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、累加和乘法相互作用 (OUD)以及9/11事件后退伍军人的吸毒过量拟议的研究还将调查短期和长期 2019冠状病毒病（COVID-19）大流行对OUD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人的主要职业目标， 在退伍军人事务部（VA）完全资助，独立的流行病学研究员，重点是 成瘾和自杀行为。CDA-2将提供领导独立团队所需的额外培训 一项研究计划，调查多方面的社会人口，身体，心理和行为 因素介导和调节成瘾和自杀行为的风险。实现这一目标的第一步 目标是完成以下培训目标：1）获得生物和行为基础的专业知识， 成瘾; 2）获得评估TBI和爆炸暴露问题的专业知识， 疾病和自杀行为，这是普遍存在于这一代退伍军人; 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;以及4） 专业发展，以实现职业独立作为VA资助的流行病学研究人员。 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） 电子医疗记录开发预测OUD和过量风险的模型。样品将包括后- 9/11年龄在18 - 65岁之间的退伍军人，在VHA接受护理，并将完成VA初次TBI 2007年10月至2020年2月之间的筛查（n~1，267，000）。我们将评估事故风险， 使用机器学习算法模型，将复发性OUD和用药过量事件作为单独的结局。我们 将检查药物过量是否1）致命和非致命，以及2）故意和无意。目标1和 我们将检查2007年10月1日至2020年2月29日期间OUD和过量事件的风险。为 探索性目标3，我们将检查2020年3月1日至 二零二五年九月三十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的预测特征 以及包含重要风险因素和相互作用的过量。有效性（灵敏度和特异性）和 将评估所有预测曲线模型的预测准确度（曲线下面积）。目的： 目的1：开发和评估结合已知和新风险因素的预测特征的性能 近端（30、60和90天）和远端（180、365、730、1095）OUD和过量的相互作用 和> 1460天）的预测间隔。假设1a： 机器学习算法将具有高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：检查性别、种族/民族、部署相关创伤（例如，tbi和 流行的精神和物质障碍），近距离爆炸暴露作为OUD风险的调节因子 还有吸毒过量假设2：将有新的风险因素和差异变量重要性影响 在亚组特异性预测特征内的OUD和过量风险。探索目标3：调查 COVID-19大流行对OUD和使用机器过量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测特征。 假设3：COVID-19大流行将直接影响OUD和过量用药的风险， 通过精神健康症状和精神状况的发作或恶化而产生的间接影响。