Performance characteristics of the immunoglobulin G-capture BED-enzyme immunoassay, an assay to detect recent human immunodeficiency virus type 1 seroconversion

Abstract

Recently, we developed an immunoglobulin G (IgG)-capture BED-enzyme immunoassay (BED-CEIA) to identify recent human immunodeficiency virus (HIV) type 1 (HIV-1) seroconversion for use in incidence estimates. We have established an algorithm for its use; developed quality control reagents to monitor the assay; and evaluated its performance for interrun, intrarun, and operator variability. Analysis of 144 individual plates, which involved multiple plate lots and several operators over more than a year, indicated that the coefficients of variation (CVs) were between 10 and 15% for raw optical density (OD) values in the dynamic range between 0.5 and 2.0 OD units; the CVs decreased to 5 to 10% when the OD was normalized (OD-n; OD-n = specimen OD/calibrator OD). The intrarun CVs were generally in the range of 5 to 10% for specimens with ODs <0.5 and less than 5% for specimens with ODs >0.5. The level of concordance between multiple plate lots (n = 6) and multiple operators (n = 7) was quite high (R(2) > 0.9). Comparison of the results of the initial and the confirmatory tests with specimens with OD-n values </=1.5 demonstrated a high degree of correlation (R(2) = 0.92); 566 (92%) of 615 of specimens tested in the two modes retained the same classification (recent or long-term infection). The values for those specimens with changed classifications (n = 49) were close to the cutoff (OD-n = 1.0), as expected. The twofold difference in the HIV IgG contents between the controls and the calibrator reagents was exploited to monitor individual plate runs by using a control plot, which was incorporated into the spreadsheet for data entry and run monitoring. This information provides baseline data for the successful transfer of BED-CEIA to other laboratories and the use of BED-CEIA for the detection of recent HIV seroconversion and the calculation of incidence estimates worldwide.

FIG. 1.

Schematic showing the various steps of BED-CEIA. Among the antibodies captured, HIV IgG is shown by solid lines, while non-HIV IgG is shown by broken lines. TMB, tetramethylbenzidine; Strep, streptavidin.

FIG. 2.

Algorithm for testing cross-sectional specimens to detect recent HIV-1 infection for incidence estimate purposes. See Materials and Methods for a description of OD-n.

FIG. 3.

Control plots showing the best-fit lines for 24 plates (A) and mean control plot for 144 plates with 99% limits (B). The x axis represents the relative levels of HIV IgG in controls and CAL. R² values are shown for the best-fit lines for a mean of 24 plates (A) or a mean of 144 plates (B).

FIG. 4.

Interoperator reproducibility of BED-CEIA runs. Six operators tested a total of 504 specimens during their training periods, and raw OD (left) or OD-n (right) values were compared with the values generated by an experienced operator (operator 1) at the Centers for Disease Control and Prevention.

FIG. 5.

Concordance between the results of initial and confirmatory tests for 625 specimens (R² = 0.92). The solid line represents the best-fit line, while the broken line represents 100% concordance (R² = 1.0).

FIG. 6.

Mean interrun CVs between initial and confirmatory tests and mean intrarun CVs for specimens tested in triplicate at various ranges of OD values.

FIG. 7.

Representation of a spreadsheet designed for data processing and quality assurance. Raw OD values are transferred electronically, and the best-fit plot indicating the acceptance or rejection of the data for the plate is generated.