Nomogram for preoperative estimation of liver cirrhosis in hepatitis B virus-related hepatocellular carcinoma patients

Rongyun Mai, Jiazhou Ye, Rong Liang, Jie Zeng, Zhongrong Long, Xianmao Shi, Tao Bai, Lequn Li, Guobin Wu, Feixiang Wu

Abstract


Objective: The degree of liver cirrhosis is one of the mostimportant diagnostic and prognostic assessments in chronicliver disease. Among the etiologies of liver cirrhosis, hepatitis Bvirus (HBV) infection-induced liver damage is most commonin Asia-Pacific regions, particularly in China. Many currentconventionally used preoperative estimation of liver cirrhosismodels, such as the model for end-stage liver disease (MELD)score, aspartate aminotransferase to platelet ratio index (APRI)score, fibrosis index based on the 4 factor (FIB-4), and Fornsindex have limitations. An accurate preoperative individualizedestimation of liver cirrhosis is very important for surgeonsto precisely plan and perform liver resection procedures.This study aimed to develop a nomogram for preoperativeestimation of liver cirrhosis in HBV-related HCC patients.

Methods: Data on 1,044 consecutive HBV-related HCCpatients who underwent curative liver resection at AffiliatedTumor Hospital of Guangxi Medical University betweenSeptember 2013 and December 2016 were included in thisstudy. All patients were assigned into a derivation set(n = 783) and validation set (n = 261) according to the 3:1matching principle. In the derivation set, univariate andmultivariate logistic analyses were performed to identify therisk factors associated with liver cirrhosis. The β coefficientsof multivariate logistic analysis were used to construct anovel nomogram model for estimating liver cirrhosis risk.Next, a validation set was conducted to validate the model’sperformance. The predictive discrimination and calibrationability of the novel nomogram model were assessed in termsof its area under the receiver operating characteristic curve(AUC) and calibration curve and compared with four currentlyconventionally used prediction models for liver cirrhosis.

Results: A total of 424 patients (40.6%) were confirmed tohave liver cirrhosis after surgery in the entire set, including329 patients (42.0%) in the derivation set and 95 patients (36.4%)in the validation set. The baseline characteristics between thetwo sets were not significantly different. In the derivationset, univariate and multivariate analyses showed that the riskfactors associated with liver cirrhosis were positive hepatitis Bsurface antigen [OR: 1.725; 95% confidence interval (95% CI):1.030–2.875; P = 0.036], platelet count (OR: 0.994; 95% CI:0.991–0.996; P < 0.001), prothrombin time (OR: 1.258; 95%CI: 1.054–1.501; P = 0.011), and clinically significant portalhypertension (OR: 2.794; 95% CI: 1.049–7.442; P = 0.040). Byincorporating these four factors, the nomogram showed anarea under the receiver operating characteristic curve (AUC)of 0.707 (95% CI 0.670–0.743; P < 0.001) and 0.721 (95% CI0.646–0.796; P < 0.001) in preoperative estimation of livercirrhosis in the derivation and validation sets, respectively,and showed satisfactory goodness-of-fit calibration curves.Compared to current conventionally used prediction models,the AUC of the nomogram (0.716) for predicting liver cirrhosiswas significantly greater than that of the MELD score (0.557),APRI score (0.598) , FIB-4 (0.597), and Frons (0.665) for thederivation set and superior in the validation set (correspondingAUC: 0.714 vs. 0.483–0.583).

Conclusions: This novel nomogram enables preoperativeprediction of liver cirrhosis in HBV-related HCC patients,which is critical for surgeons to precisely plan and performliver resection procedures.

DOI: 10.20892/j.issn.2095-3941.2018.S083


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