Adolescent Outcomes of Post-operative Opioid EXposure

This project will collect data from a diverse sample of 10,000 dyads, including adolescent (aged 12-17 years, inclusive) patients undergoing major surgery with planned post-operative opioid analgesic use. and a parent (Total N=20,000) to identify ROUD that may occur within the 12 months after surgery. The resulting data repository will allow clinician scientists and physicians to develop research-informed pain management protocols for adolescent surgical patients to reduce the risk of OUD in this vulnerable population. Using a novel combination of data sources (electronic medical records (EMR), patient-reported outcomes, and parent-reported outcomes), this study investigates the twin problems of postoperative pain and ROUD in adolescent surgical patients. Using real-world evidence to evaluate longitudinal behaviors and outcomes. This project will provide insights into the associations between post-operative pain management and ROUD in adolescents. Findings could yield information about potential risks, which may inform future studies and ultimately lead to new screening tools to assess risk before opioid prescribing, updated protocols for managing pain intra-operatively and post-operatively in high-risk populations, updated patient and family education materials to reduce risks for those undergoing painful procedures, and revised recommendations for monitoring for patients being treated for pain. Furthermore, the project enables the establishment of an infrastructure among pediatric surgical centers that can be used for future projects to evaluate the best postoperative outcomes for youth. Aim 1: Determine the frequency of ROUD. Because this is a novel measure of ROUD, we will use the first 1,000 samples to estimate the incidence of ROUD developing among adolescents in the year following surgery with post-operative opioid analgesia. Aim 2: We will use machine learning models to develop and validate screening algorithms designed to detect "early warning signs" or Risk factors for Opioid Use Disorder (ROUD). The features in these models will include the pre-, intra- and postoperative EMR-derived data elements, as well as the survey data. We will fit the models against the primary outcome described above. Model evaluation will be done via cross-validation, where the models will be fit on a randomly selected training set of patients and evaluated on the remaining held-out subset of the patients to be used exclusively for testing the performance of the final models. Aim 3: Developing and validating a prediction model designed to predict a clinical trajectory of post-surgical opioid use and pain. We will use clustering to identify typical patient trajectories of opioid use, and we will use similar prediction and validation techniques as in Aim 1 to build a prediction model for these trajectories.