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Systemic Inflammatory Response Syndrome Is a Major Contributor to COVID-19–Associated Coagulopathy

2020· article· en· W3036529476 on OpenAlexfundno aff
Paul Masi, Guillaume Hékimian, Manon Lejeune, Juliette Chommeloux, Cyrielle Desnos, Marc Pineton de Chambrun, Isabelle Martin‐Toutain, Ania Nieszkowska, Guillaume Lebreton, Nicolas Bréchot, Matthieu Schmidt, Charles Edouard Luyt, Alain Combes, Corinne Frère

Bibliographic record

VenueCirculation · 2020
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsnot available
FundersMcGill University
KeywordsBoulevardCoagulopathyMedicineCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Internal medicineVirologyHistoryInfectious disease (medical specialty)Disease

Abstract

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Coronavirus disease 2019 (COVID-19) is associated with a systemic coagulopathy 1 favoring thromboembolic complications, which occur in 15% to 30% of critically ill patients with COVID-19. 2,3This coagulopathy remains poorly documented and data on thrombin generation and fibrinolysis are lacking.We characterized the coagulation and fibrinolysis profiles of patients with CO-VID-19 with acute respiratory distress syndrome (ARDS).From October 2019 to April 2020, 28 consecutive patients with severe ARDS were referred to our tertiary intensive care unit and included in this prospective single-center cohort study.The protocol was approved by a research ethics committee (CPP Ouest III, 2019-A01160-57), and the study was conducted in accordance with the Declaration of Helsinki.Informed consent was obtained from patients or their relatives.Blood samples were collected on admission for a comprehensive coagulation/fibrinolytic pathways analysis.To better assess the in vivo dynamics of clot formation, stabilization, and lysis, we used a global coagulation assay assessing changes in viscoelastic properties of whole blood.We compared 11 patients with ARDS included before the COVID-19 pandemic (influenza pneumonia, n=4; bacterial pneumonia, n=2; other causes of ARDS, n=5) with 17 patients with COVID-19.Baseline characteristics of the patients with and without COVID-19 did not differ and are presented in the Table .Briefly, the median age was 45 years; most patients were men (68%), overweight (32.1%), or obese (57.1%); and a few of them had additional comorbidities.On admission, all patients were receiving thromboprophylaxis according to current guidelines.Pulmonary embolism was incidentally diagnosed in 3 out of 17 patients with CO-VID-19.Coagulation and fibrinolysis profiles are presented in the Table.In addition, von Willebrand factor antigen and activity did not differ between groups and were 3-to 4-fold higher than the upper limit of normal range (median, 4.44 and 2.86 IU/mL, respectively, in the overall population).Compared with patients without COVID-19, patients with COVID-19 exhibited significantly higher levels of procoagulant factors, mainly fibrinogen (median, 810 mg/dL versus 710; P=0.03), factor V (median, 1.53 IU/mL versus 0.73; P<0.0001), factor VIII (median, 2.97 IU/mL versus 1.61; P=0.03), and acute phase reactants including C-reactive protein (P=0.05) and α1-acid glycoprotein (P=0.02).All of these measures were strongly correlated with each other (P<0.05 for all correlations).In contrast, antithrombin, protein C, and protein S levels were within the normal range and did not differ between groups.Prothrombin fragment 1 and 2 levels did not differ between patients with and without COVID-19 and were 2-to 3-fold higher than the upper limit of normal range.Thrombin-antithrombin complex levels were increased in both groups but significantly lower in patients with COVID-19 (median, 7.69 µg/L versus 22.63; P=0.03).Fibrinolysis profiles showed factor XIII, plasminogen, and

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.163
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.161
Threshold uncertainty score0.845

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.163
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.058
GPT teacher head0.385
Teacher spread0.327 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations121
Published2020
Admission routes1
Has abstractyes

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