Rosiglitazone and Myocardial Infarction in Patients Previously Prescribed Metformin
Bibliographic record
Abstract
OBJECTIVE: Rosiglitazone was found associated with approximately a 43% increase in risk of acute myocardial infarction (AMI) in a two meta-analyses of clinical trials. Our objective is to estimate the magnitude of the association in real-world patients previously treated with metformin. RESEARCH DESIGN AND METHODS: We conducted a nested case control study in British Columbia using health care databases on 4.3 million people. Our cohort consisted of 158,578 patients with Type 2 diabetes who used metformin as first-line drug treatment. We matched 2,244 cases of myocardial infarction (AMI) with up to 4 controls. Conditional logistic regression models were used to estimate matched odds ratios for AMI associated with treatment with rosiglitazone, pioglitazone and sulfonylureas. RESULTS: In our cohort of prior metformin users, adding rosiglitazone for up to 6 months was not associated with an increased risk of AMI compared to adding a sulfonylurea (odds ratio [OR] 1.38; 95% confidence interval [CI], 0.91-2.10), or compared to adding pioglitazone (OR for rosi versus pio 1.41; 95% CI, 0.74-2.66). There were also no significant differences between rosiglitazone, pioglitazone and sulfonylureas for longer durations of treatment. Though not significantly different from sulfonylureas, there was a transient increase in AMI risk associated with the first 6 months of treatment with a glitazone compared to not using the treatment (OR 1.53; 95% CI, 1.13-2.07) CONCLUSIONS: In our British Columbia cohort of patients who received metformin as first-line pharmacotherapy for Type 2 diabetes mellitus, further treatment with rosiglitazone did not increase the risk of AMI compared to patients who were treated with pioglitazone or a sulfonylurea. Though not statistically significantly different compared from each other, an increased risk of AMI observed after starting rosiglitazone or sulfonylureas is a matter of concern that requires more research.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".