Predictive analysis of economic and clinical outcomes in total knee arthroplasty: Identifying high-risk patients for increased costs and length of stay.

Abstract

The purpose of this study was to predict high-risk patients who experience significant increases in hospital charges and length of stay (LOS) following specific postoperative complications. This study analyzed over two million patients from the Nationwide Inpatient Sample database undergoing elective total knee arthroplasty (TKA) for primary osteoarthritis. Baseline demographics, clinical characteristics and incidence of postoperative complications were examined. A neural network model was utilized to predict high-risk patients who fall into the top 25% for both LOS and total hospital charges after complications such as sepsis or surgical site infection (SSI). The most common complications were blood loss anaemia (14.6%), acute kidney injury (1.6%) and urinary tract infection (0.9%). Patients with complications incurred significantly higher total charges (mean $66,804) and longer LOS (mean 2.9 days) compared to those without complications (mean $58,545 and 2.1 days, respectively). The neural network model demonstrated strong predictive performance, with an area under the curve of 0.83 for the training set and 0.78 for the testing set. Key complications like sepsis and SSIs significantly impacted hospital charges and LOS. For example, a 57-year-old patient with diabetes and sepsis had a 100% probability of being in the top 25% for both total charges and LOS. Postoperative complications in TKA patients significantly increase hospital charges and LOS. The neural network model effectively predicted high-risk patients after specific complications occurred, offering a potential tool for improving patient management and resource allocation. Level III.