Predictors of new persistent opioid use after surgery in adults

作者：Kathryn H. Gessner, John S. Preisser, Emily Pfaff, Rujin Wang, Kellie M Walters, Robert L. Bradford, Marshall Clark, Mark Ehlers, Matthew E. Nielsen · 发表于：Anesthesiology and Perioperative Science · 年份：2025 · DOI：10.1007/s44254-024-00083-1 · 被引用次数：5 · 研究领域：Opioid Use Disorder Treatment、Anesthesia and Pain Management、Pain Management and Opioid Use

Purpose: Persistent opioid use is one of the most common post-operative complications. Identification of at-risk patients pre-operatively is key to reducing post-operative opioid use. We sought to develop a predictive model for persistent post-operative opioid used and to determine if geographic factors from community databases improve model prediction based solely on electronic health records (EHRs) and claims data. Methods: EHR and claims data for 4,116 opioid-naïve surgical patients older than 18 in North Carolina were linked with census tract-level unemployment data from the American Community Survey and Centers for Disease Control and Prevention data on opioid prescriptions and deaths attributed to drug poisoning. Primary outcome was new persistent opioid use and covariates included patient factors from EHR, claims data, and geographic factors. Multivariable logistic regression models of potential risk factors were evaluated. Results: 6.0% of patients developed new persistent opioid use. Associated risk factors based on multivariable logistic regressions include age (adjusted odds ratio [AOR] 1.08; 95% confidence interval [CI] 1.00, 1.16), back and neck pain (1.82; 1.39, 2.39), joint disorders (1.58; 1.18, 2.11), mood disorders (1.71; 1.28, 2.28), opioid retail prescription (1.04; 1.00, 1.07) and drug poisoning rates (1.33; 1.09, 1.62). On Monte-Carlo cross-validation, the addition of geographic factors to EHRs and claims may modestly improve prediction performance (area...