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 抑郁症是全球疾病负担的领先与心理健康有关的贡献者。我们显示了 先前的研究表明，通用抗抑郁药（ADM）和简短的心理干预措施，例如 我们在文化上适应的行为激活版本，健康活动计划（HAP），有效 在初级保健患者中缓解。但是，并不是每个人都对任何干预做出回应，并且 类似的总成果可以掩盖响应中的大量个人变异性。我们提议的目标 研究是为了查看我们是否可以通过通过 个性化分配给两种治疗方法之一；此外，我们的目的是确定那些 不太可能对任何一种治疗做出反应，应转交给专业护理。为了实现这一目标，我们将使用 基于广泛的基线主持人制定精确治疗规则（PTR）的机器学习 可以在常规护理环境中评估可行的。我们的主要假设是那些随机的患者 偶然地进行最佳干预措施，如PTR所示，汇款和恢复的可能性比 那些不是的人。此外，我们假设使用我们的PTR选择每个 个人患者将被证明比让事情变得更具成本效益。我们还计划探索 次要问题，例如我们是否可以通过将PTR包括在内来增强调解测试 与我们所谓的介体的相互作用（调解）。此外，我们计划探索我们是否可以 通过基因分型并计算多基因风险评分来增强我们PTR的规范效用 基于非常大的样本全基因组关联研究。我们将在受控的 在印度的初级保健环境中的试验，我们有两项进行抑郁症治疗试验的记录 几十年。我们计划将1500个个人随机hap或ADM随机，并在第一个1000上生成我们的PTR 随机对患者进行随机对其进行测试。这将是第一个测试是否 精密医学可用于通过预测差异来增强抑郁症治疗结果 WHO建议在初级保健中建议的两种治疗方法。同时，我们 还应该能够识别这两种治疗方法中的任何一种的基线预测指标，以便 确定一开始应转诊至专业护理的患者。我们的发现有可能使 不仅 印度也在全球初级保健环境中，因此支持NIMH RFA-的实践目标 MH-18-701。