The galactomannan enzyme immunoassay (GM-EIA) is widely utilized for the diagnosis of invasive aspergillosis (IA). There is inconsistent reproducibility of results between centers when the assay is processed manually. Automation of EIAs can reduce variation. This study investigated the semiautomation of the GM-EIA on the DS2 (Dynex) platform in the following three stages: (i) DS2 GM-EIA method validation with experimental samples, (ii) DS2 retesting of case-defined clinical samples, and (iii) a 12-month audit of DS2 GM-EIA performance. In stage i, Bland-Altman analysis demonstrated a reduced variance between optical density index (ODI) values for samples processed on two DS2 platforms (mean difference, -0.02; limits of agreement [LOA], -0.19 to 0.14) compared with the variance between samples processed manually and on a DS2 platform (mean difference, 0.02; LOA, -0.25 to 0.3). In stage ii, 100% (14/14 samples) qualitative agreement was observed for serum samples from patients with IA, with no significant change in the ODI values when samples were processed on the DS2 platform. A significant decrease in ODI values was observed for control serum samples on the DS2 platform (difference, 0.01; P = 0.042). In stage iii, a significant reduction in the frequency of equivocal results, from 5.56% (136/2,443 samples) to 1.56% (15/961 samples), was observed after DS2 automation (difference, 4.0%; 95% confidence interval [CI], 2.7 to 5.2%; P < 0.01), with an equivalent increase in negative results. This study demonstrates that GM-EIA automation may reduce intersite variability. Automation does not have an impact on the repeatability of truly positive results but contributes to a reduction in false-positive (equivocal) GM-EIA results, reducing the need to retest a significant proportion of samples.
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