Identifying treatment-resistant depression in automated databases

在自动化数据库中识别难治性抑郁症

• 批准号: 8110228
• 负责人: Darren Toh
• 金额: $ 9.86万
• 依托单位: HARVARD PILGRIM HEALTH CARE, INC.
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-05-01 至 2013-10-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8110228
• 关键词: Identifying; treatment; resistant; depression; automated

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.

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种抗抑郁药充分治疗后未能缓解，大约35%的患者在接受两种抗抑郁药治疗后仍有症状。新的药理学（例如，非典型抗精神病药物增强）和非药物（例如，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护理的更大和更多样化的TRD患者群体中现有治疗选择的长期比较有效性和安全性的信息很少。随机对照试验可能无法及时填补这一知识空白，因为其样本量相对较小，选择性研究人群，短期随访，成本高，耗时长，考虑的治疗策略有限。另一方面，电子医疗保健数据库记录了大量患者的临床遭遇，使得研究真实的世界利用模式以及比较TRD各种治疗选择的风险和获益成为可能。由于这些数据库准确地记录了抗抑郁治疗的变化，这是TRD的强有力指标，因此使用这些数据库的研究具有很大的潜力，可以补充随机试验，提供急需的临床信息，以帮助提高TRD患者的护理质量。我们提出了一项队列研究，使用一个大型非营利健康计划的行政索赔数据，纵向随访和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状态，我们还将研究抗抑郁治疗的治疗顺序和现有TRD治疗策略的利用模式。这项研究将大大提高我们使用电子医疗保健数据库来评估TRD治疗选择的广泛关键问题的能力。该项目将使用参与HMO研究网络的健康计划之一的数据，HMO研究网络是一个由15个美国健康计划组成的联盟，为1100万地理和人口统计学上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范，因此本研究中开发的算法可直接应用于整个网络，为使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TRD各种治疗策略的长期比较有效性和安全性。通过识别和针对TRD患者，该研究还可能对护理提供产生直接的临床影响，因为参与研究的研究团队、健康计划和提供系统具有长期而密切的关系，可以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：我们将开发和验证算法，以识别行政索赔数据库中的难治性抑郁症。该研究将为使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，并最终导致更好的护理质量。