RESUMEN

BACKGROUND: As the number of revision total knee arthroplasty (TKA) continues to rise, close attention has been paid to factors influencing postoperative length of stay (LOS). The aim of this study is to develop generalizable machine learning (ML) algorithms to predict extended LOS following revision TKA using data from a national database. METHODS: 23,656 patients undergoing revision TKA between 2013 and 2020 were identified using the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database. Patients with missing data and those undergoing re-revision or conversion from unicompartmental knee arthroplasty were excluded. Four ML algorithms were applied and evaluated based on their (1) ability to distinguish between at-risk and not-at-risk patients, (2) accuracy, (3) calibration, and (4) clinical utility. RESULTS: All four ML predictive algorithms demonstrated good accuracy, calibration, clinical utility, and discrimination, with all models achieving a similar area under the curve (AUC) (AUCLR=AUCRF=AUCHGB=0.75, AUCANN=0.74). The most important predictors of prolonged LOS were found to be operative time, preoperative diagnosis of sepsis, and body mass index (BMI). CONCLUSIONS: ML models developed in this study demonstrated good performance in predicting extended LOS in patients undergoing revision TKA. Our findings highlight the importance of utilizing nationally representative patient data for model development. Prolonged operative time, preoperative sepsis, BMI, and elevated preoperative serum creatinine and BUN were noted to be significant predictors of prolonged LOS. Knowledge of these associations may aid with patient-specific preoperative planning, discharge planning, patient counseling, and cost containment with revision TKA.