Predicting persistent opioid use after surgery using electronic health record and patient-reported data

Background: More than 100 million surgeries take place annually in the United States, and more than 90% of surgical patients receive an opioid prescription. A sizable minority of these patients will go on to use opioids long-term, contributing to the national opioid epidemic. Methods: The objective of this study was to develop and validate a model to predict persistent opioid use after surgery. Participants included surgical patients (>18 years old) enrolled in a cohort study at an academic medical center between 2015 and 2018. Persistent opioid use was defined as filling opioid prescriptions in postdischarge days 4 to 90 and 91 to 180. Predictors included electronic health record data, state prescription drug monitoring data, and patient-reported measures. Three models were developed: a full, a restricted, and a minimal model using a derivation and validation cohort. Results: Of 24,040 patients, 4,879 (20%) experienced persistent opioid use. In the validation cohort, the full, restricted, and minimal model had C-statistics of 0.87 (95% CI 0.86-0.88), 0.86 (0.85-0.88), and 0.85 (0.84-0.87), respectively. All models performed better among patients with preoperative opioid use compared to opioid-naive patients (P < .001). The models slightly overpredicted risk in the validation cohort. The net benefit of using the restricted model to refer patients for preoperative counseling was 0.072 to 0.092, which is superior to evaluating no patients (net benefit of 0) or all patients (net benefit of-0.22 to-0.63). Conclusion: This study developed and validated a prediction model for persistent opioid use using accessible data resources. The models achieved strong performance, outperforming prior published models. (c) 2022 Elsevier Inc. All rights reserved.