Optimized cutoffs of gamma-glutamyl transpeptidase-to-platelet ratio, aspartate aminotransferase-to-platelet ratio index, and fibrosis-4 scoring systems for exclusion of cirrhosis in patients with chronic hepatitis B.

Accurate prediction of the extent of fibrosis is of great clinical importance in patients infected with chronic hepatitis B (CHB). This study aimed to compare the performance of gamma-glutamyl transpeptidase-to-platelet ratio (GPR), aspartate aminotransferase-to-platelet ratio index (APRI), and fibrosis-4 (FIB-4) in evaluating liver fibrosis stages and to identify optimized cutoffs to exclude cirrhosis. Consecutive patients with CHB with liver biopsies were enrolled and randomly divided into derivation and validation cohorts. Areas under the receiver operating characteristic curve were used to evaluate the diagnostic performance of APRI, FIB-4, and GPR to distinguish fibrosis stages. New cutoffs with a sensitivity of at least 90% and a negative predictive value (NPV) of more than 95% were identified. A total of 880 individuals were enrolled in this study. The derivation data set consisted of 617 patients, with 82 patients with cirrhosis. In the validation cohort (n = 263), 29 patients had cirrhosis. APRI, FIB-4, and GPR had comparable diagnostic performance for diagnosing significant fibrosis. GPR outperformed APRI (p < 0.05) in the prediction of cirrhosis. A newly identified GPR score of 0.35 had a sensitivity and NPV of 93.9% and 98.0%, respectively, and misclassified 5 of 82 (6.1%) patients with cirrhosis in the derivation group. All new cutoffs identified in this study also reached our goal in the validation cohort. The new GPR score could rule out a larger proportion of individuals without cirrhosis, and the subgroup analysis showed more stable performance. However, the lower cutoff dose increases the need for further testing compared to the conventional cutoff. Conclusion: A newly identified cutoff for GPR (<0.35) could rule out more patients without cirrhosis compared to APRI and FIB-4 and have low misclassification rates.