Pain Medicine最新文献

Objective: To evaluate the effectiveness of genicular nerve radiofrequency ablation (GnRFA) for chronic knee pain due to osteoarthritis or persistent post-surgical knee pain (PPSP).

Methods: Population: Adults ≥ 18 years with chronic knee pain due to osteoarthritis (OA) or PPSP. Intervention: GnRFA. Comparison: Sham, placebo, active treatments, or no comparator. Outcomes: Proportion of individuals with pain score reductions of ≥50% or ≥2 points or ≥30% improvement in functional measures at 1, 3, 6, 12, 18, and 24 months. Search strategy and risk of bias assessment: Ovid MEDLINE, EMBASE, Web of Science, and Cochrane Library were searched through April 2024 (PROSPERO ID CRD42024552068). Cochrane Risk of Bias 2, Risk of Bias In Non-Randomized Studies-of Interventions and National Heart, Lung, and Blood Institute quality assessment tools were used accordingly.

Results: The search identified 1849 records, with 226 full-texts reviewed and 28 studies included (11 randomized controlled trials and 17 observational studies, totaling 2218 participants). Pooled success rates for ≥50% pain reduction in both OA and PPSP were 51% (95% CI: 49%-54%) at 6 months, 43% (95% CI: 40%-47%) at 12 months, and 58% (95% CI: 48%-67%) at 24 months. Large lesions showed higher pooled success rates compared to small lesions at 12 months (55% (95%CI: 51%-59%) vs 34% (95%CI: 26%-43%)).

Conclusions: GnRFA is effective in reducing knee pain in the majority of patients with osteoarthritis when large lesion techniques are used with moderate-certainty evidence, according to GRADE. Alternatively, there is low quality evidence that GnRFA results in treatment benefit for individuals with PPSP. These conclusions, however, are limited by small subgroup sample sizes and the lack of a meta-analysis.

Objective: The transition from hospital to home is a high-risk period for medication errors, particularly in patients receiving opioids. We constructed and validated a medication deviation risk prediction (MDRP) model in patients with cancer pain during the hospital-to-home transition.

Methods: The medication deviation assessment table was constructed to determine whether there was a medication deviation in the MDRP modeling group. Univariate analysis and logistic regression were used to analyze influencing factors. The model's goodness of predictive effect was tested with the Hosmer-Lemeshow (H-L) test and receiver operating characteristic (ROC) curves. External validation was performed with the same methods, and a simple risk scoring scale was developed.

Results: In the modeling group, 33.33% (51/153) had medication deviation, while 66.67% (102/153) had no medication deviation. Brief Pain Inventory score, number of comorbidities, presence of long-term caregivers, medication adherence, and presence of anxiety/depression were the 5 independent influencing factors in the construction of the MDRP model (P < .05). The H-L test yielded P = .402, and the area under the ROC curves (AUC) was 0.875, with sensitivity at 0.765 and specificity at 0.882. The validation group results were consistent with the modeling group. A simple risk scoring scale was developed, with a total score of 6, a cutoff value of 4, and an AUC of 0.886. The predictive accuracy of the scoring scale was 86.90%.

Conclusion: The MDRP model for patients with cancer pain had high sensitivity and specificity. The simple risk scoring scale was convenient and practical in clinical practice.

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).