Establishment of risk stratification model for developing cirrhosis in patients with HBeAg-negative chronic hepatitis B virus infection / 中华检验医学杂志

ABSTRACT

Objective:To explore the independent predictive factors of cirrhosis in patients with hepatitis B e antigen (HBeAg)-negative chronic hepatitis B virus (HBV) infection, and establish a nomogram model based on clinical laboratory data and analyze the predictive value of this model.Methods:The laboratory data of 596 patients with HBeAg-negative chronic HBV infection and 677 patients with hepatitis B cirrhosis, who were hospitalized in the First Hospital of China Medical University from 2011 to 2021, were retrospectively analyzed. Patients were randomly divided into training group ( n=892) and validation group ( n=381) at the ratio of 7∶3. The independent predictive factors of cirrhosis were analyzed by univariate logistic regression, multiple collinearity test and multivariate logistic regression. The nomogram model was established and the prediction value of this model was evaluated. Results:According to multivariate logistic regression analysis, hepatitis B core antibody ( OR=1.492, 95% CI 1.316-1.706), glutamine transpeptidase ( OR=1.015, 95% CI 1.010-1.022), platelet ( OR=0.986, 95% CI 0.982-0.988) and albumin ( OR=0.853, 95% CI 0.824-0.882) were independent predictors of cirrhosis ( P<0.05), and the nomogram was established based on the four indicators. Receiver operating characteristic curves showed that area under the curve of the nomogram was 0.933 (95% CI 0.916-0.950), and that of the validation group was 0.931 (95% CI 0.905-0.956). The calibration curves indicated the nomogram model was highly consistent with the actual outcome. Decision curves and clinical impact curves confirmed that the model had high net benefit and good clinical application performance. Conclusions:Hepatitis B core antibody, glutamine transpeptidase, platelet and albumin are independent predictors of cirrhosis among patients with HBeAg-negative chronic HBV infection. The newly developed nomogram model based on these factors could be used to predict cirrhosis risk in these patients.