Construction of a Risk Prediction Model for Intra-abdominal Infection in Patients After Hepatectomy

doi:10.12104/j.issn.1674-4748.2025.12.012

Abstract：Objective: To explore the risk factors for intra-abdominal infection in patients after hepatectomy and to construct a risk prediction model. Methods: A total of 182 patients who underwent hepatectomy at a tertiary hospital from June 2022 to December 2023 were selected as the study subjects. According to whether they developed intra-abdominal infection postoperatively, the patients were divided into an infected group (26 cases) and a non-infected group (156 cases). Logistic regression analysis was used to identify the risk factors for intra-abdominal infection after hepatectomy, and a nomogram was constructed using R software. The model was validated using receiver operating characteristic (ROC) curves and calibration models. Results: The incidence of intra-abdominal infection after hepatectomy was 14.29%. Logistic regression analysis showed that the risk factors included age ≥60 years, comorbid diabetes, operation time ≥300 minutes, serum albumin level <35 g/L, and intra-abdominal drainage tube placement duration ≥7 days. Based on these risk factors, a nomogram prediction model was constructed. The nomogram for predicting the risk of intra-abdominal infection after hepatectomy was drawn based on the predicted probability Logit(P). The area under the ROC curve (AUC) was 0.814 [95% CI (0.653, 0.912)], with a sensitivity of 85.3% and a specificity of 84.5%, indicating that the model has good predictive ability. Conclusion: Due to multiple factors, the risk of intra-abdominal infection is high in patients after partial hepatectomy. Using the constructed risk prediction model can help identify high-risk patients early and provide targeted interventions to reduce the occurrence of intra-abdominal infections.