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

Thrombosis is a major clinical complication of COVID-19 infection. COVID-19 patients show changes in coagulation factors 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. We developed and validated a neural net for the prediction of COVID-19-related thrombosis. The neural net was developed based on the hemostatic and general (laboratory) variables of 149 confirmed COVID-19 patients from two cohorts: at the time of hospital admission (cohort 1 including 133 patients) and at ICU admission (cohort 2 including 16 patients). Twenty-six patients suffered from thrombosis during their hospital stay: 19 patients in cohort 1 and 7 patients in cohort 2. The neural net predicts COVID-19 related thrombosis based on C-reactive protein (relative importance 14%), sex (10%), thrombin generation (TG) time-to-tail (10%), α2-Macroglobulin (9%), TG curve width (9%), thrombin-α2-Macroglobulin complexes (9%), plasmin generation lag time (8%), serum IgM (8%), TG lag time (7%), TG time-to-peak (7%), thrombin-antithrombin complexes (5%), and age (5%). This neural net can predict COVID-19-thrombosis at the time of hospital admission with a positive predictive value of 98%-100%.

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

Thrombosis is a major clinical complication of COVID-19 infection. COVID-19 patients show changes in coagulation factors 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. We developed and validated a neural net for the prediction of COVID-19-related thrombosis. The neural net was developed based on the hemostatic and general (laboratory) variables of 149 confirmed COVID-19 patients from two cohorts: at the time of hospital admission (cohort 1 including 133 patients) and at ICU admission (cohort 2 including 16 patients). Twenty-six patients suffered from thrombosis during their hospital stay: 19 patients in cohort 1 and 7 patients in cohort 2. The neural net predicts COVID-19 related thrombosis based on C-reactive protein (relative importance 14%), sex (10%), thrombin generation (TG) time-to-tail (10%), α2-Macroglobulin (9%), TG curve width (9%), thrombin-α2-Macroglobulin complexes (9%), plasmin generation lag time (8%), serum IgM (8%), TG lag time (7%), TG time-to-peak (7%), thrombin-antithrombin complexes (5%), and age (5%). This neural net can predict COVID-19-thrombosis at the time of hospital admission with a positive predictive value of 98%-100%.

Population-wide persistent hemostatic changes after vaccination with ChAdOx1-S.

Various vaccines were developed to reduce the spread of the Severe Acute Respiratory Syndrome Cov-2 (SARS-CoV-2) virus. Quickly after the start of vaccination, reports emerged that anti-SARS-CoV-2 vaccines, including ChAdOx1-S, could be associated with an increased risk of thrombosis. We investigated the hemostatic changes after ChAdOx1-S vaccination in 631 health care workers. Blood samples were collected 32 days on average after the second ChAdOx1-S vaccination, to evaluate hemostatic markers such as D-dimer, fibrinogen, α2-macroglobulin, FVIII and thrombin generation. Endothelial function was assessed by measuring Von Willebrand Factor (VWF) and active VWF. IL-6 and IL-10 were measured to study the activation of the immune system. Additionally, SARS-CoV-2 anti-nucleoside and anti-spike protein antibody titers were determined. Prothrombin and fibrinogen levels were significantly reduced after vaccination (-7.5% and -16.9%, p < 0.0001). Significantly more vaccinated subjects were outside the normal range compared to controls for prothrombin (42.1% vs. 26.4%, p = 0.026) and antithrombin (23.9% vs. 3.6%, p = 0.0010). Thrombin generation indicated a more procoagulant profile, characterized by a significantly shortened lag time (-11.3%, p < 0.0001) and time-to-peak (-13.0% and p < 0.0001) and an increased peak height (32.6%, p = 0.0015) in vaccinated subjects compared to unvaccinated controls. Increased VWF (+39.5%, p < 0.0001) and active VWF levels (+24.1 %, p < 0.0001) pointed toward endothelial activation, and IL-10 levels were significantly increased (9.29 pg/mL vs. 2.43 pg/mL, p = 0.032). The persistent increase of IL-10 indicates that the immune system remains active after ChAdOx1-S vaccination. This could trigger a pathophysiological mechanism causing an increased thrombin generation profile and vascular endothelial activation, which could subsequently result in and increased risk of thrombotic events.

Vascular activation is a strong predictor of mortality in coronavirus disease 2019 patients on the ICU.

Respiratory failure in coronavirus disease 2019 (COVID-19) patients is one of the most frequent causes for referral to the ICU. A significant percentage of these patients does not survive the infection due to thromboembolic complications. Furthermore, the vascular system seems also to be involved in the pathogenesis. To investigate the role of hemostasis and endothelium on the outcome of COVID-19 patients admitted to the ICU. Blood was drawn from 16 ICU COVID-19 patients for hemostatic analysis. Patients were followed-up till discharge (n = 11) or death (n = 5). Parameters related to both coagulation and fibrinolysis, though disturbed, were not associated with mortality. Contrarily, activated Von Willebrand factor was increased and ADAMTS13 levels were decreased by two-fold in nonsurvivors compared with survivors. Our data established the involvement of the Von Willebrand factor-ADAMTS13 axis in the COVID-19 pathogenesis, thereby demonstrating that these plasma proteins seem to be strong predictors for ICU mortality.