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的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型风险因素的多方面，加性和乘法相互作用 （OUD）9/11后退伍军人中的用药过量。拟议的研究还将研究短期和长期 2019年冠状病毒病（COVID-19）大流行对OUD和用药过量的风险的影响。 培训计划：CDA-2培训计划将有助于申请人成为一个职业目标 退伍军人事务部（VA）的全部资助，独立的流行病学研究员，重点 成瘾和自杀行为。 CDA-2将提供必要的额外培训，以领导独立 研究计划，研究了多方面的社会人口统计学，身体，心理和行为 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的：1）以生物和行为基础获得专业知识 瘾; 2）在评估TBI和爆炸，精神科问题方面获得专业知识 疾病和自杀行为，在这一代退伍军人中普遍存在； 3）获得专家 健康数据科学采用的高级分析技术，包括机器学习算法；和4） 作为VA资助​​的流行病学研究人员，专业发展以实现职业独立性。 研究设计与方法：拟议的研究将使用退伍军人卫生管理局（VHA） 电子病历以开发预测OUD和过量风险的模型。样本将包括 9/11年龄18-65岁的退伍军人在VHA中获得护理，并将完成VA Primary TBI 屏幕2007年10月至2020年2月（n〜1,267,000）。我们将评估事件的风险和 使用机器学习算法模型，重复的OUD和过量事件是单独的结果。我们 将检查过量是否为1）致命和非致命以及2）故意和无意的。目的1和 2，我们将在2007年10月1日至2020年2月29日之间研究OUD和过量事件的风险。 探索目的3，我们将研究2020年3月1日和 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的预测概况 并结合了重要的风险因素和相互作用的过量。有效性（灵敏度和特异性）和 将评估所有预测概况模型的预测准确性（曲线下的区域）。目标： AIM 1：开发和评估预测概况的性能，并结合已知和新型风险因素 OUD和用药过量的互动（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进行预测间隔。假设1a： 机器学习算法将具有高有效性和预测准确性（例如，灵敏度和特异性以及 曲线下的区域> 0.8。假设1B：近端VS中的准确性和预测能力将更高。 远端预测间隔。目标2：检查性别，种族/种族，与部署相关的创伤（例如TBI和 普遍的精神病和物质疾病），并作为OUD风险的主持人接触 和用药过量。假设2：将会有新的风险因素和差异可变重要性影响 在亚组特异性预测概况中的OUD和过量的风险。探索目标3：调查 19009大流行对使用机器过量的风险的短期和长期影响 学习分类算法以开发已知和新型风险因素和相互作用的预测概况。 假设3：COVID-19大流行将对OUD和过量的风险以及一个直接影响 通过发作或加剧心理健康症状和精神疾病的间接影响。