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

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

Polycystic ovary syndrome in patients on antiepileptic drugs

Objective: This study aims to discuss the prevalence of polycystic ovary (PCO) and Polycystic ovary syndrome (PCOS) in women with epilepsy (WWE) on valproate (VPA), carbamazepine (CBZ), or phenobarbitone (PB), drug naive WWE and women with bipolar affective disorder (BPAD) on VPA. Materials and Methods: This prospective study included 190 women aged 18-45 years, who had epilepsy or BPAD (on VPA), and consented for study. Patients were grouped as Group 1 (n = 40): WWE on VPA, Group 2 (n = 50): WWE on CBZ, Group 3 (n = 50): WWE on PB, Group 4 (n = 30): drug naοve WWE, and Group 5 (n = 20): women with BPAD on VPA. All women were interviewed for medical, menstrual, drug and treatment history, nature of epilepsy, and seizure control. Chi-square test and Fisher′s exact test were done to compare results between the groups. Results: Fifty-two women (52/190; 27.4%) had menstrual disturbances, in which oligomenorrhea was the most common (55.8%). There was a significant difference in the occurrence of PCOS in patients on VPA versus normal population (P = 0.05) and patients on other antiepileptic drugs (AEDs) (P = 0.02). There was, however, no significant difference in the occurrence of PCO between patients on VPA and the untreated epileptic women. VPA group (Epilepsy + BPAD) had a significantly higher occurrence of obesity than other treatment groups (P = 0.043, OR = 2.11). Conclusions: The study observed significantly higher occurrence of PCO in patients on VPA compared to other AEDs and the normal population. The importance of proper clinical evaluation before initiating VPA is highlighted

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

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