Predicting Postoperative Pain and Opioid Use with Machine Learning Applied to Longitudinal Electronic Health Record and Wearable Data

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

Protection of Human and Animal Subjects

The protocol was reviewed and approved by the Institutional Review Board at the Johns Hopkins University School of Medicine (identifier: IRB00422898).

Publikationsverlauf

Eingereicht: 02. Dezember 2023

Angenommen: 06. Mai 2024

Accepted Manuscript online:
07. Mai 2024

Artikel online veröffentlicht:
17. Juli 2024

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