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Title: Development and Validation of a Prediction Model for Early Diagnosis of SCN1A-Related Epilepsies.
Austin Authors: Brunklaus, Andreas;Pérez-Palma, Eduardo;Ghanty, Ismael;Xinge, Ji;Brilstra, Eva;Ceulemans, Berten;Chemaly, Nicole;de Lange, Iris;Depienne, Christel;Guerrini, Renzo;Mei, Davide;Møller, Rikke S;Nabbout, Rima;Regan, Brigid M;Schneider, Amy L ;Scheffer, Ingrid E ;Schoonjans, An-Sofie;Symonds, Joseph D;Weckhuysen, Sarah;Kattan, Michael W;Zuberi, Sameer M;Lal, Dennis
Affiliation: Epilepsy Research Centre
The Florey Institute of Neuroscience and Mental Health
Medicine (University of Melbourne)
Murdoch Children's Research Institute, Melbourne, Australia
Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, USA
Department of Quantitative Health Sciences, Cleveland Clinic, USA
The Danish Epilepsy Centre, Dianalund, Denmark
Institute for Regional Health Services, University of Southern Denmark, Odense, Denmark
Applied & Translational Neurogenomics Group, VIB-Center for Molecular Neurology, VIB, Antwerp, Belgium
Neurology Department, University Hospital Antwerp, Antwerp, Belgium
Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
Cologne Center for Genomics, University of Cologne, Cologne, Germany
Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, USA
Stanley Center for Psychiatric Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
Neuroscience Department, Children's Hospital A. Meyer-University of Florence, Italy
Department of Genetics, University Medical Centre, Utrecht, Netherlands
Department of child neurology, University Hospital Antwerp, Antwerp, Belgium
Reference centre for rare epilepsies, Department of Pediatric Neurology, Hôpital Necker-Enfants Malades, Université de Paris, Paris, France
Institute of Human Genetics, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
The Pediatric Neurosciences Research Group, Royal Hospital for Children, Glasgow, UK
Institute of Health and Wellbeing, University of Glasgow, UK
Universidad del Desarrollo, Centro de Genética y Genómica, Facultad de Medicina Clínica Alemana, Santiago, Chile
The University of Melbourne, Melbourne, Australia
Royal Children's Hospital, Melbourne, Australia
Issue Date: 15-Mar-2022
Date: 2022-01-24
Publication information: Neurology 2022; 98(11): e1163-e1174
Abstract: 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 for developing Dravet syndrome versus 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. Retrospective multicenter cohort study comprising data from SCN1A-positive Dravet syndrome and GEFS+ patients consecutively referred for genetic testing (March 2001-June 2020) including age of seizure onset and a newly-developed SCN1A genetic score. A training cohort was used to develop multiple prediction models that were validated using two independent blinded cohorts. Primary outcome was the discriminative accuracy of the model predicting Dravet syndrome versus other GEFS+ phenotypes. 1018 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) versus 52.0 (SD, 57.5; p < 0.001) and young age of onset 6.0 (SD, 3.0) months versus 14.8 (SD, 11.8; p < 0.001) months, were each associated with Dravet syndrome versus 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]). The prediction model allows objective estimation at disease onset whether a child will develop Dravet syndrome versus GEFS+, assisting clinicians with prognostic counseling and decisions on early institution of precision therapies ( This study provides Class II evidence that a combined 'SCN1A genetic score and seizure onset' model distinguishes Dravet syndrome from other GEFS+ phenotypes.
DOI: 10.1212/WNL.0000000000200028
Journal: Neurology
PubMed URL: 35074891
PubMed URL:
Type: Journal Article
Appears in Collections:Journal articles

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