Assessing the Clinical and Treatment Landscape of Genetic Epilepsies Through 3,760 Individuals
Julie Xian1, Michael Kaufman1, Sarah Ruggiero1, Mark Ramos1, Alexander Gonzalez1, Amanda Back1, Lea Bailey-medley1, Stacey Cohen1, Vishnu Cuddapah1, Colin Ellis1, Natalie Ginn1, Naomi Lewin1, Laina Lusk1, Eric Marsh1, Shavonne Massey1, Xilma Ortiz-Gonzalez1, Pamela Mcdonnell1, Katie Rose Sullivan1, Katherine Taub1, Sarah Tefft1, Katherine Helbig1, Ethan Goldberg1, Ingo Helbig1, Mark Fitzgerald1
1Children's Hospital of Philadelphia
Objective:
To delineate disease trajectories and longitudinal treatment landscapes in genetic epilepsies through Real-World Data captured from routine clinical care.
Background:
Epilepsies with a presumed genetic basis account for up to two thirds of all epilepsies. A genetic diagnosis can inform personalized treatment strategies, however real-world practice is variable and the degree to which identification of an etiology translates into evidence-based treatment changes remains relatively uncharacterized.
Design/Methods:
We retrospectively assessed the clinical data of individuals with epilepsy and neurodevelopmental disorders seen at a large pediatric healthcare network across 3 years. The impact of a genetic diagnosis on subsequent clinical management was reviewed, including referrals to specialty care and epilepsy surgical evaluations and modifications to treatment strategies. Seizure outcomes across epilepsy syndromes and distinct genetic etiologies were assessed using a Common Data Element-based framework.
Results:
We included 3,760 individuals with epilepsy and neurodevelopmental disorders and analyzed clinical data across 6,818 patient encounters. The median age of study inclusion was 6.4 years, with a median age of diagnosis of 3.1 years. 1,140/3,705 (30.8%) had a genetic diagnosis, representing 370 unique genetic etiologies including recurrent chromosomal abnormalities. The most common genetic etiologies included STXBP1 (n=89), SCN1A (n=62), and SCN2A (n=44), while the most frequent diagnoses were SCN1A (n=18), KCNQ2 (n=17), and PRRT2 (n=12). Seizure frequencies were assessed for 2,311 individuals across 1,625 cumulative patient-years, and epilepsy trajectories were characterized with respect to distinct epilepsy syndromes and etiologies. Systematic evaluation of adjustments in clinical management after a genetic diagnosis demonstrated measurable effects, including changes in treatment practices.
Conclusions:
Our study provides an unbiased view of the dynamic clinical and treatment landscape through a large-scale data approach. We demonstrate measurable effects in clinical care following a genetic diagnosis and establish a systematic framework for quantifying genetic evaluations and epilepsy outcomes for future precision medicine avenues.