Pharmacological and behavioral pain treatment strategies for patients with opioid use disorder
Abstract
Introduction: A critical challenge in providing effective medical care for individuals in opioid agonist treatment (OAT) for opioid use disorder is the effective management of acute and chronic pain. While pain commonly co-occurs with opioid use disorder, there is limited research to guide effective management of pain in this population.
Areas covered: We first provide an overview of the etiology and treatment of acute and chronic pain, highlighting areas of complexity for patients receiving OAT. We then describe the search strategy, which involved a date-inclusive search for relevant terms in PubMed, EMBASE, and the Cochrane Central Register of Controlled Trials. After summarizing the results of this search on the evidence for pharmacological and behavioral treatments of acute and chronic pain for individuals on OAT, we conclude with a discussion of these findings and a summarized expert opinion on the state of the evidence.
Expert opinion: The evidence suggests that while research on effective treatment of acute and chronic pain in individuals in OAT is limited, promising work is ongoing to translate existing treatments, particularly behavioral treatments for chronic pain, to support this population. However, further research is warranted, particularly regarding pharmacological options.
Read the paper
Related studies
- Noninvasive and Point-of-Care Surface-Enhanced Raman Scattering (SERS)-Based Breathalyzer for Mass Screening of Coronavirus Disease 2019 (COVID-19) under 5 min
- De Novo Transcriptome Analysis and Detection of Antimicrobial Peptides of the American Cockroach Periplaneta americana (Linnaeus)
- Molecular Characterization and Expression Profiling of Tomato GRF Transcription Factor Family Genes in Response to Abiotic Stresses and Phytohormones
- Assembly of Tight Junction Strands: Claudin-10b and Claudin-3 Form Homo Tetrameric Building Blocks that Polymerise in a Channel-Independent Manner
- Update of the Pompe variant database for the prediction of clinical phenotypes: Novel disease‐associated variants, common sequence variants, and results from newborn screening
- Machine Learning-Based SERS Chemical Space for Two-Way Prediction of Structures and Spectra of Untrained Molecules
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.