Toward Opioid-Free Ambulatory Surgery: A Prospective Study Using Machine Learning to Predict Postoperative Opioid Use

BACKGROUND: Postoperative opioid use has the risk of dependence and diversion. We developed an opioid-sparing regimen and identified factors associated with postoperative opioid use. STUDY DESIGN: The Toward Opioid-Free Ambulatory Surgery program was developed by establishing a regimen of ibuprofen 600 mg and acetaminophen 650 mg, alternating every 3 hours, with a rescue prescription of oxycodone 5 mg (10 doses). The study included adults undergoing ambulatory operations. A machine learning (ML) model was then developed to predict postoperative opioid use. Performance was evaluated using the area under the receiver operating characteristic curve (AUC) with an 80 and 20 train-test split and repeated across 10 random seeds to assess stability. Feature selection was performed iteratively using training data, whereas model performance was evaluated on test sets. RESULTS: A total of 223 patients were prospectively enrolled (median age 50 years, 69% men, 91% White race). The most common procedure was inguinal hernia repair (49%). Forty-two percent of patients filled their opioid prescription with a median of 4 doses used. The ML model achieved a mean test AUC of 0.674 (range 0.634 to 0.732) across 10 runs. The mean sensitivity was 0.70, and the mean specificity was 0.68. Most selected factors included active cancer, age, anesthesia type, race and ethnicity, COPD history, intraoperative complications, preoperative acetaminophen use, and pain intensity. Specifically, in seed 3 (AUC 0.67), the most influential features were age (model gain 17.8%) and pain intensity (model gain 11%). CONCLUSIONS: The ML model reliably identified high-risk individuals, supporting the potential for personalized opioid-sparing strategies in outpatient surgery. This model may help identify patients likely to require opioids, enabling tailored pain management planning.