High variability of teicoplanin concentration in patients with continuous venovenous hemodiafiltration
Bibliographic record
Abstract
Abstract Introduction: Continuous venovenous hemodiafiltration (CVVHDF) may alter teicoplanin pharmacokinetics and increase the risk of incorrect dosing. The objective of this prospective observational study was to assess the effect of CVVHDF on the pharmacokinetics of teicoplanin as maintenance therapy. Methods: Blood, urine, and dialysate samples were collected to measure teicoplanin levels. CVVHDF clearance (CLCVVHDF), total clearance (CLTOTAL), and volume of distribution (Vd) were calculated by simplex‐linear modeling. The influence of CVVHDF dose on teicoplanin pharmacokinetics was assessed. Findings: Ten samples from eight patients were studied. Creatinine clearance was 3.4 ± 5.1 ml/min/1.73 m2. Three patients were anuria. The dose for CVVHDF was 32.1 ± 7.0 mL/kg/h. Vd was 1.6 ± 0.7 L/kg. T1/2 was 100.1 ± 42.7 hours. CLTOTAL of teicoplanin was 11.9 ± 5.4 mL/min and CLCVVHDF was 5.8 ± 4.2 mL/min. Contribution of CLCVVHDF to CLTOTAL was 51.2% ± 23.6%. CLCVVHDF of individual teicoplanin varied widely. Large intra‐occasion differences were also observed. Dose of CLCVVHDF did not influence overall CLTOTAL, Vd, or half‐life. The proportion of CLTOTAL due to CLCVVHDF varied widely. It was high in some cases. Discussion: In patients receiving CVVHDF, there is great variability in teicoplanin pharmacokinetics which complicates empiric approach to dosing, suggesting the need for therapeutic drug monitoring.
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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".