Building a Risk Stratification Model for Treatment Resistance in Major Depressive

建立重度抑郁症治疗抵抗的风险分层模型

• 批准号: 7791285
• 负责人: ROY H. Perlis
• 金额: $ 44.16万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-04-01 至 2012-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7791285
• 关键词: Address; Antidepressive Agents; Anxiety; Asthma; Biology; Biomedical Computing; Cardiovascular system; Clinical; Clinical Trials; Cognitive Therapy; Cohort Studies; Collection; Combination Medication; Comorbidity; DNA; Data; Data Set; Development; Disease; Disease remission; Electroconvulsive Therapy; General Hospitals; General Population; Genetic; Genetic Variation; Genotype; Health Care Costs; Health system; Healthcare; Healthcare Systems; Hospitals; Individual; Informatics; Information Systems; Insurance Claim Review; Intervention; Investigation; Literature; Major Depressive Disorder; Massachusetts; Medical; Medical Records; Medicine; Mental Depression; Methodology; Modeling; Natural Language Processing; New England; Outcome; Outpatients; Patients; Performance; Pharmaceutical Preparations; Pharmacy facility; Phenotype; Population; Productivity; Psychiatry; Quality of life; Recording of previous events; Reporting; Research; Resistance; Resources; Rheumatoid Arthritis; Risk; Risk Estimate; Risk Factors; Sampling; Societies; Standardization; Stratification; Suicide; Symptoms; System; Techniques; Testing; Thinking; Translating; Treatment Cost; Treatment Step; Treatment outcome; Triage; Validation; Woman; Work; alternative treatment; base; burden of illness; clinical practice; clinically relevant; cohort; computerized; cost; data mining; depressive symptoms; early experience; early onset; experience; functional disability; genome wide association study; genome-wide; high risk; improved; innovation; mortality; population based; prospective; public health relevance; response; success; treatment response; treatment strategy; willingness; years lived with disability

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.

描述（由申请人提供）：尽管进行了至少两次充分的抗抑郁试验，但三分之一或更多接受重度抑郁症（MDD）治疗的患者症状并未缓解。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造成了 MDD 的巨大损失。个性化医疗对于 TRD 高危人群的前景是显而易见的。如果这些人能够在病程早期得到识别，就可以对他们进行分类以进行更强化或更有针对性的干预措施，以提高缓解的可能性。例如，他们可能会更早接受认知行为治疗、更早使用联合药物治疗或更早转诊进行电惊厥治疗。随着重度抑郁症治疗方案的增多，个人可能会在接受下一步治疗之前花费数月或数年的时间进行治疗。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：而不是要求多次失败的前瞻性试验，而是可以立即为高风险个体提供参与研究的机会。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，尚未收集到可用于构建风险模型的大型且可概括的队列。其次，没有验证队列可以证明此类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。之前的研究，包括缓解抑郁症的大型多中心系统治疗替代方案（STAR*D）研究中的工作，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，这种关联最多只能产生假设。一项正在进行的研究将收集新英格兰卫生系统中 1,000 名接受前瞻性治疗结果的个体（Dep1 队列）的数据，其中包括 500 名 TRD 患者和 500 名 SSRI 反应性 MDD 患者，并完成全基因组关联研究预计于 2009 年春季进行。拟议的研究将首先使用尖端建模技术，利用现有 Dep1 中的社会人口统计学、临床和遗传预测因子来构建和交叉验证 TRD 模型队列。与此同时，它还将从同一卫生系统收集另外 1,000 名 MDD 受试者 6 个月的治疗结果。第二组 (Dep2) 将用于验证 TRD 风险分层模型。为了识别这些患者群体，本研究将利用计算机化管理数据系统、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。这种方法可以根据索赔、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。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，并且生成成本要低得多。因此，除了促进 MDD 的个性化治疗之外，拟议的研究还将建立利用大量临床人群对整个精神病学进行个性化治疗的方法。 公共健康相关性：尽管有两种或多种不同的治疗方法，三分之一或更多的重度抑郁症患者仍无法康复，在治疗早期识别这些人可能会带来更个性化的治疗方法，并有更大的成功机会。这项研究将使用统计技术，根据临床差异和遗传变异，尝试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，看看这项技术在大型卫生系统中应用时效果如何。