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）是一种通常发生的，严重损害的障碍 暴露于创伤事件（TES）之后。症状通常在暴露和进化后不久开始 有时间进行慢性或恢复。 PTSD是最可预防的精神障碍之一，因为 暴露于TES的人在初次反应环境中引起了临床关注。对照临床试验显示 早期预防干预措施可以大大降低PTSD风险。但是，这些干预措施 有非平凡的成本，使他们不可行地将其提供给所有暴露于TES的人，因为只有一个 少数人继续发展PTSD。对于许多康复的幸存者来说，他们也不需要 自发。为了具有成本效益，需要风险预测规则来确定哪些暴露的人是 考虑到样本中的预测因子可能会有所不同，PTSD的高风险 （例如，在男性和女性幸存者之间）以及与TE的不同时间滞后。许多研究 研究通过评估PTSD的潜在预测因子来收集解决此问题的纵向数据 在从初次响应医疗设置开始的受害者中，随着时间的推移参与者并使用 基线数据以预测随后的PTSD。但是，这些研究的结果经常被提出为 小组平均可能性的变化，并未以实用性，有用的方式合成 并预测个人风险。因此，我们建立了一个由 最重要的研究将其个人和项目级别的数据结合起来，以进行汇总 辅助分析以合成有关PTSD预测因子的信息。我们的具体目的是：（1）： 从16个最重要的纵向中构造单个级别数据的合并数据集 从初次响应医疗环境开始的TE受害者中PTSD的预测因子的研究。这些 研究总共评估了6,390名受访者，其中14％患有急性PTSD。 （2）：到 估计大约92％的PTSD症状轨迹的潜在生长混合物模型（LGMM） 盖子基线后1到3次评估的合并样品（n = 5,917）， 然后评估模型结果对轨迹和PTSD样本间差异的敏感性 症状措施； （3）：估计关联之间关联的幅度和跨研究一致性 基线预测因子和PTSD结果（总样本中的急性PTSD；急性中的PTSD持久性 案件； LGMM PTSD类成员资格和症状轨迹）； （4）：将结果在AIM 3中使用 为未来在初次响应设置中评估的PTSD风险因素提出建议 与软件一起促进系统数据收集并为临床决策提供信息。我们寻找 支持构建此合并数据集，进行分析结果以及 开发可在初次响应设置中使用的风险预测工具。