Modeling and validation of a survival prediction model for hepatocellular carcinoma patients after liver transplantation based on up-to-seven criteria
10.16781/j.0258-879x.2018.07.0745
- Author:
Jun-Feng DONG
1
Author Information
1. Department of Organ Transplantation, Changzheng Hospital, Navy Medical University (Second Military Medical University)
- Publication Type:Journal Article
- Keywords:
ATMD model;
Hepatocellular carcinoma;
Liver neoplasms;
Liver transplantation;
Long-term survival;
Prognosis
- From:
Academic Journal of Second Military Medical University
2018;39(7):745-752
- CountryChina
- Language:Chinese
-
Abstract:
Objective To establish a long-term survival prediction model for hepatocellular carcinoma (HCC) patients after liver transplantation based on up-to-seven (Up7) criteria, and to validate the prediction model in different liver transplantation criteria, so as to assist clinical decision-making for the treatment of HCC. Methods We retrospectively analyzed the clinical and follow-up data of 251 HCC patients who underwent liver transplantation with Up7 criteria. Stepwise regression method was used to conduct multivariate Cox regression analysis to obtain the independent predictors of long-term survival after HCC liver transplantation, and to establish the survival Cox regression prediction model. R 3.4.3 software was used to score the prediction model, and the decision tree technique was used to determine the cut-off value. The Kaplan-Meier survival curve of the HCC patients after liver transplantation was drawn to validate the prediction model in different criteria (Shanghai Fudan criteria, University of California, San Francisco [UCSF] criteria and Italy Milancriteria), and the difference between groups was analyzed by log-rank test. The receiver operating characteristic (ROC) curve was used to test the predictive effectiveness of the model. Results Multivariate Cox regression analysis suggested that α-fetoprotein (AFP), total bilirubin (T-Bil), microvascular invasion (MVI) and tumor maximal diameter (Diameter) were the independent predictors of long-term survival of HCC liver transplant recipients after liver transplantion. We built the ATMD (AFP, T-Bil, MVI, Diameter) model using these factors: h (t, x)=h0 (t) exp (0.284×Diameter [cm]+0.773×MVI [yes=1; no=0]+0.404×lg AFP [ng/mL]+0.003×T-Bil [μmol/L]). The cut-off value of ATMD model was 1.44. The scores being more than 1.44 were defined as the high-risk group, and scores being 1.44 or less were defined as the low-risk group. The cases in the high-risk and low-risk groups who met the Up7 criteria, Shanghai Fudan criteria, UCSF criteria and Italy Milan criteria had 87 and 164, 33 and 144, 29 and 134, and 29 and 131, respectively. Kaplan-Meier survival analysis showed that the cumulative survival rates of the liver transplantation recipients with Up7 criteria, Shanghai Fudan criteria, UCSF criteria or Milan criteria were significantly different between the high-and low-risk groups (P<0.001, P=0.008, P<0.001, P=0.001). The areas under the ROC curve of the three-year survival of the liver transplantation recipients predicted by ATMD model were 76.63%, 75.87%, 73.32% and 69.41%, respectively. Conclusion The ATMD model has a good survival prediction ability for the HCC patients meeting Up7 criteria, Shanghai Fudan criteria, UCSF criteria or Milan criteria. It is of great significance for preoperative decision-making and postoperative risk assessment of HCC liver transplantation recipients meeting the above criteria.
