Incidence and predictors of major gastrointestinal bleeding in patients on aspirin, low‐dose rivaroxaban, or the combination: Secondary analysis of the <scp>COMPASS</scp> randomised controlled trial
Bibliographic record
Abstract
BACKGROUND: The incidence of major gastrointestinal bleeding (GIB) in patients on low-dose direct-acting oral anticoagulants (DOACs) is relatively unknown. Estimates from randomised controlled trials (RCTs) are lacking. AIMS: To assess GIB incidence and predictors from RCT data of patients on aspirin, low-dose rivaroxaban, or both. METHODS: This was a secondary analysis of RCT data wherein patients received aspirin 100 mg daily and rivaroxaban 2.5 mg b.d., aspirin alone, or rivaroxaban 5 mg b.d. Patients were followed from 2013 to 2016 at 602 centres. Outcomes included overall, upper, and lower GIB. We employed multivariable logistic regression to yield odds ratios (ORs) and 95% confidence intervals for potential exposures. RESULTS: Among 27,395 patients, the annual incidence of GIB on rivaroxaban 2.5 mg b.d. with aspirin was 801.7 per 100,000 compared with 372.3 in 100,000 for aspirin. Age (OR 4.16, 2.53-6.82 for ≥75 vs. 55-64), peptic ulcer disease (PUD, OR 1.57, 1.01-2.44), liver disease (OR 2.09, 1.01-4.33), hypertension (OR 1.42, 1.04-1.94), and smoking (OR 1.85, 1.26-2.73) were associated with overall GIB. Kidney disease (OR 1.68, 1.12-2.51) was significantly associated with upper GIB, whereas diverticular disease (OR 3.75, 1.88-7.49) was associated with lower GIB. Addition of rivaroxaban to aspirin was associated more with lower GIB (OR 2.82, 1.64-4.84) than upper GIB (OR 1.86, 1.18-2.92). CONCLUSIONS: We established incidences and identified risk factors for GIB in users of low-dose DOACs. Novel risk factors included current or former smoking and diverticulosis. Future studies should aim to validate these risk factors.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".