Brittany B Dennis1, Pavel S Roshanov2, Monica Bawor3, James Paul4, Michael Varenbut5, Jeff Daiter5, Carolyn Plater5, Guillame Pare2, David C Marsh6, Andrew Worster7, Dipika Desai8, Lehana Thabane9, Zainab Samaan10. 1. Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Canada; Population Genomic Program, Chanchalani Research Centre, McMaster University, Hamilton, Canada. 2. Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Canada. 3. Population Genomic Program, Chanchalani Research Centre, McMaster University, Hamilton, Canada; McMaster Integrative Neuroscience Discovery & Study (MiNDS) Program, McMaster University, Hamilton, Canada. 4. Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Canada; Department of Anesthesia, McMaster University, Hamilton, Canada. 5. Ontario Addiction Treatment Centres, Richmond Hill, Ontario, Canada. 6. Ontario Addiction Treatment Centres, Richmond Hill, Ontario, Canada; Northern Ontario School of Medicine, Sudbury, Ontario, Canada; 7. Ontario Addiction Treatment Centres, Richmond Hill, Ontario, Canada; Department of Medicine, Hamilton General Hospital, Hamilton, Canada. 8. Population Genomic Program, Chanchalani Research Centre, McMaster University, Hamilton, Canada; Departments of Pediatrics and Anesthesia, McMaster University, Hamilton, Canada; 9. Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Canada; Departments of Pediatrics and Anesthesia, McMaster University, Hamilton, Canada; Centre for Evaluation of Medicine, St Joseph's Healthcare-Hamilton, Canada; Biostatistics Unit, Father Sean O'Sullivan Research Centre, St Joseph's Healthcare-Hamilton, Canada; 10. Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Canada; Population Genomic Program, Chanchalani Research Centre, McMaster University, Hamilton, Canada; Department of Psychiatry and Behavioral Neurosciences, McMaster University, Hamilton, Canada; Peter Boris Centre for Addictions Research, Canada.
Abstract
BACKGROUND: Chronic pain is implicated as a risk factor for illicit opioid use among patients with opioid addiction treated with methadone. However, there exists conflicting evidence that supports and refutes this claim. These discrepancies may stem from the large variability in pain measurement reported across studies. OBJECTIVES: We aim to determine the clinical and demographic characteristics of patients reporting pain and evaluate the prognostic value of different pain classification measures in a sample of opioid addiction patients. STUDY DESIGN: Multi-center prospective cohort study. SETTING: Methadone maintenance treatment facilities for managing patients with opioid addiction. METHODS: This study includes participants from the Genetics of Opioid Addiction (GENOA) prospective cohort study. We assessed the prognostic value of different pain measures for predicting opioid relapse. Pain measures include the Brief Pain Inventory (BPI) and patients' response to a direct pain question all study participants were asked from the GENOA case report form (CRF) "are you currently experiencing or have been diagnosed with chronic pain?" Performance characteristics of the GENOA CRF pain measure was estimated with sensitivity and specificity using the BPI as the gold standard reference. Prognostic value was assessed using pain classification as the primary independent variable in an adjusted analysis using 1) the percentage of positive opioid urine screens and 2) high-risk opioid use (= 50% positive opioid urine screens) as the dependent variables in a linear and logistic regression analyses, respectively. RESULTS: Among participants eligible for inclusion (n = 444) the BPI was found to be highly sensitive, classifying a large number of GENOA participants with pain (n = 281 of the 297 classified with pain, 94.6%) in comparison to the GENOA CRF (n = 154 of 297 classified with pain, 51.8%). Participants concordantly classified as having pain according to the GENOA CRF and BPI were found to have an estimated 7.79% increase in positive opioid urine screens (estimated coefficient: 7.79; 95% CI 0.74, 14.85: P = 0.031) and a 4 times greater odds (odds ratio [OR]: 4.10 P = 0.008; 95% CI: 1.44, 11.63) of engaging in a "high risk" level of illicit opioids use. The prognostic relevance of pain classification was not maintained for the additional participants classified by the BPI (n = 143 discordant). CONCLUSION: These results suggest that while the BPI may be more sensitive in capturing pain among patients with opioid addiction, this tool is of less value for predicting the impact of pain on illicit opioid use for opioid addiction patients on methadone maintenance treatment. The GENOA CRF showed high predictive ability, whereby patients classified according to the GENOA CRF are at serious risk for opioid relapse. Using the appropriate tool to assess pain in opioid addiction may serve to improve the current detection and management of comorbid pain. LIMITATIONS: We caution the interpretation of these result since they are still reflective of participants already maintained on an opioid substitution therapy (OST), which can largely differ from patients who drop out of methadone maintenance treatment (MMT) or never seek treatment altogether.
BACKGROUND:Chronic pain is implicated as a risk factor for illicit opioid use among patients with opioid addiction treated with methadone. However, there exists conflicting evidence that supports and refutes this claim. These discrepancies may stem from the large variability in pain measurement reported across studies. OBJECTIVES: We aim to determine the clinical and demographic characteristics of patients reporting pain and evaluate the prognostic value of different pain classification measures in a sample of opioid addictionpatients. STUDY DESIGN: Multi-center prospective cohort study. SETTING:Methadone maintenance treatment facilities for managing patients with opioid addiction. METHODS: This study includes participants from the Genetics of Opioid Addiction (GENOA) prospective cohort study. We assessed the prognostic value of different pain measures for predicting opioid relapse. Pain measures include the Brief Pain Inventory (BPI) and patients' response to a direct pain question all study participants were asked from the GENOA case report form (CRF) "are you currently experiencing or have been diagnosed with chronic pain?" Performance characteristics of the GENOA CRF pain measure was estimated with sensitivity and specificity using the BPI as the gold standard reference. Prognostic value was assessed using pain classification as the primary independent variable in an adjusted analysis using 1) the percentage of positive opioid urine screens and 2) high-risk opioid use (= 50% positive opioid urine screens) as the dependent variables in a linear and logistic regression analyses, respectively. RESULTS: Among participants eligible for inclusion (n = 444) the BPI was found to be highly sensitive, classifying a large number of GENOA participants with pain (n = 281 of the 297 classified with pain, 94.6%) in comparison to the GENOA CRF (n = 154 of 297 classified with pain, 51.8%). Participants concordantly classified as having pain according to the GENOA CRF and BPI were found to have an estimated 7.79% increase in positive opioid urine screens (estimated coefficient: 7.79; 95% CI 0.74, 14.85: P = 0.031) and a 4 times greater odds (odds ratio [OR]: 4.10 P = 0.008; 95% CI: 1.44, 11.63) of engaging in a "high risk" level of illicit opioids use. The prognostic relevance of pain classification was not maintained for the additional participants classified by the BPI (n = 143 discordant). CONCLUSION: These results suggest that while the BPI may be more sensitive in capturing pain among patients with opioid addiction, this tool is of less value for predicting the impact of pain on illicit opioid use for opioid addictionpatients on methadone maintenance treatment. The GENOA CRF showed high predictive ability, whereby patients classified according to the GENOA CRF are at serious risk for opioid relapse. Using the appropriate tool to assess pain in opioid addiction may serve to improve the current detection and management of comorbid pain. LIMITATIONS: We caution the interpretation of these result since they are still reflective of participants already maintained on an opioid substitution therapy (OST), which can largely differ from patients who drop out of methadone maintenance treatment (MMT) or never seek treatment altogether.
Authors: Tea Rosic; Leen Naji; Balpreet Panesar; Darren B Chai; Nitika Sanger; Brittany B Dennis; David C Marsh; Launette Rieb; Andrew Worster; Lehana Thabane; Zainab Samaan Journal: BMJ Open Date: 2021-01-12 Impact factor: 2.692
Authors: Caridad Ponce Martinez; Karlyn A Edwards; Corey R Roos; Mark Beitel; Anthony Eller; Declan T Barry Journal: Clin J Pain Date: 2020-09 Impact factor: 3.423