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

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

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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）提案旨在为迈克尔博士做好准备鲁德曼（Ruderman）是一名具有高级研究方法基础知识的VA精神科医生，从事VA职业作为一名精神病医生科学家进行研究，他们将提高预防自杀性的创新干预策略 - 识别和针对自杀的因果机制干涉。鲁德曼博士将通过追求和完成培训活动来实现这一目标，专家心理团队的支持，以及旨在弥合差距的研究项目的完成在因果推理研究方法和临床知识之间，以告知自杀干预措施。 CDA-1 项目最终将通过基于改进来生成有关自杀风险因果因素的试验信息鲁德曼博士提交CDA-2申请的专家启发。 Ruderman博士提议的CDA-1项目支持VA的最高临床优先事项 - 预防自杀。此外，该建议与VA的任务直接保持一致，以优先考虑可以帮助发展的研究针对性的自杀干预措施是通过找出某些退伍军人为何有自杀风险的原因。尽管VA很强用于分层有自杀风险的退伍军人的预测分析，导致自杀的机制很差理解。缺乏知识阻碍了目标和有效干预的创新可用的。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自杀干预措施的潜在因果目标。但是，很少有大规模存在的数据库将有能力协调并链接到正确的宽度和深度信息以成功地检测出因自杀等罕见（但深刻）结果的因果目标。那，这个 CDA-1建议的研究利用Amy Byers博士（CDA-1的主要导师）CSR＆D优异奖项目（CX001119），它独特形成了50岁以上的500万退伍军人的纵向队列当前，包括近12,000名自杀死亡和人口统计数据，住院，门诊，药物，实验室和发病率。 CDA-1项目将扩展拜尔斯博士对后期自杀的研究风险查看预后因素并填补该领域的显着空白，因果推断，完成 VA自杀风险研究的预测分析。此外，专注于中期生活的退伍军人是理想的因为它提供了这个理解和高度脆弱的群体中的针对性信息，他们有自杀丧生的最高生命（占所有退伍军人自杀死亡的70％），占70％以上退伍军人人口。从大型次要数据中发现候选人需要一种可以提取因果的方法有效的信息，同时还优先考虑临床实用性的可能性。数据驱动的因果方法具有这样做的潜力，但前提知识。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因果关系；然后，第二（目标 2）制定一项协议，以引起自杀潜在机制的临床专业知识，这将提供 CDA-2应用程序的必要试点信息。这种双相方法可确保专家知识是与计算分析集成，以最大程度地提高预防自杀的临床实用性。对此最后，这项为期两年CDA-1的目标和培训将使Michael Ruderman博士准备CDA-2 应用 - 以计算精神病医生的身份确定通往独立研究计划的途径进行可行的变化，减少退伍军人的自杀风险以及赋予其提供者权力的方法。