Yeramosu Teja, Farrar Jacob M, Malik Avni, Satpathy Jibanananda, Golladay Gregory J, Patel Nirav K
Virginia Commonwealth University School of Medicine, Richmond, Virginia.
Department of Orthopaedic Surgery, Virginia Commonwealth University, Richmond, Virginia.
J Arthroplasty. 2025 Jul;40(7):1807-1814. doi: 10.1016/j.arth.2024.12.006. Epub 2024 Dec 9.
Revision total hip arthroplasty (rTHA) was recently removed from the Medicare inpatient-only list. However, appropriate candidate selection for outpatient rTHA remains paramount. The purpose of this study was to evaluate the utility of a large national database using machine learning (ML) and traditional multivariable logistic regression (MLR) models in predicting early hospital discharge (EHD) (< 24 hours) following rTHA. Furthermore, this study aimed to use the trained ML models, cross-referenced with traditional MLR, to determine key perioperative variables predictive of EHD following rTHA.
Data were obtained from a large national database from 2021. Patients who had unilateral rTHA procedures were included. Demographic, preoperative, and operative variables were analyzed as inputs for the models. An ML regression model and various ML techniques were used to predict EHD and were compared using the area under the curve, calibration, Brier score, and decision curve analysis. Feature importance was identified from the overall best-performing model. Of the 3,097 patients in this study, 866 (27.96%) underwent EHD.
The random forest model performed the best overall and identified aseptic surgical indication, operative time < three hours, absence of anemia (hematocrit < 40% in men and < 35% in women), neuraxial anesthesia type, White race, men, independent functional status, body mass index > 20, age < 75 years, and the presence of home support as factors predictive of EHD. Each of these variables was also significant in the MLR model.
Each ML model and MLR displayed good performance and identified clinically important variables for determining candidates for EHD following rTHA. Machine learning (ML) techniques such as random forest may allow clinicians to accurately risk stratify their patients preoperatively to optimize resources and improve patient outcomes.
