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

通过对当前前瞻性研究的汇总分析来识别 PTSD 的风险因素

• 批准号: 8695945
• 负责人: RONALD C KESSLER
• 金额: $ 86.1万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-07-21 至 2015-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8695945
• 关键词: Accident and Emergency department; Accounting; Acute; Address; Algorithms; Attention; Biological Markers; Clinical; Collection; Computer software; Controlled Clinical Trials; Data; Data Analyses; Data Collection; Data Set; Disasters; Disease; Event; Exclusion; Exclusion Criteria; Exposure to; Female; Forcible intercourse; Foundations; Future; Gender; Goals; Growth; Healthcare; Heterogeneity; Individual; Injury; Intensive Care Units; Intervention; Interview; Longitudinal Studies; Measures; Mental disorders; Meta-Analysis; Minority; Modeling; Onset of illness; Outcome; Participant; Patients; Persons; Phase; Physiological; Policy Research; Post-Traumatic Stress Disorders; Preventive; Preventive Intervention; Principal Investigator; Prospective Studies; Publishing; Questionnaires; Recommendation; Recording of previous events; Recovery; Recruitment Activity; Reporting; Respondent; Risk; Risk Factors; Sampling; Self-Administered; Severities; Stress; Structure; Survivors; Symptoms; Techniques; Time; Traffic accidents; Trauma; Work; base; clinical decision-making; cost; cost effective; data mining; disorder risk; high risk; improved; inclusion criteria; instrument; male; pediatric trauma; predictive modeling; prospective; public health relevance; research study; response; software development; time use; tool; trauma centers

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.

创伤后应激障碍 (PTSD) 是一种常见且严重损害的疾病， 经历创伤事件（TE）后。症状通常在 TE 暴露后不久出现并逐渐发展 随着时间的推移，要么慢性化，要么恢复。 PTSD 是最可预防的精神障碍之一，因为许多 暴露于 TE 的人在第一反应环境中会受到临床关注。对照临床试验显示 通过早期预防干预可以显着降低 PTSD 风险。然而，这些干预措施 成本不菲，因此向所有接触 TE 的人提供它们是不可行的，因为只有 少数人继续发展创伤后应激障碍。对于许多康复的幸存者来说，它们也是不必要的 自发地。为了具有成本效益，需要制定风险预测规则来确定哪些人受到影响 考虑到样本之间、样本内的预测因素可能会有所不同，因此处于 PTSD 的高风险中 （例如，男性和女性幸存者之间）以及与 TE 的不同时间滞后。多项研究 研究通过评估 PTSD 的潜在预测因素收集了解决这一问题的纵向数据 在 TE 受害者中，从第一响应医疗机构开始，随着时间的推移跟踪参与者，并使用 预测后续 PTSD 的基线数据。然而，这些研究的结果经常被表述为 群体平均可能性的变化，并且没有以实用、有用的方式综合 以及个人风险的预测。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，此类研究将个人和项目级别的数据结合起来，以进行汇总 二次分析以综合有关 PTSD 预测因素的信息。我们的具体目标是：(1)： 根据 16 个最重要的纵向数据构建个人层面数据的综合数据集 从第一反应医疗机构开始对 TE 受害者的 PTSD 预测因素进行研究。这些 研究总共评估了 6,390 名受访者，其中 14% 患有急性创伤后应激障碍 (PTSD)； (2)：至 估计大约 92% 的 PTSD 症状轨迹的潜在增长混合模型 (LGMM) 合并样本（n = 5,917）在基线后使用 CAPS 进行评估一到三次， 然后评估模型结果对样本间轨迹差异和 PTSD 的敏感性 症状测量； (3)：估计之间关联的程度和交叉研究一致性 基线预测因子和 PTSD 结果（总样本中的急性 PTSD；急性 PTSD 持续性） 案例； LGMM PTSD 类别成员资格和症状轨迹）； (4): 使用目标 3 中的结果 为未来在第一响应环境中评估的 PTSD 风险因素制定建议 以及促进系统数据收集并为临床决策提供信息的软件。我们寻求 支持构建此综合数据集，执行和报告分析结果，以及 开发可在第一反应环境中使用的风险预测工具。