Prediction of sustained opioid use in children and adolescents using machine learning

Opioid misuse in the paediatric population is understudied. This study aimed to develop a machine learning classifier to differentiate between occasional and sustained opioid users among children and adolescents in outpatient settings. Data for 29,335 patients under 19 yr with recorded opioid purchases were collected from medical records. Machine learning methods were applied to predict sustained opioid use within 1, 2, or 3 yr after first opioid use, using sociodemographic information, medical history, and healthcare usage variables collected near the time of first prescription fulfilment. The models' performance was evaluated with classification and calibration metrics, and a decision curve analysis. An online tool was deployed for model self-exploration and visualisation. The models demonstrated good performance, with a 1-yr follow-up model achieving a sensitivity of 0.772, a specificity of 0.703, and an ROC-AUC of 0.792 on an independent test set, with calibration intercept and slope of 0.00 and 1.02, respectively. Decision curve analysis revealed the clinical benefit of using the model relative to other strategies. SHAP analysis (SHapley Additive exPlanations) identified influential variables, including the number of diagnoses, medical images, laboratory tests, and type of opioid used. Our model showed promising performance in predicting sustained opioid use among paediatric patients. The online risk prediction tool can facilitate compliance to such tools by clinicians. This study presents the potential of machine learning in identifying at-risk paediatric populations for sustained opioid use, potentially contributing to secondary prevention of opioid abuse.