Acute myocardial infarction with oral nonsteroidal anti-inflammatory drugs in real-world use
Bibliographic record
Abstract
Background: It is generally accepted that oral nonsteroidal anti-inflammatory drugs (NSAIDs) can increase the risk of acute myocardial infarction (MI). However, the attributes of exposure that explain any excess risk have been incompletely investigated.Objective: For each studied NSAID, the objectives were to determine the relationship between acute MI and daily dose and to precisely characterize the time course of any increased risk – in terms of onset and possible cumulative effect – as well as to assess clinical heterogeneity due to the presence of major cardiovascular risk profiles. Methods: These questions were investigated as follows: 1) A one-stage Bayesian individual patient data meta-analysis (IPD MA) was carried out using internally-valid healthcare database studies reporting MI risks with common traditional NSAIDs, celecoxib, and rofecoxib. This study characterized the acute MI risk of each NSAID according to recency of use, duration, and dose by modelling drug exposure as a multidimensional indicator variable. Adjusted median pooled odds ratios (ORs) of acute MI were calculated for each 'recency-dose-duration' category of NSAID use. The primary endpoint of the IPD MA was the posterior probability that pooled ORs of MI exceeded pre-specified risk thresholds. 2) A cohort of elderly patients from the computerized public insurance databases of Quebec, Canada (RAMQ) was created to further examine the NSAID dose-MI risk relationship and its time course. The two analytical methods employed were a standard nested case control analysis and recency-weighted cumulative exposure (WCE) analyses. WCE analyses allowed further characterizing the onset of risk and the existence of a temporal relationship between current MI risk and past NSAID exposure. 3) The clinical heterogeneity of MI associated with current NSAID use was also assessed in the RAMQ study. The joint effects of each NSAID and each of the following CV risk profiles: diabetes, hypertension, coronary heart disease, previous MI, and concurrent use of cardioprotective aspirin were evaluated on the additive scale by calculating the relative excess risk due to interaction (RERI). Results: In this doctoral research, 1) A total of 61 460 cases of acute MI in 446 763 individuals from four database studies (Provinces of Quebec and of Saskatchewan [Canada], Finland, and the UK) were meta-analyzed, of which 21 256 cases among 233 816 individuals originated from Quebec (RAMQ). In this IPD MA, compared with non-use of any NSAIDs in the year preceding the index date, risks of acute MI were increased with all current NSAID use. Onset of risk occurred within the first week of exposure. Short-term use of high daily doses (celecoxib > 200 mg, diclofenac > 100 mg, ibuprofen > 1200 mg, and naproxen > 750 mg) were found to be associated with the greatest harms, without obvious further increases in MI risk beyond the first month of treatment. With use for 1 month or less, there is at least an 80% probability of MI risk increase by 5-10% for celecoxib, by 5-30% for diclofenac, by 25-35% for ibuprofen, by 30-45% for naproxen, and by 35-100% for rofecoxib.2)The RAMQ study confirmed a dose-related increased risk of MI with all NSAIDs. Hazard ratios (95%CI) for current dose versus non-use in the preceding year, for the most common daily doses were: celecoxib 200 mg: 1.16 (1.10, 1.22), diclofenac 150 mg: 1.59 (1.38, 1.84), ibuprofen 1200 mg: 1.42 (1.17, 1.74), naproxen 750 mg: 1.38 (1.21, 1.58), and rofecoxib 25 mg: 1.54 (1.43, 1.66). Rofecoxib exhibited the strongest dose-response relationship, 2.38 (2.05, 2.76) with 50 mg, whereas dose-MI risk responses were more modest for the other NSAIDs. WCE models suggest that the risk increases immediately with exposure to NSAIDs but that doses taken in the past 2 weeks (rofecoxib), up to 1 month ago (ibuprofen and naproxen) and up to 2 months ago (diclofenac and celecoxib) may also contribute to the current risk of acute MI.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| 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".