Development of data-driven models for the flow cytometric crossmatch.
Hum Immunol
; 80(12): 983-989, 2019 Dec.
Article
en En
| MEDLINE
| ID: mdl-31530432
ABSTRACT
HLA laboratories use virtual crossmatching (VXM) to predict recipient and donor compatibility using HLA antibody data and donor HLA type. Increasingly, transplant centers are utilizing VXM as the final compatibility determination prior to transplant. However, the VXM interpretation is based on HLA experience of individual transplant centers. This study developed data-driven algorithms that predicted flow cytometric crossmatch (FCXM) outcomes using HLA antibody mean fluorescent intensity (MFI) data and donor HLA typing without the need for human interpretation.Two algorithms were evaluated; an MFI Optimal-Threshold model and a Least-Squares-Fitting model. The Optimal-Threshold model correctly determined between 81.5% and 85.5% of T or B-cell responses. A class I antibody MFI threshold of 4670 was optimal for predicting T-cell response while an antibody MFI threshold of 6180 was optimal for predicting B-cell responses. HLA class I antibodies had a 1.47-fold greater influence on FCXM outcomes than class II antibodies. HLA-B antibodies influenced T and B-cell responses more than HLA-A or -C (-Bâ¯>â¯-Aâ¯>â¯-C). The Least-Squares-Fitting model increased accuracy to 94.1% and 88.8% for T and B-cell responses, respectively. The algorithms described here provide enhanced FCXM prediction and novel insights into the influence of specific HLA antibodies on the crossmatch outcome.
Palabras clave
Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Tipificación y Pruebas Cruzadas Sanguíneas
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Prueba de Histocompatibilidad
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Linfocitos T
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Técnicas de Apoyo para la Decisión
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Trasplante de Riñón
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Citometría de Flujo
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Rechazo de Injerto
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
Idioma:
En
Revista:
Hum Immunol
Año:
2019
Tipo del documento:
Article