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Abstract Background This study aimed to evaluate the diagnostic abilities of the non-invasive serum biomarkers to predict liver fibrosis staging and evaluate the progress of hepatitis B. Methods We enrolled 433 patients with chronic HBV infection had complete medical data available for the study, who underwent percutaneous liver biopsy. The extent of fibrosis was assessed using the modified METAVIR score. The predictive values of the non-invasive serum biomarkers were evaluated by the areas under the receiving operator characteristics curves (AUROCs) with 95% confidence intervals. Results The proportion of males with progressive stages of liver fibrosis was relatively larger, and the average age of patients with cirrhosis stages is older than the non-cirrhotic stages. We found PLT, GGT, ALP, TB, FIB4 and GPR to be significantly associated with liver fibrosis in our cohort. GGT showed a sensitivity of 71.4% and specificity of 76.7% in distinguishing cirrhosis (F4) from non-cirrhotic stages (F1-3), with an AUROC of 0.775 (95%CI 0.711–0.840).The AUROCs of the GPR in distinguishing cirrhosis (F4) from non-cirrhotic stages (F1-3) was 0.794 (95%CI 0.734–0.853), but it had a lower sensitivity of 59.2%. Additionally, GGT, FIB4, and GPR could differentiate advanced fibrosis (F3-4) from non-advanced fibrosis (F1-2) among individuals with chronic hepatitis B, with AUROCs of 0.723 (95%CI 0.668–0.777), 0.729 (95%CI 0.675–0.782), and 0.760 (95%CI: 0.709–0.811) respectively. Conclusions GGT was a better biomarker to distinguish cirrhosis (F4) from non-cirrhotic stages (F1-3), while GPR was a better biomarker to identify advanced fibrosis (F3-4) and non-advanced fibrosis (F1-2) in patients with chronic hepatitis B.

Abstract Background China is a region with a high incidence of tuberculosis, and the incidence of IBD has also been rising rapidly in recent years. Differentiating Crohn’s disease(CD) from intestinal tuberculosis (ITB) has become a very challenging issue. We aimed to develop and assess a diagnostic nomogram to differentiate between CD and ITB to improve the accuracy and practicability of the model. Methods A total of 133 patients (CD 90 and ITB 43) were analyzed retrospectively. Univariate and multivariate logistic regression analysis was included to determine the independent predictive factors and establish the regression equation. On this basis, the nomogram prediction model was constructed. The discrimination, calibration and clinical efficiency of the nomogram were assessed using area under the curve(AUC), C-index, calibration curve, decision curve analysis (DCA) and clinical impact curve. Results T-SPOT positive, cobblestone appearance, comb sign and granuloma were significant predictors in differentiating CD from ITB. Base on the above independent predictors, a diagnostic nomogram was successfully established. The sensitivity, specificity, accuracy of the prediction model are 94.4%, 93.0%, 94.0% respectively. The AUC and the C-index of the prediction model are both 0.988, which suggest that the model had a good discrimination power. The calibration curve indicated a high calibration degree of the prediction model. The DCA and clinical impact curve indicated a good clinical efficiency of the prediction model which could bring clinical benefits. Conclusion A nomogram prediction model for distinguishing CD from ITB was developed and assessed, with high discrimination, calibration and clinical efficiency. It can be used as an accurate and convenient diagnostic tool to distinguish CD from ITB, facilitating clinical decision-making.