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.

这项拟议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危险因素的多方面、相加和相乘的相互作用 (OUD)和9/11后退伍军人服药过量。拟议的研究还将调查短期和长期 2019年冠状病毒病(新冠肺炎)大流行对吸毒和过量用药风险的影响。 培训计划：CDA-2培训计划将促进申请者成为 退伍军人事务部(VA)全额资助的独立流行病学研究员，重点是 上瘾和自杀行为。CDA-2将提供必要的额外培训，以领导独立的 调查多方面的社会人口、生理、心理和行为的研究计划 调节和缓和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)在生物和行为基础上获得专业知识 成瘾；2)在评估脑外伤和冲击波暴露问题方面获得专业知识，精神病学 精神障碍和自杀行为，这在这一代退伍军人中很普遍；3)获得 健康数据科学中采用的高级分析技术，包括机器学习算法；以及4) 职业发展以实现职业独立，成为退伍军人事务部资助的流行病学研究人员。 研究设计与方法：拟议的研究将使用退伍军人健康管理局(VHA) 电子病历以开发预测OUD和过量用药风险的模型。样本将包括Post- 9/11年龄在18-65岁的退伍军人，在VHA接受护理，并将完成退伍军人事务部初级TBI 2007年10月至2020年2月期间进行筛选(n~1267,000人)。我们将评估事故的风险，并 使用机器学习算法模型，将反复发生的OUD和过量服药事件作为单独的结果。我们 将检查服药过量是否1)致命和非致命，以及2)故意和非故意。对于AIMS 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。为 探索性目标3，我们将检查在2020年3月1日至 2025年9月30日。我们将使用几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的预测配置文件 以及包含重要风险因素和相互作用的过量用药。效度(敏感性和特异性)和 将评估所有预测配置文件模型的预测精度(曲线下面积)。目标： 目标1：开发和评估包含已知和新的风险因素的预测器配置文件的性能 以及近端(30、60和90天)和远端(180、365、730、1095)的OUD和过量用药的交互作用 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，灵敏度和特异度以及 曲线下面积)0.8。假设1b：近端的准确性和预测能力将高于 远端预测区间。目标2：审查性别、种族/族裔、与部署有关的创伤(例如，TBI和 普遍存在的精神和物质障碍)，以及近距离冲击波暴露作为OUD风险的调节因素 还有服药过量。假设2：将有新的风险因素和不同的变量重要性影响 在亚组特定的预测因素范围内的OUD和过量用药的风险。探索性目标3：调查 新冠肺炎大流行对OUD和过量使用机器风险的短期和长期影响 学习分类算法，以开发已知和新的风险因素和相互作用的预报器配置文件。 假设3：新冠肺炎大流行将对肥胖和过量用药的风险产生直接影响 通过精神健康症状和精神状况的出现或加重而产生的间接影响。