Evidence map›Paper›PMID 36815252›Full record

ArticleEpilepsia2023

A pharmacokinetic model of antiseizure medication load to guide care in the epilepsy monitoring unit.

Nina J Ghosn, Kevin Xie, Akash R Pattnaik, James J Gugger, Colin A Ellis, Elizabeth Sweeney, Emily Fox, John M Bernabei, Jenaye Johnson, Jacqueline Boccanfuso and 2 more

Open access · greenAbstract read
In one paragraph

Article in Epilepsia, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
2.9field-weighted citation impact, top 9% of its field
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

8 citing papers in PubMed, 13 citations in OpenAlex.

  1. Article
  2. An Implantable Device that Converses with Patients and Learns to Co-Manage Epilepsy.medRxiv : the preprint server for health sciences · 2026
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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

12 authors at 3 institutions in 1 country.

Nina J GhosnDepartment of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA.ORCID 0000-0003-3471-803X
Kevin XieDepartment of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA.ORCID 0000-0003-1849-2085
Akash R PattnaikDepartment of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA.ORCID 0000-0002-2474-1685
James J GuggerCenter for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Colin A EllisDepartment of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.ORCID 0000-0003-2152-8106
Elizabeth SweeneyPenn Statistics in Imaging and Visualization Endeavor Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Emily FoxDepartment of Statistics, Stanford University, Stanford, California, USA.
John M BernabeiDepartment of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Jenaye JohnsonCenter for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Jacqueline BoccanfusoCenter for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Brian LittDepartment of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Erin C ConradDepartment of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA.ORCID 0000-0001-8910-1817
University of Pennsylvania · USChan Zuckerberg Initiative (United States) · USPenn Center for AIDS Research · US

Funding

Ghost in the Machine: Melding Brain, Computer and BehaviorDP1NS122038 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI LITT, BRIAN · 2020 to 2024
$5.7M
Training Program in Neuroengineering and MedicineT32NS091006 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI Brian Litt · 2015 to 2026
$4.1M
Research Training Program in Disease Oriented Neuroscience (R25)R25NS065745 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI DETRE, JOHN A · 2009 to 2023
$3.0M
Guiding epilepsy surgery using network models and Stereo EEGR01NS125137 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI BASSETT, DANIELLE SMITH, LITT, BRIAN · 2022 to 2025
$2.8M
Quantitative interictal networks to localize seizure generatorsK23NS121401 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI Erin Conrad · 2022 to 2026
$1.2M
Virtual Resection to Treat EpilepsyR56NS099348 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI BASSETT, DANIELLE SMITH, LITT, BRIAN · 2021 to 2021
$552k
NINDS NIH HHS DP1 NS122038NINDS NIH HHS K23 NS121401NINDS NIH HHS R01 NS125137NINDS NIH HHS R25 NS065745NINDS NIH HHS R56 NS099348NINDS NIH HHS T32 NS091006
6 · The paper itself

Abstract

objectiveEvaluating patients with drug-resistant epilepsy often requires inducing seizures by tapering antiseizure medications (ASMs) in the epilepsy monitoring unit (EMU). The relationship between ASM taper strategy, seizure timing, and severity remains unclear. In this study, we developed and validated a pharmacokinetic model of total ASM load and tested its association with seizure occurrence and severity in the EMU.

methodsWe studied 80 patients who underwent intracranial electroencephalographic recording for epilepsy surgery planning. We developed a first order pharmacokinetic model of the ASMs administered in the EMU to generate a continuous metric of overall ASM load. We then related modeled ASM load to seizure likelihood and severity. We determined the association between the rate of ASM load reduction, the length of hospital stay, and the probability of having a severe seizure. Finally, we used modeled ASM load to predict oncoming seizures.

resultsSeizures occurred in the bottom 50th percentile of sampled ASM loads across the cohort (p < .0001, Wilcoxon signed-rank test), and seizures requiring rescue therapy occurred at lower ASM loads than seizures that did not require rescue therapy (logistic regression mixed effects model, odds ratio = .27, p = .01). Greater ASM decrease early in the EMU was not associated with an increased likelihood of having a severe seizure, nor with a shorter length of stay. SIGNIFICANCE: A pharmacokinetic model can accurately estimate ASM levels for patients in the EMU. Lower modeled ASM levels are associated with increased seizure likelihood and seizure severity. We show that ASM load, rather than ASM taper speed, is associated with severe seizures. ASM modeling has the potential to help optimize taper strategy to minimize severe seizures while maximizing diagnostic yield.

Indexed as

Drug Resistant EpilepsySeizuresElectrocorticographyHumansLength of StayLogistic Modelsantiseizure medicationEEGepilepsymodelingseizure severity

Identifiers

PMID36815252
PMCPMC10424095
OpenAlexW4321599374

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.