Low-dose rivaroxaban plus aspirin in patients with polypharmacy and multimorbidity: an analysis from the COMPASS trial
Bibliographic record
Abstract
AIMS: To analyse whether the benefits and risks of rivaroxaban plus aspirin vary in patients with comorbidities and receiving multiple drugs. In patients with coronary or peripheral artery disease, adding low-dose rivaroxaban to aspirin reduces cardiovascular events and mortality. Polypharmacy and multimorbidity are frequent in such patients. METHODS AND RESULTS: We describe ischaemic events (cardiovascular death, stroke, or myocardial infarction) and major bleeding in participants from the randomized, double-blind COMPASS study by number of cardiovascular medications and concomitant medical conditions. We compared event rates and hazard ratios (HRs) for rivaroxaban plus aspirin vs. aspirin alone by the number of medications and concomitant conditions, and tested for interaction between polypharmacy or multimorbidity and the antithrombotic regimen. The risk of ischaemic events was higher in patients with more concomitant drugs (HR 1.7, 95% confidence interval 1.5-2.1 for >4 vs. 0-2) and with more comorbidities (HR 2.3, 1.8-2.1 for >3 vs. 0-1). Multimorbidity, but not polypharmacy, was associated with a higher risk of major bleeding. The relative efficacy, safety, and net clinical benefit of rivaroxaban were not affected by the number of drugs or comorbidities. Patients taking more concomitant medications derived the largest absolute reduction in the net clinical outcome with added rivaroxaban (1.1% vs. 0.4% reduction with >4 vs. 0-2 cardiovascular drugs, number needed to treat 91 vs. 250). CONCLUSION: Adding low-dose rivaroxaban to aspirin resulted in benefits irrespective of the number of concomitant drugs or comorbidities. Multiple comorbidities and/or polypharmacy should not dissuade the addition of rivaroxaban to aspirin in otherwise eligible patients.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".