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Updated: Mar 28, 2026

High-throughput and Comprehensive Drug Surveillance Using Multisegment Injection-Capillary Electrophoresis-Mass Spectrometry
Published on: April 23, 2019
Quantification of oxycodone and morphine analytes in urine: Assessment of adherence
Michael E M Larson1, Richard L Berg2, Joyce Flanagan3
1Clinical Psychologist, Department of Pain Management, Marshfield Clinic Health System - Minocqua Center, Minocqua, Wisconsin.
Objective:
To use urine drug testing (UDT) results and other covariates to develop a model for the assessment of opioid medication prescription adherence.
Design:
Retrospective study.
Setting:
The Pain Management Clinic at one center of a large, private, multispecialty healthcare system (consisting of 52 regional centers) in northcentral and western Wisconsin.
Participants:
Seven hundred thirty-three Pain Management Clinic patients with an opioid prescription and UDT between June 1, 2007 and May 17, 2010. UDT results were available for 2,615 individual drug screens from 2,364 urine samples.
Intervention:
Patient characteristics, drug dosage, quantitative urine creatinine and drug/analyte levels, and reported adherence/nonadherence were abstracted from the electronic medical record.
Main Outcome Measures:
Adherence was categorized for all UDT results using an objective set of criteria. Drug adherence was modeled excluding samples for clinically observed adherence issues, detection of illicit substances, diagnosed addictive disorders, and/or metabolic reasons.
Results:
Considerable variability was observed for primary urine analytes, even among those prescribed the same dose and believed to be adherent and free of confounding medical issues. For all medications evaluated, only urine creatinine contributed significantly (p < 0.0001) to predictive models of adherence based on dose alone. Simulated underuse and review of identified overuse and underuse suggest that this model could provide useful adherence information.
Conclusion:
Predictive models based on urine analyte levels and clinical covariates, particularly urine creatinine, may be clinically useful for assessing opioid adherence. Future work should evaluate whether genetics or other factors can improve predictive accuracy of these models.
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