Machine Learning to Predict Length of Stay Following Revision Hip Arthroplasty

Abstract

AbstractPredicting length of stay (LOS) for revision total hip arthroplasty (rTHA) remains challenging due to the complexity of cases and increased complication rates compared with primary total hip arthroplasty. Artificial intelligence may play a role in predicting LOS after rTHA. This study aims to develop a machine learning model to predict LOS in rTHA patients based on preoperative and intraoperative factors. The American College of Surgeons National Surgical Quality Improvement Program database was used to identify patients undergoing a rTHA between 2012 and 2018. The data were used to train both random forest machine and logistic regression machine learning models to predict short LOS (≤ 1 day) or long LOS (≥ 2 days). Statistical analysis was performed to analyze differences between short and long LOS groups. Logistic regression analysis was used to calculate odds ratios associated with long LOS. A total of 4,228 patients were identified in this analysis with a mean postoperative LOS of 3.69 days. Preoperative features associated with a short LOS included male sex, noninfectious revision, spinal anesthesia, later year of operation, younger age, smoking history, and lower body mass index. Area under the receiver operating characteristic curve was calculated to measure model performance for the random forest model and logistic regression model as 0.76 and 0.78, respectively. The machine learning model presented here was able to reasonably predict LOS for revision hip arthroplasty showing promise for use as patient selection tool to identify short LOS patients.