Colistin Nephrotoxicity: Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Abstract Background Nephrotoxicity is a known adverse effect of polymyxin antibiotics, including colistin. Although previous meta-analyses have aimed to characterize colistin-associated nephrotoxicity risk relative to other antibiotics, included studies were observational in nature with high risk of confounding and heterogeneity. We conducted this systematic review and meta-analysis of exclusively randomized controlled trials (RCTs) to evaluate the incidence of nephrotoxicity associated with colistin versus minimally nephrotoxic antibiotics. Methods We searched PubMed, EMBASE, Cochrane Library, and 3 trial registries for RCTs comparing the nephrotoxicity of colistin to nonpolymyxin antibiotics. Randomized controlled trials that used aminoglycosides were excluded. Risk ratios (RRs) and 95% confidence intervals (CIs) were calculated using random-effects models. The study outcome was the rate of nephrotoxicity. Results Five RCTs with a total of 377 patients were included. Most patients received colistin for pneumonia in the intensive care unit, and the comparators were β-lactam-based regimens. Colistimethate sodium was dosed at 9 million units/day (300 mg/day of colistin base activity), with administration of a loading dose in 4 studies. The nephrotoxicity incidence in patients who received colistin was 36.2% (95% CI, 23.3% to 51.3%). The nephrotoxicity rate was significantly higher in the colistin arm than comparators (RR, 2.40; 95% CI, 1.47 to 3.91; P ≤ .001; I2 = 0%), and the number needed to harm was 5. Findings persisted upon one-study-removed-analysis. Conclusions This meta-analysis of RCTs found a colistin-associated nephrotoxicity rate of 36.2% and an increase in this risk compared with β-lactam-based regimens by 140%. Colistin should be regarded as a last-line agent and safer alternatives should be considered when possible.
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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 | 0.044 | 0.089 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.027 | 0.064 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".