Development and validation of machine learning algorithms for postoperative opioid prescriptions after TKA

Akhil Katakam; Aditya V Karhade; Joseph H Schwab; Antonia F Chen; Hany S Bedair

doi:10.1016/j.jor.2020.03.052

Development and validation of machine learning algorithms for postoperative opioid prescriptions after TKA

J Orthop. 2020 Mar 28:22:95-99. doi: 10.1016/j.jor.2020.03.052. eCollection 2020 Nov-Dec.

Authors

Akhil Katakam¹, Aditya V Karhade¹, Joseph H Schwab¹, Antonia F Chen², Hany S Bedair¹

Affiliations

¹ Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
² Department of Orthopaedic Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.

Abstract

Objective: The 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.

Methods: Five algorithms were developed for prediction of prolonged postoperative opioid prescriptions.

Results: The 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.

Conclusion: The SGB algorithm developed could help improve preoperative identification of TKA patients at risk for prolonged postoperative opioid prescriptions.

Keywords: Machine learning; Opioid; Prediction.