key: cord-0765742-32p1lzad
authors: Goswami, Julie; MacArthur, Taleen A.; Sridharan, Meera; Tange, Julie; Kirmse, Andrew J.; Lundell, Kaitlin A.; Chen, Dong; Auton, Matthew T.; Chon, Tony Y.; Hurt, Ryan T.; Salonen, Bradley R.; Ganesh, Ravindra; Erben, Young M.; Marquez, Christopher P.; Dong, Jing-Fei; Kozar, Rosemary A.; Heller, Stephanie F.; Loomis, Erica A.; Johnstone, Andrea L.; Bailey, Kent R.; Spears, Grant M.; Park, Myung S.
title: Biomarkers of thromboinflammation correlate to COVID-19 infection and admission status in emergency department patients
date: 2021-11-19
journal: Thrombosis Update
DOI: 10.1016/j.tru.2021.100090
sha: d945bc9a19ed8fca6c3472a20102991e4c25114f
doc_id: 765742
cord_uid: 32p1lzad

Background COVID-19-associated coagulopathy is incompletely understood. Objectives To characterize thrombin generation, Von Willebrand Factor (VWF), neutrophil extracellular traps (NETs), and their role in COVID-19 risk stratification in the emergency department (ED). Patients/methods Plasma samples from 67 ED COVID-19 patients were compared to 38 healthy volunteers (HVs). Thrombin generation (calibrated automated thrombogram, CAT) was expressed as lag time (LT, min), peak height (PH, min), and time to peak (ttPeak, min). Citrullinated nucleosomes and histones were quantified with ELISA, VWF antigen and activity (IU/dL) through latex immunoassay, Factor VIII (IU/dL) through one-stage optical clot detection, and VWF multimers with western blot densitometry. Wilcoxon testing and multivariable logistic regression were performed. Results presented as median [Q1, Q3]; p < 0.05 significant. Results COVID-19 patients had longer LT (4.00 [3.26, 4.67]; 2.95 [2.67, 3.10], p < 0.001) and ttPeak (7.33 [6.33, 8.04]; 6.45 [6.00, 7.50], p = 0.004), greater VWF antigen (212 [158, 275]; 110 [91, 128], p < 0.001) and Factor VIII levels (148 [106, 190]; 106 [86, 129], p < 0.001), with decreased high molecular weight multimers (Normalized multimer ratio 0.807 [0.759, 0.869]; 0.891 [0.858, 0.966], p < 0.001), than HVs. COVID-19 patients requiring admission from the ED had longer LT and ttPeak with greater VWF antigen and Factor VIII levels than those not admitted. Two and three variable models of CAT parameters and VWF correlated with COVID-19 and admission status (C-statistics 0.677 to 0.922). Conclusions Thrombin generation kinetics and VWF levels, independent of NETs, may have a role in predicting admission need for COVID-19 patients.

Stimulating Access to Research in Residency (TAM) from the National Heart, Lung, and Blood 37

Institute (NHLBI), HL146508 from the NHLBI (MA), UM1 HL120877-06 (MSP) by the Trans-38

Agency Consortium for Trauma-Induced Coagulopathy (TACTIC), EpiCypher (ALJ) is supported 39 by the NIH under award numbers R43AI134162 from the National Institute of Allergy and 40

Introduction 1

Coagulopathy remains a major source of morbidity and mortality in patients infected with 2

Coronavirus 2019 (SARS-CoV-2, . Reported venous thromboembolism (VTE) rates in 3 COVID-19 infection range from 2.9% to 31% in patients across the spectrum of severity and 4 remained high even when mortality levels were 50% lower in the fall of 2020 [1] [2] [3] [4] [5] [6] [7] [8] [9] . Autopsy studies 5 have identified diffuse macro-and microvascular thrombosis, suggesting dysregulated 6 coagulation in these patients [10, 11] . It is hypothesized that SARS-CoV-2 binding to ACE-2 7 receptors induces endothelial injury that triggers local and systemic inflammatory response with 8 resultant macro-and microvascular thrombosis [12, 13] . Cytokine storm, complement activation, 9

and neutrophil extracellular traps (NETs) have also been implicated in COVID-19-associated 10 coagulopathy [14] [15] [16] [17] . The complex interactions among inflammation, endothelial injury, and 11 coagulation, termed "thromboinflammation" or "immunothrombosis", significantly impact clinical 12 outcomes [18, 19] ; however, the exact clinical role of plasma biomarkers of thromboinflammation 13 in COVID-19 is yet to be determined. 14 Case series have shown greater levels of von Willebrand Factor (VWF) and Factor VIII in 15 COVID-19 patients and hypothesized their role in COVID-19-associated coagulopathy through 16 endothelial activation and propagation of the intrinsic pathway [20] [21] [22] [23] [24] . Endothelial injury mediates 17 development of VTE and stimulates neutrophils to form neutrophil extracellular traps (NETs), 18 which induce a positive feedback cycle by triggering further endothelial damage [25, 26] . Cohort 19 studies have demonstrated increased levels of cell-free DNA, citrullinated histone-3 (citH3), and 20 myeloperoxidase-DNA complexes (MPO-DNA) in patients with COVID-19, suggesting increased 21

NETosis [27, 28] . Autopsy studies have demonstrated the presence of NETs in the 22 microvasculature of the kidneys, lungs, and liver in deceased patients with 29, 23 30] . It is theorized that the interplay between endothelial injury and NETosis contributes to macro-24 and micro-vessel thrombosis in J o u r n a l P r e -p r o o f Thrombin (Factor IIa) is a key serine protease in the coagulation cascade. Presently, there 1 is limited information on the clinical utility of measuring thrombin generation and markers of 2 thromboinflammation in COVID-19-associated coagulopathy. In this pilot study, we characterized 3 COVID-19-associated coagulopathy in a heterogeneous cohort of patients requiring emergency 4 department evaluation, by quantifying plasma thrombin generation kinetics, NETosis, and VWF 5 activity, as compared to healthy volunteers. We also sought to determine the role of these plasma 6 biomarkers in risk stratifying COVID-19 patients for admission need in the emergency department 7 (ED). 8

Methods 11

Patients presenting to the Emergency Department (ED) at Mayo Clinic in Rochester, MN 13 who tested positive for COVID-19 were prospectively enrolled. This study was approved by the 14 Mayo Clinic Institutional Review Board and all subjects provided written informed consent. 15

Exclusion criteria included: Refusal or inability to obtain informed consent from a patient or their 16

Legal Authorized Representative (LAR), therapeutic anticoagulation (e.g., warfarin, dabigatran 17 etexilate, rivaroxaban, apixaban), an inherited or acquired coagulation disorder, active 18 malignancy, renal failure, or high-dose immunosuppression (including active chemotherapy, 19 biologic medications, chronic immune suppression for prior transplant, and high dose steroids). 20

Patients on anti-platelet medications, including aspirin, clopidogrel, ticagrelor, and non-steroidal 21 anti-inflammatory medications were included. Patients who had blood drawn after the receipt of 22 VTE chemoprophylaxis in-hospital were also excluded, to best assess baseline coagulation 23 profiles. We analyzed a total of 67 citrated plasma samples from enrolled COVID-19 patients and 24 38 citrated plasma samples from healthy volunteers. Volunteer samples utilized in this study were 25 collected under a parent study, as previously described, and samples were randomly selected to 26 include in this cohort, in order to avoid introducing bias through specific patient selection [31] . 1 Volunteers were recruited from the local community, screened for major medical conditions, and 2 written informed consent was obtained prior to sample collection [31] . The electronic medical 3 record was reviewed to obtain information on patient demographics and clinical course. Finland), as previously described [32, 33] . Assays were performed in triplicate. Corn trypsin 16 inhibitor (50 µg/mL final concentration) was added to each plasma sample prior to analysis. 17

Thrombin generation was initiated using two different reagents: 20 µL of PPP (5 pM re-lipidated 18 human tissue factor and 4 µM phospholipids, Diagnostica Stago Inc., Parsippany, NJ) reagent. 19

Then, 80 µL of platelet poor plasma was added to each well of U-bottom 96-well microtiter plates 20 (Nunc, Thermo Fischer Scientific, Rochester, NY) using a single channel pipette. After incubation 21

for 10 minutes at 37ºC, 20 µL of warmed FluCa reagent (FluCa kit, Diagnostica Stago Inc., 22

Parsippany, NJ), which contains the fluorogenic substrate and CaCl2 was added to each well via 23 an automated dispenser. Thrombin generation curves were recorded continuously for 90 minutes 24 at a rate of three readings per minute. Separate wells containing the thrombin calibrator, which 25 corrects for inner filter effects and quenching variation among individual plasmas were also 26 J o u r n a l P r e -p r o o f measured in parallel.

A 1 dedicated software program, 2

Thrombinoscope 3 (Thrombinoscope BV, 4

Maastricht, Netherlands) was 5 used to calculate thrombin 6 activity over time. As shown 7

in Figure 1 , the parameters 8 derived were lag time (LT, 9 minutes), peak height (PH, 10 nM), time to peak (ttPeak, 11 minutes), and endogenous thrombin potential (ETP, nM*minutes). standards (1000 -15.6 ng/mL and 0 ng/mL) were prepared using serial dilutions of H3R2,8,17Cit 20 recombinant designer nucleosome (dNucs, EpiCypher Catalog No. 16-1362) . Plasma samples at 21 1:4 dilution and standards were added in triplicate at a volume of 100 uL, incubated for 2 hours 22 at room temperature, and washed three times. Biotinylated H3R8Cit detection antibody was 23

added for a one-hour incubation. Signal detection was performed using Pierce TM High Sensitivity Substrates (Thermo Fisher Scientific Catalog No. N600). Signal (absorbance at 450 nm) was read 1 using EnVision TM Multilabel Plate Reader (Perkin Elmer). H3R8Cit recoveries were determined 2 by using plate-specific standard curve (sigmoidal 5PL regression analysis for absorbance vs. log 3 ([H3R2, 8, 17Cit dNuc] , ng/mL) with a lower limit of detection of 16.7 ng/mL. Citrullinated Histone 4 H3 ELISA kit was used (Cayman Chemical, Clone 11D3) to measure free histones (H3Free) 5

following the manufacturer's instructions at 1:4 dilutions. 6 7 VWF Antigen, VWF Activity, and Factor VIII Levels 8 VWF antigen (VWF: Ag) was measured using HemosIL von Willebrand Factor Antigen 9 latex immunoassay kits (Instrumentation Laboratory, Bedford, MA) with two ACL TOP coagulation 10 system analyzers, following the manufacturer's instructions. VWF activity in plasma samples was 11 measured using Hemosil von Willebrand Factor Activity latex immunoassay kits (Instrumentation 12

Laboratory, Bedford, MA) a previously described [36] . Factor VIII levels were quantified using 13 one-stage optical clot detection. 14 15

Quantitative loss of high molecular weight multimers (HMWM) was determined with in-gel 17 western blot gel electrophoresis as previously described [37] . Multimer ratios were calculated by 18 dividing the measured density of the sample range of interest (VWF multimers >10) by the 19 remaining bands in the column. The normalized multimer ratio (NMR10) is calculated by dividing 20 the patient multimer ratio for bands >10 by the pooled control multimer ratio for bands >10. A 21 control column utilizing pooled plasma from normal donors (separate from the volunteers used in 22 this study) was used to normalize VWF multimers. In addition, plasma from 27 normal donors was 23 used to derive the normalized multimer ratio band 10 (NMR10) normal range (0.84-1.13). NMR10 24 for each subject was calculated using the following equation: 25 J o u r n a l P r e -p r o o f > 10 2 − 10 ⁄ > 10 2 − 10 ⁄ 1 2

The first multimer band is ignored in this formula as it often contains proteins unassociated with 3 VWF, is most likely to be artifact, and has the highest quantitative inter-observer variability of all 4 the bands [38] . An NMR10 below the normal range (i.e., low NMR10 ratio) indicates loss of high 5 molecular weight multimers in subject plasma relative to pooled plasma from normal donors. 6 7 Statistical Analysis: 8

Data analysis was performed using SAS Version 9.4 (Cary, NC). The Wilcoxon rank sum 9

test was used to detect differences in values between COVID-19 patients and healthy volunteers, 10 as well as between COVID-19 patients who were admitted to the hospital and those who were 11 not admitted. Spearman coefficients were used to determine correlations between quantitative 12

variables. Results are presented as median and quartiles [Q1, Q3], unless otherwise specified. 13

Two-variable and three-variable logistic regression models were fitted with either COVID-19 14 (yes/no) or admission (yes/no) as the variables to be predicted. Results are expressed as odds 15 ratio (OR) with 95% confidence interval and concordance statistic (C-stat) to quantify the overall 16 strength of the model. A p-value of < 0.05 was considered statistically significant. With 67 enrolled 17 patients and 38 healthy volunteers, our study had 80% power to detect a standardized effect of 18 0.569. 19 20 21

Baseline Characteristics: 23

A total of 67 patients with COVID-19 and 38 healthy volunteers were included in this study. 24

Baseline characteristics are shown in Table 1 . In comparison to healthy volunteers, patients with 25 COVID-19 were older, more commonly on anti-platelet agents, and had greater incidences of 1 hypertension and chronic kidney disease. Of the 67 COVID-19 patients, 66 (98.5%) presented to 2 the ED with symptoms directly related to the virus, with the most common patient reported 3 symptoms being dyspnea, chest pain, cough, and fevers. The one asymptomatic patient had 4 known COVID-19 infection and presented to the emergency room with an unrelated urgent care 5 problem. Patients presented to the ED with a median of 8 [4, 10] days of symptoms prior to sample 6 collection ( of the admitted COVID-19 patients received corticosteroid therapy, mostly in the form of 13 intravenous dexamethasone during their treatment course. None of the patients were 14 administered convalescent plasma. All but one patient received VTE prophylaxis or therapeutic 15 anticoagulation within 24 hours of admission. All samples were collected prior to administration 16 of any chemoprophylaxis, which was verified through review of the medical record. The one 17 patient who did not was discharged after less than 24 hours. Among all enrolled COVID-19 1 patients, D-dimer levels were increased (lab normal reference < 220 ng/mL), as described in 2 Table 2 . Neutrophil to lymphocyte ratio (NLR) on initial labs was significantly greater in admitted 3 COVID-19 patients than non-admitted patients (5.23 [2.63, 7.18] vs. 2.79 [1.82, 5.22] , p = 0.02). 4

Five of the 67 COVID-19 patients developed symptomatic VTE (incidence 7.5%) within 90 days, 5 including 4 with symptomatic PE found on CT angiography, early after admission. One patient 6 developed an isolated DVT in the intensive care unit. Four of the five VTE patients reported > 7 7 days of COVID-19 symptoms prior to sample collection. Median D-dimer for patients who 8 developed VTE was markedly increased at 2380 ng/mL with range 957 to 9658 ng/mL. All patients 9

with VTE were started on and discharged on therapeutic anticoagulation. There was one mortality 10 in our cohort, and this was an elderly patient, who required ICU care but did not develop VTE. 11 12 

Thrombin Generation Kinetics: 15 J o u r n a l P r e -p r o o f COVID-19 patients had longer LT and ttPeak, and greater PH as compared to healthy 1 volunteers (Table 3) . COVID-19 patients who developed VTE had similar thrombin generation 2 parameters as compared to those without. COVID-19 patients who required hospital admission 3 had longer LT and ttPeak than those who were discharged from the ER, while PH and ETP were 4 not significantly different between admitted patients and those who were not admitted ( Table 4) . 5

There were no significant differences in thrombin generation parameters when COVID-19 patients 6

were examined by duration of symptoms prior to sample collection. Initial D-dimer level correlated 7

to LT (Spearman coefficient 0.336, p = 0.013), but not to any other thrombin generation 8 parameter. 9 10 patients who required hospital admission and those who did not ( Table 4) . 10 11 and Factor VIII levels, as compared to healthy volunteers (Table 3) . There was no significant 3 difference in VWF Activity to Antigen ratio between COVID-19 patients and volunteers (Table 3) . 4

Consistent with high VWF levels, Factor VIII levels were also greater in COVID-19 patients than 5 in volunteer subjects ( Table 3) . VWF multimers, expressed as NMR10, were quantitatively lower 6

in COVID-19 patients as compared to healthy volunteers ( Table 3) . NMR was low in 6 (16.2%) 7 volunteers compared to 48 (73.8%) COVID-19 patients. Admitted patients were noted to have 8 greater VWF antigen, VWF activity, Factor VIII levels, and decreased loss of VWF multimers as 9 compared to those who were not admitted ( Table 4) . VWF antigen and activity levels showed a 10 strong correlation to Factor VIII levels in the whole cohort (n = 105) including COVID-19 patients 11 and volunteers (Spearman coefficients 0.765 (p < 0.001) and 0.775 (p < 0.001) respectively). 12

Additionally, VWF Antigen, Activity and Factor VIII were correlated to D-dimers in patients with 13 COVID-19 (Spearman coefficients 0.553 (p < 0.001), 0.573 (p < 0.001), and 0.517 (p < 0.001), 14 respectively). 15 16

Fitted to COVID-19 Infection: 18

The univariate results (longer LT, ttPeak, and greater PH in COVID-19 patients) display 19 both hypo-and hypercoagulable thrombin profiles, suggesting a combination of variables could 20 be even more powerful in discriminating COVID-19 patients. Two variable models combining 21 CAT parameters or CAT parameters with VWF antigen fitted to COVID-19 infection status as the 22 dependent variable showed high concordance statistics, which ranged from 0.805 to 0.901. With 23 a three-variable logistic regression model using LT, PH, and VWF antigen, the c-statistic of 0.922 24 was achieved (Table 5b) . Logistic regression models using two variables (LT and PH, ttPeak and PH, LT and VWF 9

antigen, ttPeak and VWF antigen, PH and VWF antigen, ttPeak and D-dimer) used to predict 10 admission status of COVID-19 patients also had strong concordance, with c-statistics ranging 11 from 0.677 to 0.846 (Table 6a) . A three variable logistic regression model showed no change in 12 concordance to admission status when PH was added to variables, LT and VWF antigen ( Table  13 6b). 14 15 study adds a larger, heterogenous cohort of ED patients, that is likely reflective the population 19 presenting for urgent care at many centers, with varying degrees of severity and symptom 1 duration. Thrombin generation profiles in these patients differ from those seen in healthy 2 volunteers. We hypothesize that COVID-19 infection results in an early cascade of accelerated 3 coagulation and consumptive coagulopathy in vivo leading to the prolonged initiation of thrombin 4 generation seen with greater symptom duration. The ongoing cycle of endothelial activation and 5 dysregulated coagulation, both evidenced by our findings of increased VWF, may predispose 6 these patients to thrombotic complications, including a high rate of VTE. We additionally identify 7 a number of predictive models for admission status using thrombin generation and other plasma 8 biomarkers, particularly VWF, at initial patient evaluation. A recently published study has shown 9 that increased thrombin peak, in association with clinical features, was associated with clinical 10 worsening (escalation of care or death) in a multivariable logistic regression model [43] . 11

Ultimately, ours and similar models may also play a role in determining level of care, as studies 12 have shown longer LT and ttPeak in critically ill patients with COVID-19 as compared to non-13 critically ill patients [40] . studies, only 31% of our patients required any respiratory support. It is likely that greater 24 differences would be seen in a more severely ill patient cohort, as indicated by the observation 25 that patients in our cohort that required admission did have greater H3Free levels than healthy 26 volunteers. It is important to note that we assessed only two independent markers of NETosis, 1 specifically citrullinated histones (H3NUC) and H3Free. There are multiple additional methods of 2 quantifying NETosis, including MPO-DNA complexes and indirect measurement through cell-free 3 DNA levels, which have been previously shown to be increased in 28] . 4

While circulating cell-free DNA levels can be non-specific and from cells other than neutrophils, 5 the measurement of MPO-DNA complexes may not reliably reflect in vivo NETosis [44]. Our 6 ELSA-based assay for measuring H3NUC has been recently shown to have high antibody 7 specificity, superior nucleosome-based calibration standards, negligible intra-and inter-lot 8 variability, and robust recovery for measuring circulating NETs in human plasma [34] . We have 9 also recently demonstrated that the commercially available assay for H3Free positively correlates 10 to this newly validated assay for H3NUC. [34, 45] . 11

Our quantitative analysis of VWF multimers revealed a decrease in high molecular weight 12 (HMW) VWF multimers in COVID-19 patients, suggesting a possible consumptive process. This 13 is similar to the findings of other recently published cohort studies that have shown a decrease in 14 HMW VWF multimers and reduced ADAMTS-13 activity in 47] . It has 15 been proposed that the relative decrease in VWF multimers in COVID-19 patients may be due to 16 an early increase in VWF proteolysis, due to in vivo consumption of HMW multimers in VWF-17 platelet aggregates, or due to altered regulation of VWF multimers by 47] . In 18 our study, COVID-19 patients who required hospital admission had greater levels of VWF antigen, 19 activity, and Factor VIII, as well as lower relative levels of HMW VWF multimers than those who 20 did not. This suggests that VWF levels and multimer size may be markers of disease severity, 21 particularly in combination with thrombin generation profiles. VWF antigen levels, along with 22 thrombin generation kinetics, have potential clinical utility in the ED for predicting need for 23 admission with symptomatic COVID-19 infection. 24

This study has several limitations. First, this is a pilot study with a small number of patients, 25 limiting our ability to link plasma biomarkers with clinical outcomes, such as VTE. Additionally, 26 because we are using samples from a single time point, at patient presentation in ED, we have 1 captured a heterogeneous population of COVID-19 patients with varying disease severity and 2 symptom duration. It is likely that symptom duration and disease severity influence inflammation, 3 endothelial activation, and coagulation. We also had relatively few critically ill patients in this 4 cohort, so our results may be under-representing the coagulopathy seen in that patient population. 5

Immunothrombosis is modulated by a complex interaction of additional pathways, including 6 complement activation, which we did not evaluate in this pilot study. Complement mediated 7 microangiopathies (TMA) and a robust complement driven immune response to SARS-CoV-2 8 infection may be responsible for much of the end-organ damage observed in severe COVID-19 9

infection, including renal and pulmonary dysfunction [48, 49] . We were unable to assess the initiation of thrombin generation), peak height (PHmaximum thrombin generation at a given 4 point in the assay), time to peak (ttPeaktime to maximum thrombin generation), and 5 endogenous thrombin potential (ETP or area under the curvetotal thrombin generated). 6 were admitted only. 13 Table 3 : Plasma thrombin generation kinetics performed for COVID-19 patients and healthy 14 volunteers using calibrated automated thrombogram (CAT) and expressed as lag time (LT) in 15 minutes, peak height (PH) in nM, and time to peak (ttPeak) in minutes. Neutrophil extracellular 16 trap (NET) formation measured by quantification of citrullinated nucleosomes (H3NUC) and 17 citrullinated free histones (H3Free). LLD = lower limit of detection 16.7 ng/mL (66.8 ng/mL when 18 accounting for 1:4 dilution) for H3NUC assay. Von Willebrand Factor (VWF) antigen levels 19

(VWF: Ag) and activity, ratio of VWF activity to VWF: Ag, and Factor VIII levels. Loss of VWF 20 multimers expressed as normalized multimer ratio band 10 (NMR10). Normal range defined as 21 0.84-1.13 based on plasma from 27 normal donors. All values expressed as median and 22 quartiles [Q1, Q3]. Wilcoxon rank sum test. p < 0.05 considered significant. *ETP and H3NUC 23 are unavailable for one COVID-19 patient. ^VWF: Ag, VWF activity, VWF multimers, and Factor 1 VIII data unavailable for one healthy volunteer. 2 Table 4 : Plasma thrombin generation kinetics using the calibrated automated thrombogram 3 (CAT) and Neutrophil extracellular trap (NET) formation measured by quantification of 4 citrullinated nucleosomes (H3NUC) compared between COVID-19 patients who required 5 hospital admission vs. those who were discharged from the Emergency Department. LLD = 6 lower limit of detection 16.7 ng/mL (66.8 ng/mL when accounting for 1:4 dilution) for H3NUC 7 assay. Von Willebrand Factor (VWF) antigen levels (VWF: Ag) and activity, ratio of VWF 8 activity to VWF: Ag, and Factor VIII levels. Loss of VWF multimers expressed as normalized 9 multimer ratio band 10 (NMR10). All values expressed as median and quartiles [Q1, Q3] . 10

Wilcoxon rank sum test. p < 0.05 considered significant. *ETP and H3NUC unavailable for one 11 COVID-19 patient. 12 Table 5 : Two variable (a) and three variable (b) logistic regression models for all subjects (n = 13 105 with 67 COVID-19 patients and 38 volunteers) using COVID-19 status as dependent 14 variable with thrombin generation kinetics parameters and Von Willebrand Factor (VWF) antigen 15 as independent variables (per standard deviation increase). LT, ttPeak, and VWF: Ag log-16 transformed with outliers reduced. p < 0.05 considered significant. 17 Table 6 : Two variable (a) and three variable (b) logistic regression models for COVID-19 18 patients (n = 67) admission status as dependent variable with thrombin generation kinetics 19 parameters and Von Willebrand Factor (VWF) antigen as independent variables (per standard 20 deviation increase). LT, ttPeak, PH, and VWF: Ag log-transformed with outliers reduced. p < 21 0.05 considered significant. J o u r n a l P r e -p r o o f

☐ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

☒The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

EpiCypher is a commercial developer and supplier of platforms like those used in this study: recombinant semi-synthetic nucleosomes (dNucs), antibody validation platforms, and nucleosome based H3Cit assays (e.g. EpiCypher Catalog No. R&D143001). ALJ is an inventor on patents covering use of recombinant nucleosomes carrying histone or DNA modifications for antibody validation and assay quantification. All other authors declare no conflict of interest.

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