Abstract
Background Managing acute postoperative pain and minimizing chronic opioid use are crucial for
patient recovery and long-term well-being.
Objectives This study explored using preoperative electronic health record (EHR) and wearable
device data for machine-learning models that predict postoperative acute pain and
chronic opioid use.
Methods The study cohort consisted of approximately 347 All of Us Research Program participants who underwent one of eight surgical procedures and
shared EHR and wearable device data. We developed four machine learning models and
used the Shapley additive explanations (SHAP) technique to identify the most relevant
predictors of acute pain and chronic opioid use.
Results The stacking ensemble model achieved the highest accuracy in predicting acute pain
(0.68) and chronic opioid use (0.89). The area under the curve score for severe pain
versus other pain was highest (0.88) when predicting acute postoperative pain. Values
of logistic regression, random forest, extreme gradient boosting, and stacking ensemble
ranged from 0.74 to 0.90 when predicting postoperative chronic opioid use. Variables
from wearable devices played a prominent role in predicting both outcomes.
Conclusion SHAP detection of individual risk factors for severe pain can help health care providers
tailor pain management plans. Accurate prediction of postoperative chronic opioid
use before surgery can help mitigate the risk for the outcomes we studied. Prediction
can also reduce the chances of opioid overuse and dependence. Such mitigation can
promote safer and more effective pain control for patients during their recovery.
Keywords
acute postoperative pain - chronic opioid use - electronic health records - wearable
device data - machine learning
