• Aurora Quaye, John DiPalazzo, Kristin Kostka, Janelle M Richard, Blaire Beers-Mulroy, Meredith Peck, Robert Krulee, and Yi Zhang.
• Department of Anesthesiology and Perioperative Medicine, Maine Medical Center, Portland, ME, United States.
• Pain Med. 2024 Nov 20.

ObjectiveTo identify predictors of persistent opioid use in opioid-naïve individuals undergoing total joint arthroplasty.DesignRetrospective cohort study.SettingMaine Health System.SubjectsOpioid-naïve patients who underwent at least one total joint arthroplasty (knee, hip, or shoulder) between 2015 and 2020.MethodsLeast Absolute Shrinkage and Selection Operator (LASSO) logistic regression was used to create a predictive model for persistent opioid use after surgery from a US Electronic Health Record dataset in the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) format. 75% of the data was used to build the LASSO model using 10-fold cross-validation and 25% of the data was used to determine the optimal probability threshold for predicting the binary outcome.ResultsOut of 6432 patients, 12.3% (792) were identified as having persistent opioid use across combined total joint arthroplasties defined as at least one opioid prescription between 90 days and one year after surgery. Patients with persistent opioid use were more likely to be current smokers (OR 1.65), use antidepressants (OR 1.76), or have a diagnosis of post-traumatic stress disorder (OR 2.07), or a substance related disorder (OR 1.69). Other factors associated with persistent opioid use included back pain (OR 1.43), dementia (OR 1.65), and BMI over 40 (OR 2.50). The probability of persistent opioid use was not associated with age, sex, or ethnicity.ConclusionsThis predictive model for persistent opioid use after total joint arthroplasty shows promise as an evidence-based, validated, and standardized tool for identifying high-risk patients before surgery in order to target strategies and interventions to reduce the reliance on opioids for post-operative pain control.© The Author(s) 2024. Published by Oxford University Press on behalf of the American Academy of Pain Medicine. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

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