A Personalized Opioid Prescription Model for Predicting Postoperative Discharge Opioid Needs

Area of Science:

• Plastic Surgery
• Pain Management
• Pharmacology

Background:

• Opioid overprescribing after surgery is a significant clinical challenge.
• Current methods lack patient-specific prediction of postdischarge opioid requirements.
• There is a need for accurate tools to guide opioid prescribing.

Purpose of the Study:

• To evaluate a patient-specific opioid prescribing framework for estimating postdischarge opioid consumption.
• To assess the accuracy of a personalized opioid prescription (POP) model.

Main Methods:

• A retrospective cohort study included 116 plastic and reconstructive surgery patients.
• A personalized logarithmic regression model was generated using daily inpatient opioid consumption.
• The POP model's predictions were compared against actual postdischarge opioid use and other prescribing models.

Main Results:

• The POP model demonstrated a strong association (R2 = 0.899) with actual postdischarge opioid consumption.
• POP model accuracy was not influenced by patient demographics or procedure type.
• Increased estimated postdischarge opioid need correlated with higher odds of persistent opioid use at 4 weeks.

Conclusions:

• The POP model provides accurate estimations of postdischarge opioid consumption in plastic surgery patients.
• The model effectively predicts the risk of developing persistent opioid use.
• Clinical implementation of the POP model can optimize opioid prescribing practices.