N-acetylcysteine reduces urinary albumin excretion following contrast administration: evidence of biological effect
Bibliographic record
Abstract
BACKGROUND: There are conflicting results regarding the effectiveness of N-acetylcysteine (NAC) in attenuating contrast-induced nephropathy (CIN). NAC administration independently reduces serum creatinine, potentially confounding studies utilizing creatinine-based endpoints. Albuminuria is a marker of renal injury and spot urine albumin: creatinine ratios (ACR) reflect 24-h urine albumin excretion. We performed a pre-specified secondary analysis from our published negative randomized control trial of NAC for prevention of CIN, to determine if NAC administration reduces albuminuria after contrast exposure following cardiac catheterization. METHODS: We included study patients who had paired urine specimens obtained pre- and post-cardiac catheterization. Baseline characteristics were compared using the chi square test or Mann-Whitney U-test, as appropriate. Changes in ACR were evaluated using binomial exact test. The effect of NAC on post-cardiac catheterization changes in ACR ratio was evaluated by ordinal logistic regression. RESULTS: A total of 125 patients met inclusion criteria (pre- and post-catheterization urinalysis within 7 days). Baseline characteristics neither differ between NAC and placebo groups, nor were they different from those who were excluded. Among the patients receiving NAC, 10.7% improved their ACR ratio and 7.1% worsened; in contrast, in patients on placebo only 4.3% improved, while 21.7% worsened (P=0.015). Change in ACR ratio was not associated with change in kidney function as measured by calculated creatinine clearance or GFR. CONCLUSIONS: The results of this analysis suggest NAC may attenuate contrast-induced glomerular or tubular injury, as defined by albumin excretion, and appears to be independent of any effect on creatinine-derived measures of kidney function. Larger studies are required to confirm this observation.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".