biostudies-literatureBrunklaus ANINDS NIH HHSe1163-e1174https://www.ebi.ac.uk/biostudies/studies/S-EPMC8935441biostudies-literatureUnknown98(11)<h4>Background and objectives</h4>Pathogenic variants in the neuronal sodium channel α1 subunit gene (<i>SCN1A</i>) 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 <i>SCN1A</i>-related epilepsies.<h4>MethoNeurologyDevelopment and Validation of a Prediction Model for Early Diagnosis of <i>SCN1A</i>-Related Epilepsies.PMC8935441U54 NS108874Schneider ALSymonds JDLal DRegan BMXinge JKattan MWChemaly NZuberi SMBrunklaus APerez-Palma ENabbout RBrilstra Ede Lange IDepienne CMoller RSWeckhuysen SMei DSchoonjans ASGhanty IGuerrini RScheffer IECeulemans BfalseDevelopment and Validation of a Prediction Model for Early Diagnosis of <i>SCN1A</i>-Related Epilepsies.<h4>Background and objectives</h4>Pathogenic variants in the neuronal sodium channel α1 subunit gene (<i>SCN1A</i>) 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 <i>SCN1A</i>-related epilepsies.<h4>Metho2022-01-01T00:00:00Z2022 Mar2025-04-04T11:26:44.71Z2024-11-19T16:59:54.491ZS-EPMC89354413507489110.1212/WNL.000000000020002810.1212/wnl.0000000000200028