Population pharmacokinetics of vancomycin in paediatric patients with febrile neutropenia and augmented renal clearance: development of new dosing recommendations
Bibliographic record
Abstract
OBJECTIVES: The purpose of this study was to evaluate the influence of augmented renal clearance (ARC) on vancomycin clearance and provide dosage recommendations for paediatric patients with febrile neutropenia following HSCT. METHODS: A population pharmacokinetic analysis was performed based on a two-compartment model structure using a non-linear mixed-effect modelling approach. Monte Carlo simulations were conducted as a target attainment analysis of AUC between 400 mg·h/L and 650 mg·h/L for MRSA at an MIC of 1 mg/L. RESULTS: A total of 165 paediatric patients and 276 vancomycin serum concentrations were analysed in this study. Age, body weight, estimated glomerular filtration rate (eGFR) and fever (≥38.0°C) were identified as factors that significantly influenced vancomycin clearance. The median eGFR of the population was 143 mL/min/1.73 m2 and 34% of patients showed an eGFR ≥160 mL/min/1.73 m2, which may be classified as ARC. Our simulations showed that current dosing recommendations result in poor target attainment. In particular, children aged 6 months old to 6 years old with ARC require an initial vancomycin dose up to 35%-65% higher than the current dosing guidelines. CONCLUSIONS: ARC is frequently observed in paediatric patients with post-HSCT febrile neutropenia, resulting in a significant increase in vancomycin clearance. We propose a vancomycin dosing strategy for children with febrile neutropenia following HSCT based on eGFR, age, weight and body temperature.
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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 | 0.012 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".