Evidence map›Paper›PMID 40663627›Full record

ArticleBrain : a journal of neurology2025

Comparative effectiveness of anti-seizure medications in emulated trials using medical informatics.

Kevin Xie, Jacob Korzun, Daniel J Zhou, Ellie Chang, Nina J Ghosn, Sarah Lavelle, Arman Oganisian, Emily K Acton, Michael A Gelfand, Dan Roth and 2 more

Abstract readComparative Study
In one paragraph

Article in Brain : a journal of neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Epilepsy grand challenge 2025.Frontiers in neurology · 2025
    Article
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.

Kevin XieDepartment of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA.ORCID 0000-0003-1849-2085
Jacob KorzunDepartment of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA.
Daniel J ZhouCenter for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA.
Ellie ChangDepartment of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA.
Nina J GhosnCenter for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA.
Sarah LavelleCenter for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA.
Arman OganisianDepartment of Biostatistics, Brown University, Providence, RI 02903, USA.ORCID 0000-0002-0437-4611
Emily K ActonDepartment of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA 19104, USA.
Michael A GelfandDepartment of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA.ORCID 0000-0002-4816-8756
Dan RothDepartment of Computer and Information Science, University of Pennsylvania, Philadelphia, PA 19104, USA.
Brian LittDepartment of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA.
Colin A EllisCenter for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA.

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
Guiding epilepsy surgery using network models and Stereo EEGR01NS125137 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI BASSETT, DANIELLE SMITH, LITT, BRIAN · 2022 to 2025
$2.8M
Polygenic risk in familial epilepsyK23NS121520 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI ELLIS, COLIN · 2021 to 2025
$1.1M
National Institute of Neurological Disorders and Stroke of the National Institutes of Health DP1-NS-122038National Institute of Neurological Disorders and Stroke of the National Institutes of Health K23NS121520National Institute of Neurological Disorders and Stroke of the National Institutes of Health R01-NS-125137-01NINDS NIH HHS DP1 NS122038NINDS NIH HHS K23 NS121520NINDS NIH HHS R01 NS125137NINDS NIH HHS T32 NS091006Office of Naval Research N00014-19-1-2620PCORI HHS ME-2023C1-31348
6 · The paper itself

Abstract

Anti-seizure medications (ASMs) are often prescribed using a trial-and-error approach with a similar sequence for many patients. Comparative effectiveness data beyond the first ASM prescription are limited. Artificial intelligence can automatically extract information from electronic health records (EHRs) and augment clinical trials. We conducted a retrospective cohort comparative effectiveness study of currently available ASMs using emulated clinical trials through a causal inference- and informatics-based framework. We extracted data from EHRs using natural language processing algorithms to collect epilepsy covariates and seizure outcomes. We compared ASMs using weighted Kaplan-Meier analyses and log-rank tests with Bonferroni corrections. We compared ASMs across seizure freedom over 2 years of follow-up, and drug retention rate over 5 years of follow-up. Emulated trials included 1596 patients (8379 patient-years) for seizure freedom as outcome and 2945 patients (14 238 patient-years) for retention as outcome. For all epilepsy types, among first-line ASMs, levetiracetam and lamotrigine were superior to oxcarbazepine for seizure freedom (P < 0.001), and levetiracetam was superior to lamotrigine and oxcarbazepine for retention (P < 0.001); among second-line ASMs, lacosamide had the best retention (P < 0.001) but lower seizure freedom rate than topiramate (P = 0.032); among third-line ASMs, cenobamate had longer retention than brivaracetam, but lower seizure freedom rate than clobazam and brivaracetam (P < 0.001). For focal epilepsies, first-line ASMs achieved similar rates of seizure freedom, but levetiracetam remained superior to lamotrigine and oxcarbazepine for retention (P < 0.001); among second-line ASMs, topiramate and lacosamide were both superior to zonisamide for seizure freedom (P < 0.001), while lacosamide remained superior to topiramate and zonisamide for retention (P < 0.002); among third-line ASMs, cenobamate, brivaracetam and clobazam achieved similar retention and seizure freedom. For generalized and unclassified epilepsies, first-line ASMs achieved similar seizure freedom, but levetiracetam remained superior to lamotrigine and oxcarbazepine for retention (P < 0.02); topiramate had the best seizure freedom among second-line ASMs (P < 0.001), while lacosamide again had better retention than zonisamide (P < 0.006); there were insufficient patients included in third-line ASM trials for this subgroup. In conclusion, emulated clinical trials provide a useful framework for studying the comparative effectiveness of anti-seizure medications using real-world observational data. Our findings of differences in seizure freedom and retention between ASMs help to generate hypotheses that should be tested in future prospective, randomized comparative effectiveness trials.

Indexed as

AnticonvulsantsEpilepsyMedical InformaticsSeizuresAdultClinical Trials as TopicElectronic Health RecordsFemaleHumansLamotrigineLevetiracetamMaleMiddle AgedRetrospective StudiesTreatment OutcomeAnticonvulsantsLamotrigineLevetiracetamanti-seizure medicationscausal inferencecomparative effectivenessepilepsynatural language processing

Identifiers

PMID40663627
PMCPMC12678050

What OpenQuestion holds

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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.