A Risk Prediction Model for Long-term Prescription Opioid Use. Issue 12 (December 2021)

• Record Type: Journal Article
• Authors: Tseregounis, Iraklis E.; Tancredi, Daniel J.; Stewart, Susan L.; Shev, Aaron B.; Crawford, Andrew; Gasper, James J.; Wintemute, Garen; Marshall, Brandon D.L.; Cerdá, Magdalena; Henry, Stephen G.
• Is Part Of: Medical care. Volume 59:Issue 12(2021)
• Publication Date: 2021-12
• Subjects: Prescription Drug Monitoring Program -- opioid-naive -- long-term opioid use -- risk prediction -- dose trajectory -- opioid analgesic; Economics, Medical -- Periodicals; Insurance, Health -- Periodicals; Santé, Services de -- Administration -- Périodiques; Soins médicaux -- Périodiques; Medical economics -- Periodicals; Health insurance -- Periodicals; Medical economics -- United States -- Periodicals; Health insurance -- United States -- Periodicals; Comprehensive Health Care -- Periodicals; Personal Health Services -- Periodicals; Gezondheidszorg; Économie de la santé -- Périodiques; Santé, Services de -- Périodiques; Health insurance; Medical economics; United States; Periodicals; 362.10973
• DOI: 10.1097/MLR.0000000000001651
• Languages: English
• ISSNs: 0025-7079

Abstract

Abstract : Background: Tools are needed to aid clinicians in estimating their patients' risk of transitioning to long-term opioid use and to inform prescribing decisions. Objective: The objective of this study was to develop and validate a model that predicts previously opioid-naive patients' risk of transitioning to long-term use. Research Design: This was a statewide population-based prognostic study. Subjects: Opioid-naive (no prescriptions in previous 2 y) patients aged 12 years old and above who received a pill-form opioid analgesic in 2016–2018 and whose prescriptions were registered in the California Prescription Drug Monitoring Program (PDMP). Measures: A multiple logistic regression approach was used to construct a prediction model with long-term (ie, >90 d) opioid use as the outcome. Models were developed using 2016–2017 data and validated using 2018 data. Discrimination ( c -statistic), calibration (calibration slope, intercept, and visual inspection of calibration plots), and clinical utility (decision curve analysis) were evaluated to assess performance. Results: Development and validation cohorts included 7, 175, 885 and 2, 788, 837 opioid-naive patients with outcome rates of 5.0% and 4.7%, respectively. The model showed high discrimination ( c -statistic: 0.904 for development, 0.913 for validation), was well-calibrated after intercept adjustment (intercept, −0.006; 95% confidence interval, −0.016 to 0.004; slope, 1.049; 95% confidence interval, 1.045–1.053), and had a net benefit over a wide range of probability thresholds. Conclusions: A model for the transition from opioid-naive status to long-term use had high discrimination and was well-calibrated. Given its high predictive performance, this model shows promise for future integration into PDMPs to aid clinicians in formulating opioid prescribing decisions at the point of care. Abstract : Supplemental Digital Content is available in the text.