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：约有1000万美国人处方的长期阿片类药物治疗（LTOT）的约30％是 估计是小姐的Ooid。接收LTOT与OOID相关的危害有关，而Missuse则导致 增加剂量消耗和其他风险行为，进一步遗憾结果。但是，有差距 在知识中如何识别和治疗该患者人群，特别是当他们不遇到诊断时 通过阿片类药物使用障碍（OUD）治疗的标准。近年来，来自 疾病控制中心和退伍军人卫生管理局（VHA）已影响宽度逐渐减少 减少滥用。丁丙诺啡是一种用于疼痛和OUD的药物，也可能有效地减少 阿片类药物相关的危害，同时控制LTOT患者的疼痛，并滥用；但是，丁丙诺啡仍在 在该患者人群中进行严格的测试。因此，需要研究以确定LTOT上的患者 滥用并比较不同疗法对患者预后的效率。 意义：慢性疼痛，疼痛的LTOT和阿片类药物在退伍军人中很常见，并导致 多个与健康有关的危害。 VHA已改善疼痛护理并减少阿片类药物会损害主要 临床计划的优先级，该提案对卫生服务研究与发展做出了回应 （HSR＆D）资金公告＃HX-21-024解决与阿片类药物有关的优先事项。通过填充至关重要 证据差距，该提议将极大地影响我们治疗疼痛的方式，并最大程度地减少对退伍军人的伤害 opioid滥用。 创新和影响：该提案在许多方面具有创新性和影响力。首先，这个项目将利用 VHA公司数据仓库（CDW）的独特功能开发出一种新颖的算法来识别 LTOT的患者滥用。如果成功，此自动识别过程有可能 扩展到全国各地的VHA站点。其次，不同治疗的比较有效性将是 由模拟试验确定，这是一种创新和有效的研究设计，可以导致更大 与标准试验相比，该区域的选择偏差遭受了明显损失。治疗 在模拟试验中进行评估很容易获得，因此，如果发现特定的治疗方法可以改善患者 症状并减少不良后果，对于有阿片类药物的退伍军人来说，访问这些是可行的 全国治疗。最后，我们将收集提供者和退伍军人的反馈，以了解最好的 策略和干预措施，以扩展标识过程，并更好地告知退伍军人和提供者 基于证据的治疗选择。 具体目的：该项目的目的是1）与阿片类药物的LTOT上的一系列患者进行分类，但没有 OUD通过a）构建先前发达的增强图表审查方法，b）应用 算法结构化数据； 2）进行模拟试验以比较药理的有效性 以患者为中心和患者安全结果的治疗选择； 3）了解当前的做法， 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进的护理。 方法论：研究人群是vha患者在LTOT上，阿片类药物MISSUSE 2014-至今。提案 使用混合定量和定性方法，包括增强结构图审查，大数据集 使用顺序弹性净回归，模拟试验和定性访谈进行分类。 下一步/实施：我们希望发现对VHA领导人，开处方临床医生和 慢性疼痛的患者。如果成功，则可以将AIM 1的自动识别过程缩放到 VHA站点以及在AIM 2中评估的治疗方法有效改善症状并减少不良 结果，这些也可以广泛实施。在AIM 3中，我们将收集有关如何最好的资深投入 在各种环境中实施AIM 1和AIM 2的发现。