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OR11 Characteristics of transplant candidates with cPRA ⩾ 99.95% likely to be allocated organs from deceased donors
Abstracts / Human Immunology 79 (2018) 8–57
OR10
DATA-DRIVEN MODELING OF FLOW CYTOMETRIC CROSSMATCH: ENHANCED VIRTUAL CROSSMATCHING Eric T. Weimer, Katherine A. Newhall. University of North Carolina at Chapel Hill, Chapel Hill, NC, United States. Aim: Increasingly HLA laboratories are using virtual crossmatching to predict recipient and donor compatibility using HLA antibody data and donor HLA type. However, virtual crossmatch interpretation is based on HLA experience and expertise of individual transplant centers. The purpose of this study is to develop datadriven algorithms (DDA) that predict flow cytometric crossmatch (FXM) outcomes using HLA antibody mean fluorescent intensity (MFI) data and donor HLA typing. These methods are independent of human intuition or experience and may provide insights that are otherwise imperceptible. Methods: Two data sets consisting of 222 and 109 FXM with single antigen bead data for both HLA class I and II antibodies were used. Single antigen bead data was compiled against donor HLA antigens using the OneLambda assay and Luminex. The first DDA found the optimal MFI threshold using summation of mean fluorescent intensity data for class I and/or II that predicted either T cell or B cell mean channel shifts (MCS) above FXM cutoff. The second DDA applied a least-squares regression model to the HLA locus-specific data (second data set) to predict the actual T or B cell MCS. Results: The threshold method yielded between 84.9% and 91.1% accuracy when using class I donor specific antibody (DSA) data to predict T cell outcome, and class I and II DSA data to predict B cell outcome. Optimal MFI thresholds of 3450 and 7560 were found for T and B cell prediction. For quality assurance, prediction of T cell MCS was attempted using class II data, resulting in 61.9% accuracy. The least-squares model increased accuracy to 93.6% and 97.2% for T and B cell MCS, respectively. Class I DSA influenced T and B cell MCS more than class II. The relative importance of an individual HLA locus on T cell prediction was found to be HLA-B > A > -C. In the least-squares fitting, B64 had a large positive effect while A34 had a large negative effect on T cell MCS. Conclusions: Utilizing DDA can expand accuracy of biologic systems beyond human experience and expertise. We showed how DDA can improve HLA crossmatch by generating high-level accuracy T and B cell FXM outcomes. Further improvements to the algorithm will incorporate HLA antigen expression and HLA antibody avidity.
OR11
CHARACTERISTICS OF TRANSPLANT CANDIDATES WITH CPRA P 99.95% LIKELY TO BE ALLOCATED ORGANS FROM DECEASED DONORS Ronald Parsons, Hannah Decker, Rachel Patzer, Shalini Bumb, Harold C. Sullivan, Robert A. Bray, Howard M. Gebel. Emory University, Atlanta, GA, United States. Aim: Since the new KAS, candidates with cPRA = 100% were prioritized for deceased donor (DD) kidneys, their transplant rates increased from 2.7% to as high as 19.1%. A recent simulation (CJSAN 11:505-511, 2016), predicted that not all cPRA=100% are equally advantaged. While 75% cPRA =100% candidates were compatible with an average of 17 donors (total donors = 6141), 25% were incompatible with every donor and related to cPRA values of 99.45–>99.99% being ”rounded up” to 100%. Indeed, 91% of candidates without a compatible donor had cPRA values >99.9%. Subsequent data with actual transplants (AJT 16:1834-1847, 2016) supported those predictions. In our program, 61 cPRA = 100% candidates were transplanted from 12/04/14-01/12/18. Surprisingly, 15 recipients had cPRA values P99.95%. In this study, we identified their characteristics. Methods: HLA profiles and demographic information from 15 cPRA P99.95% recipients were compared to 30 non-transplanted case controls with cPRA P99.95%. The HLA typing data of each subject was entered into Haplostats (haplostat.org) and the top ranked phased haplotypes for each were recorded. Results: Among cPRAP99.95% recipients, 11/15 (73%) had one or both of their HLA haplotypes ranked among the top 125 CAU haplotypes. CAU haplotypes were the focus as CAU represent 67% of kidney donors. Notably, 7/15 recipients had one or both of their HLA haplotypes among the top four ranked CAU haplotypes and 6/15 received transplants from homozygous donors. Among recipients, eight were AA (53%), six were CAU and one was HIS. In contrast, among 30 case controls, 4 (13.3%) had one haplotype ranked in the top 125 and 17 had one haplotype ranked in the top 8000. For 9 candidates, neither HLA haplotype was ranked among the top 10,000 CAU haplotypes. Importantly, 25/30 (83.3%) controls were African American. Conclusions: Kidney transplant candidates with cPRAP99.95% were more likely to be transplanted when at least one of their HLA haplotypes was frequent (rank 6 125). This novel observation offers an opportunity to medically optimize a subset of highly sensitized candidates before deceased donor transplantation. It also suggests amendments may be necessary to minimize racial disparity in the KAS. Additionally, alternative approaches should be considered for candidates not likely to receive a DD offer. H.M. Gebel: 2. Consultant; Company/Organization; Astellas, Thermofisher.
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OR10
DATA-DRIVEN MODELING OF FLOW CYTOMETRIC CROSSMATCH: ENHANCED VIRTUAL CROSSMATCHING Eric T. Weimer, Katherine A. Newhall. University of North Carolina at Chapel Hill, Chapel Hill, NC, United States. Aim: Increasingly HLA laboratories are using virtual crossmatching to predict recipient and donor compatibility using HLA antibody data and donor HLA type. However, virtual crossmatch interpretation is based on HLA experience and expertise of individual transplant centers. The purpose of this study is to develop datadriven algorithms (DDA) that predict flow cytometric crossmatch (FXM) outcomes using HLA antibody mean fluorescent intensity (MFI) data and donor HLA typing. These methods are independent of human intuition or experience and may provide insights that are otherwise imperceptible. Methods: Two data sets consisting of 222 and 109 FXM with single antigen bead data for both HLA class I and II antibodies were used. Single antigen bead data was compiled against donor HLA antigens using the OneLambda assay and Luminex. The first DDA found the optimal MFI threshold using summation of mean fluorescent intensity data for class I and/or II that predicted either T cell or B cell mean channel shifts (MCS) above FXM cutoff. The second DDA applied a least-squares regression model to the HLA locus-specific data (second data set) to predict the actual T or B cell MCS. Results: The threshold method yielded between 84.9% and 91.1% accuracy when using class I donor specific antibody (DSA) data to predict T cell outcome, and class I and II DSA data to predict B cell outcome. Optimal MFI thresholds of 3450 and 7560 were found for T and B cell prediction. For quality assurance, prediction of T cell MCS was attempted using class II data, resulting in 61.9% accuracy. The least-squares model increased accuracy to 93.6% and 97.2% for T and B cell MCS, respectively. Class I DSA influenced T and B cell MCS more than class II. The relative importance of an individual HLA locus on T cell prediction was found to be HLA-B > A > -C. In the least-squares fitting, B64 had a large positive effect while A34 had a large negative effect on T cell MCS. Conclusions: Utilizing DDA can expand accuracy of biologic systems beyond human experience and expertise. We showed how DDA can improve HLA crossmatch by generating high-level accuracy T and B cell FXM outcomes. Further improvements to the algorithm will incorporate HLA antigen expression and HLA antibody avidity.
OR11
CHARACTERISTICS OF TRANSPLANT CANDIDATES WITH CPRA P 99.95% LIKELY TO BE ALLOCATED ORGANS FROM DECEASED DONORS Ronald Parsons, Hannah Decker, Rachel Patzer, Shalini Bumb, Harold C. Sullivan, Robert A. Bray, Howard M. Gebel. Emory University, Atlanta, GA, United States. Aim: Since the new KAS, candidates with cPRA = 100% were prioritized for deceased donor (DD) kidneys, their transplant rates increased from 2.7% to as high as 19.1%. A recent simulation (CJSAN 11:505-511, 2016), predicted that not all cPRA=100% are equally advantaged. While 75% cPRA =100% candidates were compatible with an average of 17 donors (total donors = 6141), 25% were incompatible with every donor and related to cPRA values of 99.45–>99.99% being ”rounded up” to 100%. Indeed, 91% of candidates without a compatible donor had cPRA values >99.9%. Subsequent data with actual transplants (AJT 16:1834-1847, 2016) supported those predictions. In our program, 61 cPRA = 100% candidates were transplanted from 12/04/14-01/12/18. Surprisingly, 15 recipients had cPRA values P99.95%. In this study, we identified their characteristics. Methods: HLA profiles and demographic information from 15 cPRA P99.95% recipients were compared to 30 non-transplanted case controls with cPRA P99.95%. The HLA typing data of each subject was entered into Haplostats (haplostat.org) and the top ranked phased haplotypes for each were recorded. Results: Among cPRAP99.95% recipients, 11/15 (73%) had one or both of their HLA haplotypes ranked among the top 125 CAU haplotypes. CAU haplotypes were the focus as CAU represent 67% of kidney donors. Notably, 7/15 recipients had one or both of their HLA haplotypes among the top four ranked CAU haplotypes and 6/15 received transplants from homozygous donors. Among recipients, eight were AA (53%), six were CAU and one was HIS. In contrast, among 30 case controls, 4 (13.3%) had one haplotype ranked in the top 125 and 17 had one haplotype ranked in the top 8000. For 9 candidates, neither HLA haplotype was ranked among the top 10,000 CAU haplotypes. Importantly, 25/30 (83.3%) controls were African American. Conclusions: Kidney transplant candidates with cPRAP99.95% were more likely to be transplanted when at least one of their HLA haplotypes was frequent (rank 6 125). This novel observation offers an opportunity to medically optimize a subset of highly sensitized candidates before deceased donor transplantation. It also suggests amendments may be necessary to minimize racial disparity in the KAS. Additionally, alternative approaches should be considered for candidates not likely to receive a DD offer. H.M. Gebel: 2. Consultant; Company/Organization; Astellas, Thermofisher.
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