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, Miklowitz David J
Massachusetts General Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States.
University of Illinois at Chicago, Chicago, IL, United States.
J Affect Disord. 2016 Oct;203:152-157. doi: 10.1016/j.jad.2016.03.064. Epub 2016 May 31.
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.
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.
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).
Cluster analyses require list-wise deletion of cases with missing data so we were unable to conduct analyses on all STEP-BD participants.
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.
我们试图探讨双相抑郁心理治疗的预测因素和调节因素（这些因素在之前的分析中是单独确定的）如何为一种衡量标准的制定提供信息，该衡量标准用于前瞻性地对强化心理治疗（IP）与系统治疗强化项目（STEP - BD）研究中药物治疗辅助的协作式照护（CC）的治疗结果进行分类。
我们对135名STEP - BD参与者进行了事后分析，使用聚类分析来识别具有相似临床特征的参与者子集，并将这个综合衡量标准作为对IP反应的调节因素和预测因素进行研究。我们使用凝聚层次聚类分析和k均值聚类来确定临床特征的内容。使用逻辑回归和Cox比例风险模型来评估所得聚类是否预测或调节了康复的可能性或直至康复的时间。
聚类分析产生了一个两类解决方案：1）“复发较少/病情较轻”和2）“慢性/复发”。复发较少/病情较轻和慢性/复发的参与者在IP中的康复率相似。复发较少/病情较轻的患者在CC中比慢性/复发的患者更有可能实现康复（p = 0.040，OR = 4.56）。对于慢性/复发的参与者，IP导致康复更快，而在复发较少/病情较轻的聚类中，CC导致康复更早（p = 0.034，OR = 2.62）。
聚类分析需要对有缺失数据的病例进行逐行删除，所以我们无法对所有STEP - BD参与者进行分析。
一种有充分效力的参数化方法可以根据疾病史区分患者，并为临床医生提供在CC与IP中具有不同预后的患者症状特征。
