Biomarkers of coagulation, endothelial function, and fibrinolysis in critically ill patients with COVID‐19: A single‐center prospective longitudinal study. (1st May 2021)

• Record Type: Journal Article
• Authors: Juneja, Ganeem K.; Castelo, Matthew; Yeh, Calvin H.; Cerroni, Samantha E.; Hansen, Bettina E.; Chessum, James E.; Abraham, Joel; Cani, Erblin; Dwivedi, Dhruva J.; Fraser, Douglas D.; Slessarev, Marat; Martin, Claudio; McGilvray, Scott; Gross, Peter L.; Liaw, Patricia C.; Weitz, Jeffrey I.; Kim, Paul Y.
• Is Part Of: Journal of thrombosis and haemostasis. Volume 19:Number 6(2021)
• Page Start: 1546
• Page End: 1557
• Publication Date: 2021-05-01
• Subjects: biomarkers -- coronavirus -- fibrinolysis -- observational study -- thrombosis; Thrombosis -- Periodicals; Hemostasis -- Periodicals; Blood coagulation disorders -- Periodicals; 616.1
• DOI: 10.1111/jth.15327
• Languages: English
• ISSNs: 1538-7933

Abstract

Abstract: Background: Immunothrombosis and coagulopathy in the lung microvasculature may lead to lung injury and disease progression in coronavirus disease 2019 (COVID‐19). We aim to identify biomarkers of coagulation, endothelial function, and fibrinolysis that are associated with disease severity and may have prognostic potential. Methods: We performed a single‐center prospective study of 14 adult COVID‐19(+) intensive care unit patients who were age‐ and sex‐matched to 14 COVID‐19(−) intensive care unit patients, and healthy controls. Daily blood draws, clinical data, and patient characteristics were collected. Baseline values for 10 biomarkers of interest were compared between the three groups, and visualized using Fisher's linear discriminant function. 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: Elevated D‐dimer was 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. 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.