Approximating Mechanisms of Suicide Risk to Innovate Interventions for Mid-to-Late-Life Veterans

近似自杀风险机制以创新中晚年退伍军人的干预措施

基本信息

批准号：
10590282
负责人：
Michael Alan Ruderman
金额：
--
依托单位：
VETERANS AFFAIRS MED CTR SAN FRANCISCO
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-01-01 至 2024-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10590282
关键词：
Award Clinical Clinical Research Clinical Trials Complement Complex Computational Technique Computer Analysis Data Data Analyses Data Linkages Data Set Elderly Ensure Ethics Etiology Focus Groups Frequencies Goals Inpatients Intention Intervention Intervention Trial Investigational Therapies K-Series Research Career Programs Knowledge Link Longitudinal cohort Mentors Methods Modeling Morbidity - disease rate National Institute of Mental Health Occupations Outcome Outpatients Patients Pattern Pharmaceutical Preparations Phase Positioning Attribute Post-Traumatic Stress Disorders Predictive Analytics Prevention program Prevention strategy Prognostic Factor Protocols documentation Provider Psychiatrist Research Research Methodology Research Personnel Research Priority Research Project Grants Risk Reduction Role Scientist Suicide Suicide attempt Suicide prevention Techniques Testing Training Training Activity Veterans Vulnerable Populations aged career cohort cost demographics design effective intervention empowerment improved innovation insight intervention program large scale data large-scale database lens longitudinal dataset machine learning prediction military veteran programs reducing suicide screening suicidal morbidity suicidal risk

项目摘要

The current 2-year career development award (CDA-1) proposal is designed to prepare Dr. Michael Ruderman, a VA Psychiatrist with foundational knowledge of advanced research methods, for a career in VA conducting research as a psychiatrist scientist who will advance innovation in suicide prevention and intervention strategies—identifying and targeting causal mechanisms for suicide that are amenable for intervention. Dr. Ruderman will accomplish this goal through the pursuit and completion of training activities, support from an expert mentoring team, and completion of a research project aimed at bridging the gap between causal inference research methods and clinical knowledge to inform suicide interventions. The CDA-1 project will ultimately generate pilot information about causal factors for suicide risk with refinement based on expert elicitation for Dr. Ruderman’s submission of a CDA-2 application. Dr. Ruderman’s proposed CDA-1 project supports VA’s top clinical priority—Preventing suicide. Moreover, this proposal is directly aligned with VA’s mandate to prioritize research that can help develop targeted suicide interventions by finding out why certain Veterans are at risk of suicide. Despite VA’s strong predictive analytics for stratifying Veterans at risk for suicide, the mechanisms leading to suicide are poorly understood. This lack of knowledge has impeded the innovation of targeted and effective interventions available. National suicide research agendas urge investigators to leverage existing data and determine potential causal targets that could define or develop effective suicide interventions. However, few large-scale databases exist that would have the capacity to harmonize and link to the right breadth and depth of information to successfully detect causal targets for a rare (yet, profound) outcome like suicide. Thus, this CDA-1 proposed research leverages Dr. Amy Byers’ (primary mentor on the CDA-1) CSR&D Merit award project (CX001119), which uniquely formed a longitudinal cohort of 5 million Veterans aged 50 years and older including, currently, nearly 12,000 suicide deaths and data on demographics, inpatient, outpatient, medications, labs, and morbidity. The CDA-1 project will expand upon Dr. Byers’ research on late-life suicide risk looking at prognostic factors and fill a significant gap in the field, causal inference, complementing predictive analytics in suicide risk research at VA. Furthermore, focusing on mid-to-late-life Veterans is ideal because it supplies targeted information in this understudied and highly vulnerable group, who have the highest number of lives lost to suicide (~70% of all Veteran suicide deaths), as well as make up over 70% of the Veteran population. Discovering candidates from large secondary data requires an approach that can extract causal information efficiently while also prioritizing likelihood of clinical utility. Data-driven causal methods have the potential to do this, but only if such methods are tightly linked with existing clinical and other scientific knowledge. Therefore, we propose an approach where we: first (Aim 1) utilize causal discovery techniques to identify preliminary causal candidates for suicide in Veterans aged 50 years and older; and, then, second (Aim 2) develop a protocol to elicit clinical expertise on potential mechanisms of suicide, which will provide necessary pilot information for a CDA-2 application. Such a biphasic approach ensures expert knowledge is integrated with computational analysis to maximize likelihood of clinical utility for suicide prevention. To this end, the aims and training of this 2-year CDA-1 will prepare Dr. Michael Ruderman to submit a CDA-2 application—clearing a path toward an independent research program as a computational psychiatrist, bridging methods to institute actionable change, reducing suicide risk for Veterans, and empowering their providers.

当前的 2 年职业发展奖 (CDA-1) 提案旨在帮助迈克尔博士做好准备 Ruderman，退伍军人管理局精神病学家，拥有先进研究方法的基础知识，适合在退伍军人事务部工作作为隐喻科学家进行研究，推动自杀预防和预防方面的创新干预策略——识别并针对自杀的因果机制鲁德曼博士将通过追求和完成培训活动来实现这一目标，专家指导团队的支持，并完成旨在缩小差距的研究项目 CDA-1 因果推理研究方法和临床知识之间的关系，为自杀干预提供信息。项目最终将生成有关自杀风险因果因素的试点信息，并根据 Ruderman 博士提交 CDA-2 申请的专家诱导。 Ruderman 博士提出的 CDA-1 项目支持 VA 的临床首要任务——预防自杀。此外，该提案与 VA 的任务直接一致，即优先考虑有助于开发的研究尽管退伍军人事务部的力量很强，但仍通过找出某些退伍军人面临自杀风险的原因来进行有针对性的自杀干预。对有自杀风险的退伍军人进行分层的预测分析，导致自杀的机制很差这种知识的缺乏阻碍了有针对性的有效干预措施的创新。国家自杀研究议程敦促调查人员利用现有数据并确定。可以定义或制定有效的自杀干预措施的潜在因果目标然而，很少有大规模的自杀干预措施。存在的数据库将有能力协调和链接到正确的广度和深度成功检测自杀等罕见（但影响深远）结果的因果目标的信息。 CDA-1 拟议的研究利用了 Amy Byers 博士（CDA-1 的主要导师）CSR&D 优异奖项目 (CX001119)，该项目独特地形成了 500 万 50 岁及以上退伍军人的纵向队列目前包括近 12,000 例自杀死亡事件以及人口统计数据、住院患者、门诊患者、 CDA-1 项目将扩展拜尔斯博士对晚年自杀的研究。风险研究预后因素并填补该领域的重大空白，因果推断，补充此外，针对中晚年退伍军人的分析是理想的选择。因为它为这个未被充分研究和高度脆弱的群体提供了有针对性的信息，他们拥有自杀造成的死亡人数最多（约占所有退伍军人自杀死亡人数的 70%），并且占退伍军人自杀死亡人数的 70% 以上退伍军人人口。从大量二手数据中发现候选者需要一种能够提取因果关系的方法有效地提供信息，同时还优先考虑数据驱动的因果方法的可能性。有潜力做到这一点，但前提是这些方法与现有的临床和其他科学紧密联系因此，我们提出了一种方法：首先（目标 1）利用因果发现技术来确定 50 岁及以上退伍军人自杀的初步候选因素，然后是第二个（目标； 2）制定一项方案，以获取有关自杀潜在机制的临床专业知识，这将提供 CDA-2 应用程序所需的试点信息这种双相方法可确保专家知识。与计算分析相结合，最大限度地提高自杀预防的临床实用性。最后，为期 2 年的 CDA-1 的目标和培训将为 Michael Ruderman 博士提交 CDA-2 做好准备应用程序——为计算心理学、桥梁等独立研究项目扫清道路采取可行的变革的方法，降低退伍军人的自杀风险，并赋予他们的提供者权力。