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Development of data-driven models for the flow cytometric crossmatch.

Eric T Weimer1, Katherine A Newhall2

  • 1Department of Pathology & Laboratory Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA.

Human Immunology
|September 19, 2019
PubMed
Summary

Data-driven algorithms accurately predict flow cytometric crossmatch outcomes, improving virtual crossmatch (VXM) reliability. This advancement offers objective insights into human leukocyte antigen (HLA) antibody influence on transplant compatibility.

Keywords:
Data-driven modelingHLAOrgan allocationVirtual crossmatch

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Area of Science:

  • Immunogenetics
  • Transplantation immunology
  • Computational biology

Background:

  • Virtual crossmatching (VXM) is increasingly used for pre-transplant compatibility assessment.
  • Current VXM interpretation relies on subjective, center-specific experience.
  • Objective prediction of crossmatch outcomes is needed to enhance VXM reliability.

Purpose of the Study:

  • To develop and evaluate data-driven algorithms for predicting flow cytometric crossmatch (FCXM) outcomes.
  • To assess the predictive accuracy of MFI Optimal-Threshold and Least-Squares-Fitting models.
  • To gain insights into the influence of specific HLA antibodies on FCXM results.

Main Methods:

  • Developed two algorithms: MFI Optimal-Threshold and Least-Squares-Fitting.
  • Utilized human leukocyte antigen (HLA) antibody mean fluorescent intensity (MFI) data and donor HLA typing.
  • Evaluated algorithm performance in predicting T-cell and B-cell responses.

Main Results:

  • The Optimal-Threshold model achieved 81.5%-85.5% accuracy.
  • Optimal MFI thresholds were identified for Class I antibodies predicting T-cell (4670) and B-cell (6180) responses.
  • Class I antibodies had a greater influence than Class II; HLA-B > HLA-A > HLA-C.
  • The Least-Squares-Fitting model improved accuracy to 94.1% (T-cell) and 88.8% (B-cell).

Conclusions:

  • Data-driven algorithms can accurately predict FCXM outcomes without human interpretation.
  • These algorithms enhance VXM prediction and provide novel insights into HLA antibody effects.
  • The findings support the objective integration of algorithmic predictions into transplant compatibility assessments.