Acetylcysteine for prevention of contrast-induced nephropathy after intravascular angiography: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Contrast-induced nephropathy is an important cause of acute renal failure. We assess the efficacy of acetylcysteine for prevention of contrast-induced nephropathy among patients undergoing intravascular angiography. METHODS: We conducted a systematic review and meta-analysis of randomized controlled trials comparing prophylactic acetylcysteine plus hydration versus hydration alone in patients undergoing intravascular angiography. Studies were identified by searching MEDLINE, EMBASE, and CENTRAL databases. Our main outcome measures were the risk of contrast-induced nephropathy and the difference in serum creatinine between acetylcysteine and control groups at 48 h. RESULTS: Fourteen studies involving 1261 patients were identified and included for analysis, and findings were heterogeneous across studies. Acetylcysteine was associated with a significantly reduced incidence of contrast-induced nephropathy in five studies, and no difference in the other nine (with a trend toward a higher incidence in six of the latter studies). The pooled odds ratio for contrast-induced nephropathy with acetylcysteine relative to control was 0.54 (95% CI, 0.32-0.91, p = 0.02) and the pooled estimate of difference in 48-h serum creatinine for acetylcysteine relative to control was -7.2 mumol/L (95% CI -19.7 to 5.3, p = 0.26). These pooled values need to be interpreted cautiously because of the heterogeneity across studies, and due to evidence of publication bias. Meta-regression suggested that the heterogeneity might be partially explained by whether the angiography was performed electively or as emergency. CONCLUSION: These findings indicate that published studies of acetylcysteine for prevention of contrast-induced nephropathy yield inconsistent results. The efficacy of acetylcysteine will remain uncertain unless a large well-designed multi-center trial is performed.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.034 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".