Longitudinal Assessment of Post-traumatic Syndromes

每年，超过4000万美国人在美国紧急部门（ED）进行评估后， 创伤暴露（TE）。虽然这些人中的大多数恢复，但一个重要的子集发展为不利的 创伤后神经精神后遗症（APNS）。这些APNS包括传统分类的结果 如创伤后应激障碍（PTSD）、抑郁症、轻微创伤性脑损伤（MTBI）和局部或 广泛的疼痛。然而，这些先前的结果定义进展有限，我们现在 我认识到，APNS的实际轨迹是多维的，包含一系列特定的 最容易理解和最佳干预目标的结果， 功能领域。本申请是根据RFA-MH-16-500提交的，建议识别和 描述这些功能域中最常见的创伤诱导的APNS的轨迹 使用RDoC分类系统。将对5,000名创伤后到艾德就诊的患者进行筛查， 招募，并将在艾德接受初始基线评估，包括采血和心理物理学， 调查和神经认知评估。他们将在未来8周内密切监测， 技术（用于连续时间监测白天生理和睡眠的手腕可穿戴设备;智能手机 应用程序用于GPS和每日“flash”调查的连续时间监测;每周基于网络的神经认知测试; 定期混合模式调查;连续唾液收集;深度表型分析[血液收集，功能磁共振成像， 心理物理评估]），然后使用类似的程序（包括深度 表型分型）。自适应采样和最新技术 统计方法将用于（1）优化表征RDoC构建轨迹的精度，以及（2） 测试理论指导的“高产”假设，评估创伤前、创伤周围和创伤后的影响。 这些轨迹和多变量RDoC上的恢复相关因素构建轨迹曲线。的 研究中丰富的纵向时间表，粒度，多维数据收集已具体 旨在评估对TE后结局最重要的结构，并测试所提出的 假设将使用嵌入式机器学习方法来制定分层的临床决策 支持模型，以识别特定、常见APNS结果的高风险个体。关系紧密的艾德 将承担这项研究的研究网络在APNS的前瞻性研究方面有着良好的记录， 非常适合进行这项极其复杂的研究。这项研究旨在成为一种资源， 整个油田（例如，它的设计和预算是为了收集和储存更多的 生物样品在NIMH生物样品库，我们可以分析，供其他研究人员使用）。

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).