Depression: The Search for Treatment-Relevant Phenotypes

描述(由申请人提供)：尽管有数十年的临床试验经验，但我们对如何最好地从严重抑郁症中实现持久恢复的理解仍然非常有限。这项研究的目的是确定抑郁症的治疗相关表型，以帮助执业临床医生实现这一目标。我们假设，一组小的、临床实用的变量包括：1)情绪障碍和常见焦虑共病的维度测量(体现在强调临床表型描述的光谱方法的评估工具中)；以及2)治疗暴露(体现在药物治疗的人群药代动力学测量和心理治疗的治疗特异性)；将在统计上显著调节主要抑郁发作的稳定时间。我们假设这一组变量在统计学上是复发时间和残留功能损害的中介变量。最后，我们假设，使用信号检测分析方法来检验这些变量与治疗结果的传统相关性(如基线严重程度、残留症状和Axis it共病)之间的关系，我们可以开发出临床上有用的算法来指导临床医生在药物治疗和心理治疗之间进行选择作为初始治疗策略。为了验证这些假设，我们将随机分配288名年龄在18岁到之间的男性和女性，他们正在当地社区诊所寻求严重抑郁发作的治疗，开始接受人际心理治疗或西酞普兰药物治疗。那些稳定的人(汉密尔顿抑郁量表和7×3周)将继续他们的初步治疗。那些没有这样做的人，将在他们的治疗方案中增加另一种治疗方法。所有稳定的受试者将进入持续治疗阶段，在此阶段，导致缓解的治疗(SSRI单独、IPT单独或联合治疗)将持续6个月。我们感兴趣的是确定哪些患者亚组对特定的治疗或治疗序列反应最好，症状完全缓解和功能恢复，并能够通过延长的井间隔维持他们的恢复和改善的功能。通过使用一组相对较小的变量，我们认为这些变量在结果预测方面具有很高的潜力来表征患者及其治疗，我们希望在拟议的研究背景下实现这一目标。COX比例风险生存回归模型和随机回归模型将被用来分析达到稳定时间、复发时间和功能损害程度与频谱评估和治疗暴露变量的关系。信号检测分析将用于确定频谱评估分数和其他临床变量的哪种组合描述了每种治疗可能稳定或复发的患者的概况。

DESCRIPTION (provided by applicant): Despite decades of clinical trial experience, our understanding of how best to achieve durable recovery from major depression remains very limited. The aim of this study is to define treatment-relevant phenotypes of depression in order to aid practicing clinicians in achieving that goal. We hypothesize that a small, clinically practical set of variables including: 1) dimensional measures of mood disorder and common anxiety comorbidities (embodied in assessment instruments emphasizing a spectrum approach to description of clinical phenotypes); and 2) treatment exposure (embodied in population pharmacokinetic measures for pharmacotherapy and treatment specificity for psychotherapy); will be statistically significant moderators of time to stabilizaton of a major depressive episode. We hypothesize that this same set of variables will be statistically significant mediators of time to relapse and residual functional impairment. Finally we hypothesize that, using signal detection analysis methods to examine the relationship of these variables and traditional correlates of treatment outcome such as baseline severity, residual symptoms and Axis It comorbidity, we can develop clinically useful algorithms to guide clinicians in choosing between pharmacotherapy and psychotherapy as an initial treatment strategy. In order to test these hypotheses, we will randomly assign 288 men and women between 18 and 64 years of age who are seeking treatment for a major depressive episode at local community clinics to begin treatment with either interpersonal psychotherapy (IPT) or SSRI (citalopram) pharmacotherapy. Those who stabilize (Hamilton Rating Scale for Depression score < 7 X 3 weeks) will continue in their initial treatment. Those who do not, will have the other treatment added to their regimen. All stabilizing subjects will enter a continuation treatment phase in which the treatment that brought about the remission (SSRI alone, IPT alone, or the combination) will be continued for 6 months. Our interest is in identifying those subgroups of patients that respond best to specific treatments or treatment sequences, achieve full remission of symptoms and return of functioning, and are able to sustain their recovery and improved functioning through an extended well interval. By using a relatively small set of variables that we believe have high potential for outcome prediction to characterize patients and their treatment, we expect to accomplish this goal in the context of the proposed study. Cox proportional hazard survival regression models and random regression models will be used to analyze the association of time to stabilization and time to relapse and degree of functional impairment) with spectrum assessments and treatment exposure variables. Signal detection analysis will be used to determine which combination of spectrum assessment scores and other clinical variables describe the profile of patients likely to stabilize or relapse with each of the treatments.