Abstract 303: Pre-operative Angiotensin Converting Enzyme Inhibitor use and outcomes in patients undergoing Isolated Coronary Artery Bypass Grafting
Bibliographic record
Abstract
Background: The association between pre-operative use of angiotensin converting enzyme inhibitors (ACEI) and outcomes after coronary artery bypass grafting (CABG) remains controversial. Our aim was to study in-hospital outcomes after isolated CABG in patients on preoperative ACEI. Methods: We performed a retrospective analysis of 8,889 patients who underwent isolated CABG from year 2000 to 2011. Primary outcome was the incidence of major adverse events (MAE) defined as a composite of in-hospital mortality, post-operative renal dysfunction, myocardial infarction, stroke, and atrial fibrillation during index hospitalization. Secondary outcomes studied were the incidence of individual components comprising MAE. Logistic regression analysis was performed. Results (Table): Of the 8,889 patients, 3,983 (45%) were on pre-operative ACEI (“ACEI group”) and 4906 (55%) were not (“no ACEI group”). The overall incidence of MAE was 38.1% (n=1518) in the “ACEI group” versus 33.6% (n=1649) in “no ACEI group”. Pre-operative ACEI use was independently associated with increased risk of MAE (OR; 1.12, 95% CI; 1.02-1.23), most of which was driven by a statistically significant increase in post-operative renal dysfunction and atrial fibrillation. Pre-operative ACEI therapy was not associated with in-hospital mortality, post-operative myocardial infarction, or stroke. Conclusion: Preoperative ACEI use was associated with an increased risk of MAE post CABG, in particular post-operative renal dysfunction and atrial fibrillation.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".