The Risk Factors and Predictive Nomogram for Length Of Stay More than 14 Days in Undergoing TKA Patients: A Secondary Analysis Based on a Cohort Study in Singapore
Abstract Objective: The aim was to explore the risk factors and establish a predictive nomogram of length of stay (LOS) more than 14 days in a undergoing Total knee arthroplasty (TKA) cohort .Methods: We used the raw data, a retrospective cohort study of 2622 patients undergoing TKA in Singapore, for secondary analysis. The LASSO regression was used to optimize feature selection for the LOS more than 14 days. The Multivariable logistic regression analysis was applied to build a predicting nomogram by using the feature selected in the LASSO regression model. In order to evaluate the prediction ability of the model, we calculated the C-index. Simultaneously, the ROC curve, the Calibration curve and the DCA curve was draw to assess the model. Finally, we used 1000 times bootstrap method to verify the accuracy of the model. Results: Finally, 100(3.81%) patients were hospitalized for more than 14 days and 2522 patients (96.19%) were less 14 days. Patient age, ASA status, type of anesthesia, operation duration, procedure description, DM, IHD, CHF, day of operation and blood transfusion were determined and incorporated into the diagnostic nomogram. The C-index was 0.797(95% CI: 0.755-0.839).The calibration curve showed that the model had good recognition ability.The DCA curve analysis showed that the risk nomogram of length of stay more than 14 days was clinically useful.The C-index is 0.763 through 1000 times bootstrapping validation.Conclusion:We used the age, ASA status, type of anesthesia, operation duration, procedure description, DM, IHD, CHF, day of operation and blood transfusion to establish the clinical prediction model , this method can conveniently predict the risk of individual patients with total knee arthroplasty length of stay for more than 14 days.