Pain Medicine最新文献

Objective: Strategies are needed for patients with chronic pain who are using opioids to safely and effectively wean opioids without worsening of pain. The objective was to measure associations between medical cannabis authorization (MCA) and opioid milligram equivalents (OME) in patients with chronic non-cancer pain.

Design: A longitudinal, retrospective cohort analysis from July 2016 to August 2019.

Setting: Electronic health record data were analyzed.

Subjects: Adult patients (≥18 years) seen in a university-based pain clinic.

Methods: Longitudinal multilevel modeling with maximum likelihood estimation.

Results: Average overall OME at the final time point was 33.4 mg/day (SE = 1.18) with increase over time of 0.45 mg/day per quarter (not statistically significant). Average OME in those without MCA was 32.60 mg/day (SE = 1.11) versus 38.51 mg/day (SE = 4.81) in those with MCA, not significantly different. Medical cannabis consultation predicted a nonsignificant decrease of 14.25 mg/day OME. Long-term opioid use was a significant predictor with a mean OME of 85.34 mg/day, 63 mg/day higher than the rest of the cohort at the final quarter (t = 5.77, SE = 10.93, P < 0.0001).

Conclusions: In this longitudinal study of electronic health record data, MCA was not associated with a statistically significant decrease in OME over time. However, patients with long-term opioid use diagnostic code demonstrated a significantly higher endpoint OME. Future prospective research is needed to establish whether there are opioid-sparing effects of cannabis in humans.

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.