David A Yee1, Michelle M Hughes1, Alexander Y Guo1, Neveen H Barakat1, Stephanie A Tse1, Joseph D Ma2, Brookie M Best3, Rabia S Atayee2. 1. Skaggs School of Pharmacy & Pharmaceutical Sciences, University of California, San Diego (UCSD), La Jolla, California. 2. Skaggs School of Pharmacy & Pharmaceutical Sciences, University of California, San Diego (UCSD), La Jolla, California; Doris A. Howell Palliative Care Service, La Jolla, California. 3. Skaggs School of Pharmacy & Pharmaceutical Sciences, University of California, San Diego (UCSD), La Jolla, California; Department of Pediatrics, UCSD School of Medicine - Rady Children's Hospital San Diego, San Diego, California.
Abstract
OBJECTIVE: To determine the relationship between urine drug testing (UDT) frequency and patient adherence for prescribed buprenorphine, carisoprodol, fentanyl, hydrocodone, methadone, morphine, and oxycodone. SETTING: Patients with pain routinely seen by private practitioners. DESIGN: A retrospective analysis was conducted on urinary excretion data analyzed by Millennium Laboratories between March 2008 and May 2011. PATIENT PARTICIPANTS: Patients in the United States with chronic pain who underwent routine UDT to confirm adherence for prescribed medications. INTERVENTIONS: Adherence for the urine drug test was defined as the presence of parent drug and/or metabolite(s) greater than or equal to the lower limit of quantitation. The percent of adherence for prescribed medications was compared to the average percent of the same in subjects with five or more visits. MAIN OUTCOMES: Correlation analyses were used to determine the relationship between adherence for prescribed medications and number of visits. RESULTS: There were 255,168 specimens submitted for testing from 166,755 individuals. When monitoring with more frequent visits (≥5 visits) adherence was higher by 1 percent for buprenorphine (89 percent vs 88 percent); 8 percent for carisoprodol (77 percent vs 69 percent); 5 percent for fentanyl (95 percent vs 90 percent); 7 percent for hydrocodone (83 percent vs 76 percent); 3 percent for methadone (96 percent vs 93 percent); 5 percent for morphine (92 percent vs 87 percent); and 8 percent for oxycodone (90 percent vs 82 percent). CONCLUSIONS: Adherence for prescribed medications is higher with frequent urine monitoring. UDT can be used as tool that may help improve this in patients with chronic pain.
OBJECTIVE: To determine the relationship between urine drug testing (UDT) frequency and patient adherence for prescribed buprenorphine, carisoprodol, fentanyl, hydrocodone, methadone, morphine, and oxycodone. SETTING:Patients with pain routinely seen by private practitioners. DESIGN: A retrospective analysis was conducted on urinary excretion data analyzed by Millennium Laboratories between March 2008 and May 2011. PATIENTPARTICIPANTS: Patients in the United States with chronic pain who underwent routine UDT to confirm adherence for prescribed medications. INTERVENTIONS: Adherence for the urine drug test was defined as the presence of parent drug and/or metabolite(s) greater than or equal to the lower limit of quantitation. The percent of adherence for prescribed medications was compared to the average percent of the same in subjects with five or more visits. MAIN OUTCOMES: Correlation analyses were used to determine the relationship between adherence for prescribed medications and number of visits. RESULTS: There were 255,168 specimens submitted for testing from 166,755 individuals. When monitoring with more frequent visits (≥5 visits) adherence was higher by 1 percent for buprenorphine (89 percent vs 88 percent); 8 percent for carisoprodol (77 percent vs 69 percent); 5 percent for fentanyl (95 percent vs 90 percent); 7 percent for hydrocodone (83 percent vs 76 percent); 3 percent for methadone (96 percent vs 93 percent); 5 percent for morphine (92 percent vs 87 percent); and 8 percent for oxycodone (90 percent vs 82 percent). CONCLUSIONS: Adherence for prescribed medications is higher with frequent urine monitoring. UDT can be used as tool that may help improve this in patients with chronic pain.
Authors: Charles E Argoff; Daniel P Alford; Jeffrey Fudin; Jeremy A Adler; Matthew J Bair; Richard C Dart; Roy Gandolfi; Bill H McCarberg; Steven P Stanos; Jeffrey A Gudin; Rosemary C Polomano; Lynn R Webster Journal: Pain Med Date: 2018-01-01 Impact factor: 3.750
Authors: David J DiBenedetto; Kelly M Wawrzyniak; Michael E Schatman; Hannah Shapiro; Ronald J Kulich Journal: J Pain Res Date: 2019-07-23 Impact factor: 3.133