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

Andreas Brunklaus, Eduardo Pérez-Palma, Ismael Ghanty, Ji Xinge, Eva Brilstra, Berten Ceulemans, Nicole Chemaly, Iris de Lange, Christel Depienne, Renzo Guerrini, Davide Mei, Rikke S. Møller, Rima Nabbout, Brigid M. Regan, Amy L. Schneider, Ingrid E. Scheffer, An Sofie Schoonjans, Joseph D. Symonds, Sarah Weckhuysen, Michael W. KattanSameer M. Zuberi, Dennis Lal

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

Abstrakt

BACKGROUND AND OBJECTIVES: 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 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 (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.

OriginalsprogEngelsk
TidsskriftNeurology
Vol/bind98
Udgave nummer11
Sider (fra-til)e1163-e1174
ISSN0028-3878
DOI
StatusUdgivet - 15. mar. 2022

Bibliografisk note

Publisher Copyright:
© 2022 American Academy of Neurology.

Fingeraftryk

Dyk ned i forskningsemnerne om 'Development and Validation of a Prediction Model for Early Diagnosis of SCN1A-Related Epilepsies'. Sammen danner de et unikt fingeraftryk.

Citationsformater