Haemostatic differences between SARS-CoV- 2 PCR-positive and -negative patients at the time of hospital admission

Background: Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV- 2) is associated with a prothrombotic phenotype with an increased risk for thrombosis. Aim(s): To investigate whether COVID-19 is associated with changes in coagulation parameters upon presentation at the emergency department and whether these changes are associated with the development of thrombotic complications in patients with SARS-CoV- 2 infection. Method(s): A single centre, cross-sectional cohort study: The MArkers in COVID-19 And Relations to Outcomes in the Netherlands (MACARON) study was conducted. All patients suspected of SARS-CoV- 2 infection referred to the emergency department of the Meander Medical Center between March-May 2020 were included. 519 patients (26% PCR positive, median age 66 (range 19-97 years), 52.2% male) were included from whom an oro-and nasopharyngeal swab was obtained for detection of SARS-CoV- 2 by polymerase chain reaction (PCR). Blood samples for laboratory analysis were obtained from all patients. Thrombosis was defined as a clinical diagnosis of venous thromboembolism or atherothrombotic event based upon radiology and laboratory results. Result(s): SARS-CoV- 2 PCR positive patients had increased fibrinogen levels (5.41 g/L vs. 4.21 g/L, p < 0.001) and decreased levels of protein C (85.1% vs. 96.1%, p < 0.001) and alpha2-macroglobulin (4.41 muM vs. 5.11 muM, p < 0.001) compared to the PCR negative patients. In addition, we found more acquired activated protein C resistance in PCR positive patients. Furthermore, we found that elevated levels of factor VIII (208% vs. 162%, p = 0.028) and von Willebrand Factor (208% vs. 186%, p = 0.038) and decreased ADAMTS-13 levels (597 ng/ml vs. 691 ng/ml, p < 0.001) were associated with increased occurrence of thrombosis in PCR positive patients (thrombosis vs. non-thrombosis). Conclusion(s): We found that PCR positive patients had a more pronounced prothrombotic phenotype with endothelial activation upon hospital admission showing that coagulation tests may be considered useful to discriminate severe cases of COVID-19 at risk for thrombosis.

Coagulation parameters predict COVID-19- related thrombosis in a neural network with a positive predictive value of 98%

Background: Thrombosis is a major complication of a SARS-CoV- 2 infection. COVID-19 patients show changes in coagulation factor levels and functional coagulation tests that indicate an important role for the coagulation system in the pathogenesis of COVID-19. However, the multifactorial nature of thrombosis complicates the prediction of thrombotic events based on a single hemostatic variable. Aim(s): We used a neural network to predict future COVID-19- related thrombosis. Method(s): We developed neural networks for the prediction of thrombosis in COVID-19 patients based on several dedicated coagulation parameters and general laboratory variables measured in plasma samples of 133 COVID-19 patients collected at the time of hospital admission (cohort 1). The neural network was validated in a second cohort of 16 COVID-19 patients admitted to the intensive care unit (cohort 2). In cohort 1 and 2, 19 and 7 patients respectively suffered from thrombosis during their hospital stay. Result(s): The neural network predicts COVID-19- related thrombosis based on C-reactive protein (relative importance 14%), sex (10%), thrombin generation (TG) time-to- tail (10%), alpha2-macroglobulin (9%), TG curve width (9%), thrombin-alpha2- macroglobulin complexes (9%), plasmin generation lag time (8%), anti-SARS- CoV- 2 serum IgM (8%), TG lag time (7%), TG time-to- peak (7%), thrombin-antithrombin complexes (5%), and age (5%). In developmental cohort 1, the neural network identified future thrombosis in COVID-19 patients with a positive predictive value of respectively 98%. The neural network accurately ruled out thrombosis in COVID-19 patients as the negative predictive value of the neural network was 86%. In validation cohort 2, the positive predictive value of the neural network was 100%, and a negative predictive value of 66%. Conclusion(s): We developed a neural network that can accurately predict the occurrence of COVID-19- related thrombosis and is a promising algorithm to apply to other COVID-19 patient cohorts. The prediction of COVID-19 related thrombosis potentially can give clinicians the opportunity to increase anticoagulant therapy in high risk patients.

Haemostatic differences between SARS-CoV-2 PCR-positive and negative patients at the time of hospital admission.

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is associated with thrombosis. We conducted a cohort study of consecutive patients, suspected of SARS-CoV-2 infection presented to the emergency department. We investigated haemostatic differences between SARS-CoV-2 PCR positive and negative patients, with dedicated coagulation analysis. The 519 included patients had a median age of 66 years, and 52.5% of the patients were male. Twenty-six percent of the patients were PCR-positive for SARS-CoV-2.PCR positive patients had increased levels of fibrinogen and (active) von Willebrand Factor (VWF) and decreased levels of protein C and α2-macroglobulin compared to the PCR negative patients. In addition, we found acquired activated protein C resistance in PCR positive patients. Furthermore, we found that elevated levels of factor VIII and VWF and decreased levels of ADAMTS-13 were associated with an increased incidence of thrombosis in PCR positive patients. In conclusion, we found that PCR positive patients had a pronounced prothrombotic phenotype, mainly due to an increase of endothelial activation upon admission to the hospital. These findings show that coagulation tests may be considered useful to discriminate severe cases of COVID-19 at risk for thrombosis.