A novel nomogram based on preoperative parameters to predict posthepatectomy liver failure in patients with hepatocellular carcinoma

Abstract

BackgroundPosthepatectomy liver failure is one of the main causes of death in patients after hepatectomy. This study intends to establish a prediction model to predict the risk of posthepatectomy liver failure and provide a scientific basis for further reducing the incidence of posthepatectomy liver failure. MethodsThis was a retrospective analysis of 1,172 patients with hepatocellular carcinoma undergoing partial hepatectomy. Using univariate and multivariate logistic regression analyses and stepwise regression, a prediction model for posthepatectomy liver failure was established based on the independent risk factors for posthepatectomy liver failure and validated by bootstrapping with 100 resamples, and the receiver operating characteristic curve was used to evaluate the predictive value of the prediction model. ResultsThe incidence rate of posthepatectomy liver failure was 22.7% (266/1172). The results showed that the indocyanine green retention rate at 15 minutes (odds ratio = 1.05, P = .002), alanine transaminase (odds ratio = 1.02, P < .001), albumin rate (odds ratio = 0.92, P < .001), total bilirubin (odds ratio = 1.04, P < .001), prothrombin time (odds ratio = 2.44, P < .001), aspartate aminotransferase-neutrophil ratio (odds ratio = 0.95, P < .001), and liver fibrosis index (odds ratio = 1.35, P < .001) were associated with posthepatectomy liver failure. These 7 independent risk factors for posthepatectomy liver failure were integrated into a nomogram prediction model, the predictive efficiency for posthepatectomy liver failure (area under the curve = 0.818, 95% confidence interval 0.789-0.848) was significantly higher than in other predictive models with a liver fibrosis index (area under the curve = 0.651), indocyanine green R15 (area under the curve = 0.669), albumin-bilirubin score (area under the curve = 0.709), albumin-indocyanine green evaluation score (area under the curve = 0.706), model for end-stage liver disease score (area under the curve = 0.636), and Child‒Pugh (area under the curve = 0.551) (all P < .001). The risk of posthepatectomy liver failure in the high-risk posthepatectomy liver failure group (score ≥152) was higher than that in the posthepatectomy liver failure low-risk group (score <152). ConclusionThis study developed and validated a nomogram model to predict the risk of posthepatectomy liver failure before surgery that can effectively predict the risk of posthepatectomy liver failure in patients with hepatocellular carcinoma.