ObjectiveTo identify the risk factors for liver failure in patients with recurrent hepatocellular carcinoma (HCC) undergoing interventional therapy after hepatectomy, and to develop a predictive nomogram. MethodsThe patients who underwent interventional therapy for recurrent HCC after hepatectomy at Haian People’s Hospital Affiliated to Nantong University from December 2018 to January 2023 were retrospectively enrolled. The patients were randomly assigned to a training set and a validation set in a 7:3 ratio. Univariate and multivariate logistic regression analyses were performed on the training set to identify the risk factors for secondary liver failure after interventional therapy for HCC. A nomogram prediction model was subsequently developed based on the identified risk factors. The discriminative ability of the predictive nomogram was evaluated using the area under the receiver operating characteristic curve (AUC), while calibration curves and decision curve analysis (DCA) were applied to assess calibration performance and clinical utility, respectively. ResultsA total of 458 patients were included (321 patients in the training set, 137 patients in the validation set), among whom 108 (23.58%) developed liver failure. Multivariate logistic regression analysis identified the following risk factors for liver failure (all P<0.05): diabetes mellitus, liver cirrhosis, Child-Pugh grade C, intraoperative blood transfusion, prolonged hepatic inflow occlusion, remnant liver volume <40%, and elevated total bilirubin level. The nomogram constructed based on these factors achieved AUC (95%CI) of 0.887 (0.843, 0.921) in the training set and 0.820 (0.735, 0.880) in the validation set. The calibration curves approximated the ideal line, and the Hosmer-Lemeshow test indicated good agreement between predictions and observations (training set: χ2=8.849, P=0.355; validation set: χ2=8.362, P=0.399). Decision curve analysis demonstrated a high net clinical benefit within threshold probability ranges of 0.02–0.93 for the training set and 0.02–0.83 for the validation set. ConclusionsThis study suggests that for patients with high-risk factors—such as diabetes, liver cirrhosis, Child-Pugh class C, intraoperative blood transfusion, prolonged hepatic inflow occlusion, small future liver remnant volume, or elevated total bilirubin levels, who undergo interventional therapy after liver cancer resection, close attention should be paid to the risk of liver failure. The nomogram prediction model constructed based on these factors demonstrates a good performance in early risk assessment of liver failure following interventional therapy.