Cao Z, et al. – Researchers here developed an algorithm based on Golgi protein 73 (GP73) and liver stiffness (LS) for further improvement of accuracy for significant fibrosis in patients with antiviral–naïve chronic hepatitis B virus infection. They recognized serum GP73 as a robust biomarker for significant fibrosis diagnosis. GP73–LS algorithm proved superior to currently available approaches regarding diagnostic accuracy. This algorithm could be used in more than 60% antiviral naïve CHB patients without resorting to liver biopsy.

Methods

• Researchers evaluated diagnostic accuracy of GP73 and developed GP73–LS algorithm in training cohort (n = 267) with an independent cohort (n = 133) for validation.

Results

• Across fibrosis stages in patients with antiviral–naive chronic HBV infection, there appeared a stepwise rising pattern of serum GP73.
• Serum GP73 seemed to have marked correlation (rho = 0.48 p < 0.001) with fibrosis stage and was identified as an independent predictor for the presence of significant fibrosis (OR, 95%CI: 1.02, 1.01–1.03, per increase of 1 ng/mL, p < 0.001).
• In this study, both LS (AUROC, 95%CI: 0.82, 0.77–0.87, accuracy: 74.7%) and GP73 (AUROC, 95%CI: 0.76, 0.71–0.82, accuracy: 71.5%) seemed well predicting significant fibrosis and outperformed APRI (AUROC, 95%CI: 0.69, 0.63–0.76, accuracy: 66%) and FIB–4 (AUROC, 95%CI: 0.66, 0.60–0.73, accuracy: 63.6%).
• Using GP73–LS algorithm, it was noticed that GP73<63 in agreement with LS<8.5 provided accuracy of 81.7% to excluded significant fibrosis.
• Accuracy of 93.3% was realized with GP73≥63 in agreement with LS≥8.5 to confirm significant fibrosis.
• Findings revealed that almost 64% or 68% of patients in the training or validation cohort could be accurately classified.