A nomogram model for predicting advanced liver fibrosis in patients with hepatitis B: A multicenter study.

Abstract

Background: Biopsy is the gold standard method for diagnosing liver fibrosis. FibroScan is a non-invasive method of diagnosing liver fibrosis, but it still faces some limitations. This study aimed to establish a nomogram model and identify patients at high risk of advanced liver fibrosis associated with hepatitis B infection.

Methods: Data were collected from 375 patients with hepatitis B who underwent liver biopsy. Patients were divided randomly into the training (n = 263) and validation sets (n = 112). Their demographic and clinical characteristics were analyzed using the least absolute shrinkage and selection operator regression (LASSO). A nomogram model was established to predict the fibrosis stage, and its performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA) and was compared with other recognized models.

Results: In total, 209 patients with non-advanced fibrosis (S0-1) and 166 patients with advanced fibrosis (S ≥ 2) were included. Hyaluronic acid (HA), laminin, total cholesterol (TC), platelet, and age were entered into the nomogram model based on the LASSO analysis. The nomogram model for predicting advanced fibrosis exhibited a relatively high AUC in the training set. Compared with FIB4 and APRI, the nomogram model showed a better agreement between the actual status and predicted status based on the calibration curve. The nomogram model showed an AUC similar to FibroScan in the validation cohort, and showed high clinical net benefits in the training and validation sets.

Conclusion: Our nomogram model can help identify patients with hepatitis B and advanced liver fibrosis.