Development and Validation of Machine Learning Algorithms for Predicting Prolonged Postoperative Opioid Use in Spinal Metastatic Disease

Introduction: Operative management of spinal metastatic disease is largely for symptom palliation and revolves around the expectation that postoperative survival will exceed the recovery period. Long-term postoperative opioid use is a clinically useful indicator of recovery. Few studies have developed machine learning models to predict this outcome in spinal metastatic disease patients.The Merative™ Marketscan® Commercial Database and Medicare Supplement were analyzed to identify adult patients who underwent surgery for extradural spinal metastatic disease between 2006 and 2023. Patients were required to have at least 6 months of continuous preadmission data, and 6 months of continuous post-discharge follow-up. The primary outcome was prolonged opioid use, defined as filling a perioperative prescription followed by another between 90- and 180-days post-discharge. Cumulative days of postoperative opioid supply was assessed as a secondary outcome. Five models (stochastic gradient boosting, support vector machine, neural network, random forest and penalized logistic regression) were trained on a 70% training sample and validated on the withheld 30%. A total of 732 patients were included, of which 341 (46.6%) had prolonged post-discharge opioid use. The random forest algorithm had the best predictive performance in terms of discrimination (area under the curve [AUC]: 0.611), calibration (intercept: 0.18, slope: 0.613) and overall accuracy (Brier score: 0.24). We developed and validated parsimonious predictive models to estimate risk of prolonged opioid use after surgery for extradural spinal metastatic disease. Integrating these models into physician- and patient-facing interfaces may improve prognostication, enhance clinical decision-making, and ultimately optimize pain management to support more tailored postoperative care strategies.