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.

项目总结与摘要