Understanding CNS Stimulant Use and Safety in Veterans with TBI

了解患有 TBI 的退伍军人的中枢神经系统兴奋剂使用和安全性

• 批准号: 10538168
• 负责人: ALI AHMED
• 金额: --
• 依托单位: U.S. DEPT/VETS AFFAIRS MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2027-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10538168
• 关键词: Accounting; Acute; Address; Adverse effects; Age; Amphetamines; Antidepressive Agents; Antipsychotic Agents; Attention deficit hyperactivity disorder; Benzodiazepines; Blinded; Central Nervous System; Central Nervous System Diseases; Central Nervous System Stimulants; Characteristics; Child; Clinical; Clinical Practice Guideline; Complex; Computers; Congresses; Data; Demographic Factors; Dextroamphetamine; Diagnosis; Educational Materials; Enrollment; Evaluation; Guidelines; Homogeneously Staining Region; Hospitalization; Individual; International Classification of Disease Codes; Intervention; Knowledge; Machine Learning; Measures; Methodology; Modafinil; Modeling; Morbidity - disease rate; Neurocognitive; Outcome; Patients; Pattern; Pharmaceutical Preparations; Polypharmacy; Population; Provider; Publications; Race; Randomized, Controlled Trials; Recommendation; Recording of previous events; Registries; Research Priority; Risk; Risk Assessment; Risk Reduction; Ritalin; Safety; Screening procedure; Software Tools; Source; Stimulant; Substance Use Disorder; Symptoms; System; TBI Patients; Testing; Time; Traumatic Brain Injury; United States Centers for Medicare and Medicaid Services; Veterans; Work; associated symptom; care seeking; cohort; cost; data registry; design; disability; evidence base; follow-up; high risk; innovation; machine learning prediction; medication safety; mild traumatic brain injury; military veteran; mortality; persistent symptom; predictive modeling; prescription stimulants; randomized controlled design; risk prediction model; sex; stimulant use

Project Summary/Abstract Background: The use of central nervous system (CNS) stimulants such as amphetamine, dextroamphetamine, methylphenidate, armodafinil and modafinil is discouraged in patients with traumatic brain injury (TBI) as they have no proven benefits and carries the FDA black-box warning of a higher risk for developing substance use disorders (SUD). Significance: TBI is a major source of morbidity and mortality for Veterans, and a top HA/ORD/HSR&D priority. Our preliminary data suggest that as of May 2021, nationwide 728,065 Veterans had a diagnosis of TBI in their EHR. The TBI registry estimates that 81% of the Veterans have mild TBI. Veterans with TBI are more likely to receive CNS stimulants than those without TBI. Our preliminary data suggests that 5.8% (42,437/728,065) of the Veterans with TBI received prescriptions for CNS stimulants, which is over 10 times higher than that in Veterans without TBI (0.56%). Findings of our preliminary study also suggest that compared to non-users of CNS stimulants, users have a higher risk of SUD. Currently, there is no evidence-based therapy for treatment of mild TBI and the VA mild TBI guidelines discourages the use of medications to ameliorate neurocognitive symptoms. However, many Veterans with TBI receive prescriptions for CNS stimulants but less is known about the safety of these drugs in Veterans with TBI. Innovation & Impact: To the best of our knowledge, the study questions have never been answered before. The key innovation of the proposed study is in the filling of the scientific knowledge gap, the potential clinical implications of the findings, and the relevance to the Veteran population. Our methodological innovation will include the use of deep machine learning approaches including the impact and the interaction scores developed by our team to quantify the results of deep learning. Specific Aims: 1) To characterize stimulant prescription pattern in Veterans with mild TBI; 2) To test the hypothesis that initiation of stimulant therapy is associated with a higher risk of incident SUD, hospitalization, and mortality in Veterans with mild TBI; and 3) To develop an explainable machine (deep) learning risk prediction model that will allow a more accurate and precise assessment of clinical benefits vs. risk of stimulants in individual Veterans. Methodology: These aims will be achieved by using the VA TBI registry and EHR data. For Aims 1 and 3, we will use all Veterans with a TBI diagnosis and any use of stimulants. For Aim 2, we will emulate the design of an RCT, using Veterans with TBI free of prevalent SUD and new prescriptions of CNS stimulants after mild TBI diagnosis. We will then conduct sensitivity analysis in the subset of Veterans with mild TBI using the Comprehensive TBI Evaluation (CTBIE) tables. Propensity score matching will be used for outcome-blinded assembly of cohorts balanced on measured covariates, and sensitivity analyses will be used to estimate impact of unmeasured confounders. Centers for Medicare & Medicaid Services (CMS) data will be used to validate the generalizability of our prediction model. Next Steps/Implementation: In additions to the traditional dissemination approaches through presentations and publications, we will share our software tools with the VA AI center for dissemination and work with our operational partners to incorporate the findings of the proposed project into clinician education materials.

项目摘要/摘要 背景：使用中枢神经系统（CNS）兴奋剂，例如苯丙胺，右旋苯丙胺， 甲基苯甲酸甲酯，Armodafinil和Modafinil在脑外伤（TBI）患者中不鼓励他们 没有可靠的好处，并带有FDA Black-Box警告，该警告较高的使用物质使用风险 疾病（SUD）。 意义：TBI是退伍军人的发病率和死亡率的主要来源，并且是最高的HA/ORD/HSR＆D优先级。 我们的初步数据表明，截至2021年5月，全国728,065名退伍军人在他们的 EHR。 TBI注册表估计，有81％的退伍军人患有轻度TBI。与TBI的退伍军人更有可能 接受中枢神经系统兴奋剂比没有TBI的兴奋剂。我们的初步数据表明5.8％（42,437/728,065） 具有TBI的退伍军人收到了CNS兴奋剂的处方，该处方是超过10倍 没有TBI的退伍军人（0.56％）。我们的初步研究的结果还表明，与非用户相比 中枢神经系统兴奋剂，用户的SUD风险更高。目前，尚无基于循证治疗的治疗 轻度TBI和VA温和TBI指南不鼓励使用药物来改善神经认知 症状。但是，许多具有TBI的退伍军人会接受中枢神经系统兴奋剂的处方，但对 这些药物在具有TBI的退伍军人中的安全性。 创新与影响：据我们所知，研究问题从未得到回答。 拟议的研究的关键创新是填补科学知识差距，潜在的临床 调查结果的含义以及与退伍军人人口的相关性。我们的方法论创新将 包括使用深度机器学习方法，包括影响和发展的互动分数 由我们的团队量化深度学习的结果。 具体目的：1）表征温和TBI的退伍军人中的刺激处方模式； 2）测试 假设刺激治疗的开始与较高的事件SUD，住院，住院， 轻度TBI的退伍军人的死亡率； 3）开发可解释的机器（深）学习风险预测 模型将允许对临床益处与兴奋剂风险进行更准确，更精确的评估 个人退伍军人。 方法论：这些目标将通过使用VA TBI注册表和EHR数据来实现。对于目标1和3，我们 将使用所有具有TBI诊断的退伍军人，并使用任何兴奋剂。对于AIM 2，我们将模仿 RCT，使用带有TBI的退伍军人没有普遍的SUD和轻度TBI后CNS兴奋剂的新处方 诊断。然后，我们将使用轻度TBI的退伍军人子集进行灵敏度分析 全面的TBI评估（CTBIE）表。倾向得分匹配将用于结果盲 在测得的协变量上平衡的队列组装，将使用灵敏度分析来估计影响 未衡量的混杂因素。医疗保险和医疗补助服务中心（CMS）数据将用于验证 我们的预测模型的概括性。 下一步/实施：除了通过演示和 出版物，我们将与VA AI的传播中心共享我们的软件工具，并与我们的合作 运营合作伙伴将拟议项目的发现纳入临床医生教育材料中。