key: cord-0682416-g4zib1ro authors: Ceballos, F. C.; Ryan, P.; GOMEZ-CASERO, R. B.; VIDAL-ALCANTARA, E. J.; MARTIN-VICENTE, M.; PEREZ-GARCIA, F.; CHURRUCA-SARASQUETA, J.; VIRSEDA-BERDICES, A.; MARTINEZ-GONZALEZ, O.; BROCHADO-KITH, O.; RAVA, M.; VILCHES-MEDKOURI, C.; BLANCA, N.; RAMIREZ MARTINEZ-ACITORES, I.; MOREIRA-ESCRICHE, P.; DE JUAN, C.; RESINO, S.; FERNANDEZ-RODRIGUEZ, A.; JIMENEZ-SOUSA, M. A. title: Is coagulation-protein consumption upon admission linked to COVID-19 severity and mortality? date: 2021-04-20 journal: nan DOI: 10.1101/2021.04.19.21255747 sha: 902b03c77aba2b2237aa64277a89a5fc03eaf1dc doc_id: 682416 cord_uid: g4zib1ro The link between coagulation system disorders and COVID-19 has not yet been fully elucidated. With the aim of evaluating the association of several coagulation proteins with COVID-19 severity and mortality, we performed a cross-sectional study in 134 patients classified according to the highest disease severity reached during the disease. We found higher levels of antithrombin, prothrombin, factor XI, factor XII and factor XIII in asymptomatic/mild and moderate COVID-19 patients than healthy individuals. Interestingly, decreased levels of antithrombin, factor XI, XII and XIII were observed in those patients who eventually developed severe illness. Additionally, survival models showed us that patients with lower levels of these coagulation proteins had an increased risk of death. In conclusion, COVID-19 provokes early increments of some specific coagulation proteins in most patients. However, lower levels of these proteins at diagnosis might 'paradoxically' imply a higher risk of progression to severe disease and COVID-19-related mortality. Coronavirus disease 2019 (COVID-19) is associated with a significant activation of the 21 coagulation cascade. While thrombosis has been classically described in acute and 22 chronic infections including respiratory diseases (1) , thrombotic risk appears to be 23 higher in COVID-19 (2). Consequently, thromboembolic complications are common in 24 hospitalized patients, especially among those in intensive care units (ICU) (3). In this 25 setting, several mechanisms of coagulation activation have been postulated (4) and 26 large dynamic fluctuations in coagulation and fibrinolysis laboratory parameters have 27 been described during disease course (5). Development of overt disseminated 28 intravascular coagulation (DIC) seems to be rare and to follow a different pattern from 29 other infection-derived DIC (5-7) , but it has been reported in up to 71% of fatal cases as 30 a late and ominous sign (8). 31 Additionally, both venous and arterial thrombotic events have been independently 32 associated with mortality (9). Several haemostatic-system abnormalities such as 33 thrombocytopenia, elevated D-dimer levels, prolonged prothrombin time (PT) or 34 activated partial thromboplastin time (APTT), decreased factor V activity, 35 hypofibrinogenemia and reduced levels of natural anticoagulants (e.g antithrombin) 36 appear with increasing disease severity and are linked to death (6, 8) (10). However, the 37 bidirectional relationship between SARS-CoV-2 and the coagulation system is still not 38 completely understood (4). A predominant increase of D-dimer is typical, and its 39 presence on admission has been repeatedly described as significantly higher in non-40 survivors (11) but scarce or no abnormalities in PT and APTT are usually found at disease 41 onset (5, 12). To date, coagulation markers measured in the early phase of COVID-19 42 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted April 20, 2021. ; https://doi.org/10.1101/2021.04.19.21255747 doi: medRxiv preprint have evidenced a complex scenario and elucidation of the pathophysiology of 43 immunothrombosis is evolving. Therefore, to continue unravelling the insights of COVID- 44 19-induced coagulopathies, we evaluate several coagulation proteins at an early stage 45 of disease onset and their association with the highest disease severity and mortality. 46 Clinical data and samples 63 Epidemiological, clinical, disease evolution data, as well as laboratory parameters such 64 as PT, international normalized ratio (INR) and APTT were collected from clinical records 65 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. Aalen's additive models. Age and sex were included as covariables. Two-sided tests were 87 used for all statistical methods. Analyses were performed using the R 4.0.3 software. 88 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. Table 4 ). A reduction in the 111 coagulation proteins concentration involves a higher risk of death, however, the effect 112 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. Figure 5) . Our study shows that coagulation protein levels are affected at the first stages of the 118 COVID-19 infection and that those early changes already reflect disease severity in its 119 acute phase. We report increased levels of antithrombin, prothrombin, factor XI, factor 120 XII and factor XIII in AM and moderate patients, compared to healthy individuals. The 121 increase in natural anticoagulant and procoagulant proteins in COVID-19 has been 122 attributed to thromboinflammatory response caused by SARS-CoV-2, which provokes 123 endotheliitis and increases the hepatic production of factors (14). These could explain 124 the increases of clotting proteins found in our study even in early and mild stages. In 125 contrast, significantly decreased levels of antithrombin, factor XI, XII and XIII were found 126 at presentation in severe with respect to moderate patients. Therefore, an early reduced 127 production or more likely increased consumption due to pulmonary or systemic 128 coagulopathy of clotting proteins in severe cases could predict a poor prognosis. Results 129 of the survival models are in accordance with previous studies (15) that show a 130 consumption of coagulation proteins among COVID-19 non-survivors. Besides, it is 131 important to note that this negative association was more pronounced in men. 132 Several limitations should be considered. First one is the limited sample size, of healthy 133 and asymptomatic cases, which could have limited the possibility to find statistical 134 significance differences in some comparisons. Second one is that we have analysed five 135 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. should consider additional factors to fully describe the COVID-19 effects over the entire 137 coagulation cascade. However, these additional factors have been extensively studied, 138 and we analysed those that were not previously addressed. 139 In conclusion, our results indicate that: 1) COVID-19 causes an early increase of some 140 specific coagulation proteins such as antithrombin, prothrombin, contact factors and 141 factor XIII in most patients, even in those who won´t suffer from clinically significant 142 disease, suggesting that commonly elevated D-dimer levels are driven by an initial 143 enhanced procoagulant state and not just by hyperfibrinolysis; 2) Although not reflected 144 in routine tests such as PT and APTT, and despite common initial hyperfibrinogenemia, 145 patients who will eventually advance to severe disease show early decreased levels of 146 these anticoagulant and procoagulant markers, suggesting factor consumption, an these 147 levels were associated with higher COVID-19 related mortality. Evolving investigations 148 will allow us to better clarify the crosstalk between the immune and clotting systems in 149 this pandemic disease. 151 The authors declare that they have no competing interests. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. We use a generalized linear mixed model (GLMM) to test the pairwise difference among disease 282 severity classes by grouping the four coagulation proteins analysed in this study. We fit a model 283 where the protein was considered a random effect: 284 285 , = ( 0 + ,0 ) + 1 2 + 3 + (1) 286 287 Where bp,0p is the random effect of each coagulation protein, β1Xi is the fixed effect of the protein 288 concentrations, β2Xj is the fixed effect of the age and β3Xk is the fixed effect of the sex. By using 289 this model, we tested whether we were able to detect general effects between disease severity 290 classes. We were also interested in study the behaviour each coagulation protein. Pairwise 291 comparisons between disease severity classes, for each protein, were carried out using 292 multivariable logistic regressions. Sex and age were included as covariables in the multivariable 293 analysis. 294 Survival Cox Proportional-Hazard model's goodness of fit was evaluated by the Harrel's 295 concordance index (C-index). This index ranges from 0 to 1 and the intuition behind it is that, if 296 the risk model is good, patients who had shorter times-to-death should have higher risk scores. 297 Values of C-index near 0.5 indicates that the risk score predictions are not better than chance in 298 determining which individual with die first. Coagulation proteins discrimination capabilities were 299 measured by the area under the receiver operating characteristic (ROC) curve. 300 301 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted April 20, 2021. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted April 20, 2021. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted April 20, 2021. coagulation protein levels were included (antithrombin, prothrombin, Factor XI, Factor XII, 394 Factor XIII) to the base model. No significant differences were found between models. 395 396 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted April 20, 2021. ; https://doi.org/10.1101/2021.04.19.21255747 doi: medRxiv preprint Acute infection as 183 a trigger for incident venous thromboembolism: Results from a population-based case-184 crossover study Coagulation and anticoagulation in COVID-19 Embolism in Patients With COVID-19: Awareness of an Increased Prevalence The coagulopathy, endotheliopathy, and vasculitis of 191 COVID-19 COVID-19-associated coagulopathy and disseminated 193 intravascular coagulation High risk of thrombosis in patients with severe SARS-CoV-2 infection: a multicenter 196 prospective cohort study Systemic thrombosis in a large cohort of COVID-199 19 patients despite thromboprophylaxis: A retrospective study Abnormal coagulation parameters are associated 202 with poor prognosis in patients with novel coronavirus pneumonia Thrombosis in Hospitalized Patients With COVID-19 in a New York City Health System Marked factor V activity elevation in severe COVID-19 is associated 209 with venous thromboembolism D-dimer levels on admission to 211 predict in-hospital mortality in patients with Covid-19 COVID-19 coagulopathy: is it disseminated intravascular 214 coagulation? Intern Emerg Med The REDCap 216 consortium: Building an international community of software platform partners COVID-19 coagulopathy: An in-depth 222 analysis of the coagulation system Harrell's concordance index (C-index) are shown for the Cox models. C-index is a goodness of fit 341 measure for models that produces risk scores. Models with higher C-index indicate a shorter 342 time-to-disease for those patients with higher risk score. A C-index's value of 0.5 entails that the 343 risk score predictions are no better than chance.