Virtual and Reality: An Analysis of the UCLA Virtual Crossmatch Exchanges

Abstract

The "virtual" crossmatch (VXM) has become a critical tool to predict the compatibility between an organ donor and a potential recipient. Yet, nonstandardized laboratory practice can lead to variability in VXM interpretation. Therefore, UCLA's VXM Exchange survey was designed to understand factors that influence the variability of VXM prediction in the presence of HLA donor-specific antibody (DSA). Thirty-six donor blood samples and 72 HLA reference sera were sent to 35 participating laboratories to perform HLA antibody testing, flow crossmatch (FXM), and VXM from 2014 to 2019, consisting of 144 T/B-cell FXM pairs and 112 T/B-cell VXM pairs. In the FXM survey, 86% T-cell FXM and 84% B-cell FXM achieved >80% concordance among laboratories. In the VXM survey, 81% T-cell VXM and 80% VXM achieved >80% concordance. The concordance between FXM and VXM was 79% for T cell and 87% for B cell. The consensus between VXM and FXM was high with strong DSA. However, significant variability was observed in sera with (1) very high titer antibodies that exit prozone effect; (2) weak-to-moderate DSA, particularly in the presence of multiple weak DSAs; and (3) DSA against lowly expressed antigens. With the increasing use the VXM, standardization and continuous learning via exchange surveys will provide better understanding and quality controls for VXM to improve accuracy across all centers.

FIGURE 1.

UCLA VXM Exchange flow chart. UCLA VXM exchange consist of 2 phases. In phase I, 2 serum samples and 4 virtual donors are provided to the laboratories to perform SAB tests and 8 VXM. In phase II, 4 recipient serum samples and lymphocytes from 2 donors are provided to perform SAB tests and 8 FXMs. In phase II, 1 recipient–donor pair will be selected from the VXM of phase I; therefore, a direct comparison between FXM and VXM can be achieved. FXM, flow cytometry crossmatch; SAB, single antigen bead test; UCLA, University of California, Los Angeles; VXM, virtual crossmatch.

FIGURE 2.

Interlaboratory variability in the reporting of MFI values. Interlaboratory variability (%CV) in the reporting of MFI values for class I and class II antibodies for Exchanges 6 to 17. A total of 12 sera were examined twice across the 13 exchanges. MFI values for the highest DSA with MFI >1000 and reported by a minimum of 5 laboratories were plotted against %CV to illustrate the variability observed among laboratories in the reporting of MFI values. %CV, coefficient of variation; DSA, donor-specific antibody; MFI, median florescence intensity.

FIGURE 3.

A comparison of interlaboratory variability for treated vs untreated serum. Comparison of interlaboratory variability (%CV) in treated (n = 11) vs untreated (n = 9) samples. The same serum sample was tested by SAB twice across 2 consecutive exchanges among laboratories. Antibody strength (mean MFI) was plotted alongside %CV for an untreated and a treated serum sample. For the treated serum (A), interlaboratory variability was between 8% and 24% CV for antibodies with >10 000 MFI. For the untreated serum (B), interlaboratory variability ranged between 20% and 40%. %CV, coefficient of variation; MFI, median florescence intensity; SAB, single antigen bead test.

FIGURE 4.

Intralaboratory variability on repeated serum samples. Each point on the Bland-Altman plot represents the change (∆) in the mean MFI reported for individual laboratories for a serum sample tested 2 times across consecutive exchanges. The boundaries on the plots represent the expected variation of the mean value reported for antibodies present in the serum. The position of the points between the 2 boundaries shows MFI for the highest antibodies tested a second time to fall within a variation of 50% of the mean value. MFI, median florescence intensity.

FIGURE 5.

Correlation of highest DSA with flow crossmatch. A, Scatter plot illustrating the positive linear relationship between highest class I DSA MFI and T-FXM MCS values. Each data point (MCS, MFI) represents a single T-FXM examined from Exchange 1 to 18 (n = 144). The correlation between highest HLA class I DSA MFI vs T-FXM MCS showed an R² of 0.8692, P < 0.001 among laboratories. B, Scatter plot illustrating the positive linear relationship between highest class I/II DSA MFI and B-cell FXM MCS values. Each data point (MCS, MFI) represents a single B-FXM examined from Exchange 1 to 18 (n = 144). The correlation between highest HLA class II DSA MFI vs B-FXM MCS showed an R² of 0.6228, P < 0.001 among laboratories. DSA, donor-specific antibody; FXM, flow cytometry crossmatch; MCS, median channel shift; MFI, median florescence intensity; T-FXM, T-cell FXM.

FIGURE 6.

Comparison of VXM prediction and actual physical crossmatch. Each bar represents the percent agreement between VXM predictions and the physical FXM for Exchanges 6 to 17 (n =112). DSAs reported for each donor in an exchange, along with reported mean MFI, are shown in the accompanying table to illustrate the influence of DSA strength on the accuracy of predictions. DSA, donor-specific antibody; FXM, flow cytometry crossmatch; MFI, median florescence intensity; VXM, virtual crossmatch.