Abstract
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.
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Article info
Publication history
Published online: February 15, 2022
Accepted:
January 7,
2022
Identification
Copyright
© 2022 Elsevier Inc. All rights reserved.
