Improving Outcomes in Depression in Primary Care in a Low Resource Setting

改善资源匮乏的初级保健中抑郁症的治疗效果

• 批准号: 10624403
• 负责人: STEVEN DENNIS HOLLON
• 金额: $ 115.28万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-18 至 2026-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10624403
• 关键词: Academia; Adherence; Advocate; Antidepressive Agents; Behavioral; Caring; Characteristics; Clinical; Collaborations; Cost Analysis; Cost Effectiveness Analysis; Detection; Disease remission; Effectiveness; European ancestry; Exclusion Criteria; Fluoxetine; Genotype; Goals; Health system; India; Individual; Intervention; Knowledge; Machine Learning; Masks; Measures; Mediation; Mediator; Mental Depression; Mental Health; Mental Health Services; National Institute of Mental Health; Nepal; Neurocognitive; Outcome; Participant; Patient-Focused Outcomes; Patients; Pharmaceutical Preparations; Policies; Precision therapeutics; Prediction of Response to Therapy; Primary Care; Probability; Psychotherapy; Quality-Adjusted Life Years; Random Allocation; Randomized; Recommendation; Recovery; Research; Research Infrastructure; Resource-limited setting; Sample Size; Sampling; Selective Serotonin Reuptake Inhibitor; Specialist; Specificity; Testing; Treatment outcome; Work; World Health Organization; burden of illness; cost effective; cost effectiveness; effectiveness evaluation; effectiveness outcome; ethnic diversity; functional outcomes; genetic predictors; genome wide association study; health care delivery; improved outcome; individual patient; individual variation; insight; optimal treatments; personalized medicine; polygenic risk score; precision medicine; primary care patient; primary care setting; programs; psychogenetics; psychologic; relative cost; relative effectiveness; response; routine care; socioeconomics; treatment optimization; treatment trial

Project Summary Abstract Depression is the leading mental health related contributor to the Global Burden of Disease. We have shown in previous studies that generic antidepressant medications (ADMs) and brief psychological interventions such as our culturally adapted version of behavioral activation, the Healthy Activity Program (HAP), are effective in achieving remission in primary care patients. However, not everyone responds to either intervention and similar aggregate outcomes can mask considerable individual variability in response. The goal of our proposed research is to see if we can enhance treatment outcomes for patients with moderate to severe depression by personalizing allocation to one of the two treatments; additionally, we aim to identify those patients who are unlikely to respond to either treatment and should be referred to specialist care. To achieve this, we will use machine learning to develop a precision treatment rule (PTR) based on a wide range of baseline moderators which are feasible to assess in routine care settings. Our primary hypothesis is that those patients randomized by chance to their optimal intervention as indicated by the PTR will be more likely to remit and recover than those who are not. Moreover, we hypothesize that using our PTR to select the optimal treatment for each individual patient will prove to be more cost-effective than leaving things to chance. We also plan to explore secondary questions, such as whether we can enhance our mediation tests by including the PTR in interactions with our purported mediators (moderated mediation). Further, we plan to explore whether we can enhance the prescriptive utility of our PTR via genotyping our sample and calculating polygenic risk scores based on very large sample Genome-Wide Association studies. We will test these hypotheses in a controlled trial in primary care settings in India where we have a record of conducting depression treatment trials for two decades. We plan to randomize 1500 individuals to either HAP or ADM and generate our PTR on the first 1000 patients randomized and then test it on the remaining 500 patients. This will be the first test of whether precision medicine can be used to enhance depression treatment outcomes through prediction of differential response to the two treatments recommended by the WHO for depression in primary care. Concurrently, we also should be able to identify baseline predictors of nonresponse to either of these two treatments, so as to identify patients who should be referred to specialist care at the outset. Our findings have the potential to make significant contributions to the prospect of optimizing the treatment of depression in primary care not just in India but also in primary care settings worldwide, and thus support the practice-related goals of NIMH RFA- MH-18-701.

项目摘要 抑郁症是导致全球疾病负担的主要心理健康相关因素。我们已经表明， 以前的研究表明，通用抗抑郁药物（ADM）和简短的心理干预， 我们的文化适应版本的行为激活，健康活动计划（HAP），是有效的 在初级保健患者中实现缓解。然而，并不是每个人都对这两种干预措施作出反应， 类似的总体结果可能掩盖相当大的个体反应差异。我们提出的目标是 研究是为了看看我们是否可以通过以下方式提高中度至重度抑郁症患者的治疗效果： 个性化分配到两种治疗之一;此外，我们的目标是确定那些患者谁是 不太可能对任何一种治疗产生反应，应转诊至专科护理。为了实现这一目标，我们将使用 机器学习，基于广泛的基线调节器开发精确治疗规则（PTR） 这在常规护理环境中是可行的。我们的主要假设是这些随机化的患者 如PTR所示，他们的最佳干预措施将更有可能缓解和恢复， 那些没有的人此外，我们假设，使用我们的PTR为每个患者选择最佳治疗方案， 个别病人将证明比听之任之更具成本效益。我们还计划探索 次要问题，例如我们是否可以通过将PTR纳入 与我们声称的调解人的互动（适度调解）。此外，我们计划探索我们是否可以 通过对样本进行基因分型和计算多基因风险评分， 基于大样本全基因组关联研究。我们将在一个受控的 我们在印度的初级保健机构进行了一项试验，我们有记录进行了两项抑郁症治疗试验， 几十年我们计划将1500名患者随机分为HAP组和ADM组，并在前1000名患者中生成PTR 然后对剩下的500名患者进行测试。这将是第一次测试， 精准医学可用于通过预测不同的抑郁症患者， 对世界卫生组织推荐的两种治疗抑郁症的初级保健的反应。同时，我们 还应该能够识别对这两种治疗无应答的基线预测因子，以便 从一开始就确定哪些病人应该被转介到专科护理。我们的发现有可能 显著贡献的前景，优化治疗抑郁症的初级保健，而不仅仅是在 印度，而且在全世界的初级保健环境中，因此支持NIMH RFA的实践相关目标- MH-18-701