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Pharmacopsychiatry 2014; 47(07): 251-258
DOI: 10.1055/s-0034-1390467
Original Paper
© Georg Thieme Verlag KG Stuttgart · New York

Modeling Longitudinal Changes in Buprenorphine Treatment Outcome for Opioid Dependence

M. I. Saleh
1   Faculty of Pharmacy, The University of Jordan, Amman, Jordan
› Author Affiliations
Further Information

Publication History

revised 18 August 2014

accepted 03 September 2014

Publication Date:
16 October 2014 (online)

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Abstract

Introduction: The present analysis describes the longitudinal change in buprenorphine treatment outcome. It also examines several participant characteristics to predict response to buprenorphine.

Methods: Participants (n=501, age>15 years) received buprenorphine/naloxone treatment for 4 weeks, and then were randomly assigned to undergo dose tapering over either 7 days or 28 days. An empirical model was developed to describe the longitudinal changes in treatment outcome. Several patient characteristics were also examined as possible factors influencing treatment outcome.

Results: We have developed a model that captures the general behavior of the longitudinal change in the probability of having an opioid-negative urine sample following buprenorphine treatment. The model captures both the initial increase (i. e., initial response) and the subsequent decrease (i. e., relapse to opioid) in the likelihood of providing an opioid-negative urine sample. Characteristics associated with successful buprenorphine treatment outcome include: having a negative urine test for drugs, having alcohol problems [assessed using alcohol domain of addiction severity index (ASI-alcohol)] at screening, being older, and receiving low cumulative buprenorphine dose. However, ASI-alcohol values were generally low which make the application of the proposed alcohol effect for patients with more severe alcohol problems questionable.

Conclusions: A novel approach for analyzing buprenorphine treatment outcome is presented in this manuscript. This approach describes the longitudinal change in the probability of providing an opioid-free urine sample instead of considering opioid use outcome at a single time point. Additionally, this model successfully describes relapse to opioid. Finally, several patient characteristics are identified as predictors of treatment outcome.

Key words

opioid - detoxification - predictors - opiates - buprenorphine - modeling - longitudinal study
 

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