Frequency of and Risk Factors for Acute Kidney Injury Associated With Vancomycin Use in the Pediatric Intensive Care Unit
Bibliographic record
Abstract
BACKGROUND: Published information evaluating frequency of and risk factors for vancomycin-induced acute kidney injury (AKI) in the pediatric intensive care unit (PICU) population is conflicting. OBJECTIVES: The primary objective was to describe the proportion of our PICU patients who developed AKI with intravenous (IV) vancomycin. The secondary objective was to describe the associated potential risk factors. METHODS: Pediatric patients (0–18 years) who received their first IV vancomycin dose in the PICU were evaluated in this retrospective chart review. AKI was defined based on Pediatric-Modified RIFLE (pRIFLE) criteria. Patient demographics, vancomycin trough concentrations, concomitant nephrotoxins, and estimated creatinine clearance changes were analyzed. RESULTS: Of 265 patients included, the primary outcome of AKI (defined by meeting any pRIFLE criteria) occurred in 62 (23.4%) patients (48 category R, 11 category I, 3 category F). Patients who received vancomycin treatment for = 5 days were more likely to develop AKI (unadjusted odds ratio [uOR]: 2.52; 95% confidence interval [CI]: 1.11–5.73), as were patients with a maximum vancomycin trough level = 20 mg/L (OR: 2.99; 95% CI: 1.54–5.78) and patients on 1 (uOR: 2.29; 95% CI: 1.12–4.66) or more concurrent nephrotoxin (uOR: 3.11; 95% CI: 1.43–6.77). Among nephrotoxins, patients receiving furosemide concomitantly with vancomycin were more likely to develop AKI (uOR: 3.47; 95% CI: 1.92–6.27). After adjustment, only furosemide was a significant predictor of risk of AKI/AKI (adjusted OR: 3.52; 95% CI: 1.88–6.62). The study was limited by its retrospective and observational design, and confounding variables. CONCLUSIONS: Patients who were receiving vancomycin with concurrent furosemide were at highest risk of developing AKI.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".