Bleeding Complications Associated With Combinations of Aspirin, Thienopyridine Derivatives, and Warfarin in Elderly Patients Following Acute Myocardial Infarction
Bibliographic record
Abstract
BACKGROUND: Combinations of aspirin with thienopyridine derivatives (clopidogrel bisulfate or ticlopidine hydrochloride) and/or warfarin sodium are increasingly being used in various cardiac conditions. However, little is known about the bleeding risks associated with these combinations, particularly in elderly individuals at the population level. This study estimates the bleeding risks associated with combinations of aspirin, thienopyridine derivatives, and warfarin in elderly patients. METHODS: We conducted a population-based observational cohort study using linked administrative databases. A total of 21,443 elderly survivors of acute myocardial infarction between 1996 and 2000 were studied. Patients were divided into 5 groups according to drug exposure: aspirin alone, warfarin alone, aspirin plus a thienopyridine derivative (antiplatelet combination), aspirin plus warfarin (anticoagulant combination), and aspirin plus warfarin plus a thienopyridine derivative (3-drug combination). Hospitalizations for bleeding events were examined. RESULTS: Hospitalizations for bleeding were observed in 1428 patients (7%). Compared with rates of patients receiving aspirin alone (0.03 per patient-year), rates of bleeding were higher among patients receiving the antiplatelet combination (0.07 per patient-year), the anticoagulant combination (0.08 per patient-year), and the 3-drug combination (0.09 per patient-year). Compared with aspirin alone, the adjusted odds ratios (95% confidence intervals) for bleeding were 1.65 (1.02-2.73) for patients receiving the antiplatelet combination and 1.92 (1.28-2.87) for patients receiving the anticoagulant combination. Only 1 of 141 patients in the 3-drug combination group had a bleeding event. CONCLUSION: In practice, antiplatelet and anticoagulant combinations lead to modest increases in bleeding risk in elderly patients, but the overall risk is small.
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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 | 0.000 | 0.000 |
| 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".