Identifying treatment-resistant depression in automated databases

描述(申请人提供)：难治性抑郁症(TRD)是一个主要的公共卫生问题；超过50%的抑郁症患者在接受一次适当的抗抑郁药物治疗后未能缓解，约35%在接受两种抗抑郁药物治疗后仍有症状。新的药理学(如非典型抗精神病药物增强)和非药理(如迷走神经刺激)干预措施已经开发出来，但关于在常规临床环境中寻求治疗的更大和更多样化的TRD患者群体中现有治疗方案的长期比较有效性和安全性的信息很少。随机对照试验可能不能及时填补这一知识空白，因为它们的样本量相对较小，选择性研究人群，短期随访，高成本，时间消耗，以及考虑的治疗策略有限。另一方面，电子保健数据库记录了大量患者的临床遭遇，使其能够研究真实世界的使用模式，以及各种治疗方案的风险和收益的比较。由于这些数据库准确地记录了抗抑郁治疗的变化，而抗抑郁治疗是TRD的有力指标，因此使用这些数据库的研究具有很大的潜力来补充随机试验，以提供急需的临床信息，以帮助提高TRD患者的护理质量。我们建议进行一项队列研究，使用来自大型非营利性医疗计划的行政索赔数据，进行纵向跟踪并链接到全文医疗记录，以评估在索赔数据库中识别TRD患者的可行性。为了了解临床实践的情况，我们还将研究TRD的抗抑郁治疗顺序和现有治疗策略的使用模式。这项研究将极大地增强我们使用电子医疗数据库评估围绕TRD治疗方案的广泛关键问题的能力。该项目将使用参与HMO研究网络的一个健康计划的数据，HMO研究网络是一个由15个美国健康计划组成的联盟，为1100万地理和人口结构不同的成员提供服务。由于网络中的所有数据库共享相同的数据规格，本研究中开发的算法可以直接应用于整个网络，为利用这些数据库进行后续研究奠定基础，以便及时和最新地评估各种TRD治疗策略的长期比较有效性和安全性。通过确定和针对TRD患者，这项研究还可能在提供护理方面具有直接的临床意义，因为研究团队、健康计划和提供系统与研究有关的长期密切关系，将研究结果整合到政策和临床实践中，以提高其成员和患者的抗抑郁治疗质量。 公共卫生相关性：我们将开发和验证在行政索赔数据库中识别治疗抗药性抑郁症的算法。这项研究将为利用这些数据库进行后续研究奠定基础，以便及时和最先进地评估各种治疗方案对难治性抑郁症的长期比较有效性和安全性，并最终带来更好的护理质量。

DESCRIPTION (provided by applicant): Treatment-resistant depression (TRD) is a major public health problem; more than 50% of depressed patients fail to remit after one adequate treatment with an antidepressant, and approximately 35% remain symptomatic after receiving two antidepressants. Novel pharmacologic (e.g., atypical antipsychotic augmentation) and nonpharmacologic (e.g., vagus nerve stimulation) interventions have been developed, but there is little information on the long-term comparative effectiveness and safety of existing treatment options in the larger and more diverse groups of TRD patients who seek care in routine clinical settings. Randomized controlled trials may not fill this knowledge gap in a timely fashion because of their relatively small sample size, selective study population, short-term follow-up, high cost, time consumption, and limited treatment strategies considered. On the other hand, electronic healthcare databases record clinical encounters of a large number of patients, making it possible to study real world utilization patterns, and comparative risks and benefits of various therapeutic options for TRD. Because these databases accurately chronicle changes in antidepressive therapies, which are strong indicators for TRD, studies that use these databases have a great potential to complement randomized trials to provide the much needed clinical information to help improve quality of care for TRD patients. We propose a cohort study to use the administrative claims data from a large non-profit health plan with longitudinal follow-up and linkage to full-text medical records to evaluate the feasibility of identifying TRD patients in claims databases. To understand the state of clinical practice, we will also examine the treatment sequences of antidepressive therapies and utilization patterns of existing treatment strategies for TRD. This study will greatly enhance our capacity to use electronic healthcare databases to assess a wide range of critical issues surrounding therapeutic options for TRD. The project will use data from one of the health plans participating in the HMO Research Network, a 15-year old consortium of 15 U.S. health plans that serve 11 million geographically and demographically diverse members. Because all databases in the Network share identical data specifications, algorithms developed in this study can be directly applied to the entire Network, laying the groundwork to conduct subsequent studies with these databases for timely and state-of-the-art assessment of the long-term comparative effectiveness and safety of various treatment strategies for TRD. By identifying and targeting patients with TRD, the study may also have direct clinical implication with regard to delivery of care, because the research team, the health plan and the delivery system involved in the study have long standing and close relationships to integrate research findings into policy and clinical practice to improve quality of antidepressive treatment of their members and patients. PUBLIC HEALTH RELEVANCE: We will develop and validate algorithms to identify treatment-resistant depression in administrative claims databases. The study will lay the groundwork to conduct subsequent studies with these databases for timely and state-of-the-art assessment of the long-term comparative effectiveness and safety of various therapeutic options for treatment-resistant depression, and eventually lead to better quality of care.