Chrome Extension
WeChat Mini Program
Use on ChatGLM

Post-total joint arthroplasty opioid prescribing practices vary widely and are not associated with opioid refill: an observational cohort study

Archives of Orthopaedic and Trauma Surgery(2023)

Cited 1|Views5
No score
Abstract
Introduction Optimized health system approaches to improving guideline-congruent care require evaluation of multilevel factors associated with prescribing practices and outcomes after total knee and hip arthroplasty. Materials and methods Electronic health data from patients who underwent a total knee or hip arthroplasty between January 2016–January 2020 in the Military Health System Data were retrospectively analyzed. A generalized linear mixed-effects model (GLMM) examined the relationship between fixed covariates, random effects, and the primary outcome (30-day opioid prescription refill). Results In the sample ( N = 9151, 65% knee, 35% hip), the median discharge morphine equivalent dose was 660 mg [450, 892] and varied across hospitals and several factors (e.g., joint, race and ethnicity, mental and chronic pain conditions, etc.). Probability of an opioid refill was higher in patients who underwent total knee arthroplasty, were white, had a chronic pain or mental health condition, had a lower age, and received a presurgical opioid prescription (all p < 0.01). Sex assigned in the medical record, hospital duration, discharge non-opioid prescription receipt, discharge morphine equivalent dose, and receipt of an opioid-only discharge prescription were not significantly associated with opioid refill. Conclusion In the present study, several patient-, care-, and hospital-level factors were associated with an increased probability of an opioid prescription refill within 30 days after arthroplasty. Future work is needed to identify optimal approaches to reduce unwarranted and inequitable healthcare variation within a patient-centered framework.
More
Translated text
Key words
Opioids,Total knee arthroplasty,Total hip arthroplasty,Pain management,Health services research
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined