Depression: The Search for Treatment-Relevant Phenotypes

抑郁症：寻找治疗相关表型

• 批准号: 6876090
• 负责人: Ellen Frank
• 金额: $ 78.44万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-05-01 至 2008-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6876090
• 关键词: anxiety; clinical research; combination therapy; computer assisted medical decision making; computer system design /evaluation; functional ability; human subject; human therapy evaluation; interview; major depression; mental disorder chemotherapy; mood disorders; outcomes research; patient oriented research; personality; pharmacokinetics; phenotype; psychotherapy; relapse /recurrence

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.

描述（由申请人提供）：尽管有几十年的临床试验经验，我们对如何最好地实现从重度抑郁症中持久恢复的理解仍然非常有限。本研究的目的是确定抑郁症的治疗相关表型，以帮助临床医生实现这一目标。我们假设，一个小的，临床上实用的变量集，包括：1）维度的措施，情绪障碍和常见的焦虑共病（体现在强调描述临床表型的光谱方法的评估工具中）;以及2）治疗暴露（体现在药物治疗的群体药代动力学指标和心理治疗的治疗特异性）;将是重性抑郁发作稳定时间的统计学显著调节因素。我们假设这组变量将是复发时间和残余功能损害的统计学显著介质。最后，我们假设，使用信号检测分析方法，以检查这些变量的关系和传统的治疗结果，如基线严重程度，残留症状和轴它合并症的相关性，我们可以开发临床上有用的算法，以指导临床医生在药物治疗和心理治疗之间的选择作为初始治疗策略。为了检验这些假设，我们将随机分配288名年龄在18岁至64岁之间的男性和女性，他们正在当地社区诊所寻求治疗重度抑郁发作，开始接受人际心理治疗（IPT）或SSRI（西酞普兰）药物治疗。那些稳定（汉密尔顿抑郁量表评分< 7 X 3周）的患者将继续其初始治疗。那些不这样做的人，将有其他治疗添加到他们的方案。所有病情稳定的受试者将进入持续治疗期，在此期间，导致缓解的治疗（SSRI单药、IPT单药或联合治疗）将持续6个月。我们的兴趣是确定那些对特定治疗或治疗序列反应最好的患者亚组，实现症状的完全缓解和功能的恢复，并且能够通过延长的井间隔维持其恢复和改善功能。通过使用一组相对较小的变量，我们认为这些变量具有很高的预测结果的潜力，以表征患者及其治疗，我们希望在拟议的研究中实现这一目标。将使用考克斯比例风险生存回归模型和随机回归模型分析至稳定时间和至复发时间以及功能损害程度与谱评估和治疗暴露变量的相关性。信号检测分析将用于确定频谱评估评分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特征。