A net clinical benefit analysis of warfarin and aspirin on stroke in patients with atrial fibrillation: a nested case–control study
Bibliographic record
Abstract
BACKGROUND: As the management of patients treated with anticoagulants and antiplatelet drugs entails balancing coagulation levels, we evaluated the net clinical benefit of warfarin and aspirin on stroke in a large cohort of patients with atrial fibrillation (AF). METHODS: A population-based cohort study of all patients at least 18 years of age with a first-ever diagnosis of chronic AF during the period 1993-2008 was conducted within the United Kingdom General Practice Research Database. A nested case-control analysis was conducted to estimate the risk of ischemic stroke and intracranial hemorrhage associated with the use of warfarin and aspirin. Cases were matched up to 10 controls on age, sex, and date of cohort entry. The adjusted net clinical benefit of warfarin and aspirin (expressed as the number of strokes prevented per 100 persons per year) was calculated by subtracting the ischemic stroke rate (prevented by therapy) from the intracranial hemorrhage (ICH) rate (increased by therapy). RESULTS: The cohort included 70,766 patients newly-diagnosed with chronic AF, of whom 5519 experienced an ischemic stroke and 689 an ICH during follow-up. The adjusted net clinical benefit of warfarin was 0.59 (95% CI: 0.45, 0.73). However, the benefit was not seen for patients below (0.08, 95%: -0.38, 0.54) and above (-0.49, 95% CI: -1.13, 0.15) therapeutic range. The net clinical benefit of warfarin, apparent after 3 months of continuous use, increased as a function of CHADS2 score. The net clinical benefit was not significant with aspirin (-0.07, 95% CI: -0.22, 0.08), though it was seen in certain subgroups. CONCLUSIONS: Warfarin provides a net clinical benefit in patients with atrial fibrillation, which is maintained with longer duration of use, particularly when used within therapeutic range. A similar net effect is not as clear with aspirin.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".