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：在约1000万美国人中，多达30%的人接受长期阿片类药物治疗（LTOT）。 估计会滥用阿片类药物接受LTOT与阿片类药物相关的危害有关，误用会导致 增加消耗的剂量和其他危险行为，进一步恶化结果。然而， 了解如何识别和治疗这一患者群体，特别是当他们不符合诊断标准时， 阿片类药物使用障碍（OUD）的药物治疗标准。近年来， 疾病控制中心和退伍军人健康管理局（VHA）已经广泛实施了减量 以减少滥用。丁丙诺啡，一种用于疼痛和OUD的药物，也可能有效地减少 阿片类药物相关的伤害，同时控制患者的疼痛LTOT与滥用;然而，丁丙诺啡还没有 在这个患者群体中进行严格的测试。因此，需要进行研究以确定LTOT患者， 滥用，并比较不同治疗对患者结局的疗效。 意义：慢性疼痛，LTOT疼痛和阿片类药物滥用在退伍军人中很常见，并导致 多种健康相关危害。VHA已经将改善疼痛护理和减少阿片类药物的危害作为一个主要的 临床计划的优先权，该提案响应了卫生服务研究和发展 (HSR&D）资助公告#HX-21-024，以解决这些阿片类药物相关的优先事项。通过填补一个关键的 证据差距，这项建议将显着影响我们治疗疼痛的方式，并尽量减少对退伍军人的伤害， 阿片类药物滥用 创新和影响：该提案在许多方面都具有创新性和影响力。首先，该项目将利用 VHA的企业数据仓库（CDW）的独特功能，以开发一种新的算法来识别 误用LTOT的患者。如果成功，这种自动识别过程有可能成为 扩展到全国各地的VHA站点。第二，不同治疗方法的比较效果将是 通过模拟试验确定，创新和有效的研究设计，可以导致更大的 与标准试验相比，标准试验在这一领域明显存在选择偏差。的治疗 在模拟试验中进行评估的药物是现成的，所以如果发现特定的治疗方法可以改善患者的病情， 症状和减少不良后果，这将是可行的退伍军人与阿片类药物滥用，以获得这些 全国治疗。最后，我们将收集来自供应商和退伍军人的反馈，以了解最好的 战略和干预措施，以扩大识别过程，并更好地告知退伍军人和供应商， 循证治疗方案。 具体目的：该项目旨在1）将LTOT患者队列分类为阿片类药物滥用，但没有 OUD通过a）建立在先前开发的增强的病历审查方法上，以及B）应用 算法的结构化数据; 2）进行模拟试验，以比较药理学的有效性 以患者为中心的治疗方案和患者安全结局;以及3）了解当前的实践， 如何通过与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。 方法学：研究人群为2014年至今接受LTOT且阿片类药物滥用的VHA患者。该提案 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 分类使用有序弹性网络回归，模拟试验和定性访谈。 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 慢性疼痛患者。如果成功，目标1的自动识别过程可以扩大到 VHA研究中心，以及目标2中评价的治疗是否能有效改善症状并减少不良反应 这些成果也可以广泛实施。在目标3中，我们将收集退伍军人的意见， 将目标1和目标2的发现落实到各种环境中的临床实践中。