Dr. Lal and colleagues have developed a statistical model that integrates genetic and clinical data to calculate the probability of developing Dravet syndrome.
According to a recent study published in Neurology, an international team of researchers co-led by Lerner Research Institute has developed and validated a prediction model to aid in the early diagnosis of Dravet syndrome, a severe epilepsy characterized by drug-resistant seizures, intellectual disability and high mortality that begins in infancy.
The majority of Dravet syndrome cases are caused by variants in the SCN1A gene, which codes for a sodium channel protein involved in the regulation of brain cell activity. However, SCN1A variants also have been linked to other epilepsies, such as genetic epilepsy with febrile seizures plus (GEFS+), a much milder syndrome that does not affect cognition.
“Dravet syndrome initially appears clinically similar to GEFS+, so clinicians must wait until distinguishing symptoms, such as developmental delay, emerge before making a definitive diagnosis,” said co-senior author Dennis Lal, PhD, assistant staff in the Genomic Medicine Institute. “However, this reliance on clinical observations alone means they often miss the opportunity for early intervention, which is critical to achieving the best patient outcomes.”
To address this issue, the researchers built a statistical model that calculates the probability of developing Dravet syndrome versus GEFS+ by integrating clinical and genetic data from an international cohort of 1018 patients with SCN1A-related Dravet syndrome or GEFS+. The researchers found that the model outperformed any previous or alternative models, indicating its utility as a clinical decision-support tool that can help clinicians differentiate between the disorders at an early stage.
Specifically, the model takes into account the age of seizure onset, which is the earliest clinical symptom that can be reliably assessed in infancy, and the likelihood a given SCN1A variant confers risk for Dravet syndrome (known as the genetic score). A higher genetic score denotes an increased risk of Dravet syndrome.
“Consider the example of a nine-month-old infant presenting with recurrent febrile seizures and a pathogenic SCN1A variant. Based on the age of onset alone, a clinician would likely estimate the risk of Dravet syndrome to be moderate, or around 50%,” noted Dr. Lal. “But with the additional information that the infant carries a variant with a high genetic score, the estimated risk could increase to more than 90%. Alternatively, a low genetic score might reduce that risk to less than 10%.”
The model, which uses data easily accessible to clinicians treating any young infant presenting with a pathogenic SCN1A variant, can be accessed for free online. The model is for education purposes only and should not substitute for medical or professional care.
“Our model can assist in the early and accurate prediction of whether a young infant or child with an SCN1A variant will develop Dravet syndrome or the milder GEFS+, which is the vital information needed to make the best decisions regarding patient management and treatment planning,” said Dr. Lal. “In addition, our approach for developing a clinical decision-support algorithm is generalizable and can be applied to many other genetic disorders where genetic and clinical data is available.”
Andreas Brunklas, MD, a pediatric neurologist at the Royal Hospital for Children; Eduardo Pérez-Palma, PhD, an associate professor at Universidad del Desarrollo; and Ismael Ghanty, MBChB, a medical student at the Royal Hospital for Children, are co-first authors on the study. Dr. Brunklas also is a co-senior author. The study was supported by the Dravet Syndrome Foundation, the German Federal Ministry of Education and Research (BMBF), the National Institute of Neurological Disorders and Stroke (part of the National Institutes of Health) and Dravet Syndrome UK.
Dr. Lal adjusted and used a statistical model based on mutation rate to predict the annual number of new cases of over 100 rare monogenetic neurodevelopmental disorders caused by de novo variants, offering previously unavailable estimates for disease burden.
Dr. Lal’s team conducted the first big data characterization of missense variants from 1,300 disease-associated genes to identify features associated with pathogenic and benign variants.
Dr. Lal’s team will perform the most comprehensive genetic analysis of focal cortical dysplasia (FCD) to confirm proposed FCD-associated genes and identify novel FCD causal genes and variants.
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