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A cluster analytic approach to identifying predictors and moderators of psychosocial treatment for bipolar depression: results from STEP-BD

Deckersbach, Thilo, Peters, Amy T., Sylvia, Louisa G., Gold, Alexandra K., Da Silva Magalhaes, Pedro Vieira, Henry, David B., Frank, Ellen, Otto, Michael W., Berk, Michael, Dougherty, Darin D., Nierenberg, Andrew A. and Miklowitz, David J. 2016, A cluster analytic approach to identifying predictors and moderators of psychosocial treatment for bipolar depression: results from STEP-BD, Journal of affective disorders, vol. 203, pp. 152-157, doi: 10.1016/j.jad.2016.03.064.

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Title A cluster analytic approach to identifying predictors and moderators of psychosocial treatment for bipolar depression: results from STEP-BD
Author(s) Deckersbach, Thilo
Peters, Amy T.
Sylvia, Louisa G.
Gold, Alexandra K.
Da Silva Magalhaes, Pedro Vieira
Henry, David B.
Frank, Ellen
Otto, Michael W.
Berk, MichaelORCID iD for Berk, Michael orcid.org/0000-0002-5554-6946
Dougherty, Darin D.
Nierenberg, Andrew A.
Miklowitz, David J.
Journal name Journal of affective disorders
Volume number 203
Start page 152
End page 157
Total pages 6
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2016-10
ISSN 0165-0327
1573-2517
Keyword(s) bipolar disorder
cluster analyses
psychotherapy
Summary Background We sought to address how predictors and moderators of psychotherapy for bipolar depression - identified individually in prior analyses - can inform the development of a metric for prospectively classifying treatment outcome in intensive psychotherapy (IP) versus collaborative care (CC) adjunctive to pharmacotherapy in the Systematic Treatment Enhancement Program (STEP-BD) study. Methods We conducted post-hoc analyses on 135 STEP-BD participants using cluster analysis to identify subsets of participants with similar clinical profiles and investigated this combined metric as a moderator and predictor of response to IP. We used agglomerative hierarchical cluster analyses and k-means clustering to determine the content of the clinical profiles. Logistic regression and Cox proportional hazard models were used to evaluate whether the resulting clusters predicted or moderated likelihood of recovery or time until recovery. Results The cluster analysis yielded a two-cluster solution: 1) "less-recurrent/severe" and 2) "chronic/recurrent." Rates of recovery in IP were similar for less-recurrent/severe and chronic/recurrent participants. Less-recurrent/severe patients were more likely than chronic/recurrent patients to achieve recovery in CC (p=.040, OR=4.56). IP yielded a faster recovery for chronic/recurrent participants, whereas CC led to recovery sooner in the less-recurrent/severe cluster (p=.034, OR=2.62). Limitations Cluster analyses require list-wise deletion of cases with missing data so we were unable to conduct analyses on all STEP-BD participants. Conclusions A well-powered, parametric approach can distinguish patients based on illness history and provide clinicians with symptom profiles of patients that confer differential prognosis in CC vs. IP.
Language eng
DOI 10.1016/j.jad.2016.03.064
Field of Research 170199 Psychology not elsewhere classified
Socio Economic Objective 920410 Mental Health
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016 Elsevier B.V.
Persistent URL http://hdl.handle.net/10536/DRO/DU:30085591

Document type: Journal Article
Collection: School of Medicine
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