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

被引：26

作者：

Katakam, Akhil ^{[1
]}

Karhade, Aditya V. ^{[1
]}

Schwab, Joseph H. ^{[1
]}

Chen, Antonia F. ^{[2
]}

Bedair, Hany S. ^{[1
]}

机构：

[1] Harvard Med Sch, Massachusetts Gen Hosp, Dept Orthopaed Surg, 55 Fruit St, Boston, MA 02114 USA

[2] Harvard Med Sch, Brigham & Womens Hosp, Dept Orthopaed Surg, Boston, MA 02115 USA

来源：

JOURNAL OF ORTHOPAEDICS | 2020年 / 22卷

关键词：

Opioid; Machine learning; Prediction; TOTAL KNEE ARTHROPLASTY; RISK-FACTORS; TRENDS; HIP; PREDICTORS; SPECIALTY; PATTERNS; PAIN;

D O I：

10.1016/j.jor.2020.03.052

中图分类号：

R826.8 [整形外科学]; R782.2 [口腔颌面部整形外科学]; R726.2 [小儿整形外科学]; R62 [整形外科学（修复外科学）];

学科分类号：

摘要：

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.

引用

页码：95 / 99

页数：5

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