Rates of hemorrhage during warfarin therapy for atrial fibrillation
Bibliographic record
Abstract
BACKGROUND: Although warfarin has been extensively studied in clinical trials, little is known about rates of hemorrhage attributable to its use in routine clinical practice. Our objective was to examine incident hemorrhagic events in a large population-based cohort of patients with atrial fibrillation who were starting treatment with warfarin. METHODS: We conducted a population-based cohort study involving residents of Ontario (age ≥ 66 yr) with atrial fibrillation who started taking warfarin between Apr. 1, 1997, and Mar. 31, 2008. We defined a major hemorrhage as any visit to hospital for hemorrage. We determined crude rates of hemorrhage during warfarin treatment, overall and stratified by CHADS(2) score (congestive heart failure, hypertension, age ≥ 75 yr, diabetes mellitus and prior stroke, transient ischemic attack or thromboembolism). RESULTS: We included 125 195 patients with atrial fibrillation who started treatment with warfarin during the study period. Overall, the rate of hemorrhage was 3.8% (95% confidence interval [CI] 3.8%-3.9%) per person-year. The risk of major hemorrhage was highest during the first 30 days of treatment. During this period, rates of hemorrhage were 11.8% (95% CI 11.1%-12.5%) per person-year in all patients and 16.7% (95% CI 14.3%-19.4%) per person-year among patients with a CHADS(2) scores of 4 or greater. Over the 5-year follow-up, 10 840 patients (8.7%) visited the hospital for hemorrhage; of these patients, 1963 (18.1%) died in hospital or within 7 days of being discharged. INTERPRETATION: In this large cohort of older patients with atrial fibrillation, we found that rates of hemorrhage are highest within the first 30 days of warfarin therapy. These rates are considerably higher than the rates of 1%-3% reported in randomized controlled trials of warfarin therapy. Our study provides timely estimates of warfarin-related adverse events that may be useful to clinicians, patients and policy-makers as new options for treatment become available.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".