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Record W4205368515 · doi:10.1016/j.tru.2022.100097

Anticoagulation as a therapeutic strategy for hospitalised patients with COVID-19

2022· article· en· W4205368515 on OpenAlexaff
Sarah Cullivan, Michelle Sholzberg, Fionnuala Ní Áinle, Barry Kevane

Bibliographic record

VenueThrombosis Update · 2022
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
FundersScience Foundation Ireland
KeywordsMedicineIntensive care medicineHeparinObservational studyConfidence intervalClinical trialOdds ratioRandomized controlled trialPandemicInternal medicineCoronavirus disease 2019 (COVID-19)Disease

Abstract

fetched live from OpenAlex

The COVID-19 pandemic has devastated the global community and continues to cause significant morbidity and mortality worldwide. The development of effective vaccines has represented a major step towards reducing transmission and illness severity but significant challenges remain, particularly in regions where vaccine access has been limited. COVID-19 is associated with hypercoagulability and increased risk of thrombosis, with greatest risk among the critically ill. Interestingly, early observational data suggested that anticoagulant therapy might improve clinical outcomes, aside from thrombotic events, in patients with COVID-19. In this review we summarise data generated from three published randomised clinical trials which have sought to determine the effect of therapeutic heparin anticoagulation on efficacy and safety outcomes in hospitalised patients with COVID-19: the multiplatform REMAP-CAP, ACTIV-4a and ATTACC randomised controlled trials and the RAPID trial. In the multiplatform REMAP-CAP, ACTIV-4a and ATTACC randomised controlled trials, therapeutic heparin was not associated with benefit in critically ill patients with COVID-19 compared with usual care (adjusted proportional odds ratio (OR) for increased organ-support free days up to day 21: 0.83; 95% credible interval, 0.67-1.03, posterior probability of futility 99.9%). Conversely, among hospitalised patients without critical illness, therapeutic heparin was associated with an increased probability of organ support-free days alive (adjusted OR, 1.27; 95% credible interval, 1.03-1.58). The RAPID trial also evaluated the effect of therapeutic heparin compared with prophylactic heparin in non-critically ill patients. In this study, therapeutic heparin did not significantly reduce the odds of the primary composite outcome (death, mechanical ventilation or intensive care unit admission) (OR 0.69; 95% confidence interval [CI], 0.43 to 1.10; p = 0.12) but was associated with a significant reduction in all-cause mortality [OR, 0.22 (95%-CI, 0.07 to 0.65)]. Collectively these studies suggest that therapeutic anticoagulation with heparin may reduce the severity of illness and potentially even confer a survival benefit in hospitalised, non-critically ill patients with COVID-19. No benefit for therapeutic anticoagulation with heparin was evident in critically ill patients with COVID-19. Therefore, while the results of additional studies in this evolving field are pending, it is important to approach decisions regarding therapeutic heparin in moderately ill hospitalised patients with COVID-19 in a measured and individualised manner.

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)0.003
metaresearch head score (Gemma)0.009
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.086
GPT teacher head0.433
Teacher spread0.347 · 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
GenreReview

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
Published2022
Admission routes1
Has abstractyes

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