Construction and Validation of a Prediction Model for Early Ambulation After Colorectal Surgery

doi:10.12104/j.issn.1674-4748.2025.12.010

Abstract：Objective: To analyze the independent risk factors for early ambulation after colorectal surgery and to construct and validate a risk prediction model. Methods: A retrospective study was conducted on 286 patients who underwent colorectal surgery at the hospital from January 2021 to January 2023. The patients were divided into a modeling set and a validation set in a 7:3 ratio, with 200 and 86 patients, respectively. Clinical data were reviewed, and the modeling set was divided into an early ambulation group (n=166) and a non-early ambulation group (n=34). Multivariate logistic regression analysis was used to identify independent risk factors, and a prediction model for early ambulation after colorectal surgery was constructed. The predictive performance of the model was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curves. Results: Logistic regression analysis showed that age, preoperative hemoglobin level, anesthesia method, and anesthesia duration were significantly associated with early ambulation after colorectal surgery (P<0.05). The constructed risk prediction model had an area under the ROC curve (AUC) of 0.860 [95% CI (0.792, 0.928)], with a sensitivity of 77.1%, specificity of 82.4%, and Youden index of 0.595. Decision curve analysis indicated that the model has considerable clinical benefit. Calibration curves showed that the model has good predictive accuracy. The validation set results showed that the model has good internal predictive performance. Conclusion: Early ambulation after colorectal surgery is influenced by factors such as age, preoperative hemoglobin level, and anesthesia method. The risk prediction model constructed based on these independent risk factors has good predictive performance and can provide reference for clinical intervention measures to promote early ambulation after surgery, shorten recovery time, and improve prognosis.