Association between angiotensin converting enzyme inhibitor or angiotensin receptor blocker use prior to major elective surgery and the risk of acute dialysis
Bibliographic record
Abstract
BACKGROUND: Some studies but not others suggest angiotensin converting enzyme inhibitor (ACEi) or angiotensin receptor blocker (ARB) use prior to major surgery associates with a higher risk of postoperative acute kidney injury (AKI) and death. METHODS: We conducted a large population-based retrospective cohort study of patients aged 66 years or older who received major elective surgery in 118 hospitals in Ontario, Canada from 1995 to 2010 (n = 237,208). We grouped the cohort into ACEi/ARB users (n = 101,494) and non-users (n = 135,714) according to whether the patient filled at least one prescription for an ACEi or ARB (or not) in the 120 days prior to surgery. Our study outcomes were acute kidney injury treated with dialysis (AKI-D) within 14 days of surgery and all-cause mortality within 90 days of surgery. RESULTS: After adjusting for potential confounders, preoperative ACEi/ARB use versus non-use was associated with 17% lower risk of post-operative AKI-D (adjusted relative risk (RR): 0.83; 95% confidence interval (CI): 0.71 to 0.98) and 9% lower risk of all-cause mortality (adjusted RR: 0.91; 95% CI: 0.87 to 0.95). Propensity score matched analyses provided similar results. The association between ACEi/ARB and AKI-D was significantly modified by the presence of preoperative chronic kidney disease (CKD) (P value for interaction < 0.001) with the observed association evident only in patients with CKD (CKD - adjusted RR: 0.62; 95% CI: 0.50 to 0.78 versus No CKD: adjusted RR: 1.00; 95% CI: 0.81 to 1.24). CONCLUSIONS: In this cohort study, preoperative ACEi/ARB use versus non-use was associated with a lower risk of AKI-D, and the association was primarily evident in patients with CKD. Large, multi-centre randomized trials are needed to inform optimal ACEi/ARB use in the peri-operative setting.
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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 | 0.000 | 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) | 0.001 | 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".