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. 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/). 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). 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. MING fonds.

Objective: Dysfunction of the microbiota–gut–brain axis is emerging as a new pathogenic mechanism in epilepsy, potentially impacting on medication response and disease outcome. We investigated the composition of the gut microbiota in a cohort of medication‐resistant (MR) and medication‐sensitive (MS) pediatric patients with epilepsy. Methods: Children with epilepsy of genetic and presumed genetic etiologies were evaluated clinically and subgrouped into MR and MS. Age‐matched healthy controls (HCs) were also recruited. A food diary was used to evaluate nutritional habits, and the Rome IV questionnaire was used to record gastrointestinal symptoms. The microbiota composition was assessed in stool samples through 16S rRNA. α‐Diversity (AD) and β‐diversity (BD) were calculated, and differential abundance analysis was performed using linear multivariable models (significance: p .adj < .05). Results: Forty‐one patients (MR:MS = 20:21) with a mean age of 7.2 years (±4.6 SD) and 27 age‐matched HCs were recruited. No significant differences in AD were found when comparing patients and HCs. Significant positive correlation was found between AD and age (Chao1 p .adj = .0004, Shannon p .adj = .0004, Simpson p .adj = .0028). BD depicted a different bacterial profile in the epilepsy groups compared to HCs (MS vs. HC: Bray–Curtis F = 1.783, p = .001; Jaccard F = 1.24, p = .001; MR vs. HC: Bray–Curtis F = 2.24, p = .001; Jaccard F = 1.364, p = .001). At the genus level, the epilepsy groups were characterized by a significant increase in Hungatella (MS vs. HC: +4.95 log ₂ change; MR vs. HC: +6.72 log ₂ change); the [Eubacterium] siraeum group changed between the MR and MS subgroups. Significance: Epileptic patients display unique gut metagenomic signatures compared to HCs. Moreover, a different ratio of the butyrate‐producing [Eubacterium] siraeum group suggests dissimilarities between patients based on the response to antiseizure medications.

Background and objectives: Eyelid myoclonia with absences (EMA) is a generalized epilepsy syndrome whose prognosis and clinical characteristics are still partially undefined. We investigated electroclinical endophenotypes and long-term seizure outcome in a large cohort of EMA patients. Methods: In this multicenter retrospective study, EMA patients with ≥5 years of follow-up were included. We investigated prognostic patterns and sustained terminal remission (STR), along with their prognostic factors. Moreover, a two-step cluster analysis was used to investigate the presence of distinct EMA endophenotypes. Results: We included 172 patients, with a median age at onset of 7 years (interquartile range (IQR) 5-10) and a median follow-up duration of 14 years (IQR 8.25-23.75). Sixty-six patients (38.4%) displayed a non-remission pattern, whereas remission and relapse patterns were encountered in 56 (32.6%) and 50 (29.1%) subjects. Early epilepsy onset, history of febrile seizures (FS) and eyelid myoclonia (EM) status epilepticus significantly predicted a non-remission pattern according to multinomial logistic regression analysis. STR was achieved by 68 (39.5%) patients with a mean latency of 14.05 years (SD ± 12.47). Early epilepsy onset, psychiatric comorbidities, and a history of FS and generalized tonic-clonic seizures (GTCS) were associated with a lower probability of achieving STR according to a Cox regression proportional hazards model. Antiseizure medication (ASM) withdrawal was attempted in 62/172 patients, and seizures relapsed in 74.2%. Cluster analysis revealed two distinct clusters with 86 patients each. Cluster 2, which we defined as “EMA-plus”, was characterized by an earlier age at epilepsy onset, higher rate of intellectual disability, EM status epilepticus, generalized paroxysmal fast activity, self-induced seizures, FS, and poor ASM response, whereas Cluster 1, the “EMA-only” cluster, was characterized by a higher rate of seizure remission and more favorable neuropsychiatric outcome. Discussion: Early epilepsy onset was the most relevant prognostic factor for poor treatment response. A long latency between epilepsy onset and ASM response was observed, suggesting the impact of age-related brain changes in EMA remission. Finally, our cluster analysis showed a clear-cut distinction of EMA patients into an EMA-plus insidious subphenotype and an EMA-only benign cluster that strongly differed in terms of remission rates and cognitive outcomes.

Epilepsy represents the third most common neurological disorder in the elderly. Antiseizure medications (ASMs) are often used not only to treat epilepsy but also other disorders in this age group. ...