Development and Validation of a Risk Prediction Model Based on Inflammatory and Nutritional Composite Indicators for...

A plethora of studies have demonstrated an association between preoperative inflammatory immunonutritional status and the prognosis of patients with hepatocellular carcinoma. Nonetheless, there is a paucity of research examining the predictive value of inflammatory immunonutritional indicators for postoperative liver failure in this patient population. This study seeks to identify independent predictors of post hepatectomy liver failure (PHLF) in patients with hepatocellular carcinoma and to develop a nomogram model.

Clinical data were collected from 760 patients diagnosed with hepatocellular carcinoma who underwent surgical treatment at a hospital in China between January 2020 and January 2024. The dataset was randomly divided into a training set (n=570, 75%) and a validation set (n=190, 25%). To identify independent predictors of PHLF in these patients, univariate analysis and least absolute shrinkage and selection operator (LASSO) regression were employed. Subsequently, a multivariate logistic regression model was developed to construct a predictive model. The predictive performance of the nomogram was evaluated using receiver operating characteristic (ROC) curve analysis, calibration curve assessment, and decision curve analysis (DCA).

AAPR, ALBI, GAR, LMR, PNI, INR, APTT, and TT are independent factors associated with PHLF in patients with hepatocellular carcinoma. The C indices for the training and validation datasets were 0.691 (95% CI: 0.634-0.747) and 0.680 (95% CI: 0.556-0.804), respectively. The area under the curve (AUC) and calibration curve analyses demonstrated the nomogram's accuracy in predicting PHLF in this patient population. Furthermore, DCA indicated that the model provides a significant clinical net benefit. A comparison was made of the predictive efficacy of the nomogram prediction model and the associated composite liver function score. ROC curves were plotted for the nomogram prediction model, Child-Pugh score and ALBI score, and AUC values were calculated, which were 0.686 (95% CI 0.635-0.737) for the prediction model, 0.558(95% CI 0.512-0.603) for the Child-Pugh score. The AUC for ALBI score was 0.577 (95% CI 0.530-0.624), indicating that this nomogram prediction model was more effective than other scoring systems in predicting the study population in our center. In this study population, the nomogram model demonstrated an AUC of 0.707 (95% CI 0.620-0.794) for Child-Pugh score grade A and 0.572 (95% CI 0.501-0.643) for Child-Pugh score grade B. For tumors with a diameter of less than 5 cm, the AUC was 0.679 (95% CI 0.608-0.749), and for patients with tumors with a diameter of at least 5 cm, the AUC was 0.715 (95% CI 0.643-0.787).

We have developed an innovative nomogram model designed to predict the incidence of PHLF in patients diagnosed with hepatocellular carcinoma. This nomogram has a good predictive value for PHLF in HCC patients and is important for clinicians to manage patients after hepatectomy.