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Record W4206616958 · doi:10.3389/fphar.2021.812018

Comparative Effectiveness and Safety of Low-Dose Oral Anticoagulants in Patients With Atrial Fibrillation

2022· article· en· W4206616958 on OpenAlexafffundabout
Sylvie Perreault, Alice Dragomir, Robert Côté, Aurélie Lenglet, Simon de Denus, Marc Dorais, Brian White‐Guay, James M. Brophy, Mireille E. Schnitzer, Marie‐Pierre Dubé, Jean‐Claude Tardif

Bibliographic record

VenueFrontiers in Pharmacology · 2022
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsMontreal Heart InstituteMcGill UniversityUniversité de Montréal
FundersRéseau Québécois de Recherche sur les MédicamentsHeart and Stroke Foundation of Canada
KeywordsAtrial fibrillationMedicinePharmacologyCardiologyIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

Aims: Observational studies of various dose levels of direct oral anticoagulants (DOACs) for patients with atrial fibrillation (AF) found that a high proportion of patients received a dose lower than the target dose tested in randomized controlled trials. There is a need to compare low-dose DOACs with warfarin or other DOACs on effectiveness and safety. Methods: Using administrative data from Quebec province, Canada, we built a cohort of new warfarin or DOAC users discharged from hospital between 2011 and 2017. We determined CHA2DS2-VASc and HAS-BLED scores, and comorbidities for 3-year prior cohort entry. The primary effectiveness endpoint was a composite of ischemic stroke/systemic embolism (SE), and secondary outcomes included a safety composite of major bleeding (MB) events and effectiveness composite (stroke/SE, death) at 1-year follow-up. We contrasted each low-dose DOAC with warfarin or other DOACs as references using inverse probability of treatment weighting to estimate marginal Cox hazard ratios (HRs). Results: The cohort comprised 22,969 patients (mean age: 80–86). We did not find a significant risk reduction for the stroke/SE primary effectiveness endpoint for DOACs vs. warfarin; however, we observed a significantly lower risk for low-dose dabigatran vs. warfarin (HR [95%CI]: 0.59 [0.42–0.81]) for effectiveness composite, mainly due to a lower death rate. The differences in effectiveness and safety composites between low-dose rivaroxaban vs. warfarin were not significant. However, low-dose apixaban had a better safety composite (HR: 0.68 [0.53–0.88]) vs. warfarin. Comparisons of dabigatran vs. apixaban showed a lower risk of stroke/SE (HR: 0.53 [0.30–0.93]) and a 2-fold higher risk of MB. The MB risk was higher for rivaroxaban than for apixaban (HR: 1.58 [1.09–2.29]). Conclusions: The results of this population-based study suggest that low-dose dabigatran has a better effective composite than warfarin. Compared with apixaban, low-dose dabigatran had a better effectiveness composite but a worse safety profile. Low-dose apixaban had a better safety composite than warfarin and other low-dose DOACs. Given that the comparative effectiveness and safety seem to vary from one DOAC to another, pharmacokinetic data for specific populations are now warranted.

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)0.015
metaresearch head score (Gemma)0.034
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 score0.015
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.034
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.025
GPT teacher head0.332
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

Citations7
Published2022
Admission routes3
Has abstractyes

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