Development and Validation of a Prediction Model for Early Diagnosis of SCN1A-Related Epilepsies
Andreas Brunklaus1, Eduardo Pérez-Palma2, Ismael Ghanty2
1From the Pediatric Neurosciences Research Group (A.B., I.G., J.D.S., S.M.Z.), Royal Hospital for Children, Glasgow; Institute of Health and Wellbeing (A.B., I.G., J.D.S., S.M.Z.), University of Glasgow, UK; Centro de Genética y Genómica, Facultad de Medicina Clínica Alemana (E.P.-P.), Universidad del Desarrollo, Santiago, Chile; Genomic Medicine Institute, Lerner Research Institute (E.P.-P., D.L.), Department of Quantitative Health Sciences (J.X., M.W.K.), and Epilepsy Center, Neurological Institute (D.L.), Cleveland Clinic, OH; Department of Genetics (E.B., I.d.L.), University Medical Centre, Utrecht, the Netherlands; Department of Child Neurology (B.C., A.-S.S.), University Hospital Antwerp, Belgium; Reference Centre for Rare Epilepsies, Department of Pediatric Neurology (N.C., R.N.), Hôpital Necker-Enfants Malades, Université de Paris, France; Institute of Human Genetics (C.D.), University Hospital Essen, University of Duisburg-Essen, Germany; Neuroscience Department (R.G., D.M.), Children's Hospital A. Meyer-University of Florence, Italy; The Danish Epilepsy Centre (R.S.M.), Dianalund, Denmark; Institute for Regional Health Services (R.S.M.), University of Southern Denmark, Odense; Department of Medicine, Epilepsy Research Centre, Austin Health (B.M.R., A.L.S., I.E.S.), and Florey and Murdoch Children's Research Institutes, Royal Children's Hospital (I.E.S.), University of Melbourne, Australia; Applied and Translational Neurogenomics Group (S.W.), VIB-Center for Molecular Neurology, VIB, Antwerp; Neurology Department (S.W.), University Hospital Antwerp; Institute Born-Bunge (S.W.), University of Antwerp, Belgium; Cologne Center for Genomics (D.L.), University of Cologne, Germany; and Stanley Center for Psychiatric Genetics (D.L.), Broad Institute of MIT and Harvard, Cambridge, MA. andreas.brunklaus@glasgow.ac.uk.
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.
