Improving urine drug test utility to mitigate prescription opioid risk

项目摘要 据估计，1亿美国人患有慢性疼痛，500万至800万人服用慢性阿片类药物 疼痛管理1999年至1999年，处方阿片类药物导致的过量死亡增加了四倍多。 2010年，反映了处方阿片类药物销售的增加。缺乏证据支持的工具 提供者在平衡处方阿片类药物治疗的潜在益处与已知风险的挑战性任务中。 指南通常建议将尿液药物检测（UDT）作为处方患者的风险监测工具 阿片类药物治疗慢性非癌症疼痛UDT可以通过检测非法或非非法的 规定的受控物质或没有规定的阿片类药物。然而，UDT具有挑战性， 解释，需要详细了解复杂的药物代谢途径，数据表明医生 在UDT解释中经常出错。观察性研究表明，31%至55%的UDT 与潜在的滥用或转移相一致，但关于供应商如何应对这些UDT的数据很少 结果为慢性疼痛患者处方阿片类药物制定有效的风险缓解策略是一项 NIH/NIDA的关键优先事项。目前的建议旨在开发和验证一种方法，以确定意外 UDT结果与使用EMR数据的误用或转移一致。我们将使用这种方法来描述 在一个接受非预期UDT治疗的大型患者队列中， 慢性阿片类药物治疗最后，我们将评估临床决策支持工具的可接受性和有效性 包括在有或没有临床反应指南的情况下对UDT结果的解释。通过指导 职业发展奖，候选人将发展必要的技能过渡到一个独立的 研究职业生涯，包括使用大型临床数据库进行研究的高级培训， 和实施科学，以及发展与研究有关的内容和方法专门知识， 慢性疼痛和成瘾。从拟议工作范围中获得的知识将为进一步研究提供信息 检测方法的性能，以识别与误用或转移一致的非预期UDT结果 作为大型临床数据研究网络中的质量指标，并对UDT临床决策进行试点测试 临床实践中的辅助工具。

PROJECT SUMMARY An estimated 100 million Americans suffer from chronic pain, and 5 to 8 million are prescribed chronic opioids for pain management. Overdose deaths due to prescription opioids more than quadrupled between 1999 and 2010, mirroring increases in prescription opioid sales. There is a dearth of evidence-based tools to support providers in the challenging task of balancing potential benefits with known risks of prescription opioid therapy. Guidelines commonly recommend urine drug testing (UDT) as a risk monitoring tool for patients prescribed opioids for chronic non-cancer pain. UDTs can identify misuse or diversion through detection of illicit or non- prescribed controlled substances or absence of prescribed opioids. However, UDTs are challenging to interpret, requiring detailed understanding of complex drug metabolism pathways, and data suggest physicians frequently err in UDT interpretation. Observational studies suggest between 31% and 55% of UDTs are consistent with potential misuse or diversion, but there are scant data on how providers respond to these UDT results. Development of effective risk mitigation strategies for patients prescribed opioids for chronic pain is a key priority for NIH/NIDA. The current proposal seeks to develop and validate a method to identify unexpected UDT results consistent with misuse or diversion using EMR data. We will use this method to characterize the rates of, predictors of, and clinical response to unexpected UDT results in a large cohort of patients receiving chronic opioid therapy. Finally, we will evaluate the acceptability and efficacy of a clinical decision support tool that includes interpretation of UDT results with or without clinical response guidance. Through the Mentored Career Development Award, the candidate will develop skills necessary for transition to an independent research career, including advanced training in research using large clinical databases, training in intervention and implementation science, and development of research-related content and methodological expertise in chronic pain and addiction. The knowledge gained from the proposed scope of work will inform further studies to test the performance of the method to identify unexpected UDT results consistent with misuse or diversion as a quality indicator in a large clinical data research network, and pilot-testing the UDT clinical decision support tool in clinical practice.