A novel predictive model of hospital stay for Total Knee Arthroplasty patients

OBJECTIVE: This study aimed to explore the main risk factors affecting Total Knee Arthroplasty (TKA) patients and develop a predictive nomogram of hospital stay. METHODS: In total, 2,622 patients undergoing TKA in Singapore were included in this retrospective cohort study. Hospital extension was defined based on the 75% quartile (Q3) of hospital stay. We randomly divided all patients into two groups using a 7:3 ratio of training and validation groups. We performed univariate analyses of the training group, in which variables with P-values < 0.05 were included and then subjected to multivariate analysis. The multivariable logistic regression analysis was applied to build a predicting nomogram, using variable P-values < 0.01. To evaluate the prediction ability of the model, we calculated the C-index. The ROC, Calibration, and DCA curves were drawn to assess the model. Finally, we verified the accuracy of the model using the validation group and by also using the C-index. The ROC curve, Calibration curve, and DCA curve were then applied to evaluate the model in the validation group. RESULTS: The final study included 2,266 patients. The 75% quartile (Q3) of hospital stay was six days. In total, 457 (20.17%) patients had hospital extensions. There were 1,588 patients in the training group and 678 patients in the validation group. Age, Hb, D.M., Operation Duration, Procedure Description, Day of Operation, Repeat Operation, and Blood Transfusion were used to build the prediction model. The C-index was 0.680 (95% CI: 0.734–0.626) in the training group and 0.710 (95% CI: 0.742–0.678) for the validation set. The calibration curve and DCA indicated that the hospital stay extension model showed good performance in the training and validation groups. CONCLUSION: To identify patients' risk factors early, medical teams need to plan a patient’s rehabilitation path as a whole. Its advantages lie in better resource allocation, maximizing medical resources, improving the functional recovery of patients, and reducing the overall cost of hospital stay and surgery, and will help clinicians in the future.