Building a Risk Stratification Model for Treatment Resistance in Major Depressive

描述（由申请人提供）：三分之一或更多的重度抑郁障碍（MDD）患者尽管进行了至少两次充分的抗抑郁药物试验，但症状并未缓解。这种难治性抑郁症（TRD）在医疗费用、功能损害和生活质量下降方面不成比例地造成了重度抑郁症的巨大成本。为TRD高风险人群提供个性化医疗的前景是显而易见的。如果这些个体能够在疾病过程的早期被识别出来，他们就可以被分类到更密集或更有针对性的干预措施中，以提高他们缓解的可能性。例如，他们可能会更早地接受认知行为治疗，更早地使用联合药物治疗，或更早地接受电休克治疗。随着重度抑郁症治疗方案的增加，个体在接受下一步治疗之前可能要花费数月或数年的时间。此外，识别这些个体的能力将促进新的个性化干预措施的发展：而不是需要多次失败的前瞻性试验，高风险个体可以立即参与研究。目前，将个性化医疗转化为临床实践存在两个主要障碍。首先，没有收集到可用于建立风险模型的大型和可推广的队列。其次，没有验证队列来证明这些模型在临床环境中表现良好。本研究建议直接解决这两个障碍。先前的研究，包括大型多中心缓解抑郁的系统治疗方案（STAR*D）研究，已经确定了治疗反应的假定临床或遗传预测因子。然而，在没有复制的情况下，这种关联充其量只是假设。一项正在进行的研究将收集在新英格兰卫生系统中接受治疗的1000名患者（Dep1队列）的数据，其中包括500名TRD患者和500名ssri反应性重度抑郁症患者，预计将于2009年春季完成一项全基因组关联研究。该研究将首先使用尖端的建模技术，在现有的Dep1队列中使用社会人口学、临床和遗传预测因子来构建和交叉验证TRD模型。与此同时，它将从同一卫生系统收集另外1000名具有6个月治疗结果的重度抑郁症受试者。第二个队列（Dep2）将用于验证TRD风险分层模型。为了确定这些患者队列，本研究将利用计算机管理数据系统、数据挖掘和自然语言处理技术，这些技术已经成功地应用于支持基于人群的研究。这种方法可以识别临床特征，如合并症、药物治疗以及基于索赔、药房数据和医疗记录的纵向结果。与使用更传统的方法获得的数据相比，由此产生的患者数据更能代表临床人群，而且生成成本也要低得多。因此，除了促进重度抑郁症的个性化治疗外，该研究还将建立一种方法，利用大量临床人群对整个精神病学进行个性化治疗。

DESCRIPTION (provided by applicant): One-third or more of individuals treated for major depressive disorder (MDD) do not experience remission of symptoms despite at least two adequate antidepressant trials. Such treatment-resistant depression (TRD) contributes disproportionately to the tremendous costs of MDD, in terms of health care costs, functional impairment, and diminished quality of life. The promise of personalized medicine for individuals at high risk for TRD is apparent. If these individuals could be recognized early in their disease course, they could be triaged to more intensive or targeted interventions to improve their likelihood of remission. For example, they might receive earlier addition of cognitive-behavioral therapy, earlier use of combination medication treatments, or earlier referral for electroconvulsive therapy. With the proliferation of treatment options in MDD, individuals can spend months or years in and out of treatment before receiving these next-step treatments. Moreover, the ability to identify these individuals would facilitate the development of new personalized interventions: rather than the requiring multiple failed prospective trials, high-risk individuals could immediately be offered study participation. At present, there are two primary obstacles to translating personalized medicine into clinical practice. First, no large and generalizable cohorts have been collected in which to build risk models. Second, no validation cohorts exist to demonstrate that such models perform well in clinical settings. The present study proposes to address these two obstacles directly. Previous investigations, including work in the large multicenter Systematic Treatment Alternatives to Relieve Depression (STAR*D) study, have identified putative clinical or genetic predictors of treatment response. However, in the absence of replication, such associations are hypothesis-generating at best. An ongoing study will collect data from 1,000 individuals treated in a New England health system for whom prospective treatment outcomes are available (the Dep1 cohort), including 500 individuals with TRD and 500 with SSRI-responsive MDD, with completion of a genome wide association study expected by spring 2009. The proposed study will first use cutting-edge modeling techniques to construct and cross-validate models of TRD using sociodemographic, clinical, and genetic predictors in the existing Dep1 cohort. In parallel, it will collect an additional 1,000 MDD subjects with 6-month treatment outcomes from the same health system. This second cohort (Dep2) will be used to validate the TRD risk stratification model. To identify these patient cohorts, this study will take advantage of computerized administrative data systems, data-mining, and natural language processing techniques that have been successfully applied to support population-based research. This approach allows identification of clinical features, such as comorbidities, medication treatments, as well as longitudinal outcomes, based on claims, pharmacy data, and medical records. The resulting patient data is far more representative of clinical populations, and far less expensive to generate, than that which could be obtained using more traditional approaches. Therefore, beyond facilitating personalized treatment of MDD, the proposed study would establish the methodology for using large clinical populations to personalize treatment in psychiatry as a whole. Public Health Relevance: A third or more of people with major depression do not get well despite two or more different treatments, and identifying these people early in treatment might allow more personalized approaches with greater chances of success. This study will use statistical techniques to try to predict who is at risk for this treatment- resistant depression, based on clinical differences and genetic variations. Then, it will examine a second group of patients to see how well this technique might work if it is applied in a large health system.