Establishment and validation of a nomogram for predicting the risk of liver inflammation in chronic HBV infection

Objective:To establish a non-invasive quantitative and visual predictive model for assessing the occurrence of significant inflammation in chronic HBV infection,and to present nomogram to validate the efficacy.Methods:A total of 180 patients with chronic HBV infection that were admitted to the Department of Infectious Liver Diseases of the First Affiliated Hospital of Hainan Medical College from January 2019 to December 2021 with informed consent and underwent liver biopsy puncture were selected,and to prevent overfitting of the model,131 patients and 49 patients were randomly divided into a model group and a validation group according to randomization,to collect the clinic information,serological examination,liver elastography and liver histopathology results.The patients were divided into non-significant inflammation and significant inflammation groups in the modeling group.The R 4.1.1 package and the rms package were used to build the column line graph model,while the Bootstrap method was applied to repeat the sampling 1000 times for internal and external validation,and the H-L goodness of fit test and ROC curve were used to assess the calibration and discrimination of the column line graph model respectively.Results:A total of 180 patients with chronic HBV infection were included,and 92 patients(51.1%)had significant inflammation.In the modeling set,67 patients(51.1%)had significant inflammation.In the modeled group,comparison of HBV DNA,PLT,ALT,AST,ALP,GGT,PAB,H.A,PⅢP,CⅣ,L.N,IL-6,LSM and HBeAg for non-significant inflammation and significant inflammation showed statistically significant differences(P<0.05).Nomogram were obtained using stepwise regression analysis to establish a predictive model for the risk of significant inflammation following chronic HBV infection.The χ^(2) values of the H-L goodness-of-fit test for the modelling and validation groups were 0.279 and 2.098,respectively,corresponding to P values of 0.87 and 0.35,suggesting that the nomogram has good predictive accuracy;the area under the ROC curve of the column line plot predicting the occurrence of significant inflammation after HBV infection for the modelling and validation groups was 0.895[95%CI(0.843-0.948)]and 0.760[95%CI(0.622-0.897)],suggesting that the column line plot model has good discrimination.Conclusion:After stepwise regression analysis,it was established that PLT,Ln(HBV-DNA),AST,C桇and LSM were more closely associated with the occurrence of significant inflammation after HBV infection,and a visualization of the occurrence of significant inflammation nomogram was established by comprehensive assessment,and the effectiveness was good.