Transporting treatment effects from clinical trials to real-world populations with co-occurring opioid and stimulant use disorders

项目摘要和摘要 在美国，与阿片类药物相关的过量现在每年造成的死亡人数是车祸的两倍 各州。与处方阿片类药物和海洛因相关的死亡率令人鼓舞的下降趋势已经黯然失色 可归因于合成阿片类药物和伴随阿片类药物的使用的过量和死亡增加以及 兴奋剂。在全国范围内，兴奋剂和阿片类药物的联合使用在过去十年中几乎翻了一番，而且 西部各州的这一比例增加了两倍多。至少有一半的阿片类药物使用障碍(OUD)患者有另一种 物质使用障碍(SUD)，多物质使用是临床常模。尽管几种药物的疗效 对肥胖症的治疗，包括OUD，已经在严格的随机对照试验(RCT)中建立， 参加这些随机对照试验的人往往与这些治疗的目标人群不同。在……里面 特别是，几乎没有证据支持目前的SUD治疗对使用多种药物的人的有效性 物质。因此，了解和加强SUD治疗对患有慢性病的人的效果 多物质使用仍然是NIDA的优先领域(PAR-20-035)。拟议的研究解决了这一关键问题 知识鸿沟通过将尖端数据科学方法与创新的数据融合技术相结合， 可移植性，以估计如果进行了SUD治疗试验的结果会是什么 在有OUD和兴奋剂使用障碍(OUD)的具有代表性的、临床相关的个体人群中 StimUD)。在目标1中，我将来自国家药物滥用治疗临床试验网络RCT的数据与 全国药物使用与健康调查。我估计药物的有效性，偶然性 管理、激励性访谈、咨询和锻炼，以增加治疗的开始和保留 并减少成人、平民、非住院美国人口中阿片类药物和兴奋剂的使用 StimUD。在我的第二个目标中，我将相同的临床试验数据与电子健康记录融合在一起，以估计 提高卵巢癌患者治疗参与度和保留率的干预措施效果 俄勒冈州卫生与科学大学卫生系统的StimUD。通过完成这些目标，我估计 对患有单纯性多发性硬化症患者的治疗的真实效果。临床医生和决策者可以 使用这些结果来指导治疗选择，以改善这些复杂、脆弱患者的健康。 通过这个奖项，我以我在流行病学和统计学方面的背景为基础，获得了削减- 边缘数据科学和信息学方法。将因果推理、机器学习和自然推理相结合 语言处理技术最大限度地提高了我研究的创新性、有效性、影响力和影响范围。我也是 接受临床成瘾医学的广泛指导，培养我的领导力，资助写作和科学 沟通技巧。这些新技能中的每一项都对我完成研究目标和发展至关重要 未来的高影响力、高竞争力的研究提案将导致成功的独立研究事业 在成瘾科学方面。

PROJECT SUMMARY AND ABSTRACT Opioid-related overdoses are now responsible for twice as many deaths per year as car accidents in the United States. Encouraging downward trends in prescription opioid- and heroin-related mortality have been eclipsed by rises in overdoses and deaths attributable to synthetic opioids and concomitant use of opioids and stimulants. Nationally, the combined use of stimulants and opioids has nearly doubled in the past decade, and more than tripled in Western states. At least half of individuals with opioid use disorder (OUD) have another substance use disorder (SUD), and polysubstance use is the clinical norm. Although the efficacy of several treatments for SUDs, including OUD, has been established in rigorous randomized controlled trials (RCTs), the people enrolled in these RCTs often differ from the populations those treatments are intended to reach. In particular, little evidence supports the effectiveness of current SUD treatments for people who use multiple substances. Consequently, understanding and enhancing the effects of SUD treatments for people with polysubstance use remains a NIDA priority area (PAR-20-035). The proposed research addresses this critical knowledge gap by combining cutting-edge data science methods with an innovative data-fusion technique, transportability, to estimate what the results of SUD treatment trials would have been had they been conducted in representative, clinically-relevant populations of individuals with OUD and stimulant use disorders (OUD + StimUD). In Aim 1, I fuse data from National Drug Abuse Treatment Clinical Trials Network RCTs with the National Survey on Drug Use and Health. I estimate the effectiveness of medications, contingency management, motivational interviewing, counseling, and exercise to increase treatment initiation and retention and reduce opioid and stimulant use in the adult, civilian, noninstitutionalized U.S. population with OUD + StimUD. In my second Aim, I fuse the same clinical trial data with electronic health records to estimate the effectiveness of interventions to increase treatment engagement and retention among patients with OUD + StimUD in the Oregon Health & Science University health system. Through completing these aims, I estimate the real-world effectiveness of treatments for individuals with OUD + StimUD. Clinicians and policymakers may use the results to guide treatment selection to improve the health of these complex, vulnerable patients. Through this award, I build upon my background in epidemiology and statistics to gain expertise in cutting- edge data science and informatics methods. Combining causal inference, machine learning, and natural language processing techniques maximizes the innovation, validity, impact, and reach of my research. I also receive extensive instruction in clinical addiction medicine and build my leadership, grant writing, and scientific communication skills. Each of these new skills are crucial to completing my research aims and developing future high-impact, highly competitive research proposals leading to a successful independent research career in addiction science.