Risk of seizure relapse and long-term outcomes after discontinuation of antiseizure medication in children with epilepsy

Prognostic interictal electroencephalographic biomarkers and models to assess antiseizure medication efficacy for clinical practice: A scoping review

Antiseizure medication (ASM) is the primary treatment for epilepsy. In clinical practice, methods to assess ASM efficacy (predict seizure freedom or seizure reduction), during any phase of the drug treatment lifecycle, are limited. This scoping review identifies and appraises prognostic electroencephalographic (EEG) biomarkers and prognostic models that use EEG features, which are associated with seizure outcomes following ASM initiation, dose adjustment, or withdrawal. We also aim to summarize the population and context in which these biomarkers and models were identified and described, to understand how they could be used in clinical practice. Between January 2021 and October 2022, four databases, references, and citations were systematically searched for ASM studies investigating changes to interictal EEG or prognostic models using EEG features and seizure outcomes. Study bias was appraised using modified Quality in Prognosis Studies criteria. Results were synthesized into a qualitative review. Of 875 studies identified, 93 were included. Biomarkers identified were classed as qualitative (visually identified by wave morphology) or quantitative. Qualitative biomarkers include identifying hypsarrhythmia, centrotemporal spikes, interictal epileptiform discharges (IED), classifying the EEG as normal/abnormal/epileptiform, and photoparoxysmal response. Quantitative biomarkers were statistics applied to IED, high-frequency activity, frequency band power, current source density estimates, pairwise statistical interdependence between EEG channels, and measures of complexity. Prognostic models using EEG features were Cox proportional hazards models and machine learning models. There is promise that some quantitative EEG biomarkers could be used to assess ASM efficacy, but further research is required. There is insufficient evidence to conclude any specific biomarker can be used for a particular population or context to prognosticate ASM efficacy. We identified a potential battery of prognostic EEG biomarkers, which could be combined with prognostic models to assess ASM efficacy. However, many confounders need to be addressed for translation into clinical practice.

Genetic generalized epilepsy: factors associated with drug resistance polytherapy

Background

Different studies investigating generalized genetic epilepsy (GGE) have shown that achieving 5 years of remission, early seizure remission, and needing antiseizure medication (ASM) monotherapy may predict favorable long-term outcomes.

Results

This is a retrospective analytical cohort study. Records of patients with GGE diagnoses at a large epilepsy center in Cairo served as the data source. 630 patients (297 male patients (47.1%) and 333 female (52.9%)) were included, their median onset age was 13 years. The follow-up period of this study was at least 4 years. 418 patients (66.1%) were early remitters, 160 patients (25.4%) were late remitters, and 52 patients (8.3%) were intractable. In addition, 367 patients (58.3%) needed a single ASM to achieve a maximum remission period (monotherapy group), while 263 patients (41.7%) needed ASM polytherapy. Stepwise regression analysis revealed that absence seizures, clusters of seizures, seizure frequency before treatment, and dose of sodium valproate (VPA) until the first remission were independent predictors for polytherapy. Moreover, absence seizures, seizure frequency before treatment, VPA dose, and catamenial seizures in females were independent predictors of intractability.

Conclusion

The majority of GGE have a favorable outcome, some clinical features could predict the need for polytherapy and failure of remission on treatment.