Quantification of a Methadone Metabolite (EDDP) in Urine: Assessment of Compliance

Objective To investigate the possibility of utilizing methadone metabolite (EDDP) to urine creatinine ratios 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 2-ethylidene-1,5-dimethyl-3,3-diphenylpyrrolidine (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 group 2 cohort, body size, gender, and age. The coefficient of determination adjusted for the number of predictor terms (R_adj²) 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_adj² = 0.49). Adjustment for creatinine through the EDDP/creatinine ratio provided a tighter relation (R_adj² = 0.95). Similarly, for group 2 data, there was a significant positive relation (P=0.001) and substantial variability (R_adj² = 0.53). Adjustment for creatinine through EDDP/creatinine ratios provided a substantially stronger relation (R_adj² = 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_adj² = 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.