Abstracts

Rationale: Variants in the SCN1A gene are one of the most common causes of genetic epilepsy. SCN1A-related epilepsy is associated with a broad phenotypic spectrum, ranging from milder epilepsy with normal cognition (generalized epilepsy with febrile seizures plus[GEFS+]) to developmental and epileptic encephalopathies such as Dravet Syndrome (DS). Recently an SCN1A prediction model was developed using European and Australian cohorts. The goal of this project was to assess the accuracy of this prediction tool in correlating a child’s SCN1A variant with their phenotype in a cohort of children at Children’s Hospital Colorado. Assessing the performance of this prediction tool in a different cohort of children is important to understanding the generalizability of this tool and utility in clinical practice.

Methods: Patients found to have a pathogenic or likely pathogenic variant in SCN1A on Invitae Behind the Seizure panels ordered from Children’s Hospital Colorado 2017-2023 were recorded. A detailed retrospective chart review was then performed. Demographic information, diagnosis (ascertained either by clinical diagnosis documented in the chart or by diagnostic criteria if no diagnosis was listed), SCN1A variant, and result from the prediction tool was recorded in a secure redcap database. Descriptive statistical analysis and calculation of concordance rates was performed.

Results: Our institution sends approximately 190 epilepsy panels per year. Over the course of 2017-2023, 35 individuals were found to have a likely pathogenic or pathogenic variant in SCN1A. Averaged over 6 years this is a rate of 6 pathogenic variants in SCN1A per year. Out of this cohort, 15 individuals had a diagnosis of Dravet Syndrome, and 16 individuals had a diagnosis of GEFS+. 4 individuals did not have a precise diagnosis other than “epilepsy” and a more specific diagnosis was not able to be determined using diagnostic criteria. Utilizing the prediction tool, the concordance rate of this population was 70%. The concordance of patients with a diagnosis of Dravet Syndrome was 87% whereas the concordance of patients with a diagnosis of GEFS+ was 69%. Of the individuals that were discordant, 33% were within 15 percentage points of the cut off for either category.

Conclusions: This study provides key information on the performance of a prediction tool for SCN1A on an American cohort. It also gives a perspective on the percentage of SCN1A variants resulting in a Dravet vs GEFS+ phenotype at our institution. Even with a smaller cohort this was similar to validation rates seen in the initial European/Australian validation studies. There was a higher rate of GEFS+ in this population when compared to the European/Australian cohorts with similar validation numbers, likely due to increased access to testing and practice variations. Overall, this study offers valuable insights for understanding the tool’s potential in current clinical care.

Funding: None