Initial EEG and Neonatal Onset Predict Genetic Epilepsy Outcomes

The Diagnostic Challenge of Pediatric Genetic Epilepsies

Pediatric epilepsy encompasses a highly heterogeneous group of disorders where establishing an underlying etiology has become the cornerstone of modern diagnostic frameworks [1]. The International League Against Epilepsy emphasizes that identifying the specific etiology at the time of diagnosis is critical, as it directly influences treatment choices and long-term management [2]. While advanced genetic testing can identify causative variants in nearly half of complex cases, the extreme clinical variability of these conditions often makes early genotype-phenotype interpretation difficult [1]. For example, conditions like developmental and epileptic encephalopathies may present with subtle initial signs but rapidly progress to severe intellectual disability and drug-resistant seizures [3]. To bridge this gap, researchers recently investigated whether specific clinical features and electroencephalogram patterns captured during the very first seizures can help clinicians predict long-term outcomes and guide early genetic reasoning.

Study Cohort and Early Clinical Assessments

Genetic epilepsies encompass a broad spectrum of disorders caused by pathogenic variants in more than 1,000 genes. Because their clinical expression is highly variable, early phenotype-genotype interpretation remains challenging for practicing physicians. However, early seizure semiology (the observable physical signs and behaviors occurring during a seizure) and baseline electroencephalogram (EEG) features may provide clinically useful information for initial diagnostic orientation. To better understand these early presentations, researchers conducted a retrospective study at Bambino Gesù Children's Hospital to characterize initial clinical and EEG features in patients with genetic epilepsies and examine their associations with long-term outcomes. The cohort included 277 patients (52.3% female) who carried pathogenic or likely pathogenic variants in epilepsy-related genes. At the time of the last follow-up, the median age of the patients was 8.1 years (range 0 to 40 years). To capture the earliest stages of the disease, the investigators extracted clinical variables at seizure onset and EEG recordings performed within the first month of the initial seizure, with baseline EEG data available for 107 individuals. The researchers then tracked several specific clinical endpoints, including seizure frequency, drug resistance, movement disorders, behavioral or autism spectrum disorder comorbidities, and developmental delay or intellectual disability (DD/ID). By assessing associations between early features and these outcomes, the study aimed to quantify how initial clinical and electroencephalographic markers correlate with later disease severity, providing clinicians with potential early warning signs for complex cases.

Neonatal Onset and Initial EEG Predict Severe Outcomes

Long-term follow-up of the cohort revealed a high burden of severe clinical outcomes, underscoring the difficulty of managing genetic epilepsies. Overall, drug resistance occurred in 58.8% of the patients, and severe developmental delay or intellectual disability (DD/ID) affected 35.4% of the cohort. These high rates highlight the urgent need for early prognostic markers to guide treatment expectations and family counseling. The timing of the first seizure proved to be a critical prognostic indicator, with neonatal onset signaling a substantially worse long-term trajectory. Specifically, neonatal onset was associated with a higher rate of drug resistance (71.4%; odds ratio [OR] 2.0, 95% CI 1.05-3.77) and a higher rate of movement disorders (60.7%; OR 3.7, 95% CI 2.02-6.82). The most striking risk increase was observed in cognitive outcomes, where neonatal onset correlated with severe DD/ID in 71.4% of cases (OR 7.0, 95% CI 3.66-13.49). For practicing pediatricians and neurologists, a neonatal presentation should immediately raise suspicion for a highly refractory and developmentally severe disease course. Beyond the timing of onset, specific patterns on the initial EEG provided further prognostic value. The researchers found that slow EEG background activity and multifocal epileptiform discharges were significantly associated with both drug resistance and severe DD/ID. Identifying these specific electroencephalographic features within the first month of seizure onset can help physicians anticipate a more severe phenotype. This early recognition allows for prompt discussions with families regarding long-term prognosis and facilitates the timely assembly of a multidisciplinary care team.

Clustering Phenotypes to Guide Genetic Testing

To better understand how initial presentations correlate with underlying genetic causes, the researchers utilized hierarchical clustering analysis (a statistical method that groups patients based on shared clinical and biological patterns rather than predefined categories). By applying this technique, the investigators successfully identified distinct clusters linking early phenotypes to specific gene-level etiologies. The analysis revealed coherent genotype-phenotype groupings involving the genes SCN1A, PRRT2, STXBP1, KCNQ2, SCN2A, CHD2, SYNGAP1, and MECP2. Each of these genetic clusters was linked to specific early clinical and EEG features. For the practicing physician, this means that observing a particular combination of seizure timing, physical symptoms, and brain wave patterns can point directly toward a specific genetic target before laboratory results are finalized. This mapping of early observable traits to specific genetic mutations supports early diagnostic reasoning, potentially allowing neurologists to prioritize targeted genetic panels and tailor initial management strategies more rapidly. While these findings provide a practical framework for interpreting early epilepsy presentations, the authors noted several constraints. Limitations of the study include its retrospective design and the small number of patients per gene, which warrants larger multicenter studies for validation. Until such broad validation occurs, clinicians should use these genotype-phenotype clusters as an adjunctive tool to guide, rather than replace, comprehensive genetic testing in pediatric epilepsy cases.

References

1. Windaswara R, Rochim YN. A Systematic Review of Diagnostic Approaches for Pediatric Epilepsy: An Evidence-Based Framework from Clinical Assessment to Precision Medicine. International journal of medical science and health research. 2025. doi:10.70070/sdv3f613

2. Scheffer IE, Berkovic SF, Capovilla G, et al. ILAE classification of the epilepsies: Position paper of the ILAE Commission for Classification and Terminology. Epilepsia. 2017. doi:10.1111/epi.13709

3. Scheffer IE, Nabbout R. SCN1A‐related phenotypes: Epilepsy and beyond. Epilepsia. 2019. doi:10.1111/epi.16386