Abstract
BACKGROUND: Minimizing costs associated with the care of patients undergoing total knee arthroplasty (TKA) can reduce the burden on health systems that regularly struggle with limited resources. Predicting and reducing TKA associated length of stay (LoS) can therefore be invaluable. This study aimed to determine the factors that impact LoS in patients undergoing TKA and propose a model design to predict LoS. METHODS: A retrospective study was performed on patients undergoing TKA in a tertiary teaching hospital. Patients who underwent TKA from March 2007 to March 2021 were included in the study. Data were extracted from available electronic and paper records. Variables evaluated included: patients' demographic data, general admission data, laboratory data, transfusion, operation data, and preoperative comorbidities and medical history. Independent T-test, one-way ANOVA, and Pearson correlation were used for univariate data analysis. For multivariate analysis and model designing, multiple regression stepwise methods were used. RESULTS: 878 patients were included in this study. Mean LoS was 6.09 (SD = 1.83) with a median of 6 days. Factors found to have a significant effect on length of stay were age, revision surgery, Anesthesia type, intensive care unit admission, insurance, transfusion, preoperative hemoglobin level, and pre-operative platelet (Plt) count. Applying a multiple regression stepwise model to these variables showed that the following pre-operative factors can be predictive for LoS: revision surgery, sex, medical insurance, hemoglobin level, and Plt count. CONCLUSIONS: It was deduced that sex, revision, pre-operative hemoglobin and Plt level and health insurance were the best predictors for LoS in patients undergoing TKA.