A novel model for predicting posthepatectomy liver failure based on liver function and degree of liver resection in patients with hepatocellular carcinoma

Background The permissible liver resection rate for preventing posthepatectomy liver failure (PHLF) remains unclear. We aimed to develop a novel PHLF-predicting model and to strategize hepatectomy for hepatocellular carcinoma (HCC). Methods This retrospective study included 335 HCC patients who underwent anatomical hepatectomy at eight institutions between 2013 and 2017. Risk factors, including volume-associated liver-estimating parameters, for PHLF grade B–C were analyzed in a training set (n = 122) via multivariate analysis, and a PHLF prediction model was developed. The utility of the model was evaluated in a validation set (n = 213). Results Our model was based on the three independent risk factors for PHLF identified in the training set: volume-associated indocyanine green retention rate at 15 min, platelet count, and prothrombin time index (the VIPP score). The areas under the receiver operating characteristic curve of the VIPP scores for severe PHLF in the training and validation sets were 0.864 and 0.794, respectively. In both sets, the VIPP score stratified patients at risk for severe PHLF, with a score of 3 (specificity, 0.92) indicating higher risk. Conclusion Our model facilitates the selection of the appropriate hepatectomy procedure by providing permissible liver resection rates based on VIPP scores.This research was supported by the Japan Agency for Medical Research and Development (AMED, grant number JP19fk0210051) and the Japanese Society for the Promotion of Science (JSPS KAKENHI, grant number JP18K08706