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

将临床试验的治疗效果转移到同时发生阿片类药物和兴奋剂使用障碍的现实人群中

• 批准号: 10642922
• 负责人: Ryan Cook
• 金额: $ 17.1万
• 依托单位: OREGON HEALTH & SCIENCE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10642922
• 关键词: Abstinence; Address; Adult; Age; Aging; Amphetamines; Area; Award; Back; Benchmarking; Big Data; Cessation of life; Clinical; Clinical Treatment; Clinical Trials; Clinical Trials Network; Communication; Complex; Counseling; Data; Data Analyses; Data Element; Data Science; Data Sources; Drug abuse; Drug usage; Education; Effectiveness; Effectiveness of Interventions; Electronic Health Record; Emergency department visit; Enrollment; Epidemiology; Evidence based treatment; Exclusion; Exercise; Feedback; Funding; Future; Goals; Grant; Health; Health system; Heroin; Hospitalization; Hospitals; Income; Individual; Informatics; Instruction; Intervention; Knowledge; Leadership; Machine Learning; Mathematics; Medicine; Methods; Modeling; National Institute of Drug Abuse; Natural Language Processing; Opioid; Oregon; Outcome; Overdose; Participant; Patient Representative; Patients; Persons; Pharmaceutical Preparations; Policies; Policy Maker; Population; Race; Randomized, Controlled Trials; Recommendation; Research; Research Personnel; Research Proposals; Sampling; Science; Selection for Treatments; Severities; Source; Specificity; Stimulant; Substance Use Disorder; Surveys; System; Target Populations; Techniques; Translating; Treatment Effectiveness; Treatment Efficacy; United States; Universities; Writing; accountable care organization; addiction; attributable mortality; automobile accident; career; clinically relevant; contingency management; data fusion; effective therapy; effectiveness evaluation; efficacy evaluation; ethnic minority; illicit opioid; improved; informatics training; innovation; insight; interest; mortality; motivational enhancement therapy; novel; opioid epidemic; opioid overdose; opioid use; opioid use disorder; optimal treatments; overdose death; patient population; polysubstance use; prescription opioid; psychiatric comorbidity; psychosocial; racial minority; recruit; sex; skills; statistics; stimulant use; stimulant use disorder; substance use treatment; synthetic opioid; treatment effect; treatment strategy; treatment trial; trend

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.

项目总结和摘要 在美国，与阿片类药物有关的过量现在每年造成的死亡人数是车祸的两倍。 States.处方阿片类药物和海洛因相关死亡率令人鼓舞的下降趋势已经黯然失色 合成类阿片和同时使用类阿片造成的过量和死亡增加， 兴奋剂在全国范围内，兴奋剂和类阿片的合并使用在过去十年中几乎翻了一番， 在西方国家增加了三倍多。至少有一半的阿片类药物使用障碍（OUD）患者有另一个 物质使用障碍（SUD），多物质使用是临床规范。虽然几种药物的疗效 SUD（包括OUD）的治疗方法已在严格的随机对照试验（RCT）中确立， 参加这些随机对照试验的人通常与这些治疗所要达到的人群不同。在 特别是，很少有证据支持目前SUD治疗对使用多种药物的人的有效性。 物质.因此，了解和提高SUD治疗对患有 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， 通过将尖端的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SUD治疗试验的结果 在OUD和兴奋剂使用障碍（OUD +）患者的代表性临床相关人群中， 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 全国药物使用和健康调查。我估计药物的有效性， 管理，动机访谈，咨询和锻炼，以增加治疗的启动和保留 并减少阿片类药物和兴奋剂在成人，平民，非机构化的美国OUD +人群中的使用 兴奋剂。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相融合，以估计 提高OUD +患者治疗参与度和保留率的干预措施的有效性 俄勒冈州健康与科学大学卫生系统的StimUD。通过实现这些目标，我估计 OUD + StimUD患者治疗的真实有效性。临床医生和决策者可能 使用结果来指导治疗选择，以改善这些复杂，脆弱的患者的健康。 通过这个奖项，我在流行病学和统计学的基础上，获得切割的专业知识- 边缘数据科学和信息学方法。结合因果推理、机器学习和自然 语言处理技术最大限度地提高了我的研究的创新性、有效性、影响力和范围。我也 接受临床成瘾医学的广泛指导，并建立我的领导，赠款写作，和科学 沟通能力这些新技能中的每一项都对完成我的研究目标和发展我的研究能力至关重要。 未来的高影响力，高度竞争力的研究提案，导致成功的独立研究生涯 在成瘾科学中。