Document Type
Article
Publication Date
11-11-2022
Abstract
BACKGROUND: A third of people with juvenile myoclonic epilepsy (JME) are drug-resistant. Three-quarters have a seizure relapse when attempting to withdraw anti-seizure medication (ASM) after achieving seizure-freedom. It is currently impossible to predict who is likely to become drug-resistant and safely withdraw treatment. We aimed to identify predictors of drug resistance and seizure recurrence to allow for individualised prediction of treatment outcomes in people with JME.
METHODS: We performed an individual participant data (IPD) meta-analysis based on a systematic search in EMBASE and PubMed - last updated on March 11, 2021 - including prospective and retrospective observational studies reporting on treatment outcomes of people diagnosed with JME and available seizure outcome data after a minimum one-year follow-up. We invited authors to share standardised IPD to identify predictors of drug resistance using multivariable logistic regression. We excluded pseudo-resistant individuals. A subset who attempted to withdraw ASM was included in a multivariable proportional hazards analysis on seizure recurrence after ASM withdrawal. The study was registered at the Open Science Framework (OSF; https://osf.io/b9zjc/).
FINDINGS: Our search yielded 1641 articles; 53 were eligible, of which the authors of 24 studies agreed to collaborate by sharing IPD. Using data from 2518 people with JME, we found nine independent predictors of drug resistance: three seizure types, psychiatric comorbidities, catamenial epilepsy, epileptiform focality, ethnicity, history of CAE, family history of epilepsy, status epilepticus, and febrile seizures. Internal-external cross-validation of our multivariable model showed an area under the receiver operating characteristic curve of 0·70 (95%CI 0·68-0·72). Recurrence of seizures after ASM withdrawal (n = 368) was predicted by an earlier age at the start of withdrawal, shorter seizure-free interval and more currently used ASMs, resulting in an average internal-external cross-validation concordance-statistic of 0·70 (95%CI 0·68-0·73).
INTERPRETATION: We were able to predict and validate clinically relevant personalised treatment outcomes for people with JME. Individualised predictions are accessible as nomograms and web-based tools.
FUNDING: MING fonds.
Recommended Citation
Stevelink, Remi; Al-Toma, Dania; Jansen, Floor E.; Lamberink, Herm J.; Asadi-Pooya, Ali A.; Farazdaghi, Mohsen; Cação, Gonçalo; Jayalakshmi, Sita; Patil, Anuja; Özkara, Çiğdem; Aydın, Şenay; Gesche, Joanna; Beier, Christoph P.; Stephen, Linda J.; Brodie, Martin J.; Unnithan, Gopeekrishnan; Radhakrishnan, Ashalatha; Höfler, Julia; Trinka, Eugen; Krause, Roland; Irelli, Emanuele Cerulli; Di Bonaventura, Carlo; Szaflarski, Jerzy P.; Hernández-Vanegas, Laura E.; Moya-Alfaro, Monica L.; Zhang, Yingying; Zhou, Dong; Pietrafusa, Nicola; Specchio, Nicola; Japaridze, Giorgi; Beniczky, Sándor; Janmohamed, Mubeen; Kwan, Patrick; Syvertsen, Marte; Selmer, Kaja K.; Vorderwülbecke, Bernd J.; Holtkamp, Martin; Viswanathan, Lakshminarayanapuram G.; Sinha, Sanjib; Baykan, Betül; Altindag, Ebru; von Podewils, Felix; Schulz, Juliane; Seneviratne, Udaya; Viloria-Alebesque, Alejandro; Karakis, Ioannis; D'Souza, Wendyl J.; Sander, Josemir W.; Koeleman, Bobby P. C.; Otte, Willem M.; and Braun, Kees P. J., "Individualised Prediction of Drug Resistance and Seizure Recurrence After Medication Withdrawal in People With Juvenile Myoclonic Epilepsy: A Systematic Review and Individual Participant Data Meta-Analysis" (2022). Department of Neurology Faculty Papers. Paper 304.
https://jdc.jefferson.edu/neurologyfp/304
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
PubMed ID
36467455
Language
English
Comments
This is the author's final published version in eClinicalMedicine, Volume 53, November 2022, Article Number 101732.
The published version is available at https://doi.org/10.1016/j.eclinm.2022.101732. Copyright © 2022 The Author(s). Published by Elsevier Ltd.