Point-of-care prognostic modeling of PTSD risk after traumatic event exposure using digital biomarkers and clinical data from electronic health records in the emergency department setting (PREDICT)

项目摘要/摘要 目前，尚无准确的创伤后应激预后模型。 科室(ED)患者可在护理地点获得，无需进行临床筛查或诊断 采访。拟议的研究是基于这样的理论基础，即每年1.39亿人次的急诊室访问量提供了 在患者仍保持联系的早期阶段，主动计划基于风险的后续护理的关键窗口 与医疗保健系统的关系。虽然临床访谈仍然是筛查急性应激的黄金标准 创伤暴露后的症状，它们作为急诊室常规筛查在临床实践中的可行性是 鉴于急诊科的急症护理优先次序，这方面的工作受到严重限制。拟议研究的长期目标是 开发准确、可扩展、实用和可行的预测模型，并降低 繁重的急救程序。总体目标是使用先进的计算方法来提取 从视频和音频数据中获得创伤后应激的客观标记物，以建立一个临床读数 这将使ED临床医生能够预测创伤后应激障碍(PTSD)的风险。 根据我们的初步数据，我们假设声音和语音内容、头部运动、瞳孔放大、 凝视和情绪的面部标志性特征提供了概率信息，使我们能够识别 创伤后应激障碍的数字生物标志物。这一假设将通过追求两个特定的目标来检验，这些目标旨在分析 数字生物标记物用于预测1)谁有患创伤后应激障碍的风险以及2)将数字生物标记物与 常规可用的电子健康记录，用于在护理点预测谁将在一个月内患上创伤后应激障碍 在ED出院后，计划后续的特殊护理以及谁有患慢性创伤后应激障碍的风险。这项建议 前瞻性纵向研究将记录350名创伤幸存者的创伤后应激障碍症状。建议数 本研究具有较高的临床意义。预后模型将促进针对风险的早期干预。 对于减少延迟治疗，协助临床医生确定治疗分配的优先顺序，减少下游 医疗保健费用。本研究项目旨在提供客观、准确和可靠的数字测量 病人的福祉。这种数字生物标记物将使排放计划更加有效，并将促进 早期预防策略。创伤幸存者入院急诊室的心理健康状况 在危及生命的事件发生后具有很高的价值，是运作良好、高质量的紧急情况的基础 护理系统。SARS-CoV-2大流行、未来的灾难或其他大规模紧急情况突显了 迫切需要通过计算方法支持高负荷的急救，以更好地确定长期- 在不破坏急性护理的标准操作程序的情况下，满足长期精神卫生保健的需要。

PROJECT SUMMARY/ ABSTRACT Currently, no accurate prognostic model of posttraumatic stress following trauma exposure for emergency department (ED) patients is available at the point-of-care without requiring clinical screening or diagnostic interviews. The proposed research is based on the rationale that the 139 million annual ED visits provide a critical window to proactively plan risk-based follow-up care at an early stage, where patients are still in contact with the health care system. While clinical interviews are still the gold standard to screen for acute stress symptoms following trauma exposure, their feasibility in clinical practice as routine screenings in the ED is severely limited given the acute care priorities in the ED. The long-term goal of the proposed research is to develop a prognostic model that is accurate, scalable, practical, and feasible with low additional burden on the highly taxed ED procedures. The overall objective is to use advanced computational methods to extract objective markers for posttraumatic stress from video and audio data to build a clinical readout at the point-of- care that will enable ED clinicians to prognosticate the risk for posttraumatic stress disorder (PTSD). Based on our preliminary data, we hypothesize that voice and speech content, head movement, pupil dilation, gaze, and facial landmark features of emotion provide probabilistic information that will allow us to identify digital biomarkers for PTSD. This hypothesis will be tested by pursuing two specific aims directed at analyzing digital biomarkers to predict 1) who is at risk to develop PTSD and 2) to combine digital biomarkers with routinely available electronic health records to predict at the point of care who will develop PTSD one month after ED discharge to plan follow-up specialty care and who is at risk for chronic PTSD. This proposed prospective longitudinal study will chart PTSD symptoms in a cohort of 350 trauma survivors. The proposed research is of high clinically significance. The prognostic model will facilitate risk-targeted early interventions for curtailing delayed treatment, assist clinicians in prioritizing treatment allocation and reduce downstream health care costs. This research project aims to deliver an objective, accurate, and reliable digital measure for patients’ well-being. Such digital biomarkers will enable more efficient discharge planning and will promote early prevention strategies. The mental besides the physical well-being of trauma-survivors admitted to the ED after a life-threatening event is of high value and is the foundation of a well-functioning, high-quality emergency care system. The SARS-CoV-2 pandemic, future disasters, or other large-scale emergencies underscore the critical need to support highly charged EDs through computational methods to better determine risks of long- term mental health care needs without disrupting the standard operating procedures of acute care.