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.
ABSTRACT
Background: Immunothrombosis and coagulopathy in the lung microvasculature may lead to lung injury and disease progression in COVID-19. We aim to identify biomarkers of coagulation, endothelial function, and fibrinolysis that are associated with disease severity and may have prognostic potential. Aims: To identify biomarkers of coagulation, inflammation, and fibrinolysis that may predict clinical course and outcome of COVID-19 patients. Methods: We performed a single-center prospective study of 14 adult COVID-19(+) ICU patients who were age and sex-matched to 14 COVID-19(-) ICU patients, and healthy controls. Daily blood draws, clinical data, and patient characteristics were collected. Ten biomarkers of interest were subjected to linear discriminant analysis (LDA) to explore the discriminatory ability of biomarkers for COVID-19 status. Linear repeated measures mixed models were used to screen biomarkers for associations with mortality. Selected biomarkers were further explored and entered into an unsupervised longitudinal clustering machine learning algorithm to identify trends and targets that may be used for future predictive modelling efforts. Results: LDA identified high D-dimer as the strongest contributor in distinguishing COVID-19 status however D-dimer was not associated with survival. Variable selection identified clot lysis time, and antigen levels of soluble thrombomodulin (sTM), plasminogen activator inhibitor-1 (PAI-1), and plasminogen as biomarkers associated with death. Longitudinal multivariate k-means clustering on these biomarkers alone identified two clusters of COVID-19(+) patients -low (30%) and high (100%) mortality groups (Figure 1). Biomarker trajectories that characterized the high mortality cluster were higher clot lysis times (inhibited fibrinolysis), higher sTM and PAI-1 levels, and lower plasminogen levels. Conclusions: Longitudinal trajectories of clot lysis time, sTM, PAI-1, and plasminogen may have predictive ability for mortality in COVID-19.