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)

使用数字生物标志物和急诊科电子健康记录中的临床数据对创伤事件暴露后的 PTSD 风险进行护理点预后建模 (PREDICT)

• 批准号: 10884738
• 负责人: Katharina Schultebraucks
• 金额: $ 83.61万
• 依托单位: NEW YORK UNIVERSITY SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-10 至 2027-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10884738
• 关键词: Accident and Emergency department; Acute; Admission activity; Biological; COVID-19 pandemic; Caring; Cellular Phone; Charge; Chronic Post Traumatic Stress Disorder; Clinical; Clinical Data; Clinical assessments; Cognitive Therapy; Computer Vision Systems; Computing Methodologies; Consumption; Data; Devices; Diagnosis; Diagnostic; Digital biomarker; Disasters; Discharge Plannings; Early Intervention; Early treatment; Effectiveness; Electronic Health Record; Emergency Care; Emergency Department Physician; Emergency Department patient; Emergency Situation; Emergency department visit; Emergency response; Emotions; Event; Face; Foundations; Future; Goals; Head Movements; Health Care Costs; Healthcare Systems; Hospitals; Intervention; Interview; Life; Measures; Medicine; Mental Depression; Mental Health; Mental Health Services; Mission; Modality; Morbidity - disease rate; Mydriasis; National Institute of Mental Health; Natural Language Processing; Nature; Participant; Patient Admission; Patient Care; Patient Self-Report; Patients; Personal Satisfaction; Phenotype; Physiological; Post-Traumatic Stress Disorders; Predictive Value; Prevention; Prevention strategy; Privatization; Procedures; Prognosis; Proxy; Psyche structure; Psychometrics; Publishing; Reporting; Research; Research Project Grants; Risk; Severities; Speech; Survivors; Symptoms; Tablets; Taxes; Testing; Time; Training; Trauma; Triage; Video Recording; Videotape; Voice; Work; acute care; acute stress; biological adaptation to stress; care systems; clinical practice; clinically significant; cohort; cost; cost effective; deep learning; design; digital; digital measure; disorder risk; electronic health data; electronic health information; experience; follow-up; gaze; handheld mobile device; health application; high risk population; improved; instrument; longitudinal, prospective study; medical specialties; multimodality; neural; novel; point of care; post-traumatic stress; predictive marker; predictive modeling; professional atmosphere; prognostic; prognostic model; prognostic performance; prognostic value; prognostication; psychologic; response; risk stratification; routine screening; screening; stress symptom; support tools; telehealth; transfer learning; trauma exposure; traumatic event

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.

项目概要/摘要 目前，尚无紧急创伤暴露后创伤后应激的准确预后模型 科室 (ED) 患者可在护理点就诊，无需临床筛查或诊断 拟议的研究基于以下理由：每年 1.39 亿次急诊就诊提供了 在患者仍保持联系的早期阶段主动规划基于风险的后续护理的关键窗口 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，其在临床实践中作为急诊室常规筛查的可行性是 考虑到急诊室的急症护理优先事项，这项研究的长期目标是 开发一个准确、可扩展、实用且可行的预测模型，并且对患者的额外负担较小 ED 程序的总体目标是使用先进的计算方法来提取。 来自视频和音频数据的创伤后应激的客观标记，以在以下位置建立临床读数： 护理将使 ED 能够预测创伤后应激障碍 (PTSD) 的风险。 根据我们的初步数据，我们捕获了语音和言语内容、头部运动、瞳孔扩张、 凝视和情绪的面部标志特征提供了概率信息，使我们能够识别 PTSD 的数字生物标记将通过追求两个具体目标进行测试。 数字生物标记物可预测 1) 谁有患 PTSD 的风险，2) 将数字生物标记物与 常规可用的电子健康记录可在护理时预测谁将在一个月内患上创伤后应激障碍 (PTSD) 急诊室出院后计划后续专业护理以及有慢性 PTSD 风险的人。 前瞻性纵向研究将绘制 350 名创伤幸存者队列中的 PTSD 症状。 研究具有很高的临床意义。预后模型将有助于针对风险的早期干预。 为了减少延迟治疗，协助确定治疗分配的优先顺序并减少下游 该研究项目旨在为医疗保健成本提供客观、准确和可靠的数字测量。 此类数字生物标志物将实现更有效的出院计划，并促进患者的健康。 早期预防策略。 危及生命的事件发生后的处理具有很高的价值，是正常运行、高质量紧急情况的基础 SARS-CoV-2 大流行、未来的灾难或其他大规模紧急情况强调了这一点。 迫切需要通过计算方法来支持高负荷的 ED，以更好地确定长期风险 在不破坏急性护理标准操作程序的情况下满足长期精神卫生保健需求。