Biomarkers of coagulation, endothelial function, and fibrinolysis in critically ill patients with COVID-19: A single-center prospective longitudinal study.

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.

Anti-Thrombotic Therapy to Ameliorate Complications of COVID-19 (ATTACC): Study design and methodology for an international, adaptive Bayesian randomized controlled trial.

BACKGROUND: Mortality from COVID-19 is high among hospitalized patients and effective therapeutics are lacking. Hypercoagulability, thrombosis and hyperinflammation occur in COVID-19 and may contribute to severe complications. Therapeutic anticoagulation may improve clinical outcomes through anti-thrombotic, anti-inflammatory and anti-viral mechanisms. Our primary objective is to evaluate whether therapeutic-dose anticoagulation with low-molecular-weight heparin or unfractionated heparin prevents mechanical ventilation and/or death in patients hospitalized with COVID-19 compared to usual care. METHODS: An international, open-label, adaptive randomized controlled trial. Using a Bayesian framework, the trial will declare results as soon as pre-specified posterior probabilities for superiority, futility, or harm are reached. The trial uses response-adaptive randomization to maximize the probability that patients will receive the more beneficial treatment approach, as treatment effect information accumulates within the trial. By leveraging a common data safety monitoring board and pooling data with a second similar international Bayesian adaptive trial (REMAP-COVID anticoagulation domain), treatment efficacy and safety will be evaluated as efficiently as possible. The primary outcome is an ordinal endpoint with three possible outcomes based on the worst status of each patient through day 30: no requirement for invasive mechanical ventilation, invasive mechanical ventilation or death. CONCLUSION: Using an adaptive trial design, the Anti-Thrombotic Therapy To Ameliorate Complications of COVID-19 trial will establish whether therapeutic anticoagulation can reduce mortality and/or avoid the need for mechanical ventilation in patients hospitalized with COVID-19. Leveraging existing networks to recruit sites will increase enrollment and mitigate enrollment risk in sites with declining COVID-19 cases.