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: 10269013
• 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/10269013
• 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.

神经战争战士流行病学队列的慢性影响是为了识别 9/11名退伍军人部署的合并症表型，以比较出现 退伍军人TBI的神经感觉，神经退行性，疼痛和心理健康合并症。这 战斗机流行病学队列的边缘扩展将扩大Cenc的工作 在其中确定了9/11后退伍军人的队列，并确定了合并症表型。我们 还获得了DOD创伤注册表（DODTR）数据，并获得了军事卫生系统 （MHS）DOD心理健康中包含的住院，门诊和药房数据 数据立方体。现在，我们建议扩展以超过60万的重要数据源 部署的SM包括更广泛的9/11时代（部署和非部署） 退伍军人和其他数据来源，这些数据提供了独特的机会来检查长期 合并症表型并开发了感兴趣合并症的风险模型，例如 神经退行性疾病，SUD，心理合并症和自我伤害行为。 这些数据将使我们能够实现以下特定目标： 目标1：使用“所有来源” TBI严重性算法和NLP/文本嵌入方法，识别 MTBI在DOD和DOD+VA数据中的表型，该数据融合了急性损伤，机制 受伤和爆炸暴露。 目标2：在基线，之前和之后确定关键合并症和结果的患病率 MTBI暴露，在VA中（相关），并通过TBI严重程度和 研究小组。 目标3：使用结合MTBI表型，急性和慢性的深度学习模型 治疗方法和潜水员合并症的出现以发展风险评分 军事成果不佳并发展了关键的合并症。 目标4：使用深度学习模型来识别MTBI的最佳护理过程。 我们将使用Davinci的数据来确定接受纵向VA护理的老兵队员 （在2002财年至2019财年之间，至少每年一次或三年以上（至少一个是 2007年后，授权TBI筛查。我们还将确定没有 接受VA护理。然后，我们将分部有或不带有VA护理的那些部署，而不是 部署，创建四个研究小组：a）通过VA护理部署； b）在没有VA护理的情况下部署； c） 没有在VA护理中部署； d）未经VA护理就无法部署。我们将编译VA和DOD数据 来源并确定关键合并症（神经内分泌功能障碍，药物使用障碍， 心理健康状况，疼痛状况，睡眠状况，自我伤害行为）和TBI 特征。这些数据将用于将开发TBI的机器/深度学习模型 表型，合并症表型和模型风险评分，用于发展关键合并症， MTBI的最佳护理过程。 对这四个研究小组进行这些分析将为TBI护理途径提供信息 照亮特定目标领域，以改善急性TBI护理和随后的支持系统 TBI之后的慢性护理。