Prediction of Non-Home Discharge Following Total Hip Arthroplasty in Geriatric Patients

Abstract

Introduction: The majority of total hip arthroplasty (THA) patients are discharged home postoperatively, however, many still require continued medical care. We aimed to identify important characteristics that predict nonhome discharge in geriatric patients undergoing THA using machine learning. We hypothesize that our analyses will identify variables associated with decreased functional status and overall health to be predictive of non-home discharge. Materials and Methods: Elective, unilateral, THA patients above 65 years of age were isolated in the NSQIP database from 2018-2020. Demographic, pre-operative, and intraoperative variables were analyzed. After splitting the data into training (75%) and validation (25%) data sets, various machine learning models were used to predict non-home discharge. The model with the best area under the curve (AUC) was further assessed to identify the most important variables. Results: In total, 19,840 geriatric patients undergoing THA were included in the final analyses, of which 5194 (26.2%) were discharged to a non-home setting. The RF model performed the best and identified age above 78 years (OR: 1.08 [1.07, 1.09], P