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症（MDD）治疗的个人没有经历症状缓解，尽管至少有两个充分的抗抑郁药试验。这种难治性抑郁症（TRD）在医疗保健费用、功能障碍和生活质量下降方面对MDD的巨大成本做出了不成比例的贡献。个体化治疗TRD高危人群的前景是显而易见的。如果这些人可以在病程早期被识别出来，他们可以被分诊到更密集或有针对性的干预措施，以提高他们缓解的可能性。例如，他们可能会接受更早的认知行为治疗，更早使用联合药物治疗，或更早转诊接受电休克治疗。随着MDD治疗选择的增多，个体在接受这些下一步治疗之前可能需要数月或数年的治疗。此外，识别这些个体的能力将有助于开发新的个性化干预措施：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群组。其次，没有验证队列存在，以证明这种模型在临床环境中表现良好。本研究报告建议直接解决这两个障碍。以前的研究，包括大型多中心缓解抑郁症系统治疗替代方案（星星 *D）研究的工作，已经确定了治疗反应的推定临床或遗传预测因子。然而，在缺乏复制的情况下，这种关联充其量只能产生假设。一项正在进行的研究将收集1,000名在新英格兰卫生系统接受治疗的个体的数据，这些个体的前瞻性治疗结果是可用的（Dep 1队列），包括500名TRD个体和500名SSRI反应性MDD个体，预计将在2009年春季完成全基因组关联研究。这项拟议的研究将首先使用尖端的建模技术，在现有的Dep 1队列中使用社会人口统计学，临床和遗传预测因子构建和交叉验证TRD模型。同时，它将从同一卫生系统收集额外的1,000名MDD受试者，这些受试者具有6个月的治疗结果。第二个队列（Dep 2）将用于验证TRD风险分层模型。为了识别这些患者队列，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，这些技术已成功应用于支持基于人群的研究。这种方法允许基于索赔、药房数据和医疗记录来识别临床特征，例如合并症、药物治疗以及纵向结果。由此产生的患者数据更能代表临床人群，并且生成成本远低于使用更传统的方法获得的数据。因此，除了促进MDD的个性化治疗外，拟议的研究还将建立一种方法，用于使用大量临床人群来整体个性化精神病学治疗。 公共卫生相关性：三分之一或更多的重度抑郁症患者尽管接受了两种或更多种不同的治疗，但仍然没有康复，在治疗早期识别这些人可能会允许更个性化的方法，成功的机会更大。这项研究将使用统计技术，根据临床差异和遗传变异，试图预测谁有患这种难治性抑郁症的风险。然后，它将检查第二组患者，看看这种技术在大型卫生系统中应用的效果如何。

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.