Longitudinal Assessment of Post-traumatic Syndromes

创伤后综合症的纵向评估

• 批准号: 10019595
• 负责人: RONALD C KESSLER
• 金额: $ 391.54万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-23 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10019595
• 关键词: Accident and Emergency department; Address; Algorithms; American; Biological; Blood; Budgets; Classification; Clinical Trials; Collection; Complex; Data; Data Collection; Data Reporting; Decision Support Model; Development; Distress; Educational workshop; Emergency Department evaluation; Enrollment; Equation; Evaluation; Functional Magnetic Resonance Imaging; Functional disorder; Individual; Intervention; Measurement; Mental Depression; Methods; Minor; Modeling; Monitor; National Institute of Mental Health; Neurocognitive; Online Systems; Outcome; Pain; Pathogenesis; Pathogenicity; Patient Self-Report; Patients; Periodicity; Phenotype; Physiology; Post-Traumatic Stress Disorders; Preventive Intervention; Procedures; Psychophysics; Recovery; Research; Research Domain Criteria; Research Personnel; Resources; Risk; Saliva; Sampling; Schedule; Sleep; Statistical Methods; Structure; Surveys; Syndrome; System; Testing; Time; Trauma; Traumatic Brain Injury; Work; Wrist; biobehavior; clinical decision support; design; field study; follow-up; high risk; innovative technologies; insight; machine learning method; member; molecular marker; multidimensional data; neurocognitive test; neuropsychiatry; novel; novel strategies; post-trauma exposure; prevent; prospective; recruit; response; smartphone Application; success; tool; trauma exposure

Each year, more than 40 million Americans present to US emergency departments (EDs) for evaluation after trauma exposure (TE). While the majority of these individuals recover, an important subset develops adverse posttraumatic neuropsychiatric sequelae (APNS). These APNS include traditionally categorized outcomes such as posttraumatic stress disorder (PTSD), depression, minor traumatic brain injury (MTBI), and regional or widespread pain. However, these previous definitions of outcome have limited progress, and we now appreciate that the actual trajectories of APNS are multidimensional, incorporating a range of specific outcomes that may be best understood, and optimally targeted for intervention, by dividing across specific domains of functioning. This application, submitted in response to RFA-MH-16-500, proposes to identify and characterize the trajectories of the most common trauma-induced APNS within these domains of functioning using the RDoC classification system. 5,000 patients presenting to the ED after trauma will be screened, recruited, and will receive initial baseline evaluation in the ED, including blood collection and psychophysical, survey, and neurocognitive evaluation. They will be closely monitored over the next 8 weeks using innovative technologies (a wrist wearable for continuous-time monitoring of daytime physiology and sleep; a smart phone app for continuous-time monitoring of GPS and daily “flash” surveys; weekly web-based neurocognitive tests; periodic mixed-mode surveys; serial saliva collection; deep phenotyping [blood collection, fMRI, psychophysical evaluation]) and then followed less intensively using similar procedures (including deep phenotyping) over the remainder of a 52-week follow-up period. Adaptive sampling and state-of-the-art statistical methods will be used to (1) optimize precision in characterizing RDoC construct trajectories and (2) test theoretically-guided, “high yield” hypotheses evaluating the effects of pre-trauma, peritraumatic, and recovery-related factors on these trajectories and on multivariate RDoC construct trajectory profiles. The longitudinal schedule of rich, granular, multidimensional data collection in the study has been specifically designed to evaluate those constructs most important to post-TE outcomes and to test the proposed hypotheses. Ensemble machine learning methods will be used to develop tiered-targeted clinical decision support models to identify individuals at high risk of specific, common APNS outcomes. The close-knit ED research network that will undertake the study has a strong track record of prospective research on APNS and is ideally suited to carry out this exceedingly complex study. The study has been designed to be a resource for the entire field (for example, it has been designed and budgeted to collect and store a great many more biological samples at the NIMH Biorespository than we can analyze, for use by other investigators).

每年，有超过4000万美国人在美国急诊部门（ED）进行评估 创伤暴露（TE）。尽管大多数这些人康复，但重要的子集发展了对手 创伤后神经精神后遗症（APNS）。这些APN包括传统上分类的结果 例如创伤后应激障碍（PTSD），抑郁症，轻微的脑损伤（MTBI）和区域或区域性或 宽度疼痛。但是，这些先前对结果的定义的进展有限，我们现在 感谢APN的实际轨迹是多维的，结合了一系列特定的 通过分裂特定的结果，最好理解并最佳地了解干预的结果 功能域。该申请是针对RFA-MH-16-500提交的，要识别和 表征这些功能领域内最常见的创伤引起的APN的轨迹 使用RDOC分类系统。筛查创伤后出现在ED的5,000名患者， 被招募，并将在ED中获得最初的基线评估，包括血液收集和心理物理， 调查和神经认知评估。他们将在接下来的8周内使用创新对他们进行密切监控 技术（手腕可穿戴，用于连续时间监测白天生理和睡眠；智能手机 用于连续时间监控GP和每日“ Flash”调查的应用程序；每周基于Web的神经认知测试； 周期性混合模式调查；系列唾液系列；深度表型[血液收集，fMRI， 心理物理评估]），然后使用相似的程序进行较少的遵循（包括深度 表型）在52周的随访期内。自适应抽样和最先进的 统计方法将用于（1）在表征RDOC构造轨迹和（2）方面优化精度 测试理论指导的“高产量”假设，假设评估创伤前，腹膜周期和 这些轨迹和多元RDOC构造轨迹曲线的恢复相关因素。这 该研究中有丰富的，颗粒状的多维数据的纵向时间表已具体 旨在评估这些结构对TE后结果最重要的结构并测试所提出的构造 假设。集合机器学习方法将用于制定靶向分层的临床决策 支持模型，以识别具有特定常见APN结果的高风险的个体。紧密联系的Ed 将进行该研究的研究网络在APN和 非常适合进行这项非常复杂的研究。该研究被设计为用于 整个领域（例如，它的设计和预算用于收集和存储更多 NIMH Biorespository的生物样品比我们可以分析的，以供其他研究者使用）。