Time Is Brain: The Importance of an Accurate SCN1A Prediction Score in the Era of Precision Medicine

Background and objectives: Pathogenic variants in the neuronal sodium channel a1-subunit gene (SCN1A) are the most frequent monogenic cause of epilepsy. Phenotypes comprise a wide clinical spectrum including the severe childhood epilepsy, Dravet syndrome, characterized by drug-resistant seizures, intellectual disability and high mortality, and the milder genetic epilepsy with febrile seizures plus (GEFS+), characterized by normal cognition. Early recognition of a child's risk for developing Dravet syndrome versus GEFS+ is key for implementing disease-modifying therapies when available before cognitive impairment emerges. Our objective was to develop and validate a prediction model using clinical and genetic biomarkers for early diagnosis of SCN1A-related epilepsies. Methods: Retrospective multicenter cohort study comprising data from SCN1A-positive Dravet syndrome and GEFS+ patients consecutively referred for genetic testing (March 2001-June 2020) including age of seizure onset and a newly-developed SCN1A genetic score. A training cohort was used to develop multiple prediction models that were validated using two independent blinded cohorts. Primary outcome was the discriminative accuracy of the model predicting Dravet syndrome versus other GEFS+ phenotypes. Results: 1018 participants were included. The frequency of Dravet syndrome was 616/743 (83%) in the training cohort, 147/203 (72%) in validation cohort 1 and 60/72 (83%) in validation cohort 2. A high SCN1A genetic score 133.4 (SD, 78.5) versus 52.0 (SD, 57.5; p < 0.001) and young age of onset 6.0 (SD, 3.0) months versus 14.8 (SD, 11.8; p < 0.001) months, were each associated with Dravet syndrome versus GEFS+. A combined "SCN1A genetic score and seizure onset" model separated Dravet syndrome from GEFS+ more effectively (area under the curve [AUC], 0.89 [95% CI, 0.86-0.92]) and outperformed all other models (AUC, 0.79-0.85; p < 0.001). Model performance was replicated in both validation cohorts 1 (AUC, 0.94 [95% CI, 0.910.97]) and 2 (AUC, 0.92 [95% CI, 0.82-1.00]). Discussion: The prediction model allows objective estimation at disease onset whether a child will develop Dravet syndrome versus GEFS+, assisting clinicians with prognostic counseling and decisions on early institution of precision therapies (http:// scn1a-prediction-model.broadinstitute.org/). Classification of evidence: This study provides Class II evidence that a combined "SCN1A genetic score and seizure onset" model distinguishes Dravet syndrome from other GEFS+ phenotypes.