Development and Validation of a Prediction Model for Early Diagnosis of <i>SCN1A</i>-Related Epilepsies

Background and ObjectivesPathogenic 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 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 vs 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.MethodsWe performed a retrospective multicenter cohort study comprising data from patients with SCN1A-positive Dravet syndrome and patients with GEFS+ consecutively referred for genetic testing (March 2001–June 2020) including age at seizure onset and a newly developed SCN1A genetic score. A training cohort was used to develop multiple prediction models that were validated using 2 independent blinded cohorts. Primary outcome was the discriminative accuracy of the model predicting Dravet syndrome vs other GEFS+ phenotypes.ResultsA total of 1,018 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] vs 52.0 [SD 57.5]; p < 0.001) and young age at onset (6.0 [SD 3.0] vs 14.8 [SD 11.8] months; p < 0.001) were each associated with Dravet syndrome vs 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 0.91–0.97]) and 2 (AUC 0.92 [95% CI 0.82–1.00]).DiscussionThe prediction model allows objective estimation at disease onset whether a child will develop Dravet syndrome vs GEFS+, assisting clinicians with prognostic counseling and decisions on early institution of precision therapies (http://scn1a-prediction-model.broadinstitute.org/).Classification of EvidenceThis study provides Class II evidence that a combined SCN1A genetic score and seizure onset model distinguishes Dravet syndrome from other GEFS+ phenotypes.

Cite this document

Brunklaus, A., Pérez-Palma, E., Ghanty, I., Xinge, J., Brilstra, E., Ceulemans, B., Chemaly, N., De Lange, I., Depienne, C., Guerrini, R., Mei, D., Møller, R. S., Nabbout, R., Regan, B. M., Schneider, A. L., Scheffer, I. E., Schoonjans, A.-S., Symonds, J. D., Weckhuysen, S., … Lal, D. (2022). Development and validation of a prediction model for early diagnosis of scn1a -related epilepsies. Neurology, 98(11). https://doi.org/10.1212/WNL.0000000000200028

Subjects

child

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cohort studies

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early diagnosis

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epilepsies, myoclonic

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epilepsy

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humans

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mutation

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nav1.1 voltage-gated sodium channel

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retrospective studies

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biological marker

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sodium channel nav1.1

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scn1a protein, human

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sodium channel nav1.1

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age

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article

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clinical feature

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clinical outcome

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cohort analysis

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comparative study

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controlled study

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disease duration

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disease exacerbation

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early diagnosis

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epilepsy

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female

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genetic analysis

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genetic screening

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genetic variability

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human

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major clinical study

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male

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missense mutation

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multicenter study

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personalized medicine

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phenotype

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retrospective study

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scn1a gene

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scn1a related epilepsy

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severe myoclonic epilepsy in infancy

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validation process

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child

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clinical trial

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early diagnosis

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genetics

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mutation

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myoclonus epilepsy