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Direct Oral Anticoagulants Versus Warfarin in Patients With Atrial Fibrillation: Patient-Level Network Meta-Analyses of Randomized Clinical Trials With Interaction Testing by Age and Sex

2022· article· en· W4205709732 on OpenAlexaff

Bibliographic record

VenueCirculation · 2022
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsMcMaster UniversityPopulation Health Research Institute
FundersNational Heart, Lung, and Blood Institute
KeywordsWarfarinRandomized controlled trialAtrial fibrillationClinical trialMajor bleedingHeart failureAnticoagulant

Abstract

fetched live from OpenAlex

Background: Direct oral anticoagulants (DOACs) are preferred over warfarin for stroke prevention in atrial fibrillation. Meta-analyses using individual patient data offer substantial advantages over study-level data. Methods: We used individual patient data from the COMBINE AF (A Collaboration Between Multiple Institutions to Better Investigate Non-Vitamin K Antagonist Oral Anticoagulant Use in Atrial Fibrillation) database, which includes all patients randomized in the 4 pivotal trials of DOACs versus warfarin in atrial fibrillation (RE-LY [Randomized Evaluation of Long-Term Anticoagulation Therapy], ROCKET AF [Rivaroxaban Once Daily Oral Direct Factor Xa Inhibition Compared With Vitamin K Antagonism for Prevention of Stroke and Embolism Trial in Atrial Fibrillation], ARISTOTLE [Apixaban for Reduction in Stroke and Other Thromboembolic Events in Atrial Fibrillation], and ENGAGE AF-TIMI 48 [Effective Anticoagulation With Factor Xa Next Generation in Atrial Fibrillation–Thrombolysis in Myocardial Infarction 48]), to perform network meta-analyses using a stratified Cox model with random effects comparing standard-dose DOAC, lower-dose DOAC, and warfarin. Hazard ratios (HRs [95% CIs]) were calculated for efficacy and safety outcomes. Covariate-by-treatment interaction was estimated for categorical covariates and for age as a continuous covariate, stratified by sex. Results: A total of 71 683 patients were included (29 362 on standard-dose DOAC, 13 049 on lower-dose DOAC, and 29 272 on warfarin). Compared with warfarin, standard-dose DOACs were associated with a significantly lower hazard of stroke or systemic embolism (883/29 312 [3.01%] versus 1080/29 229 [3.69%]; HR, 0.81 [95% CI, 0.74–0.89]), death (2276/29 312 [7.76%] versus 2460/29 229 [8.42%]; HR, 0.92 [95% CI, 0.87–0.97]), and intracranial bleeding (184/29 270 [0.63%] versus 409/29 187 [1.40%]; HR, 0.45 [95% CI, 0.37–0.56]), but no statistically different hazard of major bleeding (1479/29 270 [5.05%] versus 1733/29 187 [5.94%]; HR, 0.86 [95% CI, 0.74–1.01]), whereas lower-dose DOACs were associated with no statistically different hazard of stroke or systemic embolism (531/13 049 [3.96%] versus 1080/29 229 [3.69%]; HR, 1.06 [95% CI, 0.95–1.19]) but a lower hazard of intracranial bleeding (55/12 985 [0.42%] versus 409/29 187 [1.40%]; HR, 0.28 [95% CI, 0.21–0.37]), death (1082/13 049 [8.29%] versus 2460/29 229 [8.42%]; HR, 0.90 [95% CI, 0.83–0.97]), and major bleeding (564/12 985 [4.34%] versus 1733/29 187 [5.94%]; HR, 0.63 [95% CI, 0.45–0.88]). Treatment effects for standard- and lower-dose DOACs versus warfarin were consistent across age and sex for stroke or systemic embolism and death, whereas standard-dose DOACs were favored in patients with no history of vitamin K antagonist use ( P =0.01) and lower creatinine clearance ( P =0.09). For major bleeding, standard-dose DOACs were favored in patients with lower body weight ( P =0.02). In the continuous covariate analysis, younger patients derived greater benefits from standard-dose (interaction P =0.02) and lower-dose DOACs (interaction P =0.01) versus warfarin. Conclusions: Compared with warfarin, DOACs have more favorable efficacy and safety profiles among patients with atrial fibrillation.

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)0.032
metaresearch head score (Gemma)0.058
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.032
Threshold uncertainty score0.167

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0320.058
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0110.047
Bibliometrics0.0040.005
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.443
GPT teacher head0.451
Teacher spread0.007 · 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 designMeta-analysis
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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Citations369
Published2022
Admission routes1
Has abstractyes

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