Establishment and validation of a noninvasive diagnostic model for chronic hepatitis B liver fibrosis based on LASSO regression / 临床肝胆病杂志

ABSTRACT

Objective To establish a noninvasive diagnostic model for chronic hepatitis B (CHB) liver fibrosis based on LASSO regression using serological parameters, and to investigate the value of this model in the diagnosis of CHB liver fibrosis. Methods A total of 240 patients who were diagnosed with CHB in Changzhou Second People's Hospital, Nanjing Medical University, from September 2019 to September 2021 were enrolled as subjects, and according to the results of liver biopsy and pathology, they were divided into significant liver fibrosis (stage F2-F4) group with 175 patients and non-significant liver fibrosis (stage F0-F1) group with 65 patients. The two groups were compared in terms of sex, age, blood biochemical parameters, and liver stiffness measurement (LSM) measured by two-dimensional shear wave elastography, and LASSO regression and the multivariate logistic regression analysis were used screen out the risk factors for liver fibrosis. A nomogram model was established and then verified by the receiver operating characteristic (ROC) curve, calibration curve, and decision curve. A one-way analysis of variance was used for comparison of normally distributed continuous data between multiple groups, and the least significant difference t -test was used for further comparison between two groups; the Kruskal-Wallis H test was used for comparison of non-normally distributed continuous data between groups; the chi-square test was used for comparison of categorical data between groups. Results There were significant differences between the patients with stage F3/F4 liver fibrosis and those with stage F2/F0-F1 liver fibrosis in age, alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase, gamma-glutamyl transpeptidase, total bilirubin, platelet count, procollagen type Ⅲ, type Ⅳ collagen, hyaluronic acid, and LSM (all P < 0.05). Five important variables were screened out by LASSO regression, and the logistic regression analysis showed that hyaluronic acid (odds ratio [ OR ]=1.432, P < 0.05), type Ⅳ collagen ( OR =1.243, P < 0.05), procollagen type Ⅲ( OR =1.146, P < 0.05), and LSM ( OR =1.656, P < 0.05) were the independent risk factors for liver fibrosis, while platelet count ( OR =0.567, P < 0.05) was a protective factor. Compared with the patients with stage F2/F0-F1 liver fibrosis, the patients with stage F3/F4 liver fibrosis had significantly higher score of the nomogram model, LSM, aspartate aminotransferase-to-platelet ratio index (APRI), King score, Forns index, and fibrosis-4 (FIB-4) index (all P < 0.05). The ROC curve was used to analyze the predictive value of the nomogram model, and the results showed an area under the ROC curve of 0.876, which was significantly higher than that of LSM, APRI, King score, Forns index, and FIB-4 (all P < 0.05). Calibration curve and decision curve showed that the nomogram model had acceptable consistency and benefit. Conclusion The noninvasive nomogram model based on LASSO regression is established by using serum parameters including hyaluronic acid, type Ⅳ collagen, procollagen type Ⅲ, platelet count, and LSM, and as a quantitative tool for the clinical diagnosis of CHB liver fibrosis, it has a high diagnostic efficiency and thus holds promise for clinical application.