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

Remi Stevelink, Dania Al-Toma, Floor E. Jansen, Herm J. Lamberink, Ali A. Asadi-Pooya, Mohsen Farazdaghi, Gonçalo Cação, Sita Jayalakshmi, Anuja Patil, Çiğdem Özkara, Şenay Aydın, Joanna Gesche, Christoph P. Beier, Linda J. Stephen, Martin J. Brodie, Gopeekrishnan Unnithan, Ashalatha Radhakrishnan, Julia Höfler, Eugen Trinka, Roland KrauseEmanuele Cerulli Irelli, Carlo Di Bonaventura, Jerzy P. Szaflarski, Laura E. Hernández-Vanegas, Monica L. Moya-Alfaro, Yingying Zhang, Dong Zhou, Nicola Pietrafusa, Nicola Specchio, Giorgi Japaridze, Sándor Beniczky, Mubeen Janmohamed, Patrick Kwan, Marte Syvertsen, Kaja K. Selmer, Bernd J. Vorderwülbecke, Martin Holtkamp, Lakshminarayanapuram G. Viswanathan, Sanjib Sinha, Betül Baykan, Ebru Altindag, Felix von Podewils, Juliane Schulz, Udaya Seneviratne, Alejandro Viloria-Alebesque, Ioannis Karakis, Wendyl J. D'Souza, Josemir W. Sander, Bobby P.C. Koeleman, Willem M. Otte, Kees P.J. Braun, EpiPGX Consortium

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20 Citations (Scopus)

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.

Original languageEnglish
Article number101732
Number of pages13
JournaleClinicalMedicine
Volume53
DOIs
Publication statusPublished - Nov 2022

Keywords

  • Drug resistance
  • Individual participant data
  • JME
  • Juvenile myoclonic epilepsy
  • Medication withdrawal
  • Meta-analysis
  • Multivariable prediction
  • Prediction model
  • Refractory epilepsy
  • Remission
  • Seizure recurrence

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