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Record W3095258628 · doi:10.1182/blood-2020-138699

Association between ROTEM Hypercoagulable Profile and Outcome in a Cohort of Severely Ill COVID-19 Patients Under Mechanical Ventilation

2020· article· en· W3095258628 on OpenAlexaffabout
Damian Ratano, Rebecca Caragata, Eddy Fan, Ewan C. Goligher, Rita Selby, Niall D. Ferguson, Keyvan Karkouti, Stuart A. McCluskey

Bibliographic record

VenueBlood · 2020
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsSunnybrook Health Science CentreToronto General HospitalUniversity of TorontoHealth Sciences CentreUniversity Health Network
Fundersnot available
KeywordsMedicineThromboelastometryPulmonary embolismMechanical ventilationPopulationThrombosisExact testDialysisInternal medicineAnesthesiaCoagulopathy

Abstract

fetched live from OpenAlex

Introduction: Recently published data show that COVID-19 is characterized by a hypercoagulable ROTEM profile and decreased fibrinolysis. Multiple reports suggest that severe COVID-19 infection is associated with an increased thromboembolic risk. There are limited data associating a hypercoagulable ROTEM profile and outcomes in the literature. A hypercoagulable ROTEM profile could help identify patients at risk of worse outcome and help target a study population for enhanced anticoagulation therapy or other COVID-19 therapy. Objectives: To determine if early hypercoagulability on ROTEM is associated with a higher risk of thromboembolic complications or worse outcome, in a population of mechanically ventilated COVID-19 patients transferred to a tertiary ARDS/ECMO referral center. Methods: All COVID-19 patients receiving mechanical ventilation at our center between April 3 and June 15, 2020 were assessed with ROTEM. Testing performed included at least ExTEM and FibTEM. Patients were classified as hypercoagulable (HC) or not (nHC) using the following criteria from ROTEM: ExTEM clot formation time (E_CFT)< 40 sec, alpha (∝) angle >79° and, either ExTEM or FibTEM maximum clot firmness (E_MCF or F_MCF) >70mm or >24mm, respectively. Outcomes, assessed at discharge from our ICU, included thromboembolic events (TE: composite of deep vein thrombosis, pulmonary embolism and ischemic stroke), acute kidney injury (AKI: AKIN stage >2 with or without dialysis), duration of mechanical ventilation, requirement for ECMO, and death. Difference between groups was determined using the chi-square or Fisher-exact test for categorical data and a t-test for continuous variables respectively, using p<0.05 as significant. Results: Of 59 patients included, 31 were hypercoagulable (HC) on admission. Mean (SD) age was 52 (14), and SOFA score on admission was high 15 (2). The severity of disease was similar in both groups especially for the cardiac and respiratory SOFA components (Table 1). The composite outcome of TE was not different between the groups (HC 9/31, nHC 6/28, p=0.7). The composite renal outcome was not different (HC 18/31 vs nHC 16/28). Mortality rate (29%, HC 9 vs nHC 8), and the duration of ventilation in survivors (HC 22 vs nHC 22 days) were similar in both groups. Requirement for ECMO support was similar (HC 8 vs nHC 12, p= 0.27). Patients were tested with daily viscoelastic tests and, neither fibrinolysis nor DIC were documented. All patients received TE prophylaxis and 34 received therapeutic intravenous heparin for ECMO (n=20) or TE treatment (n=14). Initial ROTEM were conducted on therapeutic heparin in 22 cases (12 HC, 10 nHC). Conclusion: Our data suggest that in a population of mechanically ventilated patients with severe COVID-19, an early hypercoagulable profile on ROTEM was not associated with an increased risk of thromboembolic events, AKI, prolonged ventilation, ECMO or death during their ICU stay. Disclosures Karkouti: Octapharma: Research Funding; Canadian blood services: Research Funding.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.081
GPT teacher head0.388
Teacher spread0.307 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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".

Quick stats

Citations2
Published2020
Admission routes2
Has abstractyes

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