Results & Conclusions

Results & Conclusions

Discussion

This study using OxyContin® utilizes a proprietary algorithm previously studied in an addiction setting in a more structured environment using healthy volunteers. The results of this study suggest that, in contrast to raw LC-MS-MS values, an algorithm that normalizes oxycodone urine drug levels for pH, specific gravity and lean body mass, using LC-MS-MS urine drug assays, discriminates well among all three of the daily doses of OxyContin® tested (80, 160 and 240 mg). These findings persisted even when conservative confidence intervals were applied to address the use of multiple group comparisons. These results may have important clinical implications in the monitoring of pain management in patients through the use of normalized UDT results to inform clinical assessment of patient adherence with a prescribed opioid regimen.

While this algorithm cannot currently be used to determine or predict the dose of a drug a given patient is taking, application of the algorithm can provide additional information that, when combined with observations of aberrant behavior, structured risk assessments (e.g., ORT, COMM), pill counts and medical chart reviews, should help clinicians assess the possibility of drug misuse or non-adherence.

The results of this study demonstrate the superior ability of this algorithm to produce adjusted urine drug levels that correlate more closely with dose than do unadjusted urine drug levels. When used with other clinical data, this methodology should provide insights about medication adherence that go beyond whether a patient is merely taking any of their prescribed opioid(s). Further, research to define the operating characteristics of the algorithm in a larger study will be helpful and will enhance the use of this innovative approach to monitor patient adherence.