A critical appraisal of heterogeneity in Obsessive-Compulsive Disorder using symptom-based clustering analysis

•Two-cluster solution was detected as the most appropriate model for analyses of YBOCS checklist symptoms.•Cluster analysis of YBOCS checklist separated OCD patients based on their severity not symptom patterns.•None of the evaluated clustering algorithms was superior to the others for clustering YB...

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Veröffentlicht in:Asian journal of psychiatry Jg. 28; S. 89 - 96
Hauptverfasser: Hasanpour, Hesam, Asadi, Sareh, Ghavamizadeh Meibodi, Ramak, Daraeian, Azin, Ahmadiani, Abolhassan, Shams, Jamal, Navi, Keivan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Netherlands Elsevier B.V 01.08.2017
Schlagworte:
Adult
Cluster Analysis
Clustering algorithms
Female
Humans
Internal Medicine
Male
Middle Aged
Obsessive-Compulsive Disorder - classification
Obsessive-Compulsive Disorder - physiopathology
OCD heterogeneity
OCD subtypes
OCD symptoms
Psychiatric Status Rating Scales - statistics & numerical data
Psychiatry
YBOCS
Young Adult
OCD subtypes
YBOCS
OCD heterogeneity
OCD symptoms
Clustering algorithms
ISSN:1876-2018, 1876-2026, 1876-2026
Online-Zugang:Volltext
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Zusammenfassung:•Two-cluster solution was detected as the most appropriate model for analyses of YBOCS checklist symptoms.•Cluster analysis of YBOCS checklist separated OCD patients based on their severity not symptom patterns.•None of the evaluated clustering algorithms was superior to the others for clustering YBOCS checklist symptoms. Obsessive-compulsive disorder (OCD) encompasses a broad range of symptoms and is commonly considered a heterogeneous condition. Attempts were made to define discrete OCD subtypes using a range of symptom-based methods including factor and cluster analyses. The present study aims to find the most appropriate clustering model based on Yale-Brown obsessive-compulsive scale (YBOCS) checklist explaining OCD heterogeneity. Five different clustering algorithms (FCM, K-means, Ward, Ward+K-means and Complete) applied on YBOCS symptoms of 216 patients with OCD. Data studied as four different sets including item-level raw data, item-based factor scores, category-level raw data and category-based factor scores and clustering results for 2 to 6 cluster solutions evaluated by four clustering indices (Davies-Bouldin, Calinski-Harabasz, Silhouettes and Dunn indices). Two-cluster solution was detected as the most appropriate model for item and category-based clustering analyses of YBOCS checklist symptoms. Patients in each cluster were characterized based on their clinical and demographic properties and results showed that they had similar patterns of symptoms but in different severities. Heterogenity of OCD based on the YBOCS-symptoms has been challenged as OCD patients were classified based on their symptom severity not their symptom patterns. More investigations need to find appropriate measures explaining OCD heterogeneity with clinical importance.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1876-2018
1876-2026
1876-2026
DOI:10.1016/j.ajp.2017.03.024