Preoperative angiotensin-converting enzyme inhibitors and angiotensin receptor blocker use and acute kidney injury in patients undergoing cardiac surgery
Bibliographic record
Abstract
BACKGROUND: Using either an angiotensin-converting enzyme inhibitor (ACEi) or an angiotensin receptor blocker (ARB) the morning of surgery may lead to 'functional' postoperative acute kidney injury (AKI), measured by an abrupt increase in serum creatinine. Whether the same is true for 'structural' AKI, measured with new urinary biomarkers, is unknown. METHODS: The TRIBE-AKI study was a prospective cohort study of 1594 adults undergoing cardiac surgery at six hospitals between July 2007 and December 2010. We classified the degree of exposure to ACEi/ARB into three categories: 'none' (no exposure prior to surgery), 'held' (on chronic ACEi/ARB but held on the morning of surgery) or 'continued' (on chronic ACEi/ARB and taken the morning of surgery). The co-primary outcomes were 'functional' AKI based upon changes in pre- to postoperative serum creatinine, and 'structural AKI', based upon peak postoperative levels of four urinary biomarkers of kidney injury. RESULTS: Across the three levels (none, held and continued) of ACEi/ARB exposure there was a graded increase in functional AKI, as defined by AKI stage 1 or worse; (31, 34 and 42%, P for trend 0.03) and by percentage change in serum creatinine from pre- to postoperative (25, 26 and 30%, P for trend 0.03). In contrast, there were no differences in structural AKI across the strata of ACEi/ARB exposure, as assessed by four structural AKI biomarkers (neutrophil gelatinase-associated lipocalin, kidney injury molecule-1, interleukin-18 or liver-fatty acid-binding protein). CONCLUSIONS: Preoperative ACEi/ARB usage was associated with functional but not structural acute kidney injury. As AKI from ACEi/ARB in this setting is unclear, interventional studies testing different strategies of perioperative ACEi/ARB use are warranted.
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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 | 0.001 | 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".