Diagnosing and Treating Veterans with Chronic Pain and Opioid Misuse

诊断和治疗患有慢性疼痛和阿片类药物滥用的退伍军人

• 批准号: 10595496
• 负责人: Amy S B Bohnert
• 金额: --
• 依托单位: VETERANS HEALTH ADMINISTRATION
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10595496
• 关键词: Address; Affect; Algorithms; American; Analgesics; Area; Buprenorphine; Caring; Centers for Disease Control and Prevention (U.S.); Classification; Clinical; Computerized Medical Record; Consumption; Country; Data; Diagnosis; Diagnostic; Documentation; Dose; Effectiveness; Elasticity; Enrollment; Evidence based treatment; Exclusion; Exhibits; Feedback; Fright; Funding; Future; General Population; Goals; Guidelines; Harm Reduction; Health; Health Services Accessibility; Health Services Research; Individual; International Classification of Disease Codes; Intervention; Interview; Knowledge; Mental Health; Methodology; Methods; Opioid; Opioid Rotation; Outcome; Overdose; Pain; Pain intensity; Pain management; Patient Self-Report; Patient-Focused Outcomes; Patients; Pharmaceutical Preparations; Pharmacological Treatment; Pharmacy facility; Population; Process; Provider; Qualitative Methods; Research; Research Design; Risk; Risk Behaviors; Safety; Sampling; Screening procedure; Selection Bias; Signs and Symptoms; Site; Speed; Structure; Symptoms; Testing; Translating; Veterans; Veterans Health Administration; Withdrawal; Work; addiction; adverse outcome; chronic pain; chronic pain management; chronic pain patient; clinical practice; clinical research site; cohort; comparative effectiveness; comparative efficacy; compare effectiveness; cost; data warehouse; diagnostic criteria; effective therapy; evidence base; experience; falls; high risk; improved; innovation; large datasets; medication for opioid use disorder; novel; opioid misuse; opioid tapering; opioid therapy; opioid use disorder; patient oriented; patient population; patient safety; prescription opioid; prescription opioid misuse; randomized, clinical trials; recruit; research and development; safety outcomes; secondary analysis; structured data; study population; symptomatic improvement; tool; treatment strategy; trial readiness

Background: As many as 30% of the ~10 million Americans prescribed long-term opioid therapy (LTOT) are estimated to misuse opioids. Receiving LTOT is associated with opioid-related harms, and misuse leads to an increase in the dose consumed and other risky behavior, further worsening outcomes. However, there is a gap in knowledge on how to identify and treat this patient population particularly when they do not meet diagnostic criteria to be treated by medications for Opioid Use Disorder (OUD). In recent years, guidelines from the Centers for Disease Control and the Veterans Health Administration (VHA) have effected widespread tapering to reduce misuse. Buprenorphine, a medication used for both pain and OUD, may also be effective in reducing opioid-related harms while controlling pain for patients on LTOT with misuse; however, buprenorphine is yet to be tested rigorously in this patient population. Therefore, studies are needed to identify patients on LTOT with misuse and to compare the efficacy of different treatments on patient outcomes. Significance: Chronic pain, LTOT for pain, and opioid misuse are common among Veterans and lead to multiple health-related harms. The VHA has made improving pain care and reducing opioid harms a major priority of clinical initiatives, and this proposal responds to the Health Services Research and Development (HSR&D) Funding Announcement #HX-21-024 to address those opioid-related priorities. By filling a crucial evidence gap, this proposal will significantly impact the way we treat pain and minimize harm for Veterans with opioid misuse. Innovation and Impact: This proposal is innovative and impactful in many ways. First, this project will utilize the unique capabilities of the VHA’s Corporate Data Warehouse (CDW) to develop a novel algorithm to identify patients on LTOT with misuse. If successful, this automated identification process has the potential to be scaled to VHA sites across the country. Second, the comparative effectiveness of different treatments will be determined by an emulated trial, an innovative and efficient study design that can lead to greater generalizability than standard trials, which suffer significantly from selection bias in this area. The treatments being evaluated in the emulated trial are readily available, so if specific treatments are found to improve patient symptoms and reduce adverse outcomes, it will be feasible for Veterans with opioid misuse to access these treatments nationwide. Finally, we will gather feedback from providers and Veterans to understand the best strategies and interventions to scale the identification process and better inform Veterans and providers of evidence-based treatment options. Specific Aims: This project aims to 1) Classify a cohort of patients on LTOT with opioid misuse but without OUD by a) building on a previously developed augmented chart review methodology and b) applying an algorithm to structured data; 2) Conduct an emulated trial to compare the effectiveness of pharmacologic treatment options on patient-centered and patient safety outcomes; and 3) Understand current practices and how to translate our findings into improved care via semi-structured interviews with providers and Veterans. Methodology: The study population is VHA patients on LTOT with opioid misuse 2014-present. The proposal uses mixed quantitative and qualitative methods including augmented structured chart review, large dataset classification using ordinal elastic net regression, emulated trials, and qualitative interviews. Next Steps/Implementation: We expect findings to be of use to VHA leaders, prescribing clinicians, and patients with chronic pain. If successful, the automated identification process from Aim 1 could be scaled to VHA sites, and if treatments evaluated in Aim 2 are effective in improving symptoms and reducing adverse outcomes, these could also be implemented widely. In Aim 3, we will gather Veteran input on how to best implement findings from Aims 1 and Aim 2 into clinical practice in a variety of settings.

背景：在接受长期阿片类药物治疗 (LTOT) 的约 1000 万美国人中，多达 30% 估计滥用阿片类药物。接受 LTOT 与阿片类药物相关的危害有关，滥用会导致 消耗剂量和其他危险行为的增加，进一步恶化结果。然而，还有一个差距 了解如何识别和治疗这些患者群体，特别是当他们不符合诊断条件时 阿片类药物使用障碍 (OUD) 药物治疗的标准。近年来，来自国家的指导方针 疾病控制中心和退伍军人健康管理局 (VHA) 已实施广泛的缩减措施 以减少误用。丁丙诺啡是一种用于止痛和 OUD 的药物，也可能有效减少 阿片类药物相关危害，同时控制误用 LTOT 患者的疼痛；然而，丁丙诺啡尚未 在该患者群体中进行严格测试。因此，需要研究来确定接受 LTOT 治疗的患者是否患有以下疾病： 误用并比较不同治疗对患者结果的疗效。 意义：慢性疼痛、LTOT 治疗疼痛和阿片类药物滥用在退伍军人中很常见，并导致 多种与健康相关的危害。 VHA 将改善疼痛护理和减少阿片类药物危害作为主要任务 临床举措的优先事项，该提案响应了卫生服务研究和开发 (HSR&D) 资助公告 #HX-21-024，以解决与阿片类药物相关的优先事项。通过填写关键 证据差距，该提案将极大地影响我们治疗疼痛的方式，并最大限度地减少对退伍军人的伤害 阿片类药物滥用。 创新和影响：该提案在很多方面都具有创新性和影响力。首先，该项目将利用 VHA 企业数据仓库 (CDW) 的独特功能可开发新颖的算法来识别 接受 LTOT 治疗误用的患者。如果成功的话，这种自动识别过程有可能被 扩展到全国各地的 VHA 站点。其次，比较不同治疗方法的效果 由模拟试验确定，这是一种创新且高效的研究设计，可以带来更大的效果 普遍性高于标准试验，标准试验在该领域受到选择偏差的严重影响。治疗方法 在模拟试验中进行评估的数据很容易获得，因此如果发现特定的治疗方法可以改善患者的情况 症状并减少不良后果，滥用阿片类药物的退伍军人可以获得这些 全国范围内的治疗。最后，我们将收集提供者和退伍军人的反馈，以了解最好的 扩大识别过程并更好地向退伍军人和提供者提供信息的策略和干预措施 基于证据的治疗选择。 具体目标：该项目旨在 1) 对一组接受 LTOT 且阿片类药物滥用但没有滥用的患者进行分类 OUD 通过 a) 建立在先前开发的增强图表审查方法的基础上，以及 b) 应用 结构化数据的算法； 2) 进行模拟试验以比较药理学的有效性 以患者为中心和患者安全结果的治疗方案； 3) 了解当前的实践和 如何通过对提供者和退伍军人的半结构化访谈将我们的发现转化为改善的护理。 方法：研究人群是 2014 年至今服用阿片类药物滥用且接受 LTOT 的 VHA 患者。提案 使用混合定量和定性方法，包括增强结构化图表审查、大型数据集 使用序数弹性网络回归、模拟试验和定性访谈进行分类。 后续步骤/实施：我们希望研究结果对 VHA 领导者、处方临床医生和 患有慢性疼痛的患者。如果成功，目标 1 的自动识别过程可以扩展到 VHA 位点，以及目标 2 中评估的治疗是否能有效改善症状和减少不良反应 成果，这些也可以广泛实施。在目标 3 中，我们将收集退伍军人的意见，了解如何最好地做到这一点 将目标 1 和目标 2 的发现应用到各种环境的临床实践中。