Major bleeding risk associated with warfarin and co‐medications in the elderly population
Bibliographic record
Abstract
PURPOSE: Warfarin management in the elderly population is complex as medicines prescribed for concomitant diseases may further increase the risk of major bleeding associated with warfarin use. We aimed to quantify the excess risk of bleeding-related hospitalisation when warfarin was co-dispensed with potentially interacting medicines. METHODS: A retrospective cohort study was undertaken over a 4-year period from July 2002 to June 2006 to examine bleeding risk associated with medications co-administered in patients taking warfarin using an administrative claims database from the Australian Department of Veterans' Affairs. All veterans aged 65 years and over who were new users of warfarin were followed until death or study end. Risk of bleeding was assessed using a Poisson GEE model adjusting for age, gender, socioeconomic status, co-morbidity index, previous bleeding related hospitalisations and indicators of health service use. RESULTS: Overall, 17661 veterans who used warfarin at any time during the study period were included. The overall incidence rate of bleeding-related hospitalisations was 4.1 (95% CI 3.7-4.6) per 100 person-years in veterans who were not receiving potentially interacting medicines. Bleeding-related hospitalisation rates were significantly increased when warfarin was co-prescribed with low-dose aspirin (Adjusted rate ratio (AdjRR) 1.44, 95% CI 1.00-2.07), clopidogrel (AdjRR 2.23, 95% CI 1.48–3.36), clopidogrel with aspirin (AdjRR 3.44, 95% CI 1.28-9.23), amiodarone (AdjRR 3.33, 95% CI 1.38–8.00) and antibiotics (AdjRR 2.34, 95% CI 1.55-3.54). CONCLUSIONS: Models assessing bleeding risk with warfarin should take account of the range of potentially harmful medicine combinations used in elderly people with comorbid conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".