Effect of Angiotensin‐Converting Enzyme Inhibitors and Angiotensin Receptor Blockers After Coronary Artery Bypass Graft Surgery: A Population‐Based Cohort Study
Bibliographic record
Abstract
Background The effect of angiotensin‐converting enzyme inhibitors/angiotensin receptor blockers (ACEI/ARBs) on major adverse cardiovascular events (MACE) in patients who undergo coronary artery bypass graft surgery is equivocal. This retrospective, population‐based cohort study evaluated effect of exposure to an ACEI/ARB on MACE using linked administrative databases that included all cardiac revascularization procedures, hospitalizations, and prescriptions for the population of British Columbia, Canada. Methods and Results All adults who underwent coronary artery bypass graft surgery between 2002 and 2020 were eligible. The primary outcome was time to MACE, defined as a composite of all‐cause death, myocardial infarction, and ischemic stroke using Cox proportional hazards models with inverse probability treatment weighting. Included were 15 439 patients and 6191 (40%) were prescribed an ACEI/ARB. Mean age was 66 years, 83% were men, and 16% had heart failure (HF). Median exposure time was 40 months. Over the 5‐year follow‐up, 1623 MACE occurred. Impact of exposure was different for patients with and without HF ( P <0.0001 for interaction). After probability‐weighting and adjustment for relevant covariates, exposure to ACEI/ARBs was associated with a lower hazard of MACE in patients with HF at 1 year (hazard ratio, 0.13 [95% CI, 0.09–0.19]) and 5 years (hazard ratio, 0.36 [95% CI, 0.30–0.44]). In patients without HF, ACEI/ARBs had a lower hazard of MACE at 1 year (hazard ratio, 0.35 [95% CI, 0.27–0.46]) and 5 years (hazard ratio, 0.66 [95% CI, 0.58–0.76]). Conclusions In this population‐based study, ACEI/ARBs were associated with a lower hazard of MACE in a cohort of patients post–coronary artery bypass graft surgery irrespective of HF status.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".