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: 10526149
• 负责人: Ryan Cook
• 金额: $ 17.23万
• 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/10526149
• 关键词: Abstinence; Address; Adult; Aging; Amphetamines; Area; Award; Back; Benchmarking; Big Data; Caring; 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; Effectiveness; Effectiveness of Interventions; Electronic Health Record; Emergency department visit; Enrollment; Epidemiology; Evidence based treatment; Exercise; Feedback; Funding; Future; Goals; Gold; Grant; Health; Health Sciences; Health system; Heroin; Hospitalization; Hospitals; 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; Population; Randomized Controlled Trials; Research; Research Personnel; Research Proposals; Sampling; Science; Selection for Treatments; Source; Specificity; Stimulant; Substance Use Disorder; Surveys; System; Target Populations; Techniques; Translating; Treatment Effectiveness; Treatment Efficacy; United States; Universities; Writing; addiction; attributable mortality; automobile accident; career; clinically relevant; contingency management; data fusion; effective therapy; effectiveness evaluation; efficacy evaluation; epidemiologic data; ethnic minority population; 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 and ethnic; recruit; 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.

项目摘要和摘要 现在，与阿片类药物相关的过量剂量每年造成两倍的死亡人数是曼联的汽车事故 国家。鼓励处方阿片类药物和海洛因相关死亡率的下降趋势已被黯然失色 通过归因于合成阿片类药物的过量服用和死亡以及伴随使用阿片类药物的死亡和死亡的增加。 兴奋剂。在全国范围内，兴奋剂和阿片类药物的综合使用在过去十年中几乎翻了一番，并且 在西方国家，两倍多。至少一半的阿片类药物使用障碍（OUD）有另一个 药物使用障碍（SUD）和多物质使用是临床规范。虽然几个的功效 在严格的随机对照试验（RCT）中，已经建立了包括OUD在内的SUD的治疗方法， 参加这些RCT的人通常与人群不同，这些治疗旨在达到。在 特别是，很少有证据支持当前使用多重人的SUD治疗的有效性 物质。因此，了解和增强了SUD治疗对患者的影响 Polysubstance使用仍然是NIDA优先区域（PAR-20-035）。拟议的研究解决了这一关键 通过将尖端数据科学方法与创新数据融合技术相结合的知识差距， 可运输性，以估计进行SUD治疗试验的结果 在代表性的，临床上与OUD和刺激性使用障碍的个体相关的人群（OUD +） 刺激）。在AIM 1中，我将来自国家药物滥用治疗临床试验网络RCT的数据融合在一起 全国药物使用和健康调查。我估计药物的有效性，应急性 管理，动机访谈，咨询和锻炼以增加治疗启动和保留 并减少成人，平民，非机构化的美国人群的阿片类药物和兴奋剂的使用 刺激。在第二个目标中，我将相同的临床试验数据与电子健康记录融合在一起，以估算 干预措施增加了OUD +患者的治疗参与度和保留率的有效性 俄勒冈健康与科学大学卫生系统的刺激。通过完成这些目标，我估计 对OUD +刺激个体治疗的现实有效性。临床医生和政策制定者可能 使用结果指导治疗选择，以改善这些复杂，脆弱的患者的健康。 通过这个奖项，我以流行病学和统计学的背景为基础，以获得削减专业知识 - 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 语言处理技术最大程度地提高了我的研究的创新，有效性，影响和影响力。我也是 接受临床成瘾医学的广泛指导，并建立我的领导力，赠款写作和科学 沟通技巧。这些新技能中的每一个对于完成我的研究目标和发展至关重要 未来的高影响力，竞争激烈的研究建议，导致成功的独立研究职业 在成瘾科学中。