Predicting postoperative opioid use with machine learning and insurance claims in opioid-naive patients.

ABSTRACT:

Background

The clinical impact of postoperative opioid use requires accurate prediction strategies to identify at-risk patients. We utilize preoperative claims data to predict postoperative opioid refill and new persistent use in opioid-naïve patients.

Methods

A retrospective study was conducted on 112,898 opioid-naïve adult postoperative patients from Optum's de-identified Clinformatics® Data Mart database. Potential predictors included sociodemographic data, comorbidities, and prescriptions within one year prior to surgery.

Results

Compared to linear models, non-linear models led to modest improvements in predicting refills - area under the receiver operating characteristics curve (AUROC) 0.68 vs. 0.67 (p < 0.05) - and performed identically in predicting new persist

SUBMITTER: Hur J 

PROVIDER: S-EPMC8373633 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature