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Record W3203455779 · doi:10.1097/aln.0000000000003999

Treatments Associated with Lower Mortality among Critically Ill COVID-19 Patients: A Retrospective Cohort Study

2021· article· en· W3203455779 on OpenAlexaff
Xu Zhao, Chan Gao, Feng Dai, Miriam M. Treggiari, Ranjit Deshpande, Lingzhong Meng

Bibliographic record

VenueAnesthesiology · 2021
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsMcGill University Health Centre
FundersYale Center for Clinical Investigation, Yale School of MedicineYale University
KeywordsMedicineRetrospective cohort studyCoronavirus disease 2019 (COVID-19)Critically ill2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)CohortCohort studyIntensive care medicineEmergency medicineMEDLINEInternal medicineVirologyOutbreakDiseaseInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

BACKGROUND: Mortality in critically ill COVID-19 patients remains high. Although randomized controlled trials must continue to definitively evaluate treatments, further hypothesis-generating efforts to identify candidate treatments are required. This study's hypothesis was that certain treatments are associated with lower COVID-19 mortality. METHODS: This was a 1-yr retrospective cohort study involving all COVID-19 patients admitted to intensive care units in six hospitals affiliated with Yale New Haven Health System from February 13, 2020, to March 4, 2021. The exposures were any COVID-19-related pharmacologic and organ support treatments. The outcome was in-hospital mortality. RESULTS: This study analyzed 2,070 patients after excluding 23 patients who died within 24 h after intensive care unit admission and 3 patients who remained hospitalized on the last day of data censoring. The in-hospital mortality was 29% (593 of 2,070). Of 23 treatments analyzed, apixaban (hazard ratio, 0.42; 95% CI, 0.363 to 0.48; corrected CI, 0.336 to 0.52) and aspirin (hazard ratio, 0.72; 95% CI, 0.60 to 0.87; corrected CI, 0.54 to 0.96) were associated with lower mortality based on the multivariable analysis with multiple testing correction. Propensity score-matching analysis showed an association between apixaban treatment and lower mortality (with vs. without apixaban, 27% [96 of 360] vs. 37% [133 of 360]; hazard ratio, 0.48; 95% CI, 0.337 to 0.69) and an association between aspirin treatment and lower mortality (with vs. without aspirin, 26% [121 of 473] vs. 30% [140 of 473]; hazard ratio, 0.57; 95% CI, 0.41 to 0.78). Enoxaparin showed similar associations based on the multivariable analysis (hazard ratio, 0.82; 95% CI, 0.69 to 0.97; corrected CI, 0.61 to 1.05) and propensity score-matching analysis (with vs. without enoxaparin, 25% [87 of 347] vs. 34% [117 of 347]; hazard ratio, 0.53; 95% CI, 0.367 to 0.77). CONCLUSIONS: Consistent with the known hypercoagulability in severe COVID-19, the use of apixaban, enoxaparin, or aspirin was independently associated with lower mortality in critically ill COVID-19 patients.

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)0.001
metaresearch head score (Gemma)0.150
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 score0.149
Threshold uncertainty score0.857

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.150
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.040
GPT teacher head0.401
Teacher spread0.361 · 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".

Quick stats

Citations8
Published2021
Admission routes1
Has abstractyes

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