Identifying Risk Factors for PTSD by Pooled Analysis of Current Prospective Studi

创伤后应激障碍（PTSD）是一种常见的严重损害性障碍， 创伤性事件（TEs）。症状通常在TE暴露后不久开始开始， 随着时间的推移，慢性或恢复。创伤后应激障碍是最可预防的精神疾病之一， 暴露于TE的人在第一反应环境中受到临床关注。对照临床试验显示 PTSD风险可以通过早期预防性干预显著降低。然而，这些干预措施 具有不小的成本，使得向所有暴露于TE的人提供它们是不可行的， 少数人会发展成创伤后应激障碍他们也是不必要的许多幸存者谁恢复 自发的为了具有成本效益，需要制定风险预测规则，以确定哪些受影响者 考虑到样本之间、样本内和样本之间的预测因素可能不同， (e.g.,在男性和女性幸存者之间）和在TE的不同时间滞后。多家研究 一些研究通过评估PTSD的潜在预测因子来收集解决这个问题的纵向数据 在TE受害者中，从第一反应医疗机构开始，随着时间的推移跟踪参与者， 基线数据来预测后续的PTSD。然而，这些研究的结果往往被呈现为 组的平均可能性的变化，并没有以一种实用，有用的方式进行综合 并预测个体风险。因此，我们成立了一个由主要研究人员组成的联盟， 最重要的是，这些研究将联合收割机的个人和项目级数据结合起来， 二次分析，以综合有关PTSD预测因子的信息。我们的具体目标是：（1）： 从16个最重要的纵向数据中构建一个个人层面数据的综合数据集， 在第一反应医疗机构中开始的TE受害者中PTSD预测因子的研究。这些 研究共评估了6，390名受访者，其中14%患有急性PTSD;（2）： 估计PTSD症状轨迹的潜在增长混合模型（LGMM）在大约92%的 合并样本（n = 5，917）在基线后使用CAPS评估1 - 3次， 然后评估模型结果对轨迹和PTSD样本间差异的敏感性 症状测量;（3）：估计以下因素之间关联的程度和跨研究一致性： 基线预测因子和PTSD结局（总样本中的急性PTSD;急性PTSD患者中的PTSD持续性） 病例; LGMM PTSD类别成员和症状轨迹）;（4）：使用目标3中的结果， 为未来在第一反应环境中评估的创伤后应激障碍风险因素制定建议 沿着软件，以促进系统数据收集并为临床决策提供信息。我们寻求 支持建立这一综合数据集，进行分析并报告分析结果， 开发可用于第一反应环境的风险预测工具。

Posttraumatic stress disorder (PTSD) is a commonly occurring and seriously impairing disorder that occurs after exposure to traumatic events (TEs). Symptoms typically begin shortly after TE exposure and evolve with time to either chronicity or recovery. PTSD is one of the most preventable mental disorders, as many people exposed to TEs come to clinical attention in first response settings. Controlled clinical trials show that PTSD risk can be significantly reduced by early preventive interventions. However, these interventions have nontrivial costs, making it infeasible to offer them to all persons exposed to TEs given that only a small minority goes on to develop PTSD. They are also unnecessary for many survivors who recovery spontaneously. To be cost-effective, risk prediction rules are needed to identify which exposed persons are at high risk of PTSD taking into consideration that predictors may vary between samples, within samples (e.g., between male and female survivors) and at different time lags from the TE. A number of research studies have collected longitudinal data addressing this issue by assessing potential predictors of PTSD among TE victims starting in first response healthcare settings, following participants over time, and using baseline data to predict subsequent PTSD. However, these studies' results have often been presented as changes in groups' average likelihood and were not synthesized in a way that would be practical, useful and predictive of individual risk. Therefore, we created a consortium of the principal investigators of the most important such studies to combine their individual- and item-level data towards carrying out a pooled secondary analysis to synthesize information about the predictors of PTSD. Our Specific Aims are: (1): To construct a consolidated dataset of individual-level data from 16 of the most important longitudinal studies of predictors of PTSD among TE victims starting in first response healthcare settings. These studies assessed a total of 6,390 respondents, 14% of whom have developed acute PTSD; (2): To estimate a latent growth mixture model (LGMM) of PTSD symptom trajectories in the roughly 92% of the consolidated sample (n = 5,917) assessed between one and three times after baseline with the CAPS and then to evaluate the sensitivity of model results to between-sample differences in trajectories and PTSD symptom measures; (3): To estimate the magnitude and cross-study consistency of associations between baseline predictors and PTSD outcomes (acute PTSD in the total sample; PTSD persistence among acute cases; LGMM PTSD class membership and symptom trajectories); (4): To use the results in Aim 3 to develop recommendations for the PTSD risk factors to be assessed in the future in first response settings along with software to facilitate systematic data collection and inform clinical decision making. We seek support to construct this consolidated dataset, to carry out and report the results of analyses, and to develop a risk prediction tool that can be used in first response settings.