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Abstract Background The purpose of this study was to explore the risk factors for postoperative infection in patients with primary hepatic carcinoma (PHC), build a nomogram prediction model, and verify the model to provide a better reference for disease prevention, diagnosis and treatment. Methods This single-center study included 555 patients who underwent hepatobiliary surgery in the Department of Hepatobiliary Surgery of Tianjin Third Central Hospital from January 2014 to December 2021, and 32 clinical indicators were selected for statistical analysis. In this study, Lasso logistic regression was used to determine the risk factors for infection after liver cancer resection, establish a predictive model, and construct a visual nomogram. The consistency index (C-index), calibration curve, and receiver operating characteristic (ROC) curve were used for internal validation, and decision curve analysis (DCA) was used to analyze the clinical applicability of the predictive model. The bootstrap method was used for intramodel validation, and the C-index was calculated to assess the model discrimination. Results Among the 555 patients, 279 patients met the inclusion criteria, of whom 48 had a postoperative infection, with an incidence rate of 17.2%. Body mass index (BMI) (P = 0.022), alpha-fetoprotein (P = 0.023), total bilirubin (P = 0.016), intraoperative blood loss (P |
