Development of an HCV infection risk stratification algorithm for patients on chronic hemodialysis

Abstract

Objective: The prevalence of hepatitis C virus (HCV) in patients on chronic hemodialysis (HD) is near 9%. Transaminases, which are lower in HD patients, are not effective in screening for HCV. Our aim was to design an HCV risk stratification strategy incorporating lowered aminotransferase levels and other clinical parameters.

Methods: Patient serum from 168 consecutive HD patients was analyzed for AST, ALT, ferritin, and hepatitis C antibody. Sensitivity, specificity, positive predictive value, and negative predictive value were calculated for lower transaminase values. Multivariate classification and regression tree analysis was used to determine the best combination of variables to predict risk for HCV infection.

Results: Median AST and ALT levels were higher in anti-HCV Ab(+) patients (p < 0.05). Applying a lower cutoff value for ALT of 16 IU/L resulted in a sensitivity of 61.1%, a specificity of 66.7%, a positive predictive value of 33.9%, and a negative predictive value of 86.0% for detection of HCV infection. Multivariate classification and regression tree analysis derived an algorithm using patient age, months on HD, and AST, resulting in a 97.2% sensitivity and a 51.9% specificity for the detection of HCV(+) HD patients.

Conclusions: A lower normal cutoff value of 18 IU/L for AST and 16 IU/L for ALT increased sensitivity and specificity for the detection of HCV infection in HD patients. An algorithm combining lower transaminases with clinical parameters improved both sensitivity and specificity in HCV detection. Prospective confirmation of this algorithm would allow more selective HCV enzyme immunoassay and polymerase chain reaction testing in dialysis units.