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Metabolic Evaluation of Epilepsy: A Diagnostic Algorithm With Focus on Treatable Conditions
- Source :
- Frontiers in Neurology, Vol 9 (2018), Frontiers in Neurology
- Publication Year :
- 2018
- Publisher :
- Frontiers Media S.A., 2018.
-
Abstract
- Although inborn errors of metabolism do not represent the most common cause of seizures, their early identification is of utmost importance, since many will require therapeutic measures beyond that of common anti-epileptic drugs, either in order to control seizures, or to decrease the risk of neurodegeneration. We translate the currently-known literature on metabolic etiologies of epilepsy (268 inborn errors of metabolism belonging to 21 categories, with 74 treatable errors), into a 2-tiered diagnostic algorithm, with the first-tier comprising accessible, affordable, and less invasive screening tests in urine and blood, with the potential to identify the majority of treatable conditions, while the second-tier tests are ordered based on individual clinical signs and symptoms. This resource aims to support the pediatrician, neurologist, biochemical, and clinical geneticists in early identification of treatable inborn errors of metabolism in a child with seizures, allowing for timely initiation of targeted therapy with the potential to improve outcomes.
- Subjects :
- 0301 basic medicine
medicine.medical_specialty
Neurology
Screening test
medicine.medical_treatment
Mini Review
Less invasive
Signs and symptoms
inborn errors of metabolism
lcsh:RC346-429
Targeted therapy
03 medical and health sciences
Epilepsy
0302 clinical medicine
medicine
lcsh:Neurology. Diseases of the nervous system
seizures
treatment
business.industry
medicine.disease
diagnostic algorithm
030104 developmental biology
metabolic epilepsy
Etiology
Identification (biology)
Neurology (clinical)
business
Algorithm
030217 neurology & neurosurgery
Subjects
Details
- Language :
- English
- ISSN :
- 16642295
- Volume :
- 9
- Database :
- OpenAIRE
- Journal :
- Frontiers in Neurology
- Accession number :
- edsair.doi.dedup.....05cac5b48a0fac72ce4d7b0ceec35d8c
- Full Text :
- https://doi.org/10.3389/fneur.2018.01016/full