Predicting flow cytometry crossmatch results from single-antigen bead testing. Academic Article uri icon

Overview

abstract

  • The aim of this study was to devise an algorithm that would predict flow cytometry crossmatch (FCXM) results using single-antigen bead (SAB) mean fluorescent intensity (MFI) levels using samples received through the National External Quality Assurance Scheme (NEQAS) 2B external proficiency testing scheme between 2019 and 2023. A total of 159 serum samples were retrospectively screened using LABScreen Single Antigen Class I and II (SAB), and 40 peripheral blood samples were human leucocyte antigen (HLA) typed with LABType SSO. Donor-specific antibodies were identified for each cell-serum combination tested, and cumulative MFI values were calculated for each test before correlating the screening result with the consensus crossmatch results for this scheme. HLA Class I MFIs were combined to predict the T cell crossmatch. For the B cell crossmatch prediction, two options were considered: (i) HLA Class II MFI values alone and (ii) HLA Class I + Class II MFIs. Receiver operating characteristic analysis was carried out to identify the combined MFI threshold that predicted NEQAS consensus results with the greatest sensitivity and specificity. HLA Class I combined MFI >5000 predicted T cell crossmatch results with 96% sensitivity, 100% specificity, 100% positive predictive value (PPV) and 92% negative predictive value (NPV). For B cell results, HLA Class I + Class II combined MFIs >11,000 gave the best model, showing 97% sensitivity, 82% specificity, 96% PPV and 85% NPV. However, for samples with only HLA Class II sensitization, combined MFIs >13,000 improved the B cell crossmatch predictions: 92% sensitivity, 95% specificity, 96% PPV and 91% NPV. Using this model, combined MFI can be used to predict the immunological risk posed by donor-specific antibodies when it is not possible to carry out an FCXM.

publication date

  • February 19, 2024

Research

keywords

  • Kidney Transplantation

Identity

Digital Object Identifier (DOI)

  • 10.1111/iji.12658

PubMed ID

  • 38374539