Identifying suicidal subtypes and dynamic indicators of increasing and decreasing suicide risk

摘要 在过去的20年里，美国总体人口自杀率稳步上升。那些拥有 在美国武装部队服役是一个高风险的亚群，其中费率以更快的速度增长 与那些从未在军队服役的人相比。新出现的研究表明， 自杀状态的几种亚型。不同亚型的个体可能遵循不同的途径获得高风险 在国家，并可能以不同的方式对治疗干预措施作出反应。到目前为止，研究还没有检查 使用包括遗传、环境、医学和心理变量在内的综合数据集的类型学。 为了解决这一知识差距，我们建议利用南得克萨斯州地区的归档数据集 创伤与复原力(强STAR)知识库组织网络指导研究，包含 来自4000多名军事人员的遗传、环境、医学和心理变量 部署前和部署后进行评估。使用这个数据集，我们将(A)确定自杀军人的亚群 人员和(B)确定自杀风险增加、减少和静态的不同模式。这样做的结果是 分析将使我们能够识别自杀风险的离散基因-表型表达，从而使我们能够 确定可用于改进风险检测和改进自杀预防的多个风险模型 干预措施。 新出现的研究进一步表明，自杀风险随时间推移的过程本质上是非线性的。不幸的是， 大多数研究随着时间的推移出现自杀风险的研究都使用了研究和数据 无法准确捕捉非线性变化过程的分析方法。要解决这个问题 知识差距，我们建议利用Strong中包含的六项临床试验的归档数据集 STAR资料库(N&gT；800)，每个都包括对抑郁症的重复评估(总共多达13次)， 创伤后应激障碍和自杀意念。多变量潜在变化得分模型，受动力系统理论的启发， 将用于对与低风险和高风险状态相关的非线性变化过程进行建模。这样做的结果是 分析将产生后验概率，该后验概率可以估计给定患者转变为高 在给定时间点的风险状态，这可能导致开发识别谁是 随着时间和时间的推移，风险将会增加。 尽管拟议的研究使用了从军事人员收集的存档数据，但拟议的方法可以 被翻译到其他人群和环境中，从而导致在检测和 自杀风险升高的个人。

ABSTRACT The U.S. general population suicide rate has increased steadily over the past 20 years. Those who have served in the U.S. Armed Forces are a high risk subgroup among which rates have increased at a faster rate as compared to those who have never served in the military. Emerging research suggests the existence of several subtypes of suicidal states. Individuals in different subtypes may follow different pathways to high risk states and may respond to treatment interventions in different ways. To date, studies have not examined typologies using integrated datasets that include genetic, environmental, medical, and psychological variables. To address this knowledge gap, we propose to leverage an archived dataset from the South Texas Region Organization Network Guiding Studies of Trauma and Resilience (STRONG STAR) Repository, which contains genetic, environmental, medical, and psychological variables from over 4000 military personnel who were assessed before and after deployment. Using this dataset, we will (a) identify subgroups of suicidal military personnel and (b) identify different patterns of increasing, decreasing, and static suicide risk. Results of this analysis will enable us to identify discrete genotype-phenotype expressions of suicide risk, thereby enabling us to identify multiple risk models that can be used to improve risk detection and refine suicide prevention interventions. Emerging research further indicates the process of suicide risk over time is nonlinear in nature. Unfortunately, the majority of studies examining the emergence of suicide risk over time have employed research and data analytic methods that are unable to accurately capture nonlinear change processes. To address this knowledge gap, we propose to leverage archived datasets from six clinical trials included in the STRONG STAR Repository (N>800), each of which includes repeated assessments (up to 13 total) of depression, PTSD, and suicide ideation. Multivariate latent change score models, informed by dynamical systems theory, will be used to model nonlinear change processes associated with low risk and high risk states. Results of this analysis will yield posterior probabilities that can estimate the likelihood of a given patient transitioning to a high risk state at a given point in time, which could lead to the development of “warning systems” that identify who will experience increased risk over time, and when. Although the proposed study uses archived data collected from military personnel, the proposed methods can be translated to other populations and settings, thereby leading to significant advances in the detection and individuals with elevated risk for suicide.