Pain
-
Randomized clinical trials have demonstrated the efficacy of opioid analgesics for the treatment of acute and chronic pain conditions, and for some patients, these medications may be the only effective treatment available. Unfortunately, opioid analgesics are also associated with major risks (eg, opioid use disorder) and adverse outcomes (eg, respiratory depression and falls). The risks and adverse outcomes associated with opioid analgesics have prompted efforts to reduce their use in the treatment of both acute and chronic pain. ⋯ These recommendations are based on the results of a background review, presentations and discussions at an IMMPACT consensus meeting, and iterative drafts of this article modified to accommodate input from the co-authors. We discuss opioid sparing definitions, study objectives, outcome measures, the assessment of opioid-related adverse events, incorporation of adequate pain control in trial design, interpretation of research findings, and future research priorities to inform opioid-sparing trial methods. The considerations and recommendations presented in this article are meant to help guide the design, conduct, analysis, and interpretation of future trials.
-
Chronic postsurgical pain (CPSP) affects an estimated 10% to 50% of adults depending on the type of surgical procedure. Clinical prediction models can help clinicians target preventive strategies towards patients at high risk for CPSP. Therefore, the objective of this systematic review was to identify and describe existing prediction models for CPSP in adults. ⋯ The most common predictors identified in final prediction models included preoperative pain in the surgical area, preoperative pain in other areas, age, sex or gender, and acute postsurgical pain. Clinical prediction models may support prevention and management of CPSP, but existing models are at high risk of bias that affects their reliability to inform practice and generalizability to wider populations. Adherence to standardized guidelines for clinical prediction model development is necessary to derive a prediction model of value to clinicians.