Development and internal validation of a predictive nomogram for early postoperative bacterial infections following liver transplantation in patients with hepatocellular carcinoma.

Background: Early postoperative bacterial infections (EPBIs) represent a significant complication following liver transplantation (LT) in individuals with hepatocellular carcinoma (HCC). However, existing predictive tools derived from general LT populations fail to account for HCC-specific risk factors, including cirrhosis-associated immune dysfunction compounded by tumor-induced immunosuppression, frequent healthcare exposures from bridging therapies, and surgical complexities unique to HCC patients. This study aimed to develop and internally validate HCC-specific predictive models for EPBI risk stratification. Methods: A retrospective cohort study was conducted on 549 consecutive HCC patients undergoing LT between 2015 and 2025. EPBI was defined as any bacterial infection occurring within 30 days postoperatively. Consecutive patients who met the inclusion criteria were enrolled. Data collectors responsible for assessing preoperative and intraoperative predictors were blinded to postoperative infection outcomes to minimize ascertainment bias, particularly for outcomes that require clinical judgment such as infection classification. Three predictive models were constructed: a conventional logistic regression model (Model 1), a stepwise regression model optimized using the Akaike information criterion (AIC; Model 2), and a least absolute shrinkage and selection operator (LASSO)-Ridge regression model (Model 3). Model performance was evaluated based on discrimination [area under the curve (AUC)], calibration, clinical utility, and internal validation using 10-fold cross-validation and bootstrapping. Results: The cohort had a median age of 55 years [interquartile range (IQR), 49-60 years], with 85.1% male patients. EPBI occurred in 250 patients (45.5%), mainly pulmonary (51.2%) and intra-abdominal (43.3%). Among 468 isolates, Gram-negative (51.7%) and Gram-positive (48.3%) bacteria were similarly distributed. Multivariate analysis identified Child-Pugh class B, prolonged operative time, extended intensive care unit (ICU) stay, and decreased postoperative estimated glomerular filtration rate (eGFR) as independent predictors. Model 2 showed fair-to-good discrimination [AUC 0.784, 95% confidence interval (CI): 0.746-0.822]. At the optimal cutoff, sensitivity was 72.4% (95% CI: 68.2-76.6%), specificity 71.2% (95% CI: 67.1-75.3%), positive predictive value (PPV) 69.8% (95% CI: 65.4-74.2%), and negative predictive value (NPV) 73.1% (95% CI: 69.0-77.2%). Decision curve analysis (DCA) demonstrated greater net benefit than treat-all or treat-none strategies at a 6.3% risk threshold. Conclusions: This study developed and internally validated a nomogram for EPBI in HCC patients undergoing LT. The model demonstrates fair discriminative ability and potential clinical utility, supporting its potential for early risk stratification and targeted prevention strategies in clinical practice. External validation is required before widespread clinical implementation.