Abstract: Objective The aim of this study was to explore the factors impacting liver fibrosis progress in patients with chronic hepatitis B (CHB). Methods A total of 135 patients with CHB were encountered in our Department of Infectious Diseases, Second Affiliated Hospital, Anhui Medical University, between 2010 and 2016, and all patients underwent liver biopsy, and followed-up for 5(7,11)years. The multivariate Logistic regression analysis was carried out, the prediction model was established and the area under the receiver operating characteristic (ROC) curve (AUC) was calculated to evaluate the diagnostic performance of the model. Results At the presentation, the histopatholotical examination showed non-fibrosis in 81 cases and significant liver fibrosis in 54 cases in our series; at the end of follow-up, we found non-cirrhosis in 111 cases and liver cirrhosis in 24 cases(from non-fibrosis in 7 cases and from significant fibrosis in 17 cases) based on clinical diagnosis; there were significant differences respect to age, antiviral treatment, serum HBV DNA loads, platelet counts (PLT), plasma prothrombin time international normalized ratio (INR), activated partial thromboplastin time (APTT) and erythrocyte distribution width (RDW) between patients with non-cirrhosis and cirrhosis (all P < 0.05); the Logistic regression analysis showed that INR (P = 0.010, OR = 369.352), APTT(P = 0.001,OR = 1.169), RDW (P = 0.035, OR = 1.402), PLT (P = 0.018, OR = 0.989), age (P = 0.024, OR =1.052), platelet to lymphocyte ratio (P/L, P = 0.044, OR = 0.983) and antiviral or not (P = 0.000, OR = 7.600) were the independent risk factors for liver cirrhosis; thereby, we established a prediction model as follows: Logit (P) = - 17.407 + 0.528×INR+0.161×APTT+0.079×Age + 2.401×Antiviral or not (no = 0, yes = 1); the AUC of the model in predicting liver cirrhosis was 0.872, with the sensitivity of 91.7% and the specificity of 75.7%. Conclusion The age and antiviral therapy could be the important factors impacting liver cirrhosis occurrence, and early intervention might prevent the disease progression.

Key words: Liver cirrhosis, Hepatitis B, Noninvasive prediction model, Diagnosis