Evidence map›Paper›PMID 40569218›Full record

ArticleEpilepsia2025

Optimizing phenobarbital dosing in critically ill patients with refractory and superrefractory status epilepticus using a population pharmacokinetic model.

Maximilian Stoschus, Moritz L Schmidbauer, Johannes Starp, Stefan Kunst, Georgios Gakis, Michael Paal, Michael Vogeser, Christina Scharf-Janssen, Uwe Liebchen, Konstantinos Dimitriadis

Abstract read
In one paragraph

Article in Epilepsia, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Maximilian StoschusDepartment of Neurology, LMU University Hospital, LMU Munich, Munich, Germany.ORCID https://orcid.org/0009-0006-4522-2856
Moritz L SchmidbauerDepartment of Neurology, LMU University Hospital, LMU Munich, Munich, Germany.ORCID https://orcid.org/0000-0001-9553-9911
Johannes StarpDepartment of Anesthesiology, LMU University Hospital, LMU Munich, Munich, Germany.ORCID https://orcid.org/0009-0002-9076-3943
Stefan KunstDepartment of Neurology, LMU University Hospital, LMU Munich, Munich, Germany.ORCID https://orcid.org/0000-0002-1237-3344
Georgios GakisDepartment of Neurology, LMU University Hospital, LMU Munich, Munich, Germany.ORCID https://orcid.org/0000-0002-7339-4659
Michael PaalInstitute of Laboratory Medicine, LMU University Hospital, LMU Munich, Munich, Germany.ORCID https://orcid.org/0000-0001-8831-0238
Michael VogeserInstitute of Laboratory Medicine, LMU University Hospital, LMU Munich, Munich, Germany.
Christina Scharf-JanssenDepartment of Anesthesiology, LMU University Hospital, LMU Munich, Munich, Germany.ORCID https://orcid.org/0000-0002-5310-4116
Uwe LiebchenDepartment of Anesthesiology, LMU University Hospital, LMU Munich, Munich, Germany.ORCID https://orcid.org/0000-0002-4375-0923
Konstantinos DimitriadisDepartment of Neurology, LMU University Hospital, LMU Munich, Munich, Germany.ORCID https://orcid.org/0000-0003-0417-2267

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveCurrent weight-based dosing fails to account for pharmacokinetic variability in refractory and superrefractory status epilepticus (RSE, SRSE). However, understanding pharmacokinetics in critically ill patients with varying degrees of organ dysfunction can improve both safety and efficacy. Hence, this study aims to quantify key pharmacokinetic variabilities to enable individualized dosing in RSE and SRSE.

methodsPatients with RSE and SRSE admitted to a neurointensive care unit of a tertiary academic center were retrospectively screened for therapeutic drug monitoring (TDM) samples of phenobarbital. Demographics, laboratory data, comedication, and dosing history were collected from electronic health records. Using a nonlinear mixed effects modeling approach via MONOLIX, a population pharmacokinetic model was developed. Optimal dosing regimens were simulated based on estimated parameters, with target attainment calculated for trough plasma phenobarbital concentrations within 18-40 mg/L.

resultsThirty-seven patients contributed 301 TDM samples. Oral bioavailability (96%), volume of distribution (V; 34.3 L), and total body clearance (CL; .38 L/h) were consistent with nonintensive care literature data. Ideal body weight (IBW) implemented as allometric scaling was the only significant covariate improving model fit, demonstrating a positive correlation with the required phenobarbital dose. Simulations identified optimal 12-h dosing strategies. Oral and intravenous dosing showed minor differences in loading doses but identical maintenance doses, with no significant impact on target attainment for both administration methods. As shown by the coefficients of variation (CVs), intensive care patients exhibited high interindividual (81.36% CV on V, 41.36% CV on CL) and interoccasion variability (36.85% CV on CL), resulting in low target attainment in simulated patients (~40%). SIGNIFICANCE: The pharmacokinetic model characterized phenobarbital pharmacokinetics in patients with RSE and SRSE, showing high oral bioavailability and IBW's impact on V and CL. High pharmacokinetic variability led to low target attainment. Model-informed precision dosing might improve target attainment in the future.

Indexed as

AnticonvulsantsDrug Resistant EpilepsyPhenobarbitalStatus EpilepticusAdultAgedCritical IllnessDose-Response Relationship, DrugDrug MonitoringFemaleHumansMaleMiddle AgedModels, BiologicalRetrospective StudiesAnticonvulsantsPhenobarbitalindividualized dosingintensive care unitmodel‐informed precision dosingneurointensive carenonlinear mixed effects modelingtherapeutic drug monitoring

Identifiers

PMID40569218
PMCPMC12605696

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.