ArticleEClinicalMedicine2022
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.
Article in EClinicalMedicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it, 38 citations in OpenAlex.
- Antiseizure Medications in Poststroke Seizures: A Systematic Review and Network Meta-Analysis.Neurology · 2025Pooled it
- Pooled it
- Factors associated with drug-resistant epilepsy in children with epilepsy: a retrospective cohort study.Translational pediatrics · 2026Article
- Article
- Ultra-refractory epilepsy: The newly described entity.Epilepsia open · 2026Article
- Challenges in antiseizure medication discontinuation decisions: A qualitative study of clinicians who see patients with epilepsy.Clinical neurology and neurosurgery · 2026Article
- Observational
- Antiseizure medication discontinuation: A mixed-methods exploration of factors considered by patients when approaching decision-making.Epilepsy research · 2025Article
- Risk factors of recurrence for epilepsy patients after antiseizure medication withdrawal: what we do and do not know?Journal of neurology · 2025Review
- Prospective evaluation of clinical characteristics, treatment efficacy, and seizure remission in adult-onset genetic generalized epilepsy.Epilepsia open · 2025Article
- Tremor as an intrinsic feature of juvenile myoclonic epilepsy.Epilepsia · 2025Article
- Epilepsy Therapies Symposium | Do We Really "Outgrow" Seizures?Epilepsy currents · 2024Review
- [Discontinuation of antiseizure medication in patients with epilepsy].Der Nervenarzt · 2024Review
- Predicting seizure recurrence after an initial seizure-like episode from routine clinical notes using large language models: a retrospective cohort study.The Lancet. Digital health · 2023Article
- Epilepsy classification using artificial intelligence: A web-based application.Epilepsia open · 2023Article
- Revisiting the concept of drug-resistant epilepsy: A TASK1 report of the ILAE/AES Joint Translational Task Force.Epilepsia · 2023Article
- Risk factors of recurrence after drug withdrawal in children with epilepsy.Frontiers in neurology · 2023Article
- Juvenile myoclonic epilepsy as a spectrum disorder: mechanisms of drug resistance and precision management.Frontiers in neurologyReview
- Exit Strategy: Balancing the Risks and Rewards of Antiseizure Medication Withdrawal.Epilepsy currentsReview
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Authors and funding
52 authors at 20 institutions in 17 countries.
Funding
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.
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