LAUSR

Background and Aims Patients with chronic hepatitis B virus infection (CBI) with concurrent nonalcoholic fatty liver disease (NAFLD) is becoming increasingly common in clinical practice, and it is quite important to identify the etiology when hepatitis occurs. A noninvasive diagnostic model was constructed to identify patients who need antihepatitis B virus (HBV) therapies [histologic activity index (HAI) ≥ 4] in patients with CBI with concurrent NAFLD by analyzing clinical routine parameters. Approach and Results In total, 303 out of 502 patients with CBI with concurrent NAFLD proven by liver biopsy from January 2017 to December 2020 in the Tianjin Second People's Hospital were enrolled and they were divided into the HBV-related inflammation (HBV-I) group (HAI ≥ 4,176 cases) and the non-HBV-I group (HAI < 4,127 cases) according to hepatic pathology. The univariate analysis and multivariate logistic regression analysis were performed on the two groups of patients, and then the HBV-I model of patients with CBI with concurrent NAFLD was constructed. The areas under receiver operating characteristic curves (AUROCs) were used to evaluate the parameters of the regression formula. Another 115 patients with CBI with concurrent NAFLD proven by liver biopsy from January 2021 to January 2022 were enrolled as the validation group. There were some statistical differences in demographic data, biochemical indicators, immune function, thyroid function, virology indicator, and blood routine indicators between the two groups (P < 0.05) and liver stiffness measurement (LSM) in the HBV-I group was significantly higher than those in the non-HBV-I group (P < 0.05). While controlled attenuation parameters (CAP) in the HBV-I group were lower than those in the non-HBV-I group (P < 0.05); (2) We developed a novel model by logistic regression analysis: HBV-I = −0.020 × CAP + 0.424 × LSM + 0.376 × lg (HBV DNA) + 0.049 × aspartate aminotransferase (AST) and the accuracy rate was 82.5%. The area under the receiver operating characteristic (AUROC) is 0.907, the cutoff value is 0.671, the sensitivity is 89.30%, the specificity is 77.80%, the positive predictive value is 90.34%, and the negative predictive value is 81.89%; (3) The AUROC of HBV-I in the validation group was 0.871 and the overall accuracy rate is 86.96%. Conclusion Our novel model HBV-I [combining CAP, LSM, lg (HBV DNA), and AST] shows promising utility for predicting HBV-I in patients with CBI with concurrent NAFLD with high sensitivity, accuracy, and repeatability, which may contribute to clinical application.