Da-Wu Zeng1, Jie-Min Zhang2, Yu-Rui Liu1, Jing Dong1, Yin-Lian Wu1, Su Lin1, Jia-Ji Jiang1, Yue-Yong Zhu3. 1. Liver center, The First Affiliated Hospital, Fujian Medical University, No. 20 Chazhong Road, 350005 Taijiang District, Fuzhou, Fujian Province, China. 2. Department of Pharmacy, The First Affiliated Hospital, Fujian Medical University, No. 20 Chazhong Road, 350005 Taijiang District, Fuzhou, Fujian, China. 3. Liver center, The First Affiliated Hospital, Fujian Medical University, No. 20 Chazhong Road, 350005 Taijiang District, Fuzhou, Fujian Province, China. Electronic address: ezhu066@sina.com.
Abstract
OBJECTIVES: To develop a cirrhosis-predicted model in chronic hepatitis B virus carriers with alanine transarninase (ALT) less than two times the upper limit of normal (ULN). METHODS: Treatment-naive patients (n=278), who had undergone liver biopsies, were randomly divided into two groups - a training group and a validation group. Thirteen bio-clinical parameters were analyzed. A liver cirrhosis-predicting model (PPT model) was constructed using multivariate analysis. The diagnostic value of the model was analyzed by the receiving operating characteristics (ROC) method and compared with other available models. RESULTS: A PPT model to predict liver cirrhosis was derived from three independent predictors of liver fibrosis [platelet count (PLT), prothrombin time (PT) and total bile acid (TBA)]. PPT model predicted cirrhosis with an area under the ROC (AUROC) curve of 0.83, a positive predictive value of 86.7% and a negative predictive value of 95.2%. Compared with APRI, FIB-4, age-AST model, AP index and APGA model, PPT model had the highest correlation coefficient (r=0.49) and greater predictive performance (AUROC of 0.83). CONCLUSIONS: The PPT model was accurate in predicting cirrhosis and may reduce the need for liver biopsy in chronic hepatitis B virus carriers with ALT less than two times ULN. Crown
OBJECTIVES: To develop a cirrhosis-predicted model in chronic hepatitis B virus carriers with alanine transarninase (ALT) less than two times the upper limit of normal (ULN). METHODS: Treatment-naive patients (n=278), who had undergone liver biopsies, were randomly divided into two groups - a training group and a validation group. Thirteen bio-clinical parameters were analyzed. A liver cirrhosis-predicting model (PPT model) was constructed using multivariate analysis. The diagnostic value of the model was analyzed by the receiving operating characteristics (ROC) method and compared with other available models. RESULTS: A PPT model to predict liver cirrhosis was derived from three independent predictors of liver fibrosis [platelet count (PLT), prothrombin time (PT) and total bile acid (TBA)]. PPT model predicted cirrhosis with an area under the ROC (AUROC) curve of 0.83, a positive predictive value of 86.7% and a negative predictive value of 95.2%. Compared with APRI, FIB-4, age-AST model, AP index and APGA model, PPT model had the highest correlation coefficient (r=0.49) and greater predictive performance (AUROC of 0.83). CONCLUSIONS: The PPT model was accurate in predicting cirrhosis and may reduce the need for liver biopsy in chronic hepatitis B virus carriers with ALT less than two times ULN. Crown