A Systematic Review of the Pharmacokinetics of Antiepileptic Drugs in Neonates With Refractory Seizures
Bibliographic record
Abstract
BACKGROUND: Neonatal seizures are associated with neurological sequelae and an increased risk of epilepsy later in life. Phenobarbital and phenytoin remain the antiepileptic drugs (AEDs) most commonly used to treat neonatal seizures, despite their suboptimal effectiveness and safety. As a result, other AEDs, such as levetiracetam and topiramate, are often used in neonates with refractory seizures, despite limited data and off-label use. OBJECTIVES: To systematically review published pharmacokinetic data for second-line AEDs used in neonates with seizures and to provide dosing recommendations for these agents in the neonatal population. METHODS: A literature search was conducted in PubMed (1949-May 2012), Medline (1950-May 2012), and Embase (1980-May 2012). Each study was ranked according to the quality of evidence it provided, based on the classification system developed by the US Preventive Services Task Force. Information extracted from each study included study design, number of subjects, gestational and postnatal age, AED dosage regimen, pharmacokinetic parameters, pharmacokinetic model, AED serum concentrations, and sampling times. RESULTS: Nineteen relevant pharmacokinetic studies involving a total of 8 different drugs were identified. No prospective, randomized, controlled studies (level I evidence) or nonrandomized controlled studies (level II-I evidence) were identified; 2 studies were prospective, nonrandomized, uncontrolled (cohort) studies (level II-2 evidence), 11 studies obtained evidence from multiple time series (level II-3 evidence), and 6 studies were case reports or descriptive studies (level III evidence). CONCLUSIONS: There are limited pharmacokinetic data for the use of carbamazepine, levetiracetam, lidocaine, paraldehyde, topiramate, valproic acid, and vigabatrin for neonates with seizures refractory to treatment with first-line antiepileptic agents. Further research is needed to elucidate target AED serum concentrations (if any) required to optimize effectiveness and minimize dose-related adverse effects in neonates.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".