Pain Medicine最新文献

Objective: Assessing opioid use disorder risk in patients prescribed long-term opioid therapy for management of chronic non-cancer pain is critical for prevention and early intervention.

Design: Case-control study.

Setting: Pain management and primary care clinics, and substance use treatment facilities.

Subjects: Participants are 1300 patients with chronic non-cancer pain (59.68% women; mean age = 49.03 years), 409 of whom developed opioid use disorder.

Methods: We compared the performance of 3 machine learning models that used the Opioid Risk Tool for Opioid Use Disorder alone with those that incorporated an expanded set of clinical predictors.

Results: The Opioid Risk Tool for Opioid Use Disorder showed strong performance (precision = 0.91; specificity = 0.96). Models that incorporated additional predictors showed improved performance on precision-recall area under the curve and F1 scores, particularly the random forest and eXtreme Gradient Boosting models. Aside from the Opioid Risk Tool for Opioid Use Disorder, the most important features in the expanded models were nicotine dependence, marital status, opioid misuse behaviors, and pain interference and catastrophizing.

Conclusions: A stepwise approach that employs the Opioid Risk Tool for Opioid Use Disorder as a preliminary screener followed by a more in-depth assessment of clinical predictors among high-risk individuals may offer a feasible strategy to optimize efficiency and precision in risk stratification. Future work should refine and validate this framework in diverse population and care settings, as well as examine its integration into clinical workflow to enhance the identification of chronic non-cancer pain patients at risk for opioid use disorder.

Objective: The use of cannabis and cannabidiol (CBD) as alternatives to opioids for managing postoperative pain has gained increasing interest, especially in orthopedic surgical contexts, where opioid dependence remains a pressing concern. This scoping review evaluates experimental studies published from 2014 to 2025 that investigated the efficacy and safety of cannabis or CBD products in managing postoperative orthopedic pain.

Design: Scoping review.

Methods: A total of 14 experimental studies met the inclusion criteria and were categorized by cannabinoid composition (CBD only, tetrahydrocannabinol [THC] only, or CBD/THC combination).

Results: Whereas CBD-only interventions showed mixed results, THC/CBD combinations demonstrated modest potential for opioid-sparing effects, with neutral safety profiles. One THC-only study reported increased opioid use and length of stay, though confounding variables were present.

Conclusions: Overall, the heterogeneity in study design, cannabinoid formulation, dosing, and patient factors limits significant conclusions. There is a critical need for standardized, prospective clinical trials to better evaluate the potential of cannabinoids in the postoperative period after orthopedic surgery.

Objective: To systematically evaluate existing approaches for identifying opioid use disorder (OUD) in administrative data sets and develop evidence-based recommendations for standardized identification methods.

Design: Systematic review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Scoping Review guidelines with comprehensive literature search and evidence synthesis for framework development.

Setting: Administrative data sets including commercial claims, Medicaid, Medicare, and electronic health records.

Subjects: In brief, 169 studies using administrative codes to identify OUD, primarily from US healthcare systems (94.7%).

Methods: Systematic search of EMBASE, MEDLINE, Google Scholar, and PubMed through February 2024. Three independent reviewers screened articles and extracted data using standardized tools. Study quality was assessed using modified Newcastle-Ottawa Scale. Framework development employed systematic integration of evidence-based components from high-quality studies.

Results: Our analysis of 169 studies revealed four distinct identification approaches: Direct diagnosis codes (36.7%), composite definitions (48.0%), overdose codes (10.1%), and medication-assisted treatment codes (1.2%). Commercial claims data predominated (60.4%), followed by Medicaid claims (10.1%) and electronic health records (7.7%). Multi-modal strategies incorporating both diagnostic and treatment codes showing superior theoretical foundation compared to single-method approaches. Substantial variation existed in reference periods, code requirements, and treatment verification approaches.

Conclusions: An evidence-based framework incorporating diagnosis codes, specific temporal requirements, validated indirect indicators, and treatment evidence provides theoretical foundation for standardized OUD identification protocols. The framework addresses known sources of misclassification while maintaining diagnostic specificity through clinical diagnostic alignment and systematic validation research programs.

Registration: Prospero (CRD42023406173) and OSF (osf.io/ru4j3).