A Nomogram Model Base On LSM To Predict Liver Histology In Chronic Hepatitis B With Alanine Aminotransferase?2 Upper Limit Of Normal

Background and objectiveAccording to WHO estimated,there are more than 257 million patients with chronic hepatitis B in the world,which is one of the leading causes of liver cirrhosis and hepatocellular carcinoma.For patients with slightly elevated alanine aminotransferase(ALT)but lower than 2 times of the normal upper limit(ULN),the latest guidelines agreed that antiviral therapy should be initiated when there are significant histological changes in the liver to inhibit the progression of fibrosis and other complications.The update of the guidelines for the prevention and treatment of chronic hepatitis B suggests higher requirements for liver histological evaluation and initiation of antiviral therapy in CHB patients with normal or slightly elevated ALT(alt ? 2ULN).The aim of this retrospective study was to establish a non-invasive nomogram predictive model for liver histological changes in patients with chronic hepatitis B with ALT ? 2ULN.MethodsThe clinical data of 323 CHB patients with ALT ? 2ULN who received liver biopsy in the first affiliated Hospital of Zhejiang University from April 2017 to December 2019 were collected retrospectively.All patients were randomly divided into two groups according to the proportion of training group: validation group = 2:1,including training group(n = 217)and validation group(n = 106).The prediction model of non-invasive liver histological changes [necrotic inflammatory activity grade(G)? 2 or fibrosis stage(S)? 2] was established in the training group,and further verified in the validation group.The non-invasive nomogram prediction model of liver histological changes was further tested for diagnostic efficacy in people who underwent antiviral and secondary liver biopsies.ResultsThe Nomogram prediction model was constructed with LSM,AGE,aspartate aminotransferase(AST)?platelet(PLT)and Hepatitis B e antigen(HBe Ag).The Nomogram prediction model showed good diagnostic efficiency for significant liver histology.In the training group,the area under the curve of the Nomogram prediction model((AUROC))was 0.800,which was higher than that of APRI:0.696,FIB-4:0.591 and LSM:0.652.In the validation group,the Nomogram predicts the AUROC=0.783 of the model.In addition,Nomogram prediction model showed excellent diagnostic efficacy(AUROC=0.890)in patients over 30 years old.In people who underwent secondary liver biopsies after antiviral therapy during follow-up,the Nomogram prediction model also showed a certain ability to predict liver histological changes(AUROC=0.771)?The calibration demonstrated that the assessments of moderate histology by nomograms were in line with liver biopsy.ConclusionsNomogram-significant liver histological prediction model may be a good tool for identifying significant liver histological changes in patients with chronic hepatitis B with ALT ? 2ULN.Besides,it can predict histological changes after therapy.