Development and validation of a non-invasive model for diagnosing HBV-related liver cirrhosis

Abstract

BackgroundLiver cirrhosis is closely related to the abnormal liver function and occurrence of liver cancer. Accurate non-invasive assessment of liver cirrhosis is of great significance for preventing disease progression and treatment decision-making. We aim to develop and validate a non-invasive diagnostic model for liver cirrhosis in patients with chronic hepatitis B (CHB). MethodsFrom July 2015 to April 2017, seven-hundred fifty-four patients with primary HBV-related liver cancer who underwent hepatectomy were reprospectively recruited. All patients were examined with 2D-SWE and serologic testing preoperatively, which were utilized for measurement of liver stiffness and serum fibrosis models. The stage of liver fibrosis was evaluated using a resected liver specimen. Least absolute shrinkage and selection operator (Lasso) regression was used for feature selection and binary logistic regression analysis was chosen to build a diagnostic model, which was presented as a nomogram and evaluated for calibration, discrimination and clinical usefulness. The performance of noninvasive model was then prospectively validated in an independent cohort (361 patients) by the ROC curve analysis. ResultsThe diagnostic model, which consists of 5 selected clinical characteristics (PIII-NP, IV-C, Hyaluronan, Platelet and Liver stiffness), showed the strongest correlation with liver fibrosis stage (ρ = 0.702, P < 0.05). Compared with APRI, FIB-4, King’s Score, and Forns Index, the model presented the optimal discrimination and the best predictive performance with the highest AUC in the training cohort (0.866, 95%CI 0.840–0.892, P < 0.05) and validation cohorts (0.852, 95%CI 0.813–0.890, P < 0.05). Decision curve analysis demonstrated that nomogram based on the model was extremely useful for diagnosing cirrhosis in patients with chronic hepatitis B. ConclusionThis study proposes a non-invasive diagnostic model that incorporates the clinical predictors which can be conveniently used in the individualized diagnosis of HBV-related liver cirrhosis.