VA-DoD Long-Term Impact of Military-Relevant Brain Injury Consortium (LIMBIC): Phenotypes of Persistent Comorbidity in Post‐9/11 Era Veterans with mTBI

神经创伤战友流行病学队列的慢性影响被开发以确定 9·11事件后退伍军人共病的表型比较 退伍军人脑外伤患者的神经感觉、神经退行性变、疼痛和心理健康共病。这个 战机流行病学队列的边缘扩展将扩展CENC开始的工作 在这项研究中，我们确定了一组9/11后退伍军人并确定了共病表型。我们 还获得了国防部创伤登记(DODTR)数据(如果有)和军事卫生系统 (MHS)包括在国防部精神卫生中的住院患者、门诊患者和药房数据 数据立方体。我们现在建议将这一重要的数据源扩展到60多万 部署SM以包括更广泛的后9/11时代(已部署和未部署 退伍军人和其他数据源，提供独特的机会来审查长期 共病表型和开发感兴趣的并存的风险模型，例如 神经退行性疾病、自闭症、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“All Sources”TBI严重性算法和NLP/Text嵌入方法，识别 纳入急性损伤的国防部和国防部+VA数据中mTBI的表型，机制 受伤和爆炸暴露。 目标2：在基线、前后确定主要合并症和结局的患病率 MTBI暴露，以及VA(如果相关)，并比较这些比率与TBI严重程度和 学习小组。 目标3：使用包含急性和慢性mTBI表型的深度学习模型 治疗方法，以及出现不同的合并症以制定风险评分 糟糕的军事结果和严重的合并症。 目标4：使用深度学习模型来确定mTBI的最佳护理流程。 我们将使用达芬奇的数据来确定接受纵向退伍军人护理的退伍军人队列 (在2002财年至19财年期间，至少每年一次(至少其中一年 在2007年强制进行脑损伤筛查之后。我们还将确定没有 接受退伍军人管理局的护理。然后，我们会将接受和不接受退伍军人管理局护理的人员归类为已部署和未部署 部署，创建四个研究小组：a)部署了退伍军人护理；b)部署了退伍军人护理；c) 没有部署退伍军人管理局；d)没有退伍军人护理部署。我们将汇编VA和DoD数据 来源并确定关键合并症(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)和脑外伤 特点。这些数据将用于开发TBI的机器/深度学习模型 表型、共病表型和发生关键合并症的模型风险分数， 和治疗创伤性脑损伤的最佳流程。 对这四个研究小组进行这些分析将为TBI提供护理和 阐明具体的目标领域，以改善急性颅脑损伤的护理和后续支持系统 颅脑损伤后的慢性护理。

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.