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.

项目总结/摘要 目前，还没有一个准确的创伤后应激的预测模型， 科室（艾德）患者可在护理点获得，无需临床筛查或诊断 采访拟议的研究是基于这样的基本原理，即每年1.39亿次艾德就诊提供了一个 在早期阶段主动计划基于风险的随访护理的关键窗口，患者仍在接触中 与医疗保健系统。虽然临床访谈仍然是筛查急性压力的金标准 创伤暴露后的症状，它们在临床实践中作为艾德常规筛查的可行性是 考虑到急诊护理的优先事项，该研究的长期目标是 开发一个准确、可扩展、实用和可行的预后模型，对患者的额外负担较低。 高税收的艾德程序。总体目标是使用先进的计算方法提取 从视频和音频数据中获得创伤后应激的客观标记，以建立一个临床读数， 使艾德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，我们假设声音和言语内容，头部运动，瞳孔扩张， 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具体目标进行测试，旨在分析 数字生物标志物来预测1）谁有患PTSD的风险，2）联合收割机数字生物标志物与 常规可用的电子健康记录，以预测在护理点谁将发展PTSD一个月 艾德出院后计划后续专科护理和谁是慢性创伤后应激障碍的风险。这一拟议 前瞻性纵向研究将在350名创伤幸存者的队列中记录PTSD症状。拟议 研究具有很高的临床意义。预后模型将促进针对风险的早期干预 为了减少延误治疗，协助临床医生优先安排治疗分配，并减少下游 医疗保健费用。该研究项目旨在提供一个客观，准确和可靠的数字测量， 病人的幸福。这种数字生物标志物将使更有效的排放规划成为可能，并将促进 早期预防策略。艾德院创伤幸存者的身心健康 在危及生命的事件发生后， 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强调， 迫切需要通过计算方法支持高度带电的ED，以更好地确定长期 长期精神卫生保健需要，而不破坏急性护理的标准操作程序。