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

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.

Patient Experiences and Clinical Outcomes in a Multidisciplinary Perioperative Transitional Pain Service.

Siloed pain management across the perioperative period increases the risk of chronic opioid use and impedes postoperative recovery. Transitional perioperative pain services (TPSs) are innovative care models that coordinate multidisciplinary perioperative pain management to mitigate risks of chronic postoperative pain and opioid use. The objective of this study was to examine patients' experiences with and quality of recovery after participation in a TPS. Qualitative interviews were conducted with 26 patients from The Johns Hopkins Personalized Pain Program (PPP) an average of 33 months after their first PPP visit. A qualitative content analysis of the interview data showed that participants (1) valued pain expectation setting, individualized care, a trusting patient-physician relationship, and shared decision-making; (2) perceived psychiatric treatment of co-occurring depression, anxiety, and maladaptive behaviors as critical to recovery; and (3) successfully sustained opioid tapers and experienced improved functioning after PPP discharge. Areas for improved patient-centered care included increased patient education, specifically about the program, continuity of care with pain specialists while tapering opioids, and addressing the health determinants that impede access to pain care. The positive patient experiences and sustained clinical benefits for high-risk complex surgical patient support further efforts to implement and adapt similar models of perioperative pain care.