Exposure to renin-angiotensin system inhibitors before kidney transplantation is associated with a decreased risk of delayed graft function
Bibliographic record
Abstract
Introduction: Animal models suggest a protective role of angiotensin-converting enzyme inhibitors (ACEi) and angiotensin-II receptor blockers (ARBs) in reducing renal and cardiac ischemia-reperfusion injury. Our aim was to determine the association between pre-transplant ACEi/ARBs use and the occurrence of delayed graft function (DGF) in patients who received a kidney transplantation from a deceased donor. Methods: Consecutive recipients between 2008 and 2021 in 2 Canadian university-affiliated centers were included in this retrospective cohort study. The main outcome was the occurrence of DGF and the exposure was use of ACEi or ARBs at the time of admission for transplantation. Mixed models were fit. Results: A total of 897 patients were included, of which 160 (18%) experienced DGF. At admission, 337 (38%) patients were exposed to ACEi/ARBs. In the multivariable analysis, pre-transplant ACEi/ARBs use was associated with a reduced risk of DGF (odds ratio: 0.60, 95% confidence interval: 0.40, 0.92). Other factors associated with DGF were recipient obesity, donor type, ethnicity, age, hypertension, and total ischemia time. Discussion: Pre-transplant use of ACEi/ARBs is associated with a lower risk of DGF in early postoperative period, which may be due to a protective effect of these agents on renal ischemia-reperfusion injury.
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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 | 0.000 | 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) | 0.003 | 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".