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.