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

HPB (Oxford). 2021 Jan;23(1):134-143. doi: 10.1016/j.hpb.2020.05.008. Epub 2020 Jun 18.


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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Carcinoma, Hepatocellular* / surgery
  • Hepatectomy / adverse effects
  • Humans
  • Liver Failure* / diagnosis
  • Liver Failure* / etiology
  • Liver Failure* / prevention & control
  • Liver Neoplasms* / surgery
  • Postoperative Complications
  • Retrospective Studies