Combining Computational Modelling and Machine Learning to Identify COVID-19 Patients with a High Thromboembolism Risk

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.

Automated summary

This study proposes a methodology that uses computational modeling and machine learning to identify COVID-19 patients with a high thromboembolism risk. Through numerical simulations and mathematical modeling, it is shown that COVID-19 increases the size of thrombus formation and the peak concentration of thrombin generation. Finally, a dataset of hemostatic responses from virtual COVID-19 patients and healthy subjects is used to train machine learning algorithms for predicting the risk of thrombosis in COVID-19 patients.