Comparative effectiveness of angiotensin-receptor blockers for preventing macrovascular disease in patients with diabetes: a population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Telmisartan, unlike other angiotensin-receptor blockers, is a partial agonist of peroxisome proliferator-activated receptor-γ, a property that has been associated with improvements in surrogate markers of cardiovascular health in small trials involving patients with diabetes. However, whether this property translates into a reduced risk of cardiovascular events and death in these patients is unknown. We sought to explore the risk of myocardial infarction, stroke and heart failure in patients with diabetes who were taking telmisartan relative to the risk of these events occurring in patients taking other angiotensin-receptor blockers. METHODS: We conducted a population-based, retrospective cohort study of Ontario residents with diabetes aged 66 years and older who started treatment with candesartan, irbesartan, losartan, telmisartan or valsartan between Apr. 1, 2001, and Mar. 31, 2011. Our primary outcome was a composite of admission to hospital for acute myocardial infarction, stroke or heart failure. We examined each outcome individually in secondary analyses, in addition to all-cause mortality. RESULTS: We identified 54,186 patients with diabetes who started taking an angiotensin-receptor blocker during the study period. After multivariable adjustment, patients who took either telmisartan (adjusted hazard ratio [HR] 0.85, 95% confidence interval [CI] 0.74-0.97) or valsartan (adjusted HR 0.86, 95% CI 0.77-0.95) had a lower risk of the composite outcome compared with patients who took irbesartan. In contrast, no significant difference in risk was seen between other angiotensin-receptor blockers and irbesartan. In secondary analyses, we found a reduced risk of admission to hospital for heart failure with telmisartan compared with irbesartan (adjusted HR 0.79, 95% CI 0.66-0.96), but no significant differences in risk were seen between angiotensin-receptor blockers in our other secondary analyses. INTERPRETATION: Compared with other angiotensin-receptor blockers, telmisartan and valsartan were both associated with a lower risk of admission to hospital for acute myocardial infarction, stroke or heart failure among older adults with diabetes and hypertension. Telmisartan and valsartan may therefore be the preferred angiotensin-receptor blockers for use in these 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".