A Logistic Regression Model for The Prediction of HBV-Related Cirrhosis

Abstract Background: Cirrhosis is one of the most severe complications at the late stage of chronic HBV infection. The liver biopsy is the gold standard for the diagnosis of liver cirrhosis. However, a liver biopsy is associated with the risk of severe complications and a high cost. It is therefore necessary to find several biomarkers for the diagnosis of HBV-related cirrhosis. Methods: The research was proceeded to evaluate the diagnostic value of hematological parameters to find the surrogate markers in HBV-related cirrhosis. The research was proceeded on the training set, which was recruited from Zhongnan Hospital, including 102 HBV-related cirrhosis and 102 healthy individuals. The levels of selected hematological parameters were analyzed. The receiver operating characteristic curves were generated to evaluate the diagnostic effectiveness of these parameters. A logistic regression model was built and validated using four validation sets consisting of 261 patients.Results: The result show that the level of RDW, MPV, MPV/PC ratio, PLR and NLR were all significantly higher in HBV-related cirrhosis patients compared to healthy individuals. Most of these parameters owned a moderate AUC in HBV-related cirrhosis patients. However, their diagnostic sensitivities or specificities were unsatisfactory. Therefore, a logistic regression model was built by combining these hematological parameters. The model showed great diagnostic value with the AUC of 0.987, sensitivity of 96.1% and specificity of 95.1%. Besides, the other four validation sets were generated to validate the logistic regression model and all showed good AUC with moderate specificities and sensitivities. Conclusions: The data indicate that the model might be substantially useful for the diagnosis of HBV-related cirrhosis.