TY - JOUR
T1 - Quantitative analysis of phenotypic elements augments traditional electroclinical classification of common familial epilepsies
AU - Epi4K Consortium
AU - Abou-Khalil, Bassel
AU - Afawi, Zaid
AU - Allen, Andrew S.
AU - Bautista, Jocelyn F.
AU - Bellows, Susannah T.
AU - Berkovic, Samuel F.
AU - Bluvstein, Judith
AU - Burgess, Rosemary
AU - Cascino, Gregory
AU - Cossette, Patrick
AU - Cristofaro, Sabrina
AU - Crompton, Douglas E.
AU - Delanty, Norman
AU - Devinsky, Orrin
AU - Dlugos, Dennis
AU - Ellis, Colin A.
AU - Epstein, Michael P.
AU - Fountain, Nathan B.
AU - Freyer, Catharine
AU - Geller, Eric B.
AU - Glauser, Tracy
AU - Glynn, Simon
AU - Goldberg-Stern, Hadassa
AU - Goldstein, David B.
AU - Gravel, Micheline
AU - Haas, Kevin
AU - Haut, Sheryl
AU - Heinzen, Erin L.
AU - Kirsch, Heidi E.
AU - Kivity, Sara
AU - Knowlton, Robert
AU - Korczyn, Amos D.
AU - Kossoff, Eric
AU - Kuzniecky, Ruben
AU - Loeb, Rebecca
AU - Lowenstein, Daniel H.
AU - Marson, Anthony G.
AU - McCormack, Mark
AU - McKenna, Kevin
AU - Mefford, Heather C.
AU - Motika, Paul
AU - Mullen, Saul A.
AU - J. O'Brien, Terence
AU - Ottman, Ruth
AU - Paolicchi, Juliann
AU - Parent, Jack M.
AU - Paterson, Sarah
AU - Petrou, Steven
AU - Petrovski, Slavé
AU - Owen Pickrell, William
N1 - Publisher Copyright:
Wiley Periodicals, Inc. © 2019 International League Against Epilepsy
PY - 2019/11/1
Y1 - 2019/11/1
N2 - 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.
AB - 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.
KW - epilepsy
KW - genetics
KW - latent class analysis
KW - phenotype
UR - http://www.scopus.com/inward/record.url?scp=85074737969&partnerID=8YFLogxK
U2 - 10.1111/epi.16354
DO - 10.1111/epi.16354
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AN - SCOPUS:85074737969
VL - 60
SP - 2194
EP - 2203
JO - Epilepsia
JF - Epilepsia
SN - 0013-9580
IS - 11
ER -