A Personalized Opioid Prescription Model for Predicting Postoperative Discharge Opioid Needs.

Publication/Presentation Date

2-1-2023

Abstract

BACKGROUND: Opioid overprescribing after surgery is common. There is currently no universal predictive tool available to accurately anticipate postdischarge opioid need in a patient-specific manner. This study examined the efficacy of a patient-specific opioid prescribing framework for estimating postdischarge 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 to 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 postdischarge opioid need. The validity of the personalized opioid prescription (POP) model was tested through comparison with actual postdischarge 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 postdischarge opioid consumption when compared to a procedure-based (R2 = 0.226; P = 0.025) or a 24-hour (R2 = 0.152; P = 0.007) model. 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 a postdischarge estimated opioid need at a rate of 1.16 per 37.5 oral morphine equivalents (P = 0.010; 95% CI, 1.04 to 1.30).

CONCLUSIONS: The POP model accurately estimates postdischarge 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.

Volume

151

Issue

2

First Page

450

Last Page

460

ISSN

1529-4242

Disciplines

Medicine and Health Sciences

PubMedID

36696335

Department(s)

Fellows and Residents

Document Type

Article

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