Abstract: Objective: To construct and validate a risk prediction model for Acute Postsurgical Pain in patients after hepatectomy, so as to provide a reference for the management of Acute Postsurgical Pain in patients after hepatectomy. Methods: The data of 477 patients undergoing hepatectomy admitted to a tertiary grade A hospital in Wenzhou from January 2019 to December 2023 were retrospectively analyzed, and they were devided into a training set (n=318) and a validation set (n=159) at a ratio of 2?:?1. The risk factors of Acute Postsurgical Pain were examined by multivariate Logistic regression analysis. A predictive model and nomogram were respectively established. The internal validation of the model was performed using the validation set and the Bootstap method, and 200 patients admitted from January to May 2024 were selected for the external validation. Results: Age, previous surgical history, preoperative chronic pain, surgical approach, and surgical duration were independent risk factors for Acute Postsurgical Pain in patients with hepatectomy. The area under the ROC curve (AUC) of the training set model was 0.764, with a sensitivity of 80.1% and a specificity of 58.6%. After Bootstrap correction, the AUC of the model was 0.765, the sensitivity was 76.9%, and the specificity was 65.5%. The results of the Hosmer-Lemeshow test for the training set showed that χ2=9.830, P=0.277. The AUC of the validation set was 0.785, the sensitivity was 77.1%, and the specificity was 66.2%. The results of the Hosmer-Lemeshow test for the validation set showed that χ2=3.399, P=0.907. The AUC of the externally validated and corrected dataset was 0.709, the sensitivity was 76.5%, and the specificity was 58.7%. The results of the Hosmer-Lemeshow test showed that χ2=7.590, P=0.475. Conclusion: The risk prediction model constructed in this study has good performance and can provide reference for clinical medical staff to early predict Acute Postsurgical Pain in patients with hepatectomy.

Key words: Acute Postsurgical Pain; hepatectomy; risk prediction model; risk factors