Information on the stage of liver fibrosis is essential in managing chronic hepatitis C (CHC) patients.
However, most models for predicting liver fibrosis are complicated and separate formulas are needed to predict significant fibrosis and cirrhosis.
In this study, investigators from the United States designed a simple model, consisting of routine laboratory data, to predict both significant fibrosis and cirrhosis in patients with CHC.
|Cirrhosis could be predicted in 81%.|
They evaluated 200 consecutive treatment-naive CHC patients who underwent liver biopsy over a 25-month period.
The researchers divided the patients into 2 sequential cohorts, a training set of 192 patients and a validation set of 78 patients.
The team found that the best model for predicting both significant fibrosis and cirrhosis in the training set of patients included platelets, aspartate aminotransferase (AST), and alkaline phosphatase.
They developed a novel index (AST to platelet ratio index (APRI)) to amplify the opposing effects of liver fibrosis on AST and platelet count.
The physicians determined that the area under receiver operating curves (AUC) of APRI for predicting significant fibrosis and cirrhosis were 0.80 and 0.89, respectively, in the training set.
When they used optimized cut-off values, significant fibrosis could be predicted accurately in 51% of patients, and cirrhosis could be predicted in 81%.
The team found that the AUC of APRI for predicting significant fibrosis and cirrhosis in the validation set were 0.88 and 0.94, respectively.
Dr Chun-Tao Wai's team concluded, "A simple index using readily available laboratory results can identify CHC patients with significant fibrosis and cirrhosis with a high degree of accuracy".
"Application of this index may decrease the need for staging liver biopsy specimens among CHC patients".