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

描述创伤结果：从创伤前风险到创伤后后遗症

• 批准号: 9309288
• 负责人: Jaimie L. Gradus
• 金额: $ 31.91万
• 依托单位: BOSTON UNIVERSITY MEDICAL CAMPUS
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-07 至 2021-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9309288
• 关键词: Accidents; Address; Attention; Categories; Cessation of life; Clinical; Cohort Studies; Data; Data Collection; Data Sources; Denmark; Diagnosis; Disease; Effectiveness; Ensure; Epidemiologic Methods; Etiology; Explosion; Family; Fire - disasters; Future; Gender; General Population; Heterogeneity; Individual; Injury; International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10); Knowledge; Life; Life Cycle Stages; Literature; Machine Learning; Marital Status; Mental disorders; Methods; Modeling; Mood Disorders; National Institute of Mental Health; Opiates; Outcome; Pathway interactions; Personality Disorders; Pharmaceutical Preparations; Pharmacology; Poisoning; Population; Post-Traumatic Stress Disorders; Pregnancy; Prospective cohort; Psychiatric Diagnosis; Psychiatry; Psychopathology; Psychotherapy; Public Health; Reaction; Registries; Reporting; Research; Research Design; Research Support; Resources; Risk; Risk Factors; Sample Size; Sampling; Science; Source; Statistical Methods; Steroids; Stress; Structure; Substance abuse problem; Symptoms; Testing; Time; Trauma; Trauma Research; Variant; Work; assault; base; cohort; epidemiology study; experience; follow-up; gender difference; member; novel; population based; prospective; resilience; sexual assault; social; time use; tool; traumatic event

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.

抽象的 背景：创伤很常见，但我们几乎没有能力预测谁会出现创伤后精神病理学。 我们对创伤后精神病理学病因学理解的一致挑战包括：(1) 关于创伤之前或同时发生的危险因素的无偏见前瞻性数据；(2) 无法对大的事件进行建模； 尽管我们知道风险因素确实会影响风险，但仍采用传统的零假设检验方法来构建全面的风险结构 不能孤立地进行；(3) 迄今为止，几乎普遍关注创伤后应激障碍 (PTSD) 的结果。 精神病理学涉及多种疾病类别的各种症状。这项研究的目的可能是： (1) 使用来自大型前瞻性人群创伤队列的数据来建立创伤后的多维类别 精神病理学，包括来自各种理论派生类别的诊断（例如压力诊断、情绪诊断） 障碍、人格障碍）和（2）发现多变量预测因子集和预测后的新相互作用 考虑到预计的样本量，我们还将了解创伤精神病理学班级成员资格和班级转变。 能够检查精神病理学和复原力方面的性别差异，以及创伤类型的差异。 研究设计：本研究将利用之前作为 R21 项目一部分收集的国家前瞻性数据（以及 增加了额外的创伤数据和更多年的随访），建立了 1995 年至 2015 年的创伤队列。 创伤队列成员将至少经历过 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广泛的家庭死亡）。 关于精神科诊断、治疗（药物和心理治疗）和社会变量的创伤和创伤后数据将 我们将使用潜在类别分析来表征多维创伤后精神病理学结果。 （包括缺乏精神病理学）和潜在转变分析，以检查班级成员的变化 机器学习统计方法将应用于广泛的风险因素数据以开发多元变量。 随着时间的推移，结果类别和类别转变的预测集将用于评估结果类别的影响。 我们的结果存在各种形式的系统误差。 意义：这项研究实现了 NIMH 的战略重点：(1) 绘制精神疾病轨迹以确定何时、 在哪里以及如何进行干预；(2) 加强 NIMH 支持的研究对公共卫生的影响。 将通过使用预治疗以最有效的方式实现创伤后精神病理学的稳健和有效的风险概况 来自完整且未经选择的人群的现有前瞻性数据。生命历程多维创伤方法。 研究是该领域未来工作中关键的下一步，精神病理学课程和多变量预测集。 作为这项研究的一部分发现的可以在其他人群中复制和扩展，以检查我们的发现的变化， 并用作更详细探索新发现的精神病理学风险和复原力途径的基础 创伤后。