Figure 3 - available via license: Creative Commons Attribution 4.0 International
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Schematic representation of blood vessel (length L, height H), rectangular clot (length l, height h), and parabolic flow velocity at the vessel inlet and above the clot. Note that the flow velocity above the clot does not vanish at the clot surface but below it at y = d − h since the clot is partially penetrable for fluid flow.
Source publication
Spontaneous blood clotting in pulmonary circulation caused by thrombo-inflammation is one of the main mortality causes during the COVID-19 disease. Blood clotting leads to reduced pulmonary circulation and blood oxygenation. Lung inflammation can be evaluated with noninvasive diagnostic techniques. However, the correlation of the severity of the in...
Contexts in source publication
Context 1
... begin with a single vessel with the clot schematically shown in Figure 3 [14]. In order to compare numerical results with the analytical solution considered below, we approximate clot shape by the rectangular domain. ...
Citations
... The model has shown that the lack of antithrombin and the excess of fibrinogen significantly influence the risk of thrombosis. In a previous study, we used a multiscale model to quantify the effect of vessel obstruction on blood circulation as a result of lung inflammation in COVID-19 [26]. We showed that an obstruction level of 5% leads to a decrease in blood flux by 12%. ...
Severe acute respiratory syndrome of coronavirus 2 (SARS-CoV-2) is a respiratory virus that disrupts the functioning of several organ systems. The cardiovascular system represents one of the systems targeted by the novel coronavirus disease (COVID-19). Indeed, a hypercoagulable state was observed in some critically ill COVID-19 patients. The timely prediction of thrombosis risk in COVID-19 patients would help prevent the incidence of thromboembolic events and reduce the disease burden. This work proposes a methodology that identifies COVID-19 patients with a high thromboembolism risk using computational modelling and machine learning. We begin by studying the dynamics of thrombus formation in COVID-19 patients by using a mathematical model fitted to the experimental findings of in vivo clot growth. We use numerical simulations to quantify the upregulation in the size of the formed thrombi in COVID-19 patients. Next, we show that COVID-19 upregulates the peak concentration of thrombin generation (TG) and its endogenous thrombin potential. Finally, we use a simplified 1D version of the clot growth model to generate a dataset containing the hemostatic responses of virtual COVID-19 patients and healthy subjects. We use this dataset to train machine learning algorithms that can be readily deployed to predict the risk of thrombosis in COVID-19 patients.
