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Record W2588513806 · doi:10.1182/blood.v104.11.528.528

Risk Factors for Bleeding in Patients with Nonvalvular Atrial Fibrillation Who Are Receiving Anticoagulant Therapy with Ximelagatran or Warfarin: Findings from the SPORTIF III and V Trials.

2004· article· en· W2588513806 on OpenAlexaff
James D. Douketis, Karin Arneklev, Samuel Z. Goldhaber, John Spandorfer, Frank Halperin, Jay Horrow

Bibliographic record

VenueBlood · 2004
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsKelowna General HospitalMcMaster University
Fundersnot available
KeywordsXimelagatranMedicineWarfarinAtrial fibrillationStroke (engine)Internal medicineCardiologyHazard ratioAnticoagulantDirect thrombin inhibitorConfidence intervalDabigatran

Abstract

fetched live from OpenAlex

Abstract Background: Ximelagatran is a novel oral direct thrombin inhibitor that is as effective as warfarin in preventing stroke and other thromboembolic complications in patients with nonvalvular atrial fibrillation (AF). Risk factors for bleeding with warfarin are known, but risk factors for bleeding with ximelagatran have not been described. Unlike warfarin, ximelagatran has a predictable anticoagulant effect, does not require anticoagulation monitoring, has a low potential for interactions with drugs, food, or alcohol, and is not affected by genetic polymorphisms. We undertook an exploratory analysis of a large patient database to identify conventional and novel risk factors for bleeding in ximelagatran-treated patients, in warfarin-treated patients, and in all patients, irrespective of treatment. Methods: We undertook a pooled analysis of the SPORTIF III and V trials trials, which included 7329 patients with nonvalvular AF who received oral ximelagatran, 36 mg twice daily, or warfarin, administered to achieve a target international normalized ratio of 2.0–3.0. Patients had nonvalvular AF and 1 or more risk factors for stroke: hypertension; age ≥75 years; previous stroke, transient ischemic attack (TIA) or systemic embolism; left ventricular dysfunction; age ≥65 years and coronary artery disease; or age ≥65 years and diabetes mellitus. Major exclusion criteria were: mitral stenosis; previous heart valve surgery; transient AF; increased risk for bleeding. Multivariate logistic regression analysis was used to identify independent risk factors for major bleeding. The hazard ratio (HR) for major bleeding, and corresponding 95% confidence interval (CI), was calculated for each variable in the regression model. Results: The Table presents risk factors in which there was a significant or a non-significant (NS) association with major bleeding in ximelagatran-treated or warfarin-treated patients, and in the combined patient population. Risk factor Ximelagatran-treated patients, HR (95% CI) Warfarin-treated patients, HR (95% CI) Combined patient population, HR (95% CI) Aspirin use 1.65 (1.07, 2.55) 2.40 (1.69, 3.42) 1.96 (1.49, 2.58) Increasing age 1.03 (1.01, 1.05) 1.06 (1.03, 1.08) 1.04 (1.03, 1.06) Prior liver disease NS 4.96 (1.57, 15.62) NS Prior stroke or TIA 1.78 (1.16, 2.73) NS NS Diabetes mellitus 1.80 (1.18, 2.75) NS 1.39 (1.05, 1.86) Asian race NS NS 1.99 (1.16, 3.42) Statin use 0.62 (0.39, 0.97) 0.61 (0.42, 0.88) 0.62 (0.39, 0.97) Conclusions: Overall, the bleeding risk was lower with ximelagatran compared with warfarin. Aspirin use and increasing age were associated with an increased risk of bleeding in both ximelagatran- and warfarin-treated patients. Statin use was associated with a decreased risk for bleeding in both groups.

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)0.007
metaresearch head score (Gemma)0.010
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 score0.007
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.007
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0020.001
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 head0.053
GPT teacher head0.291
Teacher spread0.238 · 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

Citations1
Published2004
Admission routes1
Has abstractyes

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