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Bleeding in Patients with Atrial Fibrillation Treated with Different Doses of Direct Oral Anticoagulants and Vitamin K Antagonists: A Population-Based Study

2016· article· en· W2589247971 on OpenAlexaff
Martin Ellis, Orly Avnery, Estela Derazne, Erez Battat, Sari Greenberg Dotan, Ariel Hammerman, John W. Eikelboom, Jeffrey S. Ginsberg, Jack Hirsh

Bibliographic record

VenueBlood · 2016
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineDabigatranRivaroxabanApixabanAtrial fibrillationGastrointestinal bleedingPopulationStroke (engine)EdoxabanInternal medicineWarfarinHazard ratioIncidence (geometry)Randomized controlled trialConfidence interval

Abstract

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Abstract INTRODUCTION Recent large randomized controlled trials have shown that direct oral anticoagulants (DOACs) are at least as effective as vitamin K antagonists (VKAs) for prevention of stroke or systemic embolism in patients with non-valvular atrial fibrillation (AF) and are associated with similar or lower rates of bleeding. The results for bleeding seen in Phase 3 trials might not be applicable to real world practice. Population-based studies suggest that the bleeding risk for DOACs and VKA is similar however neither the risk of bleeding associated with different doses of DOACs nor that associated with apixaban in routine clinical practice is well established. We performed a large population-based study to determine the incidence of bleeding in patients with AF beginning treatment with different doses of dabigatran, rivaroxaban, apixaban or a VKA. METHODS From the computerized database of the 4.5 million member Israeli Clalit Health Services health care provider, consecutive patients initiating a VKA or DOAC for AF between January 1, 2011 and December 31, 2014 were studied. Bleeding patients who required hospitalization were identified and key clinical and laboratory data were recorded. Incidence of bleeding was calculated during the first 20 months of treatment which was the minimum duration of treatment for all of the drugs. Adjusted hazard ratios for overall bleeding, intracranial hemorrhage (ICH) and gastrointestinal (GI) bleeding and all-cause mortality within 30 days were recorded and case fatality rates were calculated as the proportions of bleeding patients who died within 30 days. RESULTS 26184 patients initiated anticoagulants for AF: 14258 received VKA, 214 -received dabigatran 75 mg, 3563 received dabigatran 110 mg , 1410 received dabigatran 150 mg, 2570 received rivaroxaban 15 mg, 2140 received rivaroxaban 20 mg, 1227 received apixaban 2.5 mg and 802 received apixaban 5 mg. Key patient demographics and the overall and site-specific bleeding rates are shown in table 1. Hazard ratios for any bleeding, ICH and GI bleeding adjusted for age, renal failure, CHADS2 score, aspirin use and Charlson comorbidity score favored dabigatran 150 mg versus VKA (P<0.05). The case fatality rate for VKA bleeding was 11,4%, dabigatran 110mg-10.5%, dabigatran 150 mg- 6.25%. rivaroxaban 15mg- 15.5%, rivaroxaban 20 mg- 10%, apixaban 2.5 mg- 11.4% and for apixaban 5mg-7.14% CONCLUSIONS The results of our population-based non-randomized study of unselected AF patients demonstrate a decreased bleeding rate for dabigatran 150mg and an increased bleeding rate for apixaban 2.5 mg compared to VKA. There was a consistent tendency for increased bleeding in patients receiving lower versus higher doses of the NOACs which probably reflects physician tendency to select lower doses of DOACs for patients at greater risk for bleeding. Disclosures Ellis: Boehringer Ingelheim: Speakers Bureau; Bayer: Speakers Bureau; Pfizer: Speakers Bureau. Eikelboom:Pfizer: Honoraria, Research Funding; Eli Lilly: Honoraria, Research Funding; Boehringer Ingelheim: Honoraria, Research Funding; Daiichi-Sankyo: Honoraria, Research Funding; Bayer: Honoraria, Research Funding; Astra Zeneca: Honoraria, Research Funding; Bristol Myer Squibb: Honoraria, Research Funding; Sanofi-Aventis: Honoraria, Research Funding; Janssen: Honoraria, Research Funding.

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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)0.001
metaresearch head score (Gemma)0.003
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
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
Science and technology studies0.0010.000
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 head0.022
GPT teacher head0.274
Teacher spread0.252 · 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".

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Citations2
Published2016
Admission routes1
Has abstractyes

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