A machine learning algorithm performs well in predicting the risk of persistent opioid use after hand surgery, reports a study in the August issue of Plastic and Reconstructive Surgery®, the official medical journal of the American Society of Plastic Surgeons (ASPS).
Two machine learning models tested to predict persistent opioid use
The study evaluated two previously described machine learning models: one using patient-reported data from the Michigan Genomics Initiative (MGI) and one based on insurance claims data. The models were first evaluated in a large sample of general surgery patients, then in patients undergoing hand surgery, such as carpal tunnel or wrist fracture surgery.
The study focused on whether the machine learning models could predict which patients would develop persistent opioid use, based on prescriptions filled up to six months after surgery. The MGI model included 889 patients, about half of whom had previous opioid use. The claims model was limited to 439 “opioid-naive” patients, without recent opioid use.
In the MGI model, which included previous opioid users, 21% of patients developed persistent opioid use. In the insurance claims model, which excluded previous opioid users, 10% of patients had persistent opioid use.
On “area under the curve” analysis, the MGI model performed very well in identifying patients with persistent opioid use: 84% in the model trained on hand surgery data and 85% in the general surgery population. By contrast, in the claims model, predictive ability was 69% based on hand surgery data and only 52% in the full data set.
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