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Updated: Jan 14, 2026
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Establishing a nomogram for post-hepatectomy liver failure prediction in hepatocellular carcinoma based on
Danni Wang1,2, Qingmei Ma1,2, Lei Zhang1,2
1Department of Clinical Laboratory, Gansu Provincial Hospital, Lanzhou, Gansu, China.
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.
Methods:
We 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.
Results:
Among 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.
Conclusion:
We 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.
