ABSTRACT
Background: The mechanisms by which COVID-19 results in severe illness in some individuals remains poorly defined. Identification of biomarkers associated with disease severity could be useful in defining the mechanisms of COVID-19 pathology and predicting disease course. Aim(s): Identify trajectories of biomarkers of coagulation, endothelial dysfunction, and fibrinolysis that are associated with COVID-19 severity. Method(s): Longitudinal plasma samples were collected from 99 patients in the Canadian COVID-19 Prospective Cohort Study (CanCOV) (23 outpatients, 31 ward patients, and 45 intensive care unit (ICU) patients). Plasma was quantified using 1) ELISAs for plasminogen, soluble thrombomodulin (sTM), plasminogen activator inhibitor-1 (PAI-1), alpha2-antiplasmin, D-dimer, thrombin-activatable fibrinolysis inhibitor (TAFI), and fibrinogen, and 2) in-house functional assays for clot lysis times and activated TAFI (TAFIa) levels. Biomarker values were log-transformed and linear mixed effects models were used to compare trajectories in ICU and ward patients compared to outpatients from date of symptom onset. Result(s): Among the 45 ICU patients, 24 (53%) died. There were no deaths in the other patient groups. D-dimer (Fig 1A) and sTM (Fig 1B) were significantly elevated for both hospitalized and ICU cohorts when compared with outpatients. PAI-1 (Fig 1C) was significantly elevated only in the ICU group between days 1 and 40. Plasminogen (Fig 1D) significantly decreased only in the ICU group from day 25 onwards. TAFIa (Fig 1E) increased over time only in the ICU cohort, with the levels being significant from day 35. Fibrinogen (Fig 1F) displayed similar trends as plasminogen whereby only the ICU was significantly decreased from day 25. alpha2-antiplasmin, TAFI, and clot lysis times were not significantly different compared to COVID-19 outpatients Conclusion(s): D-dimer and sTM showed the strongest associations with moderate and severe COVID-19 compared to mild disease. PAI-1, plasminogen, TAFIa, and fibrinogen may additionally be useful in identifying patients who become critically ill.