학술논문

A cluster analytic approach to identifying predictors and moderators of psychosocial treatment for bipolar depression: Results from STEP-BD
Document Type
article
Source
Subject
Clinical and Health Psychology
Biomedical and Clinical Sciences
Clinical Sciences
Psychology
Depression
Rehabilitation
Mental Health
Brain Disorders
Serious Mental Illness
Mental health
Good Health and Well Being
Adult
Bipolar Disorder
Cluster Analysis
Female
Humans
Male
Middle Aged
Psychotherapy
Treatment Outcome
Young Adult
Bipolar disorder
Cluster analyses
Medical and Health Sciences
Psychology and Cognitive Sciences
Psychiatry
Biomedical and clinical sciences
Health sciences
Language
Abstract
BackgroundWe 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.MethodsWe 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.ResultsThe 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).LimitationsCluster analyses require list-wise deletion of cases with missing data so we were unable to conduct analyses on all STEP-BD participants.ConclusionsA 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.