Establishment and evaluation of a predictive model for length of hospital stay after total knee arthroplasty: A single-center retrospective study in China - Zhu, Bo; Zhang, Dejun; Sang, Maocheng; Zhao, Long; Wang, Chaoqun; Xu, Yunqiang (Frontiers in Surgery, 2023)
Background: Total knee arthroplasty (TKA) is the ultimate option for end-stage osteoarthritis, and the demand and cost of this procedure are increasing every year. The length of hospital stay (LOS) greatly affects the overall cost of joint arthroplasty. The purpose of this study is to develop and validate a prediction model that estimates the prolonged LOS risk in patients undergoing TKA using data collected on admission. Methods: Data for 694 patients after TKA collected retrospectively in our department were analyzed by logistic regression models. Multi-variable logistic regression modeling with forward stepwise elimination was used to determine reduced parameters and establish a prediction model. The discrimination efficacy, calibration efficacy, and clinical utility of the prediction model were evaluated. Results: Eight independent predictors were identified: non-medical insurance payment, CCI≥3, BMI＞25.2, surgery on Monday, age＞67.5, postoperative complications, blood transfusion, and operation time＞120.5 min had a higher probability of hospitalization for ≥6 days. The model had good discrimination [area under the curve (AUC), 0.802 95% CI, 0.754–0.850)] and good calibration (p = 0.929). A decision curve analysis proved that the nomogram was clinically effective. Conclusion: This model, based on some perioperative statistics, can accurately predict factors that prolong hospital stay in patients with TKA. Identifying of patients with an increased risk of prolonged LOS following TKA, which may aid in strategic discharge planning and reduce the burden on national health insurance system.