Primary Prevention of Myocardial Infarction in Rheumatoid Arthritis Using Aspirin: A Case-crossover Study and a Propensity Score–matched Cohort Study
Bibliographic record
Abstract
OBJECTIVE: Subjects with rheumatoid arthritis (RA) are at higher risk of developing cardiovascular disease, which is their leading cause of death. Conflicting evidence exists regarding the efficacy of aspirin (ASA) as primary prevention. We evaluated whether a protective association exists between ASA and myocardial infarction (MI) in RA subjects. METHODS: In the United Kingdom, persons age ≥ 60 years receive free ASA by prescription and 75% of use is by prescription. Subjects ≥ 60 years with RA in the population-based The Health Improvement Network database constituted our study population. We excluded patients with history of MI, angina, stroke, peripheral vascular disease, or coronary artery procedures. Our main outcome was the occurrence of fatal and nonfatal MI. We performed a case-crossover study with each subject contributing a hazard period and a control period 90 days prior to the MI. In addition, to minimize confounding by indication, a propensity score (PS)-matched cohort study was performed, considering all patients with RA with an incident prescription of low-dose ASA as our exposed group. RESULTS: We did not find a protective effect in the case-crossover study (OR 1.83, 95% CI 0.71-4.71), with 55 subjects exposed in the hazard period and 44 in the control period. Similarly, among 1836 subjects included in the PS-matched cohort study (918 ASA users and 918 ASA non-users), we did not find a protective effect of low ASA on MI (HR 1.39, 95% CI 0.87-2.23). CONCLUSION: We did not find a protective effect of ASA on MI in patients with RA when used as primary prophylaxis.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".