VA-DoD Long-Term Impact of Military-Relevant Brain Injury Consortium (LIMBIC): Phenotypes of Persistent Comorbidity in Postâ9/11 Era Veterans with mTBI

VA-DoD 军事相关脑损伤联盟 (LIMBIC) 的长期影响：后 9/11 时代患有 mTBI 退伍军人持续合并症的表型

• 批准号: 10534112
• 负责人: Mary Jo Pugh
• 金额: --
• 依托单位: VA SALT LAKE CITY HEALTHCARE SYSTEM
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10534112
• 关键词: Acute; Algorithms; Area; Behavior; Brain Injuries; Caring; Characteristics; Chronic; Chronic Care; Data; Data Sources; Epidemiology; Functional disorder; Individual; Injury; Inpatients; Learning; Mental Health; Methods; Military Personnel; Modeling; Nerve Degeneration; Nervous System Trauma; Neurodegenerative Disorders; Neurosecretory Systems; Outcome; Outpatients; Pain; Pathway interactions; Pharmacy facility; Phenotype; Prevalence; Process; Psychological reinforcement; Registries; Risk; Science; Self-Injurious Behavior; Severities; Sleep; Source; Substance Use Disorder; Support System; Text; Trauma; Veterans; Work; blast exposure; cohort; comorbidity; deep learning; deep learning model; economic outcome; health data; improved; interest; longitudinal analysis; mild traumatic brain injury; military health system; neurosensory; post 9/11; psychologic; screening; warfighter

The Chronic Effects of Neurotrauma Warfighter Epidemiology Cohort was developed to identify phenotypes of comorbidity among deployed Post-9/11 Veterans in order to compare emergence of neurosensory, neurodegenerative, pain, and mental health comorbidity in Veterans TBI. The LIMBIC extension of the Warfighter Epidemiology Cohort will extend the work begun by CENC in which we identified a cohort of Post-9/11 Veterans and identified comorbidity phenotypes. We also obtained DoD trauma registry (DODTR) data, where available, and Military Health System (MHS) inpatient, outpatient, and pharmacy data that was included in the DoD Mental Health Data Cube. We now propose to expand upon this important data source for over 600,000 deployed SM’s to include a broader cohort of Post-9/11 era (deployed and nondeployed Veterans and additional data sources that provide unique opportunities to examine long-term comorbidity phenotypes and develop risk models for comorbidities of interest such as neurodegenerative disease, SUD, psychological comorbidities, and self-harm behaviors. These data will allow us to accomplish the following specific aims: Aim 1: Using “all sources” TBI severity algorithm and NLP/text embedding methods, identify phenotypes of mTBI in DoD and DoD+VA data that incorporate acute injury, mechanism of injury, and blast exposure. Aim 2: Identify prevalence of key comorbidities and outcomes at baseline, before and after mTBI exposure, and in VA (where relevant) and compare those rates by TBI severity and study group. Aim 3: Use deep learning models that incorporate mTBI phenotype, acute and chronic treatment approaches, and emergence of diverse comorbidities to develop risk scores for poor military outcomes and developing key comorbidities. Aim 4: Use deep learning models to identify optimal processes of care for mTBI. We will use data in DaVINCI to identify a cohort of Veterans who receive longitudinal VA care (at least once a year for three or more years between FY2002 and FY19 (at least one of which is after 2007 when TBI screening was mandated. We will also identify individuals who did not receive VA care. We will then categorize those with and without VA care as deployed and not deployed, creating four study groups: a) deployed with VA care; b) deployed without VA care; c) not deployed with VA care; d) not deployed without VA care. We will compile VA and DoD data sources and identify key comorbidities (Neuroendocrine dysfunction, substance use disorder, mental health conditions, pain conditions, sleep conditions, self-harm behaviors) and TBI characteristics. Those data will be used for machine/deep learning models that will develop TBI phenotypes, comorbidity phenotypes, and model risk scores for developing key comorbidities, and optimal processes of care for mTBI. Conducting these analyses for these four study groups will inform TBI pathways of care and illuminate specific target areas to improve acute TBI care and subsequent support systems for chronic care following TBI.

神经创伤的慢性影响战士流行病学队列的发展，以确定 9/11事件后部署的退伍军人中的共病表型，以比较 神经感觉、神经退行性、疼痛和心理健康共病的研究。的 作战人员流行病学队列的LIMBIC扩展将扩展CENC开始的工作 在该研究中，我们确定了一组9/11后退伍军人，并确定了共病表型。我们 我还获得了国防部创伤登记处（DODTR）的数据（如果有的话），以及军事卫生系统 (MHS)住院病人、门诊病人和药房数据，这些数据包括在国防部精神卫生部门的数据库中。 数据立方体我们现在建议将这个重要的数据源扩展到60多万个 部署SM以包括更广泛的后9/11时代队列（部署和未部署 退伍军人和其他数据源，提供独特的机会，以检查长期 合并症表型，并为关注的合并症开发风险模型，例如 神经退行性疾病、SUD、心理共病和自残行为。 这些数据将使我们能够实现以下具体目标： 目标1：使用“所有来源”TBI严重程度算法和NLP/文本嵌入方法，识别 DoD和DoD+VA数据中的mTBI表型，包括急性损伤、 受伤和爆炸暴露 目的2：确定基线、治疗前后关键合并症的患病率和结局 mTBI暴露和VA（相关时），并按TBI严重程度和 学习小组。 目标3：使用包含mTBI表型、急性和慢性的深度学习模型 治疗方法，以及各种合并症的出现，以制定风险评分， 不良的军事结果和发展中的关键合并症。 目标4：使用深度学习模型来确定mTBI的最佳护理流程。 我们将使用DaVINCI中的数据来确定接受纵向VA护理的退伍军人队列 (at在2002财年至2019财年之间的三年或三年以上，每年至少一次（其中至少一年是 在2007年强制进行创伤性脑损伤筛查之后我们还将确定那些没有 接受护理。然后，我们将那些有和没有VA护理分类为部署和不部署 展开，创建4个研究组：a）展开VA护理; B）未展开VA护理; c） 未在VA护理下展开; d）未在无VA护理下展开。我们会收集退伍军人事务部和国防部的数据 来源和识别关键合并症（神经内分泌功能障碍，物质使用障碍， 心理健康状况、疼痛状况、睡眠状况、自残行为）和TBI 特色这些数据将用于开发TBI的机器/深度学习模型 表型、合并症表型和发展关键合并症的模型风险评分， 和最佳的mTBI护理流程。 对这四个研究组进行这些分析将为TBI的护理途径提供信息， 照亮特定的目标区域，以改善急性TBI护理和后续支持系统， TBI后的慢性护理