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