Use of prediction model may identify patients at high risk of opioid use after TKR
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Use of a prediction model with 10 clinical factors may identify patients undergoing total knee replacement who may be at high risk for future adverse outcomes from persistent opioid use and dependence, according to published results.
Seoyoung C. Kim, MD, ScD, MSCE, and colleagues used group-based trajectory modeling to identify distinct opioid use patterns among 142,089 patients aged 65 years and older from the Medicare claims database who underwent TKR with no history of high-dose opioid use in the year prior. Researchers considered persistent high-dose opioid use in the year after TKR as the primary outcome. Data were split into training and test sets, and researchers used logistic regression with least absolute shrinkage and selection operator regularization which utilized a total of 83 preoperative patient characteristics as candidate predictors. Researchers also considered a reduced model with 10 pre-specified variables, including demographics, opioid use and medication history.
Predictors of opioid use
Results showed four distinct trajectories of opioid use were identified through the group-based trajectory model, including short-term, low-dose use (group 1); moderate duration, low-dose use (group 2); moderate duration, high-dose use (group 3); and persistent high-dose use (group 4). Researchers found patients in group 4 were more likely to be younger, female, have higher baseline opioid use in the year prior to TKR, were more likely to use tobacco/alcohol and had a greater burden of comorbidities, such as depression, anxiety disorders, back pain and used multiple medications compared with group 1. Researchers also noted patients in group 4 were more likely to be “frail” and use health care services in general.
Baseline opioid use was the strongest positive predictor of persistent high-dose opioid use, while demographics, medication use, substance use and comorbidities were other variables selected in the final model, according to results. A reduced model with 10 investigator-specified predictors showed baseline opioid use, Black race and history of benzodiazepine use were the strongest predictors of persistent high-dose opioid use.
Probability of opioid use
Researchers found high discrimination in the model predicting persistent high-dose opioid use in the test set, as well as equal performance in the reduced model with 10 predictors.
“In the study of 142,089 Medicare enrollees with osteoarthritis who underwent total knee replacement surgery and had no history of high-dose opioid use, 10.6% became persistent users of high-dose opioids after surgery,” Kim told Healio Orthopedics. “We found certain preoperative characteristics including demographics (age, sex and race), history of substance abuse (opioids, alcohol and tobacco) and medication use (benzodiazepines, anxiolytics, antidepressants, anticonvulsants and NSAIDs) are predictors of persistent use of high-dose opioids after TKR. Our prediction model with 10 readily available clinical factors may help identify OA patients at high risk of future adverse outcomes from persistent opioid use and dependence after TKR.”
Perspective
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Opiate misuse persists as a significant medical and economic burden. Surgeons are compelled to take a more active role in prescription-driven misuse. Orthopedic procedures may indeed be painful. While opiate-free surgery has many appeals, an opiate-minimizing post-surgical plan should be normalized.
This study examines a large database cohort and through machine-based learning, created an algorithm to identify patients at risk for prolonged opiate use after knee replacement.
The authors confirmed baseline use of opiates and depression were significant risk factors. Use of benzodiazepines, anxiolytics and chronic pain were also risk factors for higher use. Racial differences, notably Black race, were also a strong risk factor for higher use. Future research should be directed at a better understanding, and strategies to mitigate these risk factors is warranted.
Chandrasekar Gopalakrishnan, MD, MPH, and colleagues are commended for developing this model and adding to the body of literature which helps to identify patients at risk for future adverse outcomes for persistent opioid use following total knee replacement.
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