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17 November 2022
Article (Scientific journals)
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
Stevelink, Remi; Al-Toma, Dania; Jansen, Floor E. et al.
2022In EClinicalMedicine, p. 101732
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Keywords :
Juvenile myoclonic epilepsy; Prediction model; Refractory epilepsy; Drug resistance; Medication withdrawal; Remission; Multivariable prediction; JME; Seizure recurrence; Meta-analysis; Individual participant data
Abstract :
[en] Summary 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  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.
Disciplines :
Author, co-author :
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 ;  University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Bioinformatics Core
Irelli, Emanuele Cerulli
Bonaventura, Carlo Di
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
Podewils, Felix Von
Schulz, Juliane
Seneviratne, Udaya
Viloria-Alebesque, Alejandro
Karakis, Ioannis
D'Souza, Wendyl J.
Sander, Josemir W.
Koeleman, Bobby P. C.
Otte, Willem M.
Braun, Kees P. J.
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Title :
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
Publication date :
November 2022
Journal title :
Publisher :
Elsevier, United Kingdom
Pages :
Peer reviewed :
Peer Reviewed verified by ORBi
European Projects :
FP7 - 279062 - EPIPGX - Epilepsy Pharmacogenomics: delivering biomarkers for clinical use
Funders :
CE - Commission Européenne


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