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

ABSTRACT:

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<

SUBMITTER: Epi4K Consortium 

PROVIDER: S-EPMC7145322 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature