Comparative evaluation of four fully automated immunoanalyzers for the simultaneous detection of HBeAg and Anti-HBe - Junhyup Song; Seung Jun Choi; Sinyoung Kim; Younhee Park (PLoS ONE, 2025)
Background Hepatitis B envelope Antigen (HBeAg) and anti-hepatitis B envelope Antigen (anti-HBe) are crucial markers for evaluating hepatitis B virus infection status and guiding clinical decisions. Considering the increasing prevalence of HBeAg-negative variants, accurate detection of both markers is essential. This study aimed to examine the analytical performance of four fully automated immunoanalyzers for the simultaneous detection of HBeAg and Anti-HBe and to assess the inter-platform concordance. Methods In total, 439 leftover serum samples from routine clinical testing for HBeAg or anti-HBe testing were analyzed using four immunoanalyzers: Architect i2000 and Alinity i (Abbott), Cobas e801 (Roche), and Atellica IM 1600 (Siemens). Samples not tested immediately were stored at 4 °C and analyzed within 24 hours of storage. Verification of Precision followed the CLSI EP15-A3 guidelines, and methods were compared according to the CLSI EP09-A3 guidelines. Qualitative agreement was assessed using Cohen’s κ, whereas quantitative correlation was evaluated using Deming regression and R² values. Results Architect i2000 and Alinity i exhibited the highest correlation (R² = 0.9960) and concordance (κ = 0.953 for HBeAg, κ = 0.971 for anti-HBe). Cobas e801 showed strong overall correlation with Architect i2000 (R² = 0.9838), but displayed a systematic negative bias near the cutoff, substantially reducing qualitative agreement. Atellica IM 1600 demonstrated relatively weaker correlation with the other three platforms. Conclusions Inter-platform discrepancies observed may impact clinical interpretation. As these discrepancies primarily stem from systematic bias, they can be readily mitigated by adjusting the reference values. Overall, further harmonization efforts are essential to enhance inter-platform agreement and ensure consistency in clinical decision-making.