Academic Journal
Development and Validation of a Prediction Model for Early Diagnosis of SCN1A-Related Epilepsies
| العنوان: | Development and Validation of a Prediction Model for Early Diagnosis of SCN1A-Related Epilepsies |
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| المؤلفون: | Brunklaus, A, Perez-Palma, E, Ghanty, I, Xinge, J, Brilstra, E, Ceulemans, B, Chemaly, N, de Lange, I, Depienne, C, Guerrini, R, Mei, D, Moller, RS, Nabbout, R, Regan, BM, Schneider, AL, Scheffer, IE, Schoonjans, A-S, Symonds, JD, Weckhuysen, S, Kattan, MW, Zuberi, SM, Lal, D |
| بيانات النشر: | LIPPINCOTT WILLIAMS & WILKINS |
| سنة النشر: | 2022 |
| المجموعة: | The University of Melbourne: Digital Repository |
| الوصف: | 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 ... |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| ردمك: | 978-0-00-000000-2 0-00-000000-0 |
| تدمد: | 0028-3878 1526-632X |
| Relation: | pii: WNL.0000000000200028; Brunklaus, A., Perez-Palma, E., Ghanty, I., Xinge, J., Brilstra, E., Ceulemans, B., Chemaly, N., de Lange, I., Depienne, C., Guerrini, R., Mei, D., Moller, 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), pp.E1163-E1174. https://doi.org/10.1212/WNL.0000000000200028.; http://hdl.handle.net/11343/316400 |
| الاتاحة: | http://hdl.handle.net/11343/316400 |
| رقم الانضمام: | edsbas.BA3E0A5C |
| قاعدة البيانات: | BASE |
| ردمك: | 9780000000002 0000000000 |
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| تدمد: | 00283878 1526632X |