Population Pharmacokinetics of Phenobarbital in Neonates and Infants on Extracorporeal Membrane Oxygenation and the Influence of Concomitant Renal Replacement Therapy
Bibliographic record
Abstract
The objective of this study was to describe the pharmacokinetics (PK) of intravenous phenobarbital in neonates and infants on extracorporeal membrane oxygenation (ECMO) and to provide dosing recommendations in this population. We performed a retrospective single-center PK study of phenobarbital in neonates and infants on ECMO between January 1, 2014, and December 31, 2018. We developed a population PK model using nonlinear mixed-effects modeling, performed simulations using the final PK parameters, and determined optimal dosing based on attainment of peak and trough concentrations between 20 and 40 mg/L. We included 35 subjects with a median (range) age and weight of 14 days (1-154 days) and 3.4 kg (1.6-8.1 kg), respectively. A total of 194 samples were included in the analysis. Five children (14%) contributing 30 samples (16%) were supported by continuous venovenous hemodiafiltration (CVVHDF). A 1-compartment model best described the data. Typical clearance and volume of distribution for a 3.4-kg infant were 0.038 L/h and 3.83 L, respectively. Clearance increased with age and CVVHDF. Although on ECMO, phenobarbital clearance in children on CVVHDF was 6-fold higher than clearance in children without CVVHDF. In typical subjects, a loading dose of 30 mg/kg/dose followed by maintenance doses of 6-7 mg/kg/day administered as divided doses every 12 hours reached goal concentrations. Age did not impact dosing recommendations. However, higher doses were needed in children on CVVHDF. We strongly recommend therapeutic drug monitoring in children on renal replacement therapy (excluding slow continuous ultrafiltration) while on ECMO.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".