Predictor Profiles of Opioid Use Disorders and Overdose Among Post-9/11 Veterans

9/11 事件后退伍军人中阿片类药物使用障碍和过量的预测因素

• 批准号: 10559588
• 负责人: Jennifer R Fonda
• 金额: --
• 依托单位: VA BOSTON HEALTH CARE SYSTEM
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2027-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10559588
• 关键词: Adverse event; Afghanistan; Amalgam; Anxiety; Area Under Curve; Behavioral; Biological; COVID-19 pandemic; COVID-19 pandemic effects; Caring; Complex; Computerized Medical Record; Data; Data Science; Databases; Development; Disease; Distal; Domestic Violence; Early Intervention; Epidemiology; Ethnic Origin; Event; Funding; Gender; General Population; Generations; Goals; Health Services Accessibility; High Prevalence; Individual; Intervention; Iraq; Lead; Machine Learning; Measures; Mediating; Mental Depression; Mental Health; Mental disorders; Modeling; Opioid; Outcome; Overdose; Performance; Population; Post-Traumatic Stress Disorders; Predisposition; Public Health; Race; Recording of previous events; Recurrence; Research; Research Design; Research Methodology; Research Personnel; Risk; Risk Assessment; Risk Factors; Sampling; Sensitivity and Specificity; Social Distance; Subgroup; Substance Use Disorder; Symptoms; Techniques; Testing; Training; Trauma; Traumatic Brain Injury; United States; United States Department of Veterans Affairs; Veterans; Veterans Health Administration; Work; addiction; advanced analytics; aged; blast exposure; career; chronic pain; classification algorithm; classification trees; cohort; disorder risk; epidemiology study; gradient boosting; health data; high risk; high risk population; improved; long term consequences of COVID-19; machine learning algorithm; machine learning classification; machine learning method; machine learning prediction; military veteran; novel; opioid epidemic; opioid misuse; opioid use disorder; overdose risk; pandemic disease; post 9/11; precision medicine; predictive modeling; programs; psychiatric comorbidity; psychologic; random forest; regression trees; sociodemographics; study population; suicidal behavior; targeted treatment; trauma exposure

The overall aim of this proposed study is to use machine learning prediction models to evaluate the multifaceted, additive and multiplicative interactions of known and novel risk factors for opioid use disorder (OUD) and overdose in Post-9/11 Veterans. The proposed study will also investigate the short- and long-term impact of the coronavirus disease 2019 (COVID-19) pandemic on the risk of OUD and overdose. TRAINING PLAN: The CDA-2 training plan will facilitate the applicant’s primary career goal of becoming a fully funded, independent epidemiologic researcher at the Department of Veterans Affairs (VA), with a focus on addiction and suicidal behavior. The CDA-2 will provide additional training necessary to lead an independent program of research investigating the multifaceted sociodemographic, physical, psychological, and behavioral factors mediating and moderating the risk of addiction and suicidal behavior. The first step of achieving this goal is to complete the following training aims: 1) gaining expertise in the biological and behavioral basis of addiction; 2) gaining expertise in the assessment of the problems of TBI and blast exposure, psychiatric disorders, and suicidal behavior, which is pervasive in this generation of Veterans; 3) gaining expertise in advanced analytic techniques employed in health data science, including machine learning algorithms; and 4) professional development to achieve career independence as a VA funded epidemiologic researcher. RESEARCH DESIGN & METHODS: The proposed study will use Veterans Health Administration (VHA) electronic medical records to develop models predicting OUD and overdose risk. The sample will include Post- 9/11 Veterans who are aged 18-65, receive care in the VHA, and will have completed the VA primary TBI screen between October 2007 and February 2020 (n~1,267,000). We will assess the risk of incident and recurrent OUD and overdose events, as separate outcomes, using machine learning algorithmic models. We will examine whether overdose was 1) fatal and non-fatal and 2) intentional and unintentional. For Aims 1 and 2, we will examine the risk of OUD and overdose events between October 1, 2007 and February 29, 2020. For Exploratory Aim 3, we will examine the risk of OUD and overdose events between March 1, 2020 and September 30, 2025. We will use several machine learning classification-tree modeling approaches, including classification and regression trees, random forest, and gradient boosting, to develop predictor profiles of OUD and overdose incorporating important risk factors and interactions. The validity (sensitivity and specificity) and prediction accuracy (area under the curve) will be assessed for all prediction profile models. OBJECTIVES: Aim 1: Develop and evaluate the performance of predictor profiles incorporating known and novel risk factors and interactions for OUD and overdose over proximal (30, 60, and 90 days) and distal (180, 365, 730, 1095 and >1460 days) prediction intervals using machine learning classification algorithms. Hypothesis 1a: The machine learning algorithms will have high validity and prediction accuracy (e.g., sensitivity and specificity and area under the curve) >0.8. Hypothesis 1b: Accuracy and predictive ability will be higher in the proximal vs. distal prediction intervals. Aim 2: Examine gender, race/ethnicity, deployment-related trauma (e.g., TBI and prevalent psychiatric and substance disorders), and close-blast exposure as moderators of the risk of OUD and overdose. Hypothesis 2: There will be novel risk factors and differential variable importance impacting the risk of OUD and overdose within the subgroup-specific predictor profiles. Exploratory Aim 3: Investigate the short- and long-term impact of the COVID-19 pandemic on the risk of OUD and overdose using machine learning classification algorithms to develop predictor profiles of known and novel risk factors and interactions. Hypothesis 3: The COVID-19 pandemic will have both a direct effect on the risk for OUD and overdose and an indirect effect through the onset or exacerbation of mental health symptoms and psychiatric conditions.

这项研究的总体目标是使用机器学习预测模型来评估 阿片类药物使用障碍的已知和新风险因素的多方面、加法和乘法相互作用 (OUD) 和 9/11 后退伍军人用药过量。拟议的研究还将调查短期和长期 2019 年冠状病毒病 (COVID-19) 大流行对 OUD 和用药过量风险的影响。 培训计划：CDA-2 培训计划将促进申请人成为一名 退伍军人事务部 (VA) 全额资助的独立流行病学研究员，重点关注 成瘾和自杀行为。 CDA-2 将提供领导独立机构所需的额外培训 调查多方面的社会人口、身体、心理和行为的研究计划 介导和调节成瘾和自杀行为风险的因素。实现这一目标的第一步 目标是完成以下培训目标：1）获得生物学和行为基础方面的专业知识 瘾; 2) 获得评估 TBI 和爆炸暴露、精神科问题的专业知识 疾病和自杀行为在这一代退伍军人中普遍存在； 3）获得专业知识 健康数据科学中采用的先进分析技术，包括机器学习算法；和 4) 作为 VA 资助的流行病学研究员，实现职业独立的专业发展。 研究设计和方法：拟议的研究将使用退伍军人健康管理局 (VHA) 电子病历开发预测 OUD 和过量风险的模型。该样本将包括后 18-65 岁的 9/11 退伍军人在 VHA 接受护理，并将完成 VA 初级 TBI 2007 年 10 月至 2020 年 2 月期间的屏幕（n~1,267,000）。我们将评估事件的风险并 使用机器学习算法模型将复发性 OUD 和过量事件作为单独的结果。我们 将检查过量服用是否是 1) 致命和非致命以及 2) 有意和无意。对于目标 1 和 2、我们将检查2007年10月1日至2020年2月29日期间发生OUD和过量事件的风险。 探索性目标 3，我们将检查 2020 年 3 月 1 日至 2025 年 9 月 30 日。我们将使用多种机器学习分类树建模方法，包括 分类和回归树、随机森林和梯度提升，以开发 OUD 的预测器配置文件 以及过量服用，包括重要的风险因素和相互作用。有效性（敏感性和特异性）和 将对所有预测剖面模型的预测准确性（曲线下面积）进行评估。目标： 目标 1：开发并评估包含已知和新风险因素的预测因子概况的性能 OUD 与过量近端（30、60 和 90 天）和远端（180、365、730、1095 和 >1460 天）使用机器学习分类算法的预测间隔。假设 1a： 机器学习算法将具有较高的有效性和预测准确性（例如，敏感性和特异性以及 曲线下面积）>0.8。假设 1b：近端数据比非近端数据的准确性和预测能力更高。 远端预测区间。目标 2：检查性别、种族/民族、部署相关创伤（例如 TBI 和 流行的精神疾病和物质障碍），以及近距离爆炸暴露作为 OUD 风险的调节因素 和过量。假设2：将会有新的风险因素和不同的变量重要性影响 亚组特异性预测因子中 OUD 和过量用药的风险。探索性目标 3：调查 COVID-19 大流行对 OUD 和使用机器过量的风险的短期和长期影响 学习分类算法来开发已知和新颖的风险因素和相互作用的预测配置文件。 假设 3：COVID-19 大流行将对 OUD 和用药过量的风险产生直接影响，并且 通过心理健康症状和精神状况的发作或恶化产生间接影响。