Prescription quantity and duration predict progression from acute to chronic opioid use in opioid-naïve Medicaid patients

Opiates used for acute pain are an established risk factor for chronic opioid use (COU). Patient characteristics contribute to progression from acute opioid use to COU, but most are not clinically modifiable. To develop and validate machine-learning algorithms that use claims data to predict progression from acute to COU in the Medicaid population, adult opioid naïve Medicaid patients from 6 anonymized states who received an opioid prescription between 2015 and 2019 were included. Five machine learning (ML) Models were developed, and model performance assessed by area under the receiver operating characteristic curve (auROC), precision and recall. In the study, 29.9% (53820/180000) of patients transitioned from acute opioid use to COU. Initial opioid prescriptions in COU patients had increased morphine milligram equivalents (MME) (33.2 vs. 23.2), tablets per prescription (45.6 vs. 36.54), longer prescriptions (26.63 vs 24.69 days), and higher proportions of tramadol (16.06% vs. 13.44%) and long acting oxycodone (0.24% vs 0.04%) compared to non- COU patients. The top performing model was XGBoost that achieved average precision of 0.87 and auROC of 0.63 in testing and 0.55 and 0.69 in validation, respectively. Top-ranking prescription-related features in the model included quantity of tablets per prescription, prescription length, and emergency department claims. In this study, the Medicaid population, opioid prescriptions with increased tablet quantity and days supply predict increased risk of progression from acute to COU in opioid-naïve patients. Future research should evaluate the effects of modifying these risk factors on COU incidence.Author summary: Prescription opioids in the United States contribute to opioid-related overdose deaths. Evidence suggests the Medicaid population has a greater likelihood of opioid-related mortality. However, the current standard for postoperative pain management often doesn’t account for previous opioid use and/or misuse. Machine learning can help healthcare systems identify patients at risk of progression from opioid naïve to chronic opioid use (COU) in Medicaid patients. In this cohort study of 180,000 opioid naïve Medicaid patients, machine learning models accurately identified opioid naïve patients at risk of progression to COU. Features of the initial opioid prescription were strong predictors of progression to COU, including number of tablets, prescription length, and prescription of oxycodone. We found that prescription quantity and length are discrete features related to opioid prescribing that are predictive of COU incidence. This research suggests that stronger prescription guidelines are needed for opioid prescriptions among opioid naïve patients.