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A Personalized Opioid Prescription Model for Predicting Postoperative Discharge Opioid Needs
Kevin Blum, PhD; Kevin Zhang; Jacqueline J. Chu; Roman Skoracki, MD; Jeffrey E. Janis, MD; Jenny C. Barker MD, PhD
Ohio State University
2022-01-15
Presenter: Kevin Blum
Affidavit:
This work is 100% the original work of the corresponding author Jenny C. Barker and the medical student and faculty co-author team listed. Kevin Blum, specifically completed the work for the algorithm used in this study and executed data analysis and statistical approach.
Director Name: Gregory Pearson
Author Category: Medical Student
Presentation Category: Clinical
Abstract Category: General Reconstruction
Background: Opioid overprescribing after surgery is common. There is currently no universal predictive tool available to accurately anticipate post-discharge opioid need in a patient-specific manner. This study examined the efficacy of a patient-specific opioid prescribing framework for estimating post-discharge opioid consumption.
Methods: A total of 149 patients were evaluated for a single-center retrospective cohort study of plastic and reconstructive surgery patients. Patients with length-of-stay of 2-8 days and quantifiable inpatient opioid consumption (n=116) were included. Each patient's daily postoperative inpatient opioid consumption was used to generate a personalized logarithmic regression model to estimate post-discharge opioid need. The validity of the Personalized Opioid Prescription (POP) model was tested through comparison with actual post-discharge opioid consumption reported by patients 4 weeks after surgery. The accuracy of the POP model was compared with two other opioid prescribing models.
Results: The POP model had the strongest association (R2 = 0.899, p<0.0001) between model output and post-discharge opioid consumption when compared to a Procedure-Based (R2 = 0.226, p = 0.025) or 24-Hour (R2 = 0.152, p = 0.007) models. Accuracy of the POP model was unaffected by age, gender identity, procedure type, or length-of-stay. Odds of persistent use at 4 weeks increased with post-discharge estimated opioid need at a rate of 1.16 per 37.5 OME (p=0.010, 95% CI 1.04–1.30).
Conclusions: The POP model accurately estimates post-discharge opioid consumption and risk of developing persistent use in plastic surgery patients. Use of the POP model in clinical practice may lead to more appropriate and personalized opioid prescribing.
