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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".