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.

项目概要/摘要 背景：使用中枢神经系统（CNS）兴奋剂，如苯丙胺、右旋苯丙胺、 哌醋甲酯、阿莫达非尼和莫达非尼不适合用于创伤性脑损伤 (TBI) 患者，因为它们 没有经过证实的益处，并且带有 FDA 黑箱警告，表明药物使用的风险较高 疾病（SUD）。 意义：TBI 是退伍军人发病和死亡的主要原因，也是 HA/ORD/HSR&D 的首要任务。 我们的初步数据表明，截至 2021 年 5 月，全国有 728,065 名退伍军人在其服役期间被诊断为 TBI 电子病历。 TBI 登记处估计 81% 的退伍军人患有轻度 TBI。患有 TBI 的退伍军人更有可能 与没有 TBI 的人相比，接受 CNS 兴奋剂的人更多。我们的初步数据表明，5.8% (42,437/728,065) 患有 TBI 的退伍军人收到的中枢神经系统兴奋剂处方比普通人高出 10 倍以上 没有 TBI 的退伍军人 (0.56%)。我们的初步研究结果还表明，与非用户相比 中枢神经系统兴奋剂，使用者发生 SUD 的风险较高。目前，尚无基于证据的治疗方法 轻度 TBI 和 VA 轻度 TBI 指南不鼓励使用药物来改善神经认知 症状。然而，许多患有 TBI 的退伍军人都会收到中枢神经系统兴奋剂的处方，但人们对此知之甚少。 这些药物对患有 TBI 的退伍军人的安全性。 创新与影响：据我们所知，研究问题以前从未得到解答。 该研究的关键创新在于填补科学知识空白、潜在的临床应用 研究结果的影响以及与退伍军人群体的相关性。我们的方法创新将 包括深度机器学习方法的使用，包括所产生的影响和交互分数 由我们的团队量化深度学习的结果。 具体目标： 1) 描述患有轻度 TBI 的退伍军人的兴奋剂处方模式； 2) 测试 假设开始兴奋剂治疗与发生 SUD、住院、 患有轻度 TBI 的退伍军人的死亡率； 3) 开发可解释的机器（深度）学习风险预测 该模型将允许更准确和精确地评估兴奋剂的临床益处与风险 个人退伍军人。 方法：这些目标将通过使用 VA TBI 登记和 EHR 数据来实现。对于目标 1 和 3，我们 将使用所有患有 TBI 诊断且使用过兴奋剂的退伍军人。对于目标 2，我们将模拟 RCT，使用没有流行 SUD 的 TBI 退伍军人以及轻度 TBI 后中枢神经系统兴奋剂的新处方 诊断。然后，我们将使用以下方法对患有轻度 TBI 的退伍军人子集进行敏感性分析： 综合 TBI 评估 (CTBIE) 表。倾向评分匹配将用于结果盲法 根据测量的协变量平衡队列的组装，并将使用敏感性分析来估计影响 无法测量的混杂因素。医疗保险和医疗补助服务中心 (CMS) 数据将用于验证 我们的预测模型的普遍性。 后续步骤/实施：除了通过演示和演示的传统传播方法之外 出版物，我们将与 VA AI 中心共享我们的软件工具，以进行传播并与我们的合作 业务合作伙伴将拟议项目的研究结果纳入临床医生教育材料。