Machine Learning to Predict Discharge Destination After Total Knee Arthroplasty and Total Hip Arthroplasty

$25.00

Discharge destination impacts costs and perioperative planning for primary total knee (TKA) or hip arthroplasty (THA). The purpose of this study was to create a tool to predict discharge destination in contemporary patients. Models were developed using more than 400,000 patients from the National Surgical Quality Improvement Program database. Models were compared with a previously published model using area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA). AUC on patients with TKA was 0.729 (95% confidence interval [CI]: 0.719 to 0.738) and 0.688 (95% CI: 0.678 to 0.697) using the new and previous models, respectively. AUC on patients with THA was 0.768 (95% CI: 0.758 to 0.778) and 0.726 (95% CI: 0.714 to 0.737) using the new and previous models, respectively. DCA showed substantially improved net clinical benefit. The new models were integrated into a web-based application. This tool enhances clinical decision making for predicting discharge destination following primary TKA and THA. (Journal of Surgical Orthopaedic Advances 32(4):252- 258, 2023)

Key words: total knee arthroplasty, total hip arthroplasty, machine learning, artificial intelligence, clinical risk prediction

Gregory J. Booth, MD; Jacob Cole, MD; Phil Geiger, MD; George C. Balazs, MD; Scott Hughey, MD; Natalie Nepa, MD; and Ashton Goldman, MD