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.
ABSTRACT
Background : COVID-19 infection is characterized by immunothrombosis that likely reflects hypercoagulation, endothelial dysfunction, and increased formation of neutrophil extracellular traps. Aims : In this study, we investigated the utility of immunothrombosis biomarkers to distinguish between COVID-19 patients and non-COVID septic pneumonia patients. We also investigated the prognostic utility of the biomarkers in predicting ICU mortality in the two patients groups. Methods : The participants in this study were ICU COVID-19 patients ( n = 14), ICU non-COVID septic pneumonia patients ( n = 19), and age-and sex-matched healthy controls ( n = 14). Blood samples were collected on Days 4, 7, 10, and/or 14. We measured plasma levels of the following biomarkers: thrombin-antithrombin (TAT) complexes, protein C, antithrombin, soluble TM, soluble EPCR, fibrinogen, D-dimer, cell-free DNA (cfDNA), and citrullinated histones (H3-Cit). Data analysis was based on binomial logit models and receiver operating characteristic curve analyses. Results : We identified 8 biomarkers that distinguish COVID-19 patients from healthy individuals: cfDNA, D-dimer, sEPCR, PC, sTM, fibrinogen, H3-Cit, and TAT complexes. In comparison, 4 biomarkers distinguish COVID-19 from non-COVID septic pneumonia patients: fibrinogen, sEPCR, antithrombin, and cfDNA. With respect to prognosis, the main predictors of ICU mortality differ between the two patient groups. In COVID-19 patients, non-survivors have higher sTM and H3-Cit compared with survivors. In septic pneumonia patients, non-survivor patients have lower levels of protein C and higher cfDNA levels compared with survivors. In addition, the most recent values of the biomarkers have stronger prognostic value compared to their Day 1 values. Conclusions : Our results suggest that fibrinogen, sEPCR, antithrombin, and cfDNA have utility for distinguishing COVID-19 patients from non-COVID septic pneumonia patients. Our data also suggest that the predictors of ICU mortality differ between the two patient groups: sTM and H3-Cit for COVID-19 patients, and protein C and cfDNA for non-COVID septic pneumonia patients. These findings suggests that there are pathophysiological differences between the two patients groups.