Abstract
OBJECTIVE: It is well established that adolescents and young adults are increasingly vulnerable to the effects of early opioid exposures, with the emergency department (ED) playing a critical role in such introduction. Our objective was to identify predictors of ED opioid administration (ED-RX) and prescribing at discharge (DC-RX) among adolescent and young adults using a machine learning approach. METHODS: We conducted a secondary analysis of ED visit data from the National Hospital Ambulatory Medical Care Survey from 2014 to 2018. Visits where patients were aged 10 to 24 years were included. Predictors of ED-RX and DC-RX were identified via machine learning methods. Separate weighted logistic regressions were performed to determine the association between each predictor, and ED-RX and DC-RX, respectively. RESULTS: There were 12,693 ED visits identified within the study time frame, with the majority being female (58.6%) and White (70.7%). Approximately 12.3% of all visits were administered an opioid during the ED visit, and 11.5% were prescribed one at discharge. For ED-RX, the strongest predictors were fracture injury (odds ratio [OR], 5.24; 95% confidence interval [CI], 3.73-7.35) and Southern geographic region (OR, 3.01; 95% CI, 2.14-4.22). The use of nonopioid analgesics significantly reduced the odds of ED-RX (OR, 0.46; 95% CI, 0.37-0.57). Fracture injury was also a strong predictor of DC-RX (OR, 5.91; 95% CI, 4.24-8.25), in addition to tooth pain (OR, 5.47; 95% CI, 3.84-7.69). CONCLUSIONS: Machine learning methodologies were able to identify predictors of ED-RX and DC-RX, which can be used to inform ED prescribing guidelines and risk mitigation efforts among adolescents and young adults.