AUTHOR=Wang Danni , Ma Qingmei , Zhang Lei , Hou Jinxia , Zhang Zhemei , Wu Jing , Zhang Jingheng 

TITLE=Establishing a nomogram for post-hepatectomy liver failure prediction in hepatocellular carcinoma based on pre-operative and perioperative parameters

JOURNAL=Frontiers in Medicine

VOLUME=Volume 12 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1670396

DOI=10.3389/fmed.2025.1670396

ISSN=2296-858X

ABSTRACT=Objective:Post-hepatectomy liver failure (PHLF) is a severe complication for hepatocellular carcinoma (HCC) patients post-surgery. This study explores PHLF risk factors and creates a nomogram for prediction using pre- and intraoperative factors.MethodsWe retrospectively analyzed 654 patients who underwent hepatectomy. Eligible patients were randomly divided into training and internal validation cohorts in a 7:3 ratio. Key variables for nomogram construction were determined through integrated Least Absolute Shrinkage and Selection Operator (LASSO) and multivariate logistic regression analyses. The nomogram's performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis.ResultsAmong 228 eligible patients included in the study, 55 developed PHLF. Seven independent predictors were identified and incorporated into the nomogram: liver cirrhosis, total bilirubin (TBIL), prothrombin time (PT), Albumin-Bilirubin (ALBI), fibrosis-4 index (FIB4), ascites, and intraoperative blood loss. The nomogram demonstrated excellent predictive performance, with area under the curve (AUC) of 0.880 in the training cohort and 0.879 in the validation cohort. Calibration curve and decision curve analysis show that nomogram has significant clinical application value in predicting PHLF probability.ConclusionWe have developed and validated a novel PHLF risk prediction model that integrates pre-operative and intraoperative parameters, along with various liver function scoring systems, enabling more comprehensive and accurate prediction of PHLF risk in HCC patients.