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Knowledge of the presence of cirrhosis is important for the management of patients with chronic hepatitis C (CHC). Most models for predicting cirrhosis were derived from small numbers of patients and included subjective variables or laboratory tests that are not readily available. The aim of this study was to develop a predictive model of cirrhosis in patients with CHC based on standard laboratory tests. Data from 1,141 CHC patients including 429 with cirrhosis were analyzed. All biopsies were read by a panel of pathologists (blinded to clinical features), and fibrosis stage was determined by consensus. The cohort was divided into a training set (n = 783) and a validation set (n = 358). Variables that were significantly different between patients with and without cirrhosis in univariate analysis were entered into logistic regression models, and the performance of each model was compared. The area under the receiver- operating characteristic curve of the final model comprising platelet count, AST/ALT ratio, and INR in the training and validation sets was 0.78 and 0.81, respectively. A cutoff of less than 0.2 to exclude cirrhosis would misclassify only 7.8% of patients with cirrhosis, while a cutoff of greater than 0.5 to confirm cirrhosis would misdassify 14.8% of patients without cirrhosis. The model performed equally well in fragmented and nonfragmented biopsies and in biopsies of varying lengths. Use of this model might obviate the requirement for a liver biopsy in 50% of patients with CHC. In conclusion, a model based on standard laboratory test results can be used to predict histological cirrhosis with a high degree of accuracy in 50% of patients with CHC.
Knowledge of the presence of cirrhosis is important for the management of patients with chronic hepatitis C (CHC). Most models for predicting cirrhosis were derived from small numbers of patients and included subjective variables or laboratory tests that are not available available. The aim of this study was to develop a predictive model of cirrhosis in patients with CHC based on standard laboratory tests. Data from 1,141 CHC patients including 429 with cirrhosis were read by a panel of pathologists (blinded to clinical features), and fibrosis stage was determined by consensus. The cohort was divided into a training set (n = 783) and a validation set (n = 358). Variables that were significantly different between patients with and without cirrhosis in univariate analysis were entered into logistic regression models, and the performance of each model was compared. The area under the receiver- operating characteristic curve of the final model comprises platelet count, AST / ALT ratio, and INR in the training and validation sets was 0.78 and 0.81, respectively. A cutoff of less than 0.2 to exclude cirrhosis would misclassify only 7.8% of patients with cirrhosis, while a cutoff of greater than 0.5 to confirm cirrhosis would misdassify 14.8% of patients without cirrhosis. The model performed equally well in fragmented and nonfragmented biopsies and in biopsies of varying lengths. Use of this model might obviate the requirement for a liver biopsy in 50% of patients with CHC. In conclusion, a model based on standard laboratory test results can be used to predict histological cirrhosis with a high degree of accuracy in 50% of patients with CHC.