Toward Opioid-Free Ambulatory Surgery: A Prospective Study Using Machine Learning to Predict Postoperative Opioid Use.
Document Type
Article
Publication Title
Journal of the American College of Surgeons
Abstract
BACKGROUND: Postoperative opioid use carries 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 (TOFAS) program was developed by establishing a regimen of ibuprofen 600mg and acetaminophen 650mg alternating every 3-hours with a rescue prescription of oxycodone 5mg (10 doses). The study included adults undergoing ambulatory operations. A machine learning (ML) model was then developed to predict post-operative opioid use. Performance was evaluated by area under the receiver operating characteristic curve (AUC) using an 80/20 train-test split and repeated across 10 random seeds to assess stability. Feature selection was performed iteratively using training data while model performance was evaluated on test sets.
RESULTS: 223 patients were prospectively enrolled (median age 50 years, 69% male, 91% white). The most common procedure was inguinal hernia repair (49%). 42% of patients filled their opioid prescription with median of 4 doses used. The ML model achieved a mean test AUC of 0.674 (range: 0.634-0.732) across 10 runs. Mean sensitivity was 0.70, and specificity 0.68. Most selected factors included active cancer, age, anesthesia type, race/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%).
CONCLUSION: 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.
DOI
10.1097/XCS.0000000000001803
Publication Date
1-27-2026
Recommended Citation
Renshaw S, Satija D, Aly A, Edwards P, Shannon K, Guertin M, Heh V, Poulose BK. Toward Opioid-Free Ambulatory Surgery: A Prospective Study Using Machine Learning to Predict Postoperative Opioid Use. J Am Coll Surg. 2026 Jan 27. doi: 10.1097/XCS.0000000000001803. Epub ahead of print. PMID: 41589853.