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.
Between 5 and 8 million Americans are prescribed opioids for chronic pain. Increases in prescription opioid sales have been mirrored by a quadrupling of overdose deaths due to prescription opioids between 1999 and 2010. The research project will use large clinical data sources to develop, validate, and employ a method to identify unexpected urine drug test results consistent with misuse or diversion. The method and findings will be used to inform development of a clinical decision support tool to improve the effectiveness of urine drug tests as a risk mitigation strategy.
