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A machine learning model predicts patients needing opioids after surgery, aiding personalized pain management. This opioid-sparing strategy reduces risks associated with postoperative opioid use.

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Area of Science:

  • Anesthesiology
  • Pain Management
  • Machine Learning in Healthcare

Background:

  • Postoperative opioid use presents risks of dependence and diversion.
  • Developing effective opioid-sparing strategies is crucial for patient safety.
  • Identifying patients at high risk for opioid use is essential for tailored pain management.

Purpose of the Study:

  • To develop and evaluate an opioid-sparing regimen for ambulatory surgery.
  • To create a machine learning (ML) model to predict postoperative opioid use.
  • To identify key factors associated with postoperative opioid consumption.

Main Methods:

  • Implemented the Toward Opioid-Free Ambulatory Surgery (TOFAS) program, alternating ibuprofen and acetaminophen with limited oxycodone rescue doses.
  • Developed an ML model to predict postoperative opioid use in adult patients undergoing ambulatory surgery.
  • Validated the ML model using area under the receiver operating characteristic curve (AUC) with an 80/20 train-test split and 10 random seeds.

Main Results:

  • 42% of 223 enrolled patients filled their opioid prescription, with a median of 4 doses used.
  • The ML model achieved a mean test AUC of 0.674, with sensitivity of 0.70 and specificity of 0.68.
  • Key predictors of opioid use included active cancer, age, anesthesia type, race/ethnicity, COPD history, intraoperative complications, preoperative acetaminophen use, and pain intensity.

Conclusions:

  • The developed ML model reliably identifies patients at high risk for postoperative opioid use.
  • This predictive capability supports personalized, opioid-sparing pain management strategies in outpatient settings.
  • The model facilitates tailored pain management planning, potentially reducing opioid dependence and diversion.