Abstract
ObjectiveThe aims of this study were to develop machine learning algorithms for preoperative prediction of prolonged opioid prescriptions after TKA and to identify variables that can predict the probability of this adverse outcome. MethodsFive algorithms were developed for prediction of prolonged postoperative opioid prescriptions. ResultsThe stochastic gradient boosting (SGB) model had the best performance. Age, history of preoperative opioid use, marital status, diagnosis of diabetes, and several preoperative medications were predictive of prolonged postoperative opioid prescriptions. ConclusionThe SGB algorithm developed could help improve preoperative identification of TKA patients at risk for prolonged postoperative opioid prescriptions.
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