The SCN1A -Epilepsy Prediction Model

Pathogenic variants in the neuronal sodium-channel α1-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 developing Dravet syndrome versus GEFS+ is key for implementing gene-specific, disease-modifying therapies before cognitive impairment emerges

Based on a well-phenotyped international cohort of 1,018 SCN1A -positive Dravet syndrome and GEFS+ patients, we used data from five countries (training cohort, N=743) to develop multiple prediction models. We employed data easily available for any young child presenting with recurrent seizures and a pathogenic SCN1A variant, including age of seizure onset and the genetic variant, combined with a newly-developed SCN1A genetic prediction score. We validated the model using two independent blinded cohorts from Australia (N=203) and Belgium (N=72).

A high SCN1A genetic score and young age of onset, were each associated with Dravet syndrome (P<0.001). A combined ' SCN1A genetic score & seizure onset' model separated Dravet syndrome from GEFS+ more effectively (area under the curve [AUC]=0.89) and outperformed all other models (AUC=0.79-0.85; P<0.001). Model performance was replicated in both validation cohorts (AUC-Australia=0.94; AUC-Belgium=0.92).

Figure 1

Study overview. We used a supervised machine learning approach to generate a prediction model using genetic data ( SCN1A genetic score) and clinical data (age of seizure onset in months) from 743 patients (training cohort). We tested the prediction model with two independent blinded validation cohorts (n= 275) and integrated it into the SCN1A -epilepsy prediction model.