Dravet syndrome in South African infants: Tools for an early diagnosis

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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Dravet Syndrome: Early Diagnosis and Emerging Therapies

Journal of Pediatric Epilepsy ◽

10.1055/s-0039-1692171 ◽

2019 ◽

Vol 08 (02) ◽

pp. 031-037

Author(s):

Tyler J. Burr ◽

Karen L. Skjei

Keyword(s):

Status Epilepticus ◽

Early Diagnosis ◽

Seizure Control ◽

Febrile Seizures ◽

Epileptic Encephalopathy ◽

Dravet Syndrome ◽

Long Term Outcomes ◽

Treatment Algorithms ◽

Emerging Therapies

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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Improving Early Diagnosis of Rare Diseases Using Natural Language Processing in Unstructured Medical Records: An Illustration From Dravet Syndrome

10.21203/rs.3.rs-149382/v1 ◽

2021 ◽

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 BackgroundThe 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 two 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 two 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.MethodsData 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 four 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 two years and using a series of logistic regressions. ResultsWe found significative higher representation of concepts related to seizures’ phenotype 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. ConclusionsNarrative medical reports of individuals younger than two 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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Early diagnosis and experimental treatment with fenfluramine via the Investigational New Drug mechanism in a boy with Dravet syndrome and recurrent status epilepticus

Epileptic Disorders ◽

10.1684/epd.2021.1345 ◽

2021 ◽

Vol 23 (6) ◽

pp. 954-956

Author(s):

Sara Trowbridge ◽

Annapurna Poduri ◽

Heather Olson

Keyword(s):

Status Epilepticus ◽

Early Diagnosis ◽

Experimental Treatment ◽

Dravet Syndrome ◽

New Drug ◽

Drug Mechanism

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Use of the Mentzer index will assist in early diagnosis of iron deficiency in South African children

South African Medical Journal ◽

10.7196/samjnew.8589 ◽

2015 ◽

Vol 105 (9) ◽

pp. 702

Author(s):

D Lawrie ◽

D K Glencross

Keyword(s):

Iron Deficiency ◽

Early Diagnosis ◽

South African ◽

African Children ◽

South African Children

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The Three-dimensional structure of frozen-hydrated nudaurelia capensis β virus

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100127682 ◽

1987 ◽

Vol 45 ◽

pp. 650-651

Author(s):

N. H. Olson ◽

T. S. Baker ◽

Wu Bo Mu ◽

J. E. Johnson ◽

D. A. Hendry

Keyword(s):

South African ◽

Rna Virus ◽

Carbon Film ◽

Three Dimensional ◽

Dimensional Structure ◽

Electron Dose ◽

Total Electron ◽

Three Dimensional Structure ◽

Negative Stain ◽

Dimensional Reconstruction

Nudaurelia capensis β virus (NβV) is an RNA virus of the South African Pine Emperor moth, Nudaurelia cytherea capensis (Lepidoptera: Saturniidae). The NβV capsid is a T = 4 icosahedron that contains 60T = 240 subunits of the coat protein (Mr = 61,000). A three-dimensional reconstruction of the NβV capsid was previously computed from visions embedded in negative stain suspended over holes in a carbon film. We have re-examined the three-dimensional structure of NβV, using cryo-microscopy to examine the native, unstained structure of the virion and to provide a initial phasing model for high-resolution x-ray crystallographic studiesNβV was purified and prepared for cryo-microscopy as described. Micrographs were recorded ∼1 - 2 μm underfocus at a magnification of 49,000X with a total electron dose of about 1800 e-/nm2.

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Red Flags: Barriers to Listening and Spoken Language in Children with Hearing Loss

Perspectives on Hearing and Hearing Disorders in Childhood ◽

10.1044/hhdc24.1.11 ◽

2014 ◽

Vol 24 (1) ◽

pp. 11-18

Author(s):

Andrea Bell ◽

K. Todd Houston

Keyword(s):

Hearing Loss ◽

Early Diagnosis ◽

Skill Acquisition ◽

Dynamic Process ◽

Spoken Language ◽

Red Flags ◽

Careful Monitoring ◽

Auditory Development ◽

Children With Hearing Loss ◽

Family Centered

To ensure optimal auditory development for the acquisition of spoken language, children with hearing loss require early diagnosis, effective ongoing audiological management, well fit and maintained hearing technology, and appropriate family-centered early intervention. When these elements are in place, children with hearing loss can achieve developmental and communicative outcomes that are comparable to their hearing peers. However, for these outcomes to occur, clinicians—early interventionists, speech-language pathologists, and pediatric audiologists—must participate in a dynamic process that requires careful monitoring of countless variables that could impact the child's skill acquisition. This paper addresses some of these variables or “red flags,” which often are indicators of both minor and major issues that clinicians may encounter when delivering services to young children with hearing loss and their families.

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