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. (November 2022)

• Record Type: Journal Article
• Authors: 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.; Braun, Kees P.J.;
• Is Part Of: EClinicalMedicine. Volume 53(2022)
• Publication Date: 2022-11
• Subjects: Juvenile myoclonic epilepsy -- Prediction model -- Refractory epilepsy -- Drug resistance -- Medication withdrawal -- Remission -- Multivariable prediction -- JME -- Seizure recurrence -- Meta-analysis -- Individual participant data; Medicine -- Research -- Periodicals; Medical policy -- Periodicals; Clinical Medicine; Health Policy; Public Health; Medical policy; Medicine -- Research; Periodical; Electronic journals; Periodicals; 613
• DOI: 10.1016/j.eclinm.2022.101732
• Languages: English
• ISSNs: 2589-5370

Abstract

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: 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 .