Research paperA cluster analytic approach to identifying predictors and moderators of psychosocial treatment for bipolar depression: Results from STEP-BD
Introduction
Bipolar disorder, characterized by one or more periods of elevated mood, classically alternating with depressive episodes, is associated with high rates of disability (Calabrese et al., 2003). The foundation of treatment for bipolar disorder is usually pharmacotherapy. However, pharmacotherapy alone often fails to bring patients to full and sustained remission (Sachs et al., 2007). Therefore, pharmacotherapy is often paired with adjunctive psychotherapy to improve response, quality of life and prolong remission. Psychosocial treatments such as group or individual psychoeducation (Colom, 2010), cognitive behavioral therapy (CBT) (Thase et al., 2014), family focused therapy (FFT) (Miklowitz et al., 2000), interpersonal and social rhythm therapy (IPSRT) (Frank et al., 2000) and online adaptations (Lauder et al., 2015) combined with pharmacotherapy have been shown to improve medication adherence, acute mood symptoms, reduce functional impairment, and reduce likelihood of relapse (Miklowitz et al., 2006).
Although combination treatment yields improvements for many patients, there remains great variability in clinical response. Whereas mania is often relatively well controlled by pharmacotherapy (Post et al., 2014), bipolar depression is more often chronic and difficult to treat (Kohler et al., 2014). Bipolar disorder is also complicated by high rates of co-morbidity with anxiety or related disorders (Freeman et al., 2002), substance and alcohol use (Post and Kalivas, 2013), obesity (Liu et al., 2013), and medical problems (McElroy and Keck, 2014). In addition, many patients do not seek treatment in the initial stages of illness, bringing a chronic history of mood recurrences into treatment (Fagiolini et al., 2013).
Many of these variables have been shown to directly affect the potency of psychosocial treatments for bipolar depressive episodes. In prior studies, our group has investigated clinical predictors and moderators of response to adjunctive psychotherapy (Deckersbach et al., 2014, Peters et al., 2014a, Peters et al., 2015) using a large, cross-national randomized controlled trial that was part of the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD). In STEP-BD, acutely depressed individuals with bipolar disorder were randomized to one of three intensive (up to 30 sessions over 9 months) psychotherapies (CBT, IPSRT, or FFT) plus pharmacotherapy or to collaborative care (a 3-session psychoeducation intervention) plus pharmacotherapy (Miklowitz et al., 2007a). Thus, STEP-BD contains a large, nationally representative sample in a controlled trial of multiple psychotherapies. Our previous work has shown that repeated mood episodes and prolonged illness duration (Peters et al., 2014b), as well as medical co-comorbidity (Peters et al., 2015) are associated with overall treatment resistance, and that patients with co-morbid anxiety disorders and body mass index within a normal range respond better to intensive psychotherapy than those without comorbid anxiety disorders (Deckersbach et al., 2014).
Although these findings yielded valuable insights towards selection of intensive versus brief treatments, many patients with bipolar disorder exhibit several of the traits described above. From a treatment perspective, it is a challenge to know which of these selected findings should guide clinical decision-making. Cluster analysis provides a potentially elegant solution for combining these variables into well-powered and more clinically relevant metrics. Cluster analysis identifies subgroups of participants that are more similar on a set of variables than to individuals in other clusters. This analysis can help address the question of how numerous predictors and moderators of treatment β identified individually in prior analyses β can inform the development of a metric for prospectively classifying treatment outcome in bipolar disorder. Cluster analyses have also been shown to produce predictor/moderator sets with larger effect sizes than obtained with any of the individual variables of which the analytic solution is composed (Wallace et al., 2013). The primary aim of this study was to use a parametric method for creating a single combined variable from multiple individual clinical characteristics (Kraemer, 2013) and to evaluate whether the combined metric predicted or moderated response to intensive psychotherapy or collaborative care in the STEP-BD study.
Section snippets
Study Design
STEP-BD was a multi-site, longitudinal study funded by the National Institute of Mental Health that examined course of illness, treatment effectiveness, and outcomes for individuals with bipolar disorder. The institutional review boards of the respective study sites approved the study protocol. The study incorporated several clinical trials that evaluated the efficacy of various treatment programs for bipolar disorder including antidepressants, mood stabilizing medications, atypical
Study Sample
Demographic and clinical characteristics for the total included sample that had all available measures (135 of 293 in the original sample), stratified by clusters, are presented in Table 1. Participants in the selected subsample resembled the full sample on the majority of demographic and clinical characteristics with a few exceptions. Participants in this subsample were on average 3 years younger (p=.003), had slightly poorer global functioning ratings (M=57.64 vs. 54.96, p=.014), slightly
Discussion
We utilized a novel approach to the analysis of treatment response prediction and moderation (Kraemer, 2013) to develop profiles of patients likely to benefit from more and less intensive psychotherapies for bipolar depression. Using this exploratory approach, we found that a combination of variables related to illness course (older age, earlier age at onset, longer illness duration, more manic episodes, and more depressive episodes) produced clear profiles of patients in an advanced/chronic
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