Abstract 304: The Outcomes of Pre-Procedural Angiotensin Converting Enzyme Inhibitor Therapy in patients undergoing Percutaneous Coronary Intervention
Bibliographic record
Abstract
Background: Post-procedural outcomes associated with the use of angiotensin converting enzyme inhibitors (ACEI) in patients undergoing percutaneous coronary intervention (PCI) have not been studied. We aimed to determine the association between ACEI use and adverse cardiovascular/ renal outcomes in these patients. Methods: We performed retrospective analysis of 15,485 consecutive patients who underwent percutaneous coronary intervention from January 1, 2000 to June 30, 2011. Primary outcome was the incidence of major adverse events (MAE) defined as a composite of mortality, post PCI renal dysfunction, myocardial infarction, and stroke during index hospitalization for PCI. Secondary outcome was the incidence of aforementioned components of MAE analyzed separately. Logistic regression and multivariate analyses were performed. Results: Of the patients undergoing PCI, 6,600 (43%) received pre-PCI ACEI and 8,885 (57%) did not. Patients on ACEI were more likely to be older (64.6 ± 10.9 years vs. 63.6 ± 12.1 years; p<0.0001), have diabetes (41% vs. 26%; p<0.0001), prior renal insufficiency (19% vs. 15%; p<0.0001) and ejection fraction less than 35% (9.6% vs. 7%; p<0.0001). There were no significant associations between pre-operative ACEI use and primary and secondary outcomes (table). Conclusion: Pre-operative ACEI use is not associated with major adverse events in patients undergoing PCI. Key Words: angiotensin converting enzyme inhibitor; percutaneous coronary intervention
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 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".