摘要
BACKGROUND: The prognostic prediction of liver transplantation(LT) guides the donor organ allocation. However, there is currently no satisfactory model to predict the recipients’ outcome, especially for the patients with HBV cirrhosis-related hepatocellular carcinoma(HCC). The present study was to develop a quantitative assessment model for predicting the post-LT survival in HBV-related HCC patients.METHODS: Two hundred and thirty-eight LT recipients at the Liver Transplant Center, First Affiliated Hospital, Zhejiang University School of Medicine between 2008 and 2013 were included in this study. Their post-LT prognosis was recorded and multiple risk factors were analyzed using univariate and multivariate analyses in Cox regression.RESULTS: The score model was as follows: 0.114×(Child-Pugh score)-0.002×(positive HBV DNA detection time)+0.647×(number of tumor nodules)+0.055×(max diameter of tumor nodules)+0.231×ln AFP+0.437×(tumor differentiation grade).The receiver operating characteristic curve analysis showed that the area under the curve of the scoring model for predicting the post-LT survival was 0.887. The cut-off value was 1.27, which was associated with a sensitivity of 72.5% and a specificity of 90.7%, respectively.CONCLUSION: The quantitative score model for predicting post-LT survival proved to be sensitive and specific.
BACKGROUND: The prognostic prediction of liver transplantation(LT) guides the donor organ allocation. However, there is currently no satisfactory model to predict the recipients’ outcome, especially for the patients with HBV cirrhosis-related hepatocellular carcinoma(HCC). The present study was to develop a quantitative assessment model for predicting the post-LT survival in HBV-related HCC patients.METHODS: Two hundred and thirty-eight LT recipients at the Liver Transplant Center, First Affiliated Hospital, Zhejiang University School of Medicine between 2008 and 2013 were included in this study. Their post-LT prognosis was recorded and multiple risk factors were analyzed using univariate and multivariate analyses in Cox regression.RESULTS: The score model was as follows: 0.114×(Child-Pugh score)-0.002×(positive HBV DNA detection time)+0.647×(number of tumor nodules)+0.055×(max diameter of tumor nodules)+0.231×ln AFP+0.437×(tumor differentiation grade).The receiver operating characteristic curve analysis showed that the area under the curve of the scoring model for predicting the post-LT survival was 0.887. The cut-off value was 1.27, which was associated with a sensitivity of 72.5% and a specificity of 90.7%, respectively.CONCLUSION: The quantitative score model for predicting post-LT survival proved to be sensitive and specific.
基金
supported by grants from National S&T Major Project(2012ZX10002017)
the Foundation for Innovative Research Groups of the National Natural Science Foundation of China(81121002)
the National Natural Science Foundation of China(81200331)