Pharmacokinetics of Vancomycin in Pediatric Patients Receiving Intermittent Hemodialysis or Hemodiafiltration
Bibliographic record
Abstract
Introduction Vancomycin is a common antibiotic used to treat hemodialysis (HD) or hemodiafiltration (HDF)-related infections in pediatric patients, but optimal dosing remains unknown. This is the first observational study to characterize the pharmacokinetics and evaluate dosing of vancomycin in this population. Methods Eligible patients received IV vancomycin 10 mg/kg per dose postdialysis followed by a series of serum vancomycin concentrations collected before, immediately after, 1 hour after, and 4 hours after dialysis. The pharmacokinetic parameters were estimated using 1- and 2-compartment models and a nonlinear least-squares algorithm. Results Among 42 vancomycin courses in 16 patients, 1 compartment model had the best fit for observed data. The net drug removal was 43 ± 13% (39% for HD and 50% for HDF) from an average 3-hour HD/HDF session. The mean elimination constant was 0.28 h −1 (standard deviation [SD], 0.11 h −1 ) during the intradialytic period compared with 0.0049 h −1 (SD, 0.004 h −1 ) when off dialysis. The mean volume of distribution was 0.65 (SD, 0.19) L/kg. Duration of dialysis session and mode of dialysis (HD vs. HDF) were significant predictors of vancomycin pharmacokinetic parameters. Half-life was shorter for HDF compared with HD (2.1 vs. 3.5 hours). Conclusions Based on the simulations, an initial vancomycin dose of 10 mg/kg per dose and redosing postdialysis was optimal to achieve a vancomycin concentration range of 5 to 12 mg/L at 4 hours postdialysis and 24-hour area under the curve over minimum inhibitory concentration of ≥400 hours. Therapeutic drug monitoring is necessary to account for residual variability in vancomycin elimination in pediatric patients receiving HD/HDF.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".