Using Real‐World Data to Externally Evaluate Population Pharmacokinetic Models of Dexmedetomidine in Children and Infants
Bibliographic record
Abstract
Dexmedetomidine is a sedative used in both adults and off-label in children with considerable reported pharmacokinetic (PK) interindividual variability affecting drug exposure across populations. Several published models describe the population PKs of dexmedetomidine in neonates, infants, children, and adolescents, though very few have been externally evaluated. A prospective PK dataset of dexmedetomidine plasma concentrations in children and young adults aged 0.01-19.9 years was collected as part of a multicenter opportunistic PK study. A PubMed search of studies reporting dexmedetomidine PK identified five population PK models developed with data from demographically similar children that were selected for external validation. A total of 168 plasma concentrations from 102 children were compared with both population (PRED) and individualized (IPRED) predicted values from each of the five published models by quantitative and visual analyses using NONMEM (v7.3) and R (v4.1.3). Mean percent prediction errors from observed values ranged from -1% to 120% for PRED, and -24% to 60% for IPRED. The model by James et al, which was developed using similar "real-world" data, nearly met the generalizability criteria from IPRED predictions. Other models developed using clinical trial data may have been limited by inclusion/exclusion criteria and a less racially diverse population than this study's opportunistic dataset. The James model may represent a useful, but limited tool for model-informed dosing of hospitalized children.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".