Abstract
Background
Postoperative recovery after routine total hip arthroplasty (THA) can lead to the development of prolonged opioid use but there are few tools for predicting this adverse outcome. The purpose of this study was to develop machine learning algorithms for preoperative prediction of prolonged post-operative opioid use after THA.
Methods
A retrospective review of electronic health records was conducted at two academic medical centers and three community hospitals to identify adult patients who underwent THA for osteoarthritis between January 1st, 2000 and August 1st, 2018. Prolonged postoperative opioid prescriptions were defined as continuous opioid prescriptions after surgery to at least 90 days after surgery. Five machine learning algorithms were developed to predict this outcome and were assessed by discrimination, calibration, and decision curve analysis.
Results
Overall, 5507 patients underwent THA, of which 345 (6.3%) had prolonged postoperative opioid prescriptions. The factors determined for prediction of prolonged postoperative opioid prescriptions were: age, duration of pre-operative opioid exposure, preoperative hemoglobin, and certain preoperative medications (anti-depressants, benzodiazepines, non-steroidal anti-inflammatory drugs, and beta-2-agonists). The elastic-net penalized logistic regression model achieved the best performance across discrimination (c-statistic = 0.77), calibration, and decision curve analysis. This model was incorporated into a digital application able to provide both predictions and explanations; available here: https://sorg-apps.shinyapps.io/thaopioid/
Conclusion
If externally validated in independent populations, the algorithms developed in this study could improve preoperative screening and support for THA patients at high-risk for prolonged postoperative opioid use. Early identification and intervention in high-risk cases may mitigate the long-term adverse consequence of opioid dependence.
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