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Quantitative analysis of phenotypic elements augments traditional electroclinical classification of common familial epilepsies.

Publication ,  Journal Article
Epi4K Consortium,
Published in: Epilepsia
November 2019

OBJECTIVE: Classification of epilepsy into types and subtypes is important for both clinical care and research into underlying disease mechanisms. A quantitative, data-driven approach may augment traditional electroclinical classification and shed new light on existing classification frameworks. METHODS: We used latent class analysis, a statistical method that assigns subjects into groups called latent classes based on phenotypic elements, to classify individuals with common familial epilepsies from the Epi4K Multiplex Families study. Phenotypic elements included seizure types, seizure symptoms, and other elements of the medical history. We compared class assignments to traditional electroclinical classifications and assessed familial aggregation of latent classes. RESULTS: A total of 1120 subjects with epilepsy were assigned to five latent classes. Classes 1 and 2 contained subjects with generalized epilepsy, largely reflecting the distinction between absence epilepsies and younger onset (class 1) versus myoclonic epilepsies and older onset (class 2). Classes 3 and 4 contained subjects with focal epilepsies, and in contrast to classes 1 and 2, these did not adhere as closely to clinically defined focal epilepsy subtypes. Class 5 contained nearly all subjects with febrile seizures plus or unknown epilepsy type, as well as a few subjects with generalized epilepsy and a few with focal epilepsy. Family concordance of latent classes was similar to or greater than concordance of clinically defined epilepsy types. SIGNIFICANCE: Quantitative classification of epilepsy has the potential to augment traditional electroclinical classification by (1) combining some syndromes into a single class, (2) splitting some syndromes into different classes, (3) helping to classify subjects who could not be classified clinically, and (4) defining the boundaries of clinically defined classifications. This approach can guide future research, including molecular genetic studies, by identifying homogeneous sets of individuals that may share underlying disease mechanisms.

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Published In

Epilepsia

DOI

EISSN

1528-1167

Publication Date

November 2019

Volume

60

Issue

11

Start / End Page

2194 / 2203

Location

United States

Related Subject Headings

  • Phenotype
  • Pedigree
  • Neurology & Neurosurgery
  • Male
  • Latent Class Analysis
  • Humans
  • Female
  • Epileptic Syndromes
  • Electroencephalography
  • 3209 Neurosciences
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Epi4K Consortium, . (2019). Quantitative analysis of phenotypic elements augments traditional electroclinical classification of common familial epilepsies. Epilepsia, 60(11), 2194–2203. https://doi.org/10.1111/epi.16354
Epi4K Consortium, Wayne N. “Quantitative analysis of phenotypic elements augments traditional electroclinical classification of common familial epilepsies.Epilepsia 60, no. 11 (November 2019): 2194–2203. https://doi.org/10.1111/epi.16354.
Epi4K Consortium, Wayne N. “Quantitative analysis of phenotypic elements augments traditional electroclinical classification of common familial epilepsies.Epilepsia, vol. 60, no. 11, Nov. 2019, pp. 2194–203. Pubmed, doi:10.1111/epi.16354.
Journal cover image

Published In

Epilepsia

DOI

EISSN

1528-1167

Publication Date

November 2019

Volume

60

Issue

11

Start / End Page

2194 / 2203

Location

United States

Related Subject Headings

  • Phenotype
  • Pedigree
  • Neurology & Neurosurgery
  • Male
  • Latent Class Analysis
  • Humans
  • Female
  • Epileptic Syndromes
  • Electroencephalography
  • 3209 Neurosciences