Longitudinal Assessment of Post-traumatic Syndromes

每年，超过4000万美国人在事后向美国急诊室(ED)提交评估 创伤暴露(TE)。虽然这些人中的大多数人都恢复了健康，但有一个重要的亚群出现了不利的情况 创伤后神经精神后遗症(APNS)。这些APN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，这些以前对结果的定义取得的进展有限，我们现在 认识到APN的实际轨迹是多维的，包含了一系列特定的 结果可能是最容易理解的，也可能是干预的最佳目标，通过划分具体的 功能领域。本申请是针对RFA-MH-16-500提交的，建议确定和 描述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。将对5000名创伤后到急诊室就诊的患者进行筛查， 招募，并将在ED接受初步基线评估，包括采血和心理物理， 调查和神经认知评估。在接下来的8周内，他们将使用创新的 技术(可佩戴的手腕，用于连续监测白天的生理和睡眠；智能手机 用于持续监测GPS和每日“闪存”调查的应用程序；每周基于网络的神经认知测试； 定期混合模式调查；连续唾液采集；深度表型[血液采集，功能磁共振成像， 心理物理评估])，然后使用类似的程序进行不那么密集的跟踪(包括深度 表型)在52周随访期的剩余时间内。自适应采样和最新技术 统计方法将被用来(1)优化表征RDoC构建轨迹的精确度和(2) 以理论为指导的测试，评估创伤前、肿瘤周围和 这些轨迹和多变量RDoC上的恢复相关因素构成了轨迹曲线。这个 在研究中对丰富的、细粒度的、多维的数据收集进行了具体的纵向计划 旨在评估对TE后结果最重要的结构，并测试建议的 假设。将使用集成机器学习方法来开发分级目标临床决策 支持模型以确定特定的、常见的APNS结果的高风险个体。紧密相连的边缘 将进行这项研究的研究网络在APN和APN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。这项研究旨在成为以下方面的资源 整个字段(例如，它的设计和预算是为了收集和存储更多的 NIMH生物资源库的生物样本比我们可以分析的更多，以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).