Optimizing opioid use disorder treatment with naltrexone or buprenorphine.


Journal

Drug and alcohol dependence
ISSN: 1879-0046
Titre abrégé: Drug Alcohol Depend
Pays: Ireland
ID NLM: 7513587

Informations de publication

Date de publication:
01 11 2021
Historique:
received: 26 05 2021
revised: 26 07 2021
accepted: 27 07 2021
pubmed: 18 9 2021
medline: 15 12 2021
entrez: 17 9 2021
Statut: ppublish

Résumé

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. 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. 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. 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.

Sections du résumé

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.

Identifiants

pubmed: 34534863
pii: S0376-8716(21)00526-3
doi: 10.1016/j.drugalcdep.2021.109031
pmc: PMC8595679
mid: NIHMS1741410
pii:
doi:

Substances chimiques

Delayed-Action Preparations 0
Narcotic Antagonists 0
Buprenorphine 40D3SCR4GZ
Naltrexone 5S6W795CQM

Types de publication

Journal Article Randomized Controlled Trial Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

109031

Subventions

Organisme : NIDA NIH HHS
ID : R00 DA042127
Pays : United States

Informations de copyright

Copyright © 2021 Elsevier B.V. All rights reserved.

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Auteurs

Kara E Rudolph (KE)

Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, United States. Electronic address: kr2854@cumc.columbia.edu.

Iván Díaz (I)

Division of Biostatistics, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States.

Sean X Luo (SX)

Department of Psychiatry, School of Medicine, Columbia University, and New York State Psychiatric Institute, New York, NY, United States.

John Rotrosen (J)

Department of Psychiatry, School of Medicine, New York University, New York, NY, United States.

Edward V Nunes (EV)

Department of Psychiatry, School of Medicine, Columbia University, and New York State Psychiatric Institute, New York, NY, United States.

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Classifications MeSH