Use of an algorithm applied to urine drug screening to assess adherence to an oxycontin regimen.

OBJECTIVE: This study examined the ability of an algorithm applied to urine drug levels of oxycodone in healthy adult volunteers to differentiate among low, medium, and high doses of OxyContin.

PARTICIPANTS AND INTERVENTIONS: Thirty-six healthy volunteers were randomized to receive 80, 160, or 240 mg of daily OxyContin to steady state while under a naltrexone blockade. During days 3 and 4 of the study, urine samples of all participants were collected, and oxycodone levels detected in the urine were obtained using a liquid chromatography-mass spectrometry (LC-MS-MS) assay.

OUTCOME MEASURES: The concordance was calculated for raw and adjusted LC-MS-MS urine oxycodone values within each study participant between their third and fourth day values. Also, an analysis of medians was calculated for each of the dosage groupings using Bonett-Price confidence intervals for both raw and adjusted LC-MS-MS values.

RESULTS: The concordance correlation coefficient for the raw LC-MS-MS values between days 3 and 4 was 0.689 (95% confidence intervals = 0.515, 0.864), whereas the concordance correlation coefficient for the LC-MS-MS values using the algorithm (ie, normalized values) was 0.882 (95% confidence intervals = 0.808, 0.956). Because of greater variability in the raw values, some overlap was observed in the confidence intervals of the various OxyContin doses, whereas no overlap was observed in the normalized confidence intervals regardless of the application of a Bonferroni adjustment.

CONCLUSIONS: In contrast to raw LC-MS-MS values, an algorithm that normalizes oxycodone urine drug levels for pH, specific gravity, and lean body mass discriminates well among all three of the daily doses of OxyContin tested (80, 160, and 240 mg), even with correcting for multiple analyses.