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  • Springer Science and Business Media LLC  (1)
  • Pacchiarotti, Isabella  (1)
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  • Springer Science and Business Media LLC  (1)
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    In: Translational Psychiatry, Springer Science and Business Media LLC, Vol. 10, No. 1 ( 2020-07-19)
    Abstract: A cross-diagnostic, post-hoc analysis of the BRIDGE-II-MIX study was performed to investigate how unipolar and bipolar patients suffering from an acute major depressive episode (MDE) cluster according to severity and duration. Duration of index episode, Clinical Global Impression-Bipolar Version-Depression (CGI-BP-D) and Global Assessment of Functioning (GAF) were used as clustering variables. MANOVA and post-hoc ANOVAs examined between-group differences in clustering variables. A stepwise backward regression model explored the relationship with the 56 clinical-demographic variables available. Agglomerative hierarchical clustering with two clusters was shown as the best fit and separated the study population ( n  = 2314) into 65.73% (Cluster 1 (C1)) and 34.26% (Cluster 2 (C2)). MANOVA showed a significant main effect for cluster group ( p   〈  0.001) but ANOVA revealed that significant between-group differences were restricted to CGI-BP-D ( p   〈  0.001) and GAF ( p   〈  0.001), showing greater severity in C2. Psychotic features and a minimum of three DSM-5 criteria for mixed features (DSM-5-3C) had the strongest association with C2, that with greater disease burden, while non-mixed depression in bipolar disorder (BD) type II had negative association. Mixed affect defined as DSM-5-3C associates with greater acute severity and overall impairment, independently of the diagnosis of bipolar or unipolar depression. In this study a pure, non-mixed depression in BD type II significantly associates with lesser burden of clinical and functional severity. The lack of association for less restrictive, researched-based definitions of mixed features underlines DSM-5-3C specificity. If confirmed in further prospective studies, these findings would warrant major revisions of treatment algorithms for both unipolar and bipolar depression.
    Type of Medium: Online Resource
    ISSN: 2158-3188
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2020
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