Comparative effectiveness of ACE inhibitors and angiotensin receptor blockers in patients with prior myocardial infarction
Bibliographic record
Abstract
Objective: Although ACE inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) are commonly prescribed for patients with coronary artery disease, whether these medications are similarly effective is still a subject of intense debate. Our objective was to compare the clinical effectiveness of ACEIs and ARBs in patients with prior myocardial infarction (MI). Methods: All residents older than 65 years, alive on 1 April 2012, with a prior MI were included. Propensity weighting was used to balance potentially confounding baseline covariates between the treatment groups. The primary outcome was a composite of cardiovascular death, hospitalisation for MI or unstable angina at 3 years. Results: Our cohort included 59 353 patients with MI; their mean age was 77 years and 40% were women. In the propensity-weighted cohort, the primary outcome occurred in 6.5% in the ACEI group and 5.7% in the ARB group at 1 year (HR comparing ACEI with ARB 1.14, 95% CI 1.05 to 1.23, p<0.001). At 3 years, the primary outcome occurring in 16.0% with ACEIs and 15.1% with ARBs (HR 1.07; 95% CI 1.02 to 1.12; p<0.001). A significant interaction with sex was observed, with women prescribed ACEIs having a higher hazards (HR 1.17; 95% CI 1.10 to 1.26) compared with ARBs, while no significant difference was seen among men (HR 1.00; 95% CI 0.93 to 1.06, interaction p<0.001). Conclusions: Despite previous concerns regarding ARBs, we found that they had slightly lower rates of adverse clinical cardiovascular outcomes among older patients with MI compared with ACEIs. The observed difference in clinical outcomes may be related to a sex difference in effectiveness.
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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 | 0.004 | 0.012 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".