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Predicting Persistent Opioid Use after Hand Surgery: A Machine Learning Approach.

Natalie B Baxter¹, Allister Z Ho², Jacqueline N Byrd^3,4

¹University of Michigan Medical School.

Plastic and Reconstructive Surgery

|September 29, 2023

Summary

This summary is machine-generated.

Machine learning models can predict persistent opioid use after hand surgery. The Michigan Genomics Initiative model showed strong predictive performance, aiding early intervention for opioid addiction prevention.

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Area of Science:

Orthopedic Surgery
Data Science
Pharmacology

Background:

Persistent opioid use is a significant concern following hand surgery.
Identifying at-risk patients is crucial for preventing opioid addiction.

Purpose of the Study:

To evaluate machine learning algorithms for predicting persistent opioid use in hand surgery patients.
To compare the performance of different machine learning models.

Main Methods:

Two algorithms (Michigan Genomics Initiative and claims model) were trained using general and hand surgery datasets.
Models were tested on hand surgery patient data (n=889) from 2015-2018.
Model discrimination was measured using the area under the receiver operating curve (AUC).

Main Results:

The Michigan Genomics Initiative model achieved AUCs of 0.84 (hand surgery data) and 0.85 (full cohort).
The claims model demonstrated lower performance with AUCs of 0.69 (hand surgery data) and 0.52 (opioid-naive cohort).
21% of hand surgery patients developed persistent opioid use.

Conclusions:

Machine learning, particularly the Michigan Genomics Initiative model, shows promise in identifying patients at risk for persistent opioid use after hand surgery.
Early identification can facilitate interventions to prevent opioid addiction.