Establishment and evaluation of a predictive model for length of hospital stay after total knee arthroplasty: A single-center retrospective study in China

• Published in: Frontiers in surgery 2023-04, Vol.10, p.1102371-1102371
• Main Authors: Zhu, Bo, Zhang, Dejun, Sang, Maocheng, Zhao, Long, Wang, Chaoqun, Xu, Yunqiang
• Format: Article
• Language: English
• Subjects: length of hospital stay (LOS); nomogram; payment method; prediction model; Surgery; total knee arthroplasty (TKA)
• ISSN: 2296-875X; 2296-875X
• DOI: 10.3389/fsurg.2023.1102371

Total knee arthroplasty (TKA) is the ultimate option for end-stage osteoarthritis, and the demand 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 was to develop and validate a predictive model using perioperative data to estimate the risk of prolonged LOS in patients undergoing TKA. 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. Eight independent predictors were identified: non-medical insurance payment, Charlson Comorbidity Index (CCI) ≥ 3, body mass index (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 (  = 0.929). A decision curve analysis proved that the nomogram was clinically effective. This study identified risk factors for prolonged hospital stay in patients after TKA. It is important to recognize all the factors that affect hospital LOS to try to maximize the use of medical resources, optimize hospital LOS and ultimately optimize the care of our patients.