Comparative Analysis of the Diagnostic Performance of Six Major Echinococcus granulosus Antigens Assessed in a Double-Blind, Randomized Multicenter Study
Open Access
- 1 June 2005
- journal article
- clinical trial
- Published by American Society for Microbiology in Journal of Clinical Microbiology
- Vol. 43 (6), 2764-2770
- https://doi.org/10.1128/jcm.43.6.2764-2770.2005
Abstract
The serodiagnosis of hydatid disease is a valuable instrument for clinical diagnosis and epidemiological surveillance of high-risk populations. In the past decade a wealth of reports on the diagnostic performance of numerous antigens have been produced. However, their diagnostic value has been estimated under different conditions, using different serum collection, therefore precluding their direct comparison. Here we report an unbiased comparison of the same batch of six major E. granulosus antigens, namely, hydatid cyst fluid (HCF), native antigen B (AgB), two recombinant AgB subunits, an AgB-derived synthetic peptide, and recombinant cytosolic malate dehydrogenase from E. granulosus (EgMDH), against the same serum collection. The double-blind analysis was performed using a standardized protocol and receiver operating characteristic (ROC) data analysis by a network of six South American laboratories. High intercenter reproducibility was attained, and the intralaboratory analysis allowed the comparative ranking of the antigen panel. HCF, AgB, and its AgB8/1 subunit exhibited equivalent diagnostic efficiencies, 81.4% ± 0.5%, 81.3% ± 0.6%, and 81.9% ± 2.0%, respectively; with a more favorable balance toward specificity in the case of the last antigen. The diagnostic efficiencies for the other three antigens were 76.8% ± 6.8%, 69.1% ± 2.7%, and 66.8% ± 2.1%, for the peptide, the AgB8/2 subunit, and the EgMDH, respectively. The study also included an analysis of batch-to-batch variation in the diagnostic performance of different HCF regional preparations. Based on these results, a suggested recommendation on the use of these antigens was drawn.Keywords
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