Development and validation of a nomogram model for predicting acute hepatic dysfunction post-intervention in primary hepatocellular carcinoma: a retrospective case-control study.

objectivesTo identify independent risk factors for acute liver function damage (ALFD) following interventional treatment in hepatocellular carcinoma (HCC) patients and to develop and validate a novel Nomogram predictive model.

methodsA retrospective analysis of 362 HCC patients diagnosed from January 2021 to October 2023 was conducted, dividing them into a training set (n = 253) and an internal validation set (n = 109) using the bootstrap method. Significant factors were first screened through univariate analysis and the Least Absolute Shrinkage and Selection Operator (LASSO) method, followed by Logistic regression to determine independent risk factors. A Nomogram was constructed using R language, with its discriminative ability and consistency assessed by the area under the ROC curve (AUC) and calibration plots, and its clinical utility evaluated through decision curve analysis (DCA).

resultsThree independent influencing factors were identified from the multivariate logistic regression analysis of the training set: Child-Pugh (OR: 0.34, 95%CI:0.21-0.52, P < 0.001), APRI (OR: 3.47,95%CI:2.07-6.51,P < 0.001), and FIB-4 (OR: 1.30, 95%CI:1.16-1.47, P < 0.001), which were included in the Nomogram. This Nomogram predicted an AUC value for ALFD post-TACE in HCC patients of 0.900 in the training set and 0.854 in the internal validation set. Calibration curves also demonstrated that the nomogram's predictions were close to the ideal curve, with predictions consistent with actual outcomes, and the DCA curve showed benefits for all patients. These conclusions were also confirmed in the validation set.

conclusionThe newly developed Nomogram provides a highly predictive tool for risk assessment of ALFD post-TACE in HCC patients, serving as a potent instrument for personalized diagnosis and treatment.