عنوان انگلیسی مقاله:
Development of Machine Learning Algorithms for Prediction of Sustained Postoperative Opioid Prescriptions After Total Hip Arthroplasty
ترجمه فارسی عنوان مقاله:
توسعه الگوریتم های یادگیری ماشین برای پیش بینی نسخه های افیونی پس از عمل پایدار پس از آرتروپلاستی کامل باسن
Sciencedirect - Elsevier - The Journal of Arthroplasty, 34 (2019) 2272-2278: doi:10:1016/j:arth:2019:06:013
Aditya V. Karhade, BE, Joseph H. Schwab, MD, MS, Hany S. Bedair, MD *
Background: Postoperative recovery after 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 is to develop machine learning algorithms for preoperative prediction of prolonged opioid prescriptions
Methods: A retrospective review of electronic health records was conducted at 2 academic medical
centers and 3 community hospitals to identify adult patients who underwent THA for osteoarthritis
between January 1, 2000 and August 1, 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 opioid exposure, preoperative hemoglobin, and preoperative medications (antidepressants,
benzodiazepines, nonsteroidal anti-inflammatory drugs, and beta-2-agonists). The elasticnet
penalized logistic regression model achieved the best performance across discrimination
(c-statistic ¼ 0.77), calibration, and decision curve analysis. This model was incorporated into a digital
application able to provide both predictions and explanations (available at https://sorg-apps.shinyapps.
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 prescriptions. Early identification and intervention in high-risk cases may mitigate the
long-term adverse consequence of opioid dependence.
Level of Evidence: III.
Keywords: arthroplasty | machine learning | opioid use | orthopedic surgery | prediction | total hip arthroplasty