Characterizing Trauma Outcomes: From Pre-trauma Risk to Post-trauma Sequelae

摘要 背景：创伤是常见的，但我们几乎没有能力预测谁会发展为创伤后精神病理学。 对我们理解创伤后精神病理学病因的持续挑战包括：(1)获得 创伤之前或同时发生的风险因素的无偏见的前瞻性数据；(2)无法对大型模型进行建模 使用传统零假设检验方法的全面风险结构，尽管知道风险因素会 不是孤立运作的；以及(3)迄今几乎普遍关注创伤后应激障碍的结果 精神病理学可能涉及跨越多个障碍类别的各种症状。这项研究的目的是 (1)使用来自大型预期人群创伤队列的数据来建立创伤后的多维分类 包括各种理论派生类别的诊断的精神病理学(例如，压力诊断、情绪 障碍，人格障碍)和(2)发现多变量预测集合和新的交互作用，预测后 随着时间的推移，创伤精神病理学的班级成员资格和班级转换。鉴于预计的样本量，我们还将 能够检查在精神病理学和恢复力方面的性别差异，以及按创伤类型的差异。 研究设计：这项研究将利用以前作为R21项目一部分收集的国家预期数据(和 增加了额外的创伤数据和更多年的后续行动)，以建立1995-2015年的创伤队列。 创伤队列成员将至少经历10种创伤事件中的一种(即火灾/爆炸、事故和 袭击、中毒、危及生命的疾病/伤害、与怀孕有关的创伤和家庭突然死亡)。广泛的预- 关于精神诊断、治疗(药物和心理治疗)和社会变量的创伤和创伤后数据将 被包括在内。我们将使用潜在类别分析来描述创伤后精神病理学结果的多维特征 (包括没有精神病理学)和潜在的过渡分析，以检查班级成员的变化 时间到了。将机器学习统计方法应用于扩展的风险因素数据，以建立多元 随着时间的推移，结果类和类转换的预测符集。偏差分析将用于评估以下方面的影响 对我们的结果有各种形式的系统误差。 影响：这项研究实现了NIMH的战略重点：(1)绘制精神疾病轨迹图，以确定何时， 在哪里以及如何干预，以及(2)加强NIMH支持的研究对公共健康的影响。我们的方法 将以最有效的方式获得创伤后精神病理学的强大和有效的风险概况，通过使用预 现有的预期数据来自全人群和未选择的人群。创伤的生命历程多维处理方法 研究是这一领域的关键下一步。在未来的工作中，精神病理类和多变量预测集 作为这项研究的一部分，可以在其他人群中复制和扩展，以检查我们发现的变化， 并被用作更详细地探索新发现的精神病风险和恢复能力的途径的基础 在创伤之后。

Abstract Background: Trauma is common, but we have little ability to predict who will develop post-trauma psychopathology. Consistent challenges to our understanding of the etiology of post-trauma psychopathology include: (1) obtaining unbiased prospective data on risk factors preceding or concurrent with trauma; (2) the inability to model large comprehensive risk structures with traditional null hypothesis testing methods, despite the knowledge that risk factors do not operate in isolation; and (3) the almost universal focus on PTSD outcomes to date, while post-trauma psychopathology likely involves various symptoms spanning multiple disorder categories. The aims of this study are to (1) use data from a large, prospective population trauma cohort to establish multidimensional classes of post-trauma psychopathology which include diagnoses from various theoretically derived categories (e.g., stress diagnoses, mood disorders, personality disorders) and (2) to discover multivariate predictor sets and novel interactions which predict post- trauma psychopathology class membership and class transitions over time. Given the projected sample size we will also be able to examine gender differences in psychopathology and resilience, as well as differences by trauma type. Study Design: This study will make use of national prospective data previously assembled as part of an R21 project (and augmented with additional trauma data and more years of follow-up) to establish a trauma cohort from 1995 – 2015. Trauma cohort members will have experienced at least one of 10 traumatic events (i.e., fires/explosions, accidents and assaults, poisoning, life-threatening illness/injury, pregnancy-related trauma and sudden family deaths). Extensive pre- trauma and post-trauma data on psychiatric diagnoses, treatment (medication and psychotherapy) and social variables will be included. We will use latent class analyses to characterize multidimensional post-trauma psychopathology outcomes (including the absence of psychopathology) and latent transition analyses to examine changes in class membership over time. Machine learning statistical methods will be applied to the expansive risk factor data to develop multivariate predictor sets for outcome classes and class transitions over time. Bias analyses will be used to assess the impact of various forms of systematic error on our results. Implications: This study fulfills NIMH’s strategic priorities of (1) charting mental illness trajectories to determine when, where, and how to intervene and (2) strengthening the public health impact of NIMH-supported research. Our approach will achieve robust and valid risk profiles of post-trauma psychopathology in the most efficient way possible by using pre- existing prospective data from a full and unselected population. A life course multidimensional approach to trauma research is a critical next step in this field. In future work, psychopathology classes and multivariate predictor sets discovered as part of this study can be replicated and expanded in other populations to examine variations of our findings, and used as the basis for a more detailed exploration of newly discovered pathways to psychopathology risk and resilience following trauma.