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）：尽管至少有两项适当的抗抑郁药试验，但接受重大抑郁症（MDD）治疗的患者中有三分之一或更多的人没有症状缓解。这种耐药性抑郁症（TRD）在医疗保健成本，功能障碍和生活质量下降方面，对MDD的巨大成本的贡献不成比例。显而易见的是，对有TRD高风险的个体的个性化医学的承诺显而易见。如果这些人在疾病的早期可以得到认可，则可以将他们分为更密集或有针对性的干预措施，以提高他们的缓解可能性。例如，他们可能会更早地接受认知行为疗法，早期使用联合用药治疗或更早的电击疗法转诊。随着MDD治疗方案的扩散，个人可以在接受这些下一步的治疗之前花费数月或几年的时间。此外，识别这些人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，而是立即提供研究参与。目前，将个性化医学转化为临床实践有两个主要障碍。首先，没有收集大型且可概括的人群来建立风险模型。其次，没有验证队列可以证明此类模型在临床环境中的表现良好。本研究建议直接解决这两个障碍。先前的研究，包括在大型多中心系统治疗替代方案中的工作（Star*d）研究，已经确定了治疗反应的推定临床或遗传预测指标。但是，在没有复制的情况下，这种关联充其量是假设生成的。 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现有DEP1队列中的预测因子。同时，它将收集来自同一卫生系统的6个月治疗结果的另外1,000名MDD受试者。第二个队列（DEP2）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化的管理数据系统，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。由此产生的患者数据比使用更传统的方法获得的临床人群的代表性要大得多，而产生的患者数据要比可以获得的。因此，除了促进对MDD的个性化治疗外，拟议的研究还将建立使用大量临床人群来个性化精神病学治疗的方法。 公共卫生相关性：尽管两种或多种不同的治疗方法，大抑郁症患者中的三分之一或更多，并且在治疗的早期确定这些人可能会允许更具个性化的方法，并获得更大的成功机会。这项研究将使用统计技术来试图根据临床差异和遗传变异来预测谁有这种耐药性抑郁症的风险。然后，它将检查第二组患者，以了解如果将其应用于大型卫生系统，则该技术的工作状况如何。