistic understanding of the pathophysiological changes induced by COVID-19 in the underlying thrombogenic substrate of AF, which is a crucial element for treatment planning.

To overcome this issue, we propose to build multi-scale models of (patho-)physiological blood flow and coagulation by combining clinical data from AF and COVID-19 patients to create a virtual cohort of COVID-19/AF patients (see figure). Blood flow models of the left atrium will be based on imaging datasets (CMR and echocardiography) from 12 AF patients equally split between sinus rhythm (normal state) or fibrillation (arrhythmic state), updated with values from blood samples of 40 COVID-19/AF patients, acquired as part of an ongoing study on COVID-19 at St Thomas’ Hospital (Dr Williams).

These models will be used to achieve three main objectives:

  1. Elucidate the interplay between COVID-19 and AF in terms of blood coagulation.

  2. Predict disease-specific thrombus formation dynamics.

  3. Test new blood biomarkers to tailor stroke risk stratification to COVID-19/AF patients and inform treatment.


Our existing models of blood flow and coagulation in the left atrium will be used as a starting point. The candidate will further develop them to include disease-specific coagulation models, and to simulate the surgical LAA occlusion and the thrombolytic action of varying doses of heparin. A virtual cohort of 480 COVID-19/AF patients will be generated and used as a testbed for stroke risk predictions based on blood biomarkers using principles of Bayesian inference and machine learning.


Given the rising AF prevalence (1-2% in the general population) and the overwhelming long-term effects of COVID-19, this disease combination carries and will continue carrying a significant socio-economic burden if effective treatment strategies are not devised in a timely manner. This study will contribute key information for a better understanding of the disease mechanisms, which constitutes a necessary step for an effective patient stratification and treatment planning.

Adelaide Oleg project image edited