Quantifying dimensional severity of obsessive-compulsive disorder for neurobiological research.
Prog Neuropsychopharmacol Biol Psychiatry
; 79(Pt B): 206-212, 2017 10 03.
Article
em En
| MEDLINE
| ID: mdl-28673486
ABSTRACT
Current research to explore genetic susceptibility factors in obsessive-compulsive disorder (OCD) has resulted in the tentative identification of a small number of genes. However, findings have not been readily replicated. It is now broadly accepted that a major limitation to this work is the heterogeneous nature of this disorder, and that an approach incorporating OCD symptom dimensions in a quantitative manner may be more successful in identifying both common as well as dimension-specific vulnerability genetic factors. As most existing genetic datasets did not collect specific dimensional severity ratings, a specific method to reliably extract dimensional ratings from the most widely used severity rating scale, the Yale-Brown Obsessive Compulsive Scale (YBOCS), for OCD is needed. This project aims to develop and validate a novel algorithm to extrapolate specific dimensional symptom severity ratings in OCD from the existing YBOCS for use in genetics and other neurobiological research. To accomplish this goal, we used a large data set comprising adult subjects from three independent sites the Brazilian OCD Consortium, the Sunnybrook Health Sciences Centre in Toronto, Canada and the Hospital of Bellvitge, in Barcelona, Spain. A multinomial logistic regression was proposed to model and predict the quantitative phenotype [i.e., the severity of each of the five homogeneous symptom dimensions of the Dimensional YBOCS (DYBOCS)] in subjects who have only YBOCS (categorical) data. YBOCS and DYBOCS data obtained from 1183 subjects were used to build the model, which was tested with the leave-one-out cross-validation method. The model's goodness of fit, accepting a deviation of up to three points in the predicted DYBOCS score, varied from 78% (symmetry/order) to 84% (cleaning/contamination and hoarding dimensions). These results suggest that this algorithm may be a valuable tool for extracting dimensional phenotypic data for neurobiological studies in OCD.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Escalas de Graduação Psiquiátrica
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Índice de Gravidade de Doença
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Algoritmos
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Transtorno Obsessivo-Compulsivo
Tipo de estudo:
Clinical_trials
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Prognostic_studies
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Risk_factors_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2017
Tipo de documento:
Article