A Personalized Opioid Prescription Model for Predicting Postoperative Discharge Opioid Needs | Journal Article CME:-Journal Article CME: A Personalized Opioid Prescription Model for Predicting Postoperative Discharge Opioid Needs Article

Journal Article CME: A Personalized Opioid Prescription Model for Predicting Postoperative Discharge Opioid Needs Article

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The article addresses the opioid epidemic in the U.S., highlighting the risks of opioid overprescription, especially post-surgery. It focuses on the need for a personalized approach in prescribing opioids after surgical discharge, aiming to prevent opioid misuse and dependency. This study introduces a Personalized Opioid Prescription (POP) model that uses inpatient opioid consumption data from surgery patients to estimate their postdischarge opioid needs accurately.<br /><br />The study was conducted on 149 plastic and reconstructive surgery patients, with 116 patients fitting the complete criteria for analysis, including a 2- to 8-day hospital stay and quantifiable inpatient opioid consumption. The POP model utilizes a logarithmic regression formula based on each patient's daily inpatient opioid use to project postdischarge needs. The model's accuracy was tested against actual patient-reported opioid consumption four weeks after surgery and compared to two other existing models—a procedure-based model and a 24-hour model.<br /><br />Results showed the POP model had a strong correlation (R² = 0.899) between estimated and actual opioid consumption postdischarge, outperforming the other models. It remained consistent across variables such as age, gender identity, length of stay, procedure type, and previous opioid use. The study suggests that overprescription leads to increased consumption and indicates the potential for the POP model to identify patients at risk for persistent opioid use. It found a 16% increased risk of prolonged opioid use for every additional 37.5 oral morphine equivalents prescribed.<br /><br />The article suggests integrating the POP model into electronic health records to assist in precise, patient-specific opioid prescribing, potentially reducing the risks associated with opioid misuse. It acknowledges limitations, such as the exclusion of patients with shorter hospital stays and outpatient procedures, and calls for future prospective validations across various medical disciplines.

Keywords

opioid epidemic

overprescription

personalized approach

POP model

surgical discharge

opioid misuse

opioid dependency

plastic surgery

logarithmic regression

electronic health records