Construction and evaluation of a nomogram risk prediction model for post-hepatectomy liver failure in hepatocellular carcinoma patients with hepatitis B infection at low viral load

Objective‍ ‍To investigate the influencing factors for post-hepatectomy liver failure (PHLF) in hepatocellular carcinoma (HCC) patients with HBV infection at low viral load, and then construct a risk prediction model. Methods‍ ‍A total of 403 HCC patients who underwent initial hepatectomy in the First Affiliated Hospital of Army Medical University between January 1, 2015 and March 1, 2023 were recruited, and randomly assigned into a training set and a verification set in a ratio of 7:3. Lasso regression and multivariate logistic regression analyses were applied to screen the risk factors for occurrence of PHLF, and based on these identified factors, a nomogram prediction model was constructed. Receiver operating characteristic (ROC) curve analysis (area under the curve, AUC), calibration curve analysis, decision curve analysis, and clinical impact curve analysis were preformed to assess the predictive efficacy of the model. Results‍ ‍History of anti-viral therapy, history of drinking, logHBsAg, and international normalized ratio (INR) were independent influencing factors for the occurrence of PHLF in HCC patients with HBV infection at low viral load. The model established based on these indicators demonstrated excellent discriminative capabilities in both the training and validation sets, with an AUC value of 0.744 and 0.737, respectively. Calibration curve analysis indicated our model of high accuracy (training: P=0.995; validation: P=0.701), and decision curve analysis and clinical impact curve analysis displayed that our model provided greater clinical benefit. Conclusion‍ ‍Our model can effectively evaluate the risk of PHLF in HCC patients with HBV infection at low viral load, and shows good predictive performance, which has certain guiding significance for timely identification of high-risk populations.