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.

项目摘要摘要 抑郁症是导致全球疾病负担的主要心理健康因素。我们已经在 之前的研究表明，非专利抗抑郁药物(ADMS)和简单的心理干预，如 我们的行为激活的文化适应版本，健康活动计划(HAP)，在 在初级保健患者中实现缓解。然而，并不是每个人都对干预和 类似的综合结果可能掩盖了反应中相当大的个体差异。我们提议的目标是 研究是为了看看我们是否可以通过以下方式提高中度到重度抑郁症患者的治疗结果 个性化分配到两种治疗之一；此外，我们的目标是识别那些 对任何一种治疗都不太可能有反应，应该转介到专家护理。为了实现这一点，我们将使用 机器学习以开发基于广泛的基线主持人的精确治疗规则(PTR) 在常规护理环境中评估是可行的。我们的主要假设是，这些患者随机 碰巧，PTR所示的最佳干预措施更有可能缓解和恢复，而不是 那些不是的人。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 个别病人将被证明比听天由命更具成本效益。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 与我们所谓的调解人的互动(主持调解)。此外，我们计划探索我们是否可以 通过对我们的样本进行基因分型并计算多基因风险分数，增强我们PTR的处方效用 基于非常大的样本全基因组关联研究。我们将在受控的情况下测试这些假设 在印度的初级保健环境中进行试验，在那里我们有进行两次抑郁症治疗试验的记录 几十年。我们计划将1500人随机分配到HAP或ADM，并在前1000人生成我们的PTR 患者随机选择，然后在剩下的500名患者身上进行测试。这将是第一次考验 精确医学可用于通过预测差异来提高抑郁症的治疗结果 对世界卫生组织推荐的两种初级保健抑郁症治疗方法的反应。同时，我们 也应该能够识别对这两种治疗中的任何一种无反应的基线预测因素，以便 在一开始就确定哪些患者应该被转介到专科护理。我们的发现有可能使 对优化初级保健中抑郁症治疗的前景做出了重大贡献 印度以及世界各地的初级保健环境，从而支持NIMH RFA与实践相关的目标- MH-18-701。