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

目前的2年职业发展奖（CDA-1）的建议是为了准备博士迈克尔 Ruderman是一名退伍军人事务部精神病学家，拥有先进研究方法的基础知识， 作为一名精神病学家进行研究，推动自杀预防的创新， 干预策略-识别和针对自杀的因果机制， 干预鲁德曼博士将通过追求和完成培训活动来实现这一目标， 得到专家指导小组的支持，并完成了一个旨在弥合差距的研究项目 因果推理研究方法和临床知识之间的联系，为自杀干预提供信息。CDA-1 该项目将最终产生关于自杀风险因果因素的试点信息，并根据以下因素进行细化： 鲁德曼博士提交CDA-2申请的专家诱导。 Ruderman博士提出的CDA-1项目支持VA的首要临床优先事项-预防自杀。 此外，这项建议直接符合VA的任务，即优先考虑有助于发展的研究。 有针对性的自杀干预，找出为什么某些退伍军人有自杀的风险。尽管VA的强大 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 明白这种知识的缺乏阻碍了有针对性的有效干预措施的创新 available.国家自杀研究议程敦促调查人员利用现有数据， 潜在的因果目标，可以定义或制定有效的自杀干预措施。然而，少数大型 现有的数据库将有能力协调和链接到正确的广度和深度， 信息，以成功地检测因果目标的罕见（但深刻）的结果，如自杀。因此， CDA-1建议的研究利用了Amy Byers博士（CDA-1的主要导师）的CSR&D优异奖 项目（CX 001119），该项目独特地形成了一个由500万50岁及以上退伍军人组成的纵向队列 包括，目前，近12,000例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项目将扩大拜尔斯博士对晚年自杀的研究 风险看预后因素，填补了该领域的重大空白，因果推理，补充 自杀风险研究的预测分析此外，专注于中年到晚年的退伍军人是理想的 因为它提供了有针对性的信息，在这个研究不足和高度脆弱的群体，谁拥有 自杀死亡人数最多（约占所有退伍军人自杀死亡人数的70%），占所有退伍军人自杀死亡人数的70%以上。 退伍军人人口。 从大量的次级数据中发现候选者需要一种可以提取因果关系的方法 有效地提供信息，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 有潜力做到这一点，但只有当这些方法与现有的临床和其他科学方法紧密联系在一起时， 知识因此，我们提出了一种方法，其中我们：首先（目标1）利用因果发现技术， 确定50岁及以上退伍军人自杀的初步因果候选人;然后，第二（目的 2)制定一项方案，以征求关于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。这种双相方法确保了专家知识 与计算分析相结合，以最大限度地提高预防自杀的临床实用性。本 最后，这个为期2年的CDA-1的目标和培训将准备迈克尔·鲁德曼博士提交CDA-2 应用程序清除路径走向一个独立的研究计划，作为一个计算精神病学家， 方法来制定可行的变化，降低退伍军人的自杀风险，并赋予他们的供应商。

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.