Opioid reduction and enhanced recovery in orthopaedic surgery (OREOS): a protocol for a feasibility randomised controlled trial in patients undergoing total knee arthroplasty

BACKGROUND

Knee arthritis is a leading cause of limited function and long-term disability in older adults. Despite a technically successful total knee arthroplasty (TKA), around 20% of patients continue to have persisting pain with reduced function, and low quality of life. Many of them continue using opioids for pain control, which puts them at risk for potential long-term adverse effects such as dependence, overdose and risk of falls. Although persisting pain and opioid use after TKA have been recognised to be important issues, individual strategies to decrease their burden have limitations and multi-component interventions, despite their potential, have not been well studied. In this study, we propose a multi-component pathway including personalized pain management, facilitated by a pain management coordinator. The objectives of this pilot trial are to evaluate feasibility (recruitment, retention, and adherence), along with opioid-free pain control at 8 weeks after TKA.

METHODS

This is a protocol for a multicentre pilot randomised controlled trial using a 2-arm parallel group design. Adult participants undergoing unilateral total knee arthroplasty will be considered for inclusion and randomised to control and intervention groups. Participants in the intervention group will receive support from a pain management coordinator who will facilitate a multicomponent pain management pathway including (1) preoperative education on pain and opioid use, (2) preoperative risk identification and mitigation, (3) personalized post-discharge analgesic prescriptions and (4) continued support for pain control and recovery up to 8 weeks post-op. Participants in the control group will undergo usual care. The primary outcomes of this pilot trial are to assess the feasibility of participant recruitment, retention, and adherence to the interventions, and key secondary outcomes are persisting pain and opioid use.

DISCUSSION

The results of this trial will determine the feasibility of conducting a definitive trial for the implementation of a multicomponent pain pathway to improve pain control and reduce harms using a coordinated approach, while keeping an emphasis on patient centred care and shared decision making.

TRIAL REGISTRATION

Prospectively registered in Clinicaltrials.gov (NCT04968132).

The reporting quality and transparency of orthopaedic studies using Bayesian analysis requires improvement: A systematic review

Background

Bayesian methods are being used more frequently in orthopaedics. To advance the use and transparent reporting of Bayesian studies, reporting guidelines have been recommended. There is currently little known about the use or applications of Bayesian analysis in orthopedics including adherence to recommended reporting guidelines. The objective is to investigate the reporting of Bayesian analysis in orthopedic surgery studies; specifically, to evaluate if these papers adhere to reporting guidelines.

Methods

We searched PUBMED to December 2nd, 2020. Two reviewers independently identified studies and full-text screening. We included studies that focused on one or more orthopaedic surgical interventions and used Bayesian methods.

Results

After full-text review, 100 articles were included. The most frequent study designs were meta-analysis or network meta-analysis (56%, 95% CI 46-65) and cohort studies (25%, 95% CI 18-34). Joint replacement was the most common subspecialty (33%, 95% CI 25-43). We found that studies infrequently reported key concepts in Bayesian analysis including, specifying the prior distribution (37-39%), justifying the prior distribution (18%), the sensitivity to different priors (7-8%), and the statistical model used (22%). In contrast, general methodological items on the checklists were largely well reported.

Conclusions

There is an opportunity to improve reporting quality and transparency of orthopaedic studies using Bayesian analysis by encouraging adherence to reporting guidelines such as ROBUST, JASP, and BayesWatch. There is an opportunity to better report prior distributions, sensitivity analyses, and the statistical models used.