Kara E Rudolph1, Iván Díaz2, Sean X Luo3, John Rotrosen4, Edward V Nunes3. 1. Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, United States. Electronic address: kr2854@cumc.columbia.edu. 2. Division of Biostatistics, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States. 3. Department of Psychiatry, School of Medicine, Columbia University, and New York State Psychiatric Institute, New York, NY, United States. 4. Department of Psychiatry, School of Medicine, New York University, New York, NY, United States.
Abstract
BACKGROUND: Relapse rates during opioid use disorder (OUD) treatment remain unacceptably high. It is possible that optimally matching patients with medication type would reduce risk of relapse. Our objective was to learn a rule by which to assign type of medication for OUD to reduce risk of relapse, and to estimate the extent to which risk of relapse would be reduced if such a rule were used. METHODS: This was a secondary analysis of an open-label randomized controlled, 24-week comparative effectiveness trial of injection extended-release naltrexone (XR-NTX), delivered approximately every 28 days, or daily sublingual buprenorphine-naloxone (BUP-NX) for treating OUD, 2014-2017 (N = 570). Outcome was a binary indicator of relapse to regular opioid use during the 24 weeks of outpatient treatment. RESULTS: We found that applying an estimated individualized treatment rule-i.e., a rule that assigns patients with OUD to either XR-NTX or BUP-NX based on their individual characteristics in such a way that risk of relapse is minimized-would reduce risk of relapse by 24 weeks by 12% compared to randomly assigned treatment. CONCLUSIONS: The number-needed-to-treat with the estimated treatment rule to prevent a single relapse is 14. A simpler, alternative estimated rule in which homeless participants would be treated with XR-NTX and stably housed participants would be treated with BUP-NX performed similarly. These results provide an estimate of the amount by which a relatively simple change in clinical practice could be expected to improve prevention of OUD relapse.
BACKGROUND: Relapse rates during opioid use disorder (OUD) treatment remain unacceptably high. It is possible that optimally matching patients with medication type would reduce risk of relapse. Our objective was to learn a rule by which to assign type of medication for OUD to reduce risk of relapse, and to estimate the extent to which risk of relapse would be reduced if such a rule were used. METHODS: This was a secondary analysis of an open-label randomized controlled, 24-week comparative effectiveness trial of injection extended-release naltrexone (XR-NTX), delivered approximately every 28 days, or daily sublingual buprenorphine-naloxone (BUP-NX) for treating OUD, 2014-2017 (N = 570). Outcome was a binary indicator of relapse to regular opioid use during the 24 weeks of outpatient treatment. RESULTS: We found that applying an estimated individualized treatment rule-i.e., a rule that assigns patients with OUD to either XR-NTX or BUP-NX based on their individual characteristics in such a way that risk of relapse is minimized-would reduce risk of relapse by 24 weeks by 12% compared to randomly assigned treatment. CONCLUSIONS: The number-needed-to-treat with the estimated treatment rule to prevent a single relapse is 14. A simpler, alternative estimated rule in which homeless participants would be treated with XR-NTX and stably housed participants would be treated with BUP-NX performed similarly. These results provide an estimate of the amount by which a relatively simple change in clinical practice could be expected to improve prevention of OUD relapse.
Authors: Roger D Weiss; Jennifer Sharpe Potter; Margaret L Griffin; Scott E Provost; Garrett M Fitzmaurice; Katherine A McDermott; Emily N Srisarajivakul; Dorian R Dodd; Jessica A Dreifuss; R Kathryn McHugh; Kathleen M Carroll Journal: Drug Alcohol Depend Date: 2015-03-06 Impact factor: 4.492
Authors: Yih-Ing Hser; David Huang; Andrew J Saxon; George Woody; Andrew L Moskowitz; Abigail G Matthews; Walter Ling Journal: J Addict Med Date: 2017 Jan/Feb Impact factor: 3.702