Early diagnosis and experimental treatment with fenfluramine via the Investigational New Drug mechanism in a boy with Dravet syndrome and recurrent status epilepticus

AbstractDravet's syndrome (DS) or severe myoclonic epilepsy of infancy is a rare, genetic, and infantile-onset epileptic encephalopathy. DS presents with recurrent febrile seizures and/or febrile status epilepticus in developmentally normal infants, and subsequently evolves into a drug-resistant mixed-seizure disorder with developmental arrest or regression. As many defining clinical features of DS do not become evident until 3 to 4 years of age, diagnosis is often delayed. Early seizure control, particularly the prevention of status epilepticus in infancy, has been shown to correlate with better long-term outcomes. Thus, early diagnosis and seizure control is crucial. Several treatment algorithms have been published in recent years to guide antiepileptic drug selection and escalation. Last year, two agents, stiripentol and cannabidiol, were approved by the U.S. Food and Drug Administration specifically for use in DS, and a third has been submitted (fenfluramine). Additional therapies, including serotonin modulators lorcaserin and trazodone, verapamil, and several first-in-class medications, are currently in various phases of investigation.

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Reply to Dravet, C. Different Outcomes of Acute Encephalopathy after Status Epilepticus in Patients with Dravet Syndrome. How to Avoid Them? Comment on “De Liso et al. Fatal Status Epilepticus in Dravet Syndrome. Brain Sci. 2020, 10, 889”

Brain Sciences ◽

10.3390/brainsci11060811 ◽

2021 ◽

Vol 11 (6) ◽

pp. 811

Author(s):

Paola De Liso ◽

Virginia Pironi ◽

Massimo Mastrangelo ◽

Domenica Battaglia ◽

Dana Craiu ◽

...

Keyword(s):

Status Epilepticus ◽

Dravet Syndrome ◽

Acute Encephalopathy ◽

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Improving early diagnosis of rare diseases using Natural Language Processing in unstructured medical records: an illustration from Dravet syndrome

Orphanet Journal of Rare Diseases ◽

10.1186/s13023-021-01936-9 ◽

2021 ◽

Vol 16 (1) ◽

Author(s):

Tommaso Lo Barco ◽

Mathieu Kuchenbuch ◽

Nicolas Garcelon ◽

Antoine Neuraz ◽

Rima Nabbout

Keyword(s):

Natural Language Processing ◽

Early Diagnosis ◽

Natural Language ◽

Language Processing ◽

Rare Diseases ◽

Febrile Seizures ◽

Epileptic Encephalopathy ◽

Dravet Syndrome ◽

Written Information ◽

Medical Reports

Abstract Background The growing use of Electronic Health Records (EHRs) is promoting the application of data mining in health-care. A promising use of big data in this field is to develop models to support early diagnosis and to establish natural history. Dravet Syndrome (DS) is a rare developmental and epileptic encephalopathy that commonly initiates in the first year of life with febrile seizures (FS). Age at diagnosis is often delayed after 2 years, as it is difficult to differentiate DS at onset from FS. We aimed to explore if some clinical terms (concepts) are significantly more used in the electronic narrative medical reports of individuals with DS before the age of 2 years compared to those of individuals with FS. These concepts would allow an earlier detection of patients with DS resulting in an earlier orientation toward expert centers that can provide early diagnosis and care. Methods Data were collected from the Necker Enfants Malades Hospital using a document-based data warehouse, Dr Warehouse, which employs Natural Language Processing, a computer technology consisting in processing written information. Using Unified Medical Language System Meta-thesaurus, phenotype concepts can be recognized in medical reports. We selected individuals with DS (DS Cohort) and individuals with FS (FS Cohort) with confirmed diagnosis after the age of 4 years. A phenome-wide analysis was performed evaluating the statistical associations between the phenotypes of DS and FS, based on concepts found in the reports produced before 2 years and using a series of logistic regressions. Results We found significative higher representation of concepts related to seizures’ phenotypes distinguishing DS from FS in the first phases, namely the major recurrence of complex febrile convulsions (long-lasting and/or with focal signs) and other seizure-types. Some typical early onset non-seizure concepts also emerged, in relation to neurodevelopment and gait disorders. Conclusions Narrative medical reports of individuals younger than 2 years with FS contain specific concepts linked to DS diagnosis, which can be automatically detected by software exploiting NLP. This approach could represent an innovative and sustainable methodology to decrease time of diagnosis of DS and could be transposed to other rare diseases.

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