[肝胆速递] 两种乙肝表面抗原定量方法的比较

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两种乙肝表面抗原定量方法的比较

A comparison of two assays for quantification of Hepatitis B surface Antigen in patients with chronic hepatitis B

[肝胆速递]：两种商业化的乙肝表面抗原定量方法（Architect HBsAg QT 和 Elecsys HBsAg）检测结果高度一致，均可用于临床。

http://www.journalofclinicalvirology.com/article/PIIS1386653211001454/abstract?rss=yes

Milan J. Sonnevelda, Vincent Rijckborsta, Charles A.B. Boucherb, Louwerens Zwangc, Matthias F.C. Beersmab, Bettina E. Hansenad, Harry L.A. Janssena

Abstract
Background and objectives

Serum Hepatitis B surface Antigen (HBsAg) levels correlate with hepatitis B virus intrahepatic covalently closed circular DNA and may predict response to treatment. Currently, 2 commercial platforms are available for HBsAg quantification in clinical practice, the Architect HBsAg QT and the Elecsys HBsAg. We aimed to directly compare the results of these assays.

Study design

HBsAg levels were measured in 1427 serum samples from HBeAg-positive chronic hepatitis B patients who participated in a randomized trial of peginterferon alfa-2b±lamivudine. Samples were extracted from our serum bank, thawed, and subsequently analysed for HBsAg levels using both assays.

Results

Of 1427 samples, 242 (17%) were taken before and 1185 during the treatment phase of the study. Distribution of HBV genotypes was 447 (31%) genotype A, 125 (9%) B, 210 (15%) C and 534 (37%) D. Correlation between Architect and Elecsys results was high (r=0.96, p<0.001). By Bland–Altman analysis, agreement between the two assays was close (mean difference between Architect and Elecsys: −0.01logIU/mL, 95% CI: −0.55–0.52logIU/mL), also when analysed separately for HBV genotypes A–D. Additionally, the performance of our recently published stopping rule for HBeAg-positive patients treated with peginterferon was comparable: the negative predictive values were 96% and 98% for Elecsys and Architect, respectively.

Conclusions

There is a high correlation and close agreement between quantitative HBsAg measurements conducted with the Architect and the Elecsys. Clinical prediction rules derived from data from one platform can be applied on the other; both can therefore be used in clinical practice.