The Association Between Bleeding and the Incidence of Warfarin Discontinuation in Patients with Atrial Fibrillation
Bibliographic record
Abstract
AIM: While bleeding is a well-known complication of warfarin use and is thought to be a contributory cause of treatment discontinuation, studies quantifying this association are limited. The objective of this study was to quantify the association between bleeding events and subsequent warfarin discontinuation in patients with nonvalvular atrial fibrillation (NVAF). METHODS: A nested case-control analysis was conducted within a cohort of patients with NVAF newly treated with warfarin. All patients who discontinued warfarin (at least 60 days from last day of warfarin supply) during follow-up were identified as cases and matched with up to 10 controls on age, sex, and duration of follow-up. The index date was defined as the date of warfarin treatment discontinuation of the cases. Conditional logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) of warfarin treatment discontinuation associated with a bleeding event in the 60 days before the index date. RESULTS: The cohort included 24,243 patients who initiated warfarin treatment, of whom 13,482 discontinued treatment during follow-up (cases). Bleeding was associated with an increased risk of warfarin treatment discontinuation (3.55% vs. 0.85%; OR, 4.31; 95% CI, 3.87-4.81). When including only bleeds as the first listed diagnosis, the unadjusted OR was 4.64 (95% CI, 4.10-5.26), and the adjusted OR was 4.65 (95% CI, 4.10-5.27). CONCLUSIONS: Bleeding was significantly associated with warfarin discontinuation, and thus, the selection of an effective treatment regimen associated with a lower bleeding rate could be a desirable treatment approach.
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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 | 0.002 | 0.009 |
| 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 | 0.001 | 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".