Quantification of oxycodone and morphine analytes in urine: Assessment of adherence.

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.

Quantification of a methadone metabolite (EDDP) in urine: assessment of compliance.

OBJECTIVE: To investigate the possibility of utilizing the ratio of the methadone metabolite, 2-ethylidene-1,5-dimethyl-3,3-diphenylpyrrolidine (EDDP), to urine creatinine to develop a regression model that would predict drug adherence in patients prescribed methadone for either pain management or drug addiction. DESIGN: Retrospective study. SETTING: Marshfield Clinic-Lakeland Center, one of 41 regional centers that make up Marshfield Clinic, a large, private, multi-specialty healthcare institution in central Wisconsin. PARTICIPANTS: Patients receiving methadone treatment for substance abuse or chronic pain. Group 1 was an initial pilot group consisting of 7 patients who were followed for a 4-month period. Group 2 consisted of 33 patients who were followed over a 28-month period. METHODS: Age, gender, weight, height, methadone dosage, quantitative urine creatinine and EDDP levels, reported compliance/non-compliance, and relevant clinical cofactors were retrospectively abstracted from the patients' medical records. Log-log regression analyses were used to model EDDP and the EDDP/creatinine ratio from urine screening results as functions of methadone dose, and in the larger cohort (group 2), body size, gender and age. The coefficient of determination adjusted for the number of predictor terms (R(adj)(2)) was reported as a measure of model fit. RESULTS: For group 1 data, there was a significant positive relation (P<0.001) but also substantial variability (R(adj)(2) = 0.49). Adjustment for creatinine through the EDDP/creatinine ratio provided a tighter relation (R(adj)(2) = 0.95). Similarly, for group 2 data, there was a significant positive relation (P=0.001) and substantial variability (R(adj)(2) = 0.53). Adjustment for creatinine through EDDP/creatinine ratios provided a substantially stronger relation (R(adj)(2) = 0.73). Gender and age showed no evidence of association with the EDDP/creatinine ratio (P=0.60 and P=0.51, respectively). Body size was significant in the model, both when measured by body surface area and by lean body weight, and improved the prediction when added to our model (R(adj)(2) = 0.80). CONCLUSION: For the first time, urine analyses may be used to monitor methadone over- or under-use in a clinical setting, regardless of the state of patient hydration or the manipulation of a sample by addition of another substance, such as bleach, soap, or even methadone, which could render an appropriate sample inappropriate or an inappropriate sample appropriate. A similar approach may prove useful for other drug treatments, allowing for more accurate monitoring of commonly abused prescription medications.