Development and Validation of a Prediction Model for Early Diagnosis of SCN1A-Related Epilepsies

Brunklaus, Andreas; Perez-Palma, Eduardo; Ghanty, Ismael; Xinge, Ji; Brilstra, Eva; Ceulemans, Berten; Chemaly, Nicole; de Lange, Iris; Depienne, Christel; Guerrini, Renzo; Mei, Davide; Moller, Rikke S.; Nabbout, Rima; Regan, Brigid M.; Schneider, Amy L.; et. al.


Background and Objectives Pathogenic variants in the neuronal sodium channel alpha 1 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. Methods We 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. Results A 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]). Discussion The 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 ( 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.

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Título según WOS: Development and Validation of a Prediction Model for Early Diagnosis of SCN1A-Related Epilepsies
Título de la Revista: NEUROLOGY
Volumen: 98
Número: 11
Fecha de publicación: 2022
Página de inicio: E1163
Página final: E1174


Notas: ISI