Seeking Standardized Definitions for HLA-incompatible Kidney Transplants: A Systematic Review : Transplantation

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Original Clinical Science—General

Seeking Standardized Definitions for HLA-incompatible Kidney Transplants: A Systematic Review

Jatana, Sukhdeep S. MD1; Zhao, Hedi MD1; Bow, Laurine M. PhD2; Cozzi, Emanuele MD, PhD3,4; Batal, Ibrahim MD5; Horak, Tillie6; Amar-Zifkin, Alexandre MLIS7; Schinstock, Carrie MD8; Askar, Medhat MD, PhD9,10; Dadhania, Darshana M. MD, MS11; Cooper, Matthew MD12; Naesens, Maarten MD, PhD13; Kraus, Edward S. MD6; Sapir-Pichhadze, Ruth MD, PhD, FRCPC1,14;  on behalf of the Banff Antibody-Mediated Injury Working Group

Author Information
Transplantation 107(1):p 231-253, January 2023. | DOI: 10.1097/TP.0000000000004262
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Abstract

INTRODUCTION

HLA incompatibility between donors and recipients is associated with inferior transplant outcomes.1-9 Immunological risk is assessed in histocompatibility laboratories mainly based on serologic10-21 and cell-based assays.22-36 Various definitions have been used in the transplantation literature to describe HLA-incompatible transplants. This is because the risk for immune-mediated injury varies by the degree of donor and recipient antigen, allele, and molecular mismatch, as well as by the presence of donor-specific HLA antibodies (DSAs), HLA antibody titer, and crossmatch results.1-9,37-48 Despite this, there are currently no standards for reporting on HLA compatibility between donors and recipients.

The degree of immunological risk varies as a function of the assay studied and the threshold used to determine donor:recipient HLA incompatibility. To compare immunological risk across molecular, serologic, and cell-based methods, it is important to assess the prognostic significance of HLA incompatibility to kidney transplant outcomes. In addition, as transplant outcomes determined by HLA incompatibility are dependent on population characteristics, clinical setting, and the immune-modulatory therapies applied over the entire posttransplantcourse,22,24,25,33,34,49-62 it is also important to consider their modifying effects when designing studies and analyzing data.

To inform the care of transplant patients, there is a need for international collaboration on clinical trials and observational studies. Large studies will offer sufficient statistical power and diverse populations will facilitate generalizability of findings. For this purpose, it is important to ensure standardized definitions of incompatible transplants and consistent measurement and reporting of immunological risk. This systematic review sought to assess to what extent a predefined set of variables pertaining to HLA incompatibility was reported in contemporary peer-reviewed publications. We further conducted a narrative synthesis to describe the direction and size of the estimated effects of each definition of HLA incompatibility on transplant outcomes, captured factors that might explain differences between eligible studies, and assessed the strength of evidence.

MATERIALS AND METHODS

Study Design

We conducted a systematic review to describe how HLA-incompatible kidney transplants were defined, the therapeutic interventions administered to prevent alloimmune injury, and the outcomes considered. Institutional ethics approval was not sought as we relied on publicly accessible evidence.

Literature Search Strategy

Concepts pertaining to HLA incompatibility were identified in consultation with the Banff Antibody-Mediated Injury Working Group (Banff AMI-WG) (Table S1, SDC, https://links.lww.com/TP/C493). Given that solid-phase assays were more widely implemented over the last decade, the electronic databases MEDLINE, EMBASE, and the Cochrane library were searched from January 2015 to August 2019. To ensure comprehensive capture of the relevant literature, 2 independent search strategies addressing key concepts were developed by information experts at Johns Hopkins and McGill University Health Center. Search strategies for the databases and details on the database platforms can be found in Table S2, SDC, https://links.lww.com/TP/C493. Search results were combined, deduplicated, and underwent selection using bibliographic data.

Study Selection

Titles and abstracts were screened for eligibility using a standardized questionnaire in Microsoft Excel. We included full-text peer-reviewed articles in all languages, which discussed original research in kidney transplant recipients across age groups (pediatric and adult). Studies assessing clinical outcomes (eg, rejection, graft failure) in the presence of HLA incompatibility at the allele and/or molecular (eg, T- and/or B-cell epitope) level, pretransplant solid phase, and T- and B-cell-based (by flow or complement-dependent cytotoxicity [CDC]) assays, were included. Articles considering HLA incompatibility solely at the antigen level were also captured but detailed information on study characteristics was not collected for these articles. Eligible study designs included interventional (eg, randomized controlled trials) and analytical studies (ie, experimental observational studies). Articles discussing allo-sensitization solely defined by measured and/or calculated panel reactive antibodies (PRAs) but without information on donor specificity, were excluded. We focused on studies including only kidney transplants and excluded studies on nonsolid organ transplants, solid organ transplants other than kidney, and multiorgan transplants (combined with kidney). Studies discussing laboratory methods rather than HLA incompatibility as determinants of relevant clinical outcomes were excluded. Finally, nonoriginal research (eg, commentaries and letters) and conference abstracts as well as studies discussing ABO-incompatible transplants, non-HLA antibodies, and those with <20 participants, were also excluded. Selected titles and abstracts were imported for a full-text review into custom forms on Microsoft Access. Similar selection criteria were considered for the full-text review phase, with eligible articles proceeding to the data collection phase. The title and abstract as well as the full-text screening were conducted by 2 reviewers independently. Disagreements were resolved by consensus.

Data Collection Process

Data collection focused on a standard set of variables that was generated in consultation with the Banff AMI-WG. Study characteristics, histocompatibility, outcomes, and therapy-related variables were collected by content experts using standardized forms in Microsoft Access. Study characteristics included information on participating centers, study design, and period. Recipient characteristics included age group (pediatric versus adult) and sex (% female). Donor characteristics included donor type. Information on other donor characteristics (eg, age or percentage of expanded criteria donors) was not reported in a standardized fashion across articles, and hence was not included with the study characteristics.

We collected data on completeness of HLA molecular typing. For studies discussing preformed DSAs, we collected information on sera treatment, vendor, DSA type (eg, IgM, complement-binding, IgG subtype), and information considered for DSA assignment (eg, median fluorescence intensity [MFI] thresholds and cross reactive groups [CREGs]). Information on T- and B-cell crossmatches by CDC, anti-human globulin CDC, or flow cytometry, and the thresholds defining a positive flow crossmatch were also collected. When available, we collected data on molecular HLA compatibility, and the threshold used to discriminate patients at high versus low risk for immune-mediated injuries.

Outcomes of interest included delayed graft function, estimated glomerular filtration rate, proteinuria, rejection (including type and the classification era), graft failure, and patient survival, as well as the incidence of infection, cancer, and cardiovascular outcomes. Finally, we gathered information on desensitization, induction, and maintenance of immunosuppression, as well as therapies applied for rejection events. The Access database template for study selection and data collection is available upon request to the corresponding author (Note S1).

Data Analysis, Risk of Bias Assessment, and Synthesis

Data management was performed using Python and Statistical Analysis System 9.4 (SAS Institute, Cary, NC). We conducted descriptive statistics to provide parameters of distributions for each of the collected variables. Figures were generated using GraphPad Prism version 9.3.1 for MacOS (GraphPad Software, San Diego, CA). Given significant qualitative heterogeneity in the published literature, we opted to provide narrative synthesis on how incompatible transplants were defined and their implications for a frequently studied hard clinical endpoint—death-censored graft failure (DCGF). We did not exclude articles referring to the same cohort as we did not conduct a meta-analysis. Risk of bias and applicability were assessed for articles discussing DCGF by adapting the Prediction model Risk Of Bias ASsessment Tool (PROBAST)63 and focusing on the participant (2 signaling questions), predictor (3 signaling questions), and analysis (9 signaling questions) domains. The overall risk of bias was deemed low (if there were no relevant shortcomings), high (if at least 1 domain had high risk of bias), or unclear (if at least 1 domain was unclear, and all other domains had low risk of bias). Based on the applicability classifications for each domain, an overall judgment regarding the applicability of the model was assigned as “low concern,” “high concern,” or “unclear” when all domains showed low concern, 1 or more domains showed high concern, and only if 1 or more domains were judged as “unclear” while all other domains were of “low concern,” respectively. Data collection and PROBAST application were completed by 2 reviewers independently. Disagreements were resolved by a third reviewer.

RESULTS

The literature search combining 2 independent searches conducted by information specialists from Johns Hopkins University and McGill University Health Center yielded 11 396 and 19 702 records, respectively. Of those, 24 442 references were removed based on bibliographic data verified by an information specialist. A total of 6656 records underwent title and abstract screening. The full-text review was undertaken for 886 eligible articles; of these 78 articles discussed HLA incompatibility at the level of the antigen mismatch only,64-141 whereas 163 articles assessed incompatibility by molecular genotyping, molecular mismatch analysis, and/or pretransplant DSA verification by solid-phase and/or by cell-based assays.3,142-303 The Preferred Reporting Items for Systematic reviews and Meta-Analyses diagram outlining the article selection process is provided in Figure 1.304

F1
Figure 1.:
Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) flow diagram. Outlines the studies excluded at various stages of selection, including literature search, title and abstract screening, and full-text screening. From: Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. αDuplicates identified in the reference manager and articles meeting exclusion criteria based on publications before 2015 and bibliographic data verified by the information specialist (T.H.). Abs, antibodies; DSA, donor-specific antibody; JH, Johns Hopkins.

Study Characteristics

The characteristics of the 163 included articles are presented in Table 1. Most articles reported on cohort studies (n = 143; 87.7%) with adult (n = 90; 55.2%) or a combination of adult and pediatric (n = 54; 33.1%) participants. Female participants made up less than half of the study population in most of the eligible articles. Living, deceased, or both donor types were reported in 27 (16.6%), 24 (14.7%), and 103 (63.2%) studies, respectively. Nine articles did not mention donor type. A total of 62 studies reported on cohort periods commencing between 2000 and 2007, 61 between 2008 and 2013, 27 preceding 2000, and 3 commencing ≥2014. Studies were often conducted in a single center (n = 126; 77.3%) and originated in Europe (n = 84; 51.5%), North America (n = 44; 27.0%), and Asia (n = 32; 19.6%).

TABLE 1. - Study characteristics
Characteristics Categories Number %
Study design Interventional design
Randomized controlled trials 4 2.5
Nonrandomized 3 1.8
Observational studies
Cohort 143 87.7
Case-control 8 4.9
Case series 2 1.2
Cross sectional 3 1.8
Study population age group Adult 90 55.2
Pediatric 2 1.2
Both adult and pediatric 54 33.1
Not reported 17 10.4
Study population sex distribution, % female <25 2 1.2
25–50 101 62.0
≥50–75 36 22.1
≥75 2 1.2
Not reported 22 13.5
Study center(s) Multicenter 36 22.1
Single-center 126 77.3
Not reported 1 0.6
Study start date (y) <2000 26 16.0
2000–2007 62 38.0
2008–2013 61 37.4
≥2014 3 1.8
Not reported 11 6.8
Continent a Africa 2 1.2
Asia 32 19.6
Australia 2 1.2
Europe 84 51.5
North America 44 27.0
South America 6 3.7
Donor type Living 27 16.6
Deceased 24 14.7
Both living and deceased 103 63.2
Not reported 9 5.5
aSome studies may report on >1 continent.

HLA Incompatibility

HLA incompatibility was assessed as antigen, allele, and molecular mismatches as well as using solid-phase and cell-based assays. Figure 2A and B outline information on HLA typing methods and resolution. Donor and recipient genotyping was conducted by molecular methods in 30.7% and 35.6% for HLA class I and class II, respectively. Molecular typing was reported only in a third of the articles and was typically at the allele-group level. HLA-C was infrequently available and, of the HLA class II loci, DQB1 and DRB1 were most frequently mentioned (Figure 2C). HLA incompatibility was assessed by pretransplant CDC (B cell: n = 54, 33.1% and T cell: n = 76, 46.6%) followed by flow (B cell: n = 51, 31.3% and T cell: n = 52, 31.9%) crossmatch (Table 2). Molecular HLA incompatibility was assessed in 9.8% of the articles using HLAMatchmaker and PIRCHE-II algorithms. There was heterogeneity in the loci considered and resolution of HLA types from which the molecular HLA incompatibility was inferred. Studies typically described HLA incompatibility at multiple levels (eg, solid-phase and cell-based).

TABLE 2. - HLA incompatibility by cell-based methods
Crossmatch type Number % a
B-cell crossmatch Articles reporting on B-cell crossmatch 84 51.5
B-cell CDC crossmatch Used 54 33.1
Not used 3 1.8
Other 0 0.0
Not reported 106 65.0
B-cell Amos crossmatch Used 2 1.2
Not used 9 5.5
Other 0 0.0
Not reported 152 93.3
B-cell flow crossmatch Used 51 31.3
Not used 20 12.3
Other b 1 0.6
Not reported 91 55.8
B-cell crossmatch MESF threshold Not reported 163 100.0
B-cell channel shift threshold c 2 SD 0 0.0
3 SD 2 1.2
Other b 20 12.3
Not reported 142 87.1
T-cell crossmatch Articles reporting on T-cell crossmatch 98 60.1
T-cell CDC crossmatch Used 76 46.6
Not used 4 2.5
Other 0 0.0
Not reported 83 50.9
T-cell AHG-CDC crossmatch Used 15 9.2
Not used 13 8.0
Other 0 0.0
Not reported 135 82.8
T-cell flow crossmatch Used 52 31.9
Not used 23 14.1
Other 1 0.6
Not reported 87 53.4
T-cell crossmatch MESF threshold Not reported 163 100.0
T-cell channel shift threshold c 2 SD 0 0.0
3 SD 2 1.2
Other d 16 9.8
Not reported 146 89.6
aPercentage considering the total number of eligible articles (N = 163).
bOther includes median channel shift of 50–300, SD of 2.3.
cSome studies may report >1 option.
dOther includes median channel shift dependent/ independent if primary graft, other SD values.
AHG, anti-human globulin; CDC, complement-dependent cytotoxicity; MESF, molecules of equivalent soluble fluorochrome; SD, standard deviation (a positive crossmatch will be determined based on the number of SD the fluorescence with serum from tested patient is stronger than that with negative controls).

F2
FIGURE 2.:
Reporting on HLA typing. A, HLA typing method. Other includes typing by imputation. Studies may report >1 typing method (eg, serological typing for class I and molecular typing for class II). B, HLA typing resolution. C, HLA class and loci. Proportions delineated by locus. All values are calculated based on a denominator of 163 eligible articles. NR, not reported.

Pretransplant DSA was discussed in 87.7% of the eligible articles. Studies primarily reported on IgG DSA (Figure 3A). DSA verification relied primarily on MFI thresholds (Figure 3B) and was performed using assays from 2 main vendors. We observed heterogeneity between studies in MFI threshold considered for DSA assignment (range: 300–10 000) depending on HLA locus (Figure 3C) and serum processing (Figure 3D). When C1q assays were performed, the MFI threshold was typically 500. Reporting on serum sample processing before solid-phase assay analysis was incomplete in 81% of the articles. Details on whether, among others, reactivity against candidates’ (self) antigens, patients’ sensitization history, and eplets or CREGs were considered when assigning DSAs, were not consistently provided.

F3
FIGURE 3.:
Preformed DSA by solid-phase assays. A, DSA assay type. B, Approach for DSA verification. Other includes combinations of MFI and signal-to-background ratio, relative intensity scores, alternative scoring system, and consideration of epitope reactivity. C, MFI threshold for DSA. D, Sera pretreatment for DSA measurement. Other includes dilution and fetal calf serum as well as sera heating for C1q DSA measurements. All values are calculated based on a denominator of 163 eligible articles. Ab, antibody; C’, complement-binding DSA; CREG, crossreactive group analysis; DSA, donor-specific antibody; DTT, dithiothreitol; EDTA, ethylenediaminetetraacetic acid; IgG, immunoglobulin G; IgM, immunoglobulin M; MFI, median fluorescence intensity; NR, not reported.

Outcomes

Table 3 presents the outcomes of the included studies. Graft failure and rejection were the most frequently studied outcomes of HLA incompatibility. Less than half of the studies reported on kidney function (creatinine or estimated glomerular filtration rate), posttransplant DSA, patient survival, infection, cancer, and cardiovascular complications. Despite the selection of contemporary publications, there was noticeable heterogeneity in the Banff classification applied (spanning the years 1997–2017; Figure 4A), and the assigned rejection diagnosis (Figure 4B). There was also heterogeneity in the literature with regard to how graft failure events were defined and analyzed, including DCGF, all-cause graft loss (graft failure and death with function), missing information on censoring, considering death as a competing risk,225,233,303 and defining graft failure as a binary endpoint.

TABLE 3. - Outcomes
Outcome a Number %
Delayed graft function 53 32.5
Serum creatinine 36 22.1
eGFR 61 37.4
Posttransplant DSA 63 38.7
Rejection 133 81.6
Graft failure a Graft failure 122 74.8
Death-censored graft failure 72 59.0 b
Overall graft loss (graft failure + death with function) 34 27.9 b
Graft failure (Y/N) 28 23.0 b
Other 3 2.5 b
Not specified 12 9.9 b
Patient survival 63 38.7
Infection 37 22.7
Cancer 9 5.5
Cardiovascular complications 8 4.9
aStudies may have reported on >1 outcome.
bPercentage of articles reporting on graft failure (N = 122).
DSA, donor-specific antibody; eGFR, estimated glomerular filtration rate; N, no; Y, yes.

F4
FIGURE 4.:
Banff rejection data. A, Banff classification (y). Other includes combinations of Banff classifications. B, Banff rejection diagnosis. Some articles reported on multiple diagnoses and considered combinations of active, chronic active, and chronic features. All values are calculated based on a denominator of 163 eligible articles. ABMR, antibody-mediated rejection; NR, not reported; TCMR, T-cell mediated rejection.

Treatment

Table 4 summarizes the immunomodulatory interventions reported in the included studies. Choices of induction and maintenance immunosuppression therapies were reported in most studies (n = 129, 79.1% and n = 140, 85.9%, respectively), whereas desensitization and rejection therapies were reported in 70 (42.9%) and 72 (44.2%) studies, respectively. Induction agents included IL-2 receptor inhibitors (n = 98; 76.0%), lymphocyte-depleting agents (n = 103; 79.8%), and steroids (n = 31; 24.0%). Maintenance immunosuppression regimens included tacrolimus (n = 136; 97.1%), mycophenolate (n = 133; 95.0%), and steroids (n = 126; 90.0%). Few studies reported on cyclosporine, azathioprine, mammalian target of rapamycin (mTOR) inhibitors, leflunomide, and belatacept. Pretransplant desensitization primarily included IVIG (n = 38; 54.3%), plasmapheresis (n = 42, 60.0%), and rituximab (n = 35; 50.0%). Eculizumab, C1-esterase inhibitors, bortezomib, IdeS/imlifidase, and tocilizumab were infrequently used. Protocols for rejection included steroids (n = 55; 76.4%), lymphocyte-depleting agents (n = 41; 56.9%), IVIG (n = 48; 66.7%), plasmapheresis (n = 52; 72.2%), and rituximab (n = 32; 44.4%). IL-2 receptor inhibitors, eculizumab, C1-esterase inhibitors, cyclophosphamide, bortezomib, tocilizumab, and splenectomy were infrequently mentioned.

TABLE 4. - Therapeutic interventions
Treatment type Number %
Desensitization Desensitization therapy reported a Yes 70 42.9
Pretransplant desensitization reported a Yes 64 39.3
Pretransplant desensitization type Anti-CD20 antibody 35 54.7 b
IVIG 38 59.4 b
Interleukin-6 receptor antagonist 1 1.6 b
Plasmapheresis 42 65.6 b
Proteosome inhibitor 4 6.3 b
Complement inhibitor 2 3.1 b
Other c 6 9.4 b
Not specified 7 10.9 b
Posttransplant desensitization reported a Yes 7 4.3
Posttransplant desensitization type Anti-CD20 antibody 2 28.6 b
IVIG 4 57.1 b
Interleukin-6 receptor antagonist 0 0 b
Plasmapheresis 3 42.9 b
Proteosome inhibitor 0 0 b
Complement inhibitor 0 0 b
Other d 1 14.3 b
Not specified 2 28.6 b
Induction Induction therapy reported Yes 129 79.1
Induction type a Steroids 31 24.0 b
Interleukin-2 inhibitor 98 76.0 b
Lymphocyte depleting 103 79.8 b
Other e 20 15.5 b
Not specified 4 3.1 b
Maintenance Maintenance therapy reported Yes 140 85.9
Maintenance immunosuppression a Tacrolimus 136 97.1 b
Cyclosporine 70 50.0 b
Mycophenolate 133 95.0 b
Azathioprine 18 12.9 b
Steroids 126 90.0 b
mTOR inhibitor 23 17.1 b
Leflunomide 1 0.7 b
Other f 13 9.3 b
Not specified 1 0.7 b
Rejection Rejection treatment reported Yes 72 44.2
Rejection treatment type a Steroids 55 76.4 b
Lymphocyte depleting 41 56.9 b
Interleukin-2 receptor inhibitor 1 1.4 b
IVIG 48 66.7 b
Anti-CD20 antibody (rituximab) 32 44.4 b
Complement inhibitor (C5 inhibitor eculizumab, C1-esterase inhibitor) 7 9.7 b
Cyclophosphamide 0 0 b
Proteosome inhibitor (bortezomib) 7 9.7 b
Interleukin-6 receptor antagonist tocilizumab 1 1.4 b
Plasmapheresis 52 72.2 b
Splenectomy 0 0 b
Other g 3 4.2 b
Not specified 5 6.9 b
aMore than 1 treatment option could have been applied.
bPercentage of subset of articles reporting on desensitization (pretransplant [N = 64], posttransplant [N = 7], induction [N = 129], maintenance [N = 140], and rejection therapy [N = 72], respectively).
cOther includes immunoabsorption and immunoglobulin G endopeptidase.
dOther includes immunoabsorption.
eOther includes alemtuzumab, eculizumab, alefacept, rituximab, anti-CD 20 antibody, IVIG, and bortezomib.
fOther includes belatacept, staurosporine, mizoribine, and proliferation signal inhibitor.
gOther includes mycophenolate and antibody-mediated rejection–specific therapy.
mTOR, mammalian target of rapamycin.

HLA Incompatibility and the Risk of DCGF

Of the eligible articles, we identified 13 articles that provided hazard ratio (HR) estimates for DCGF associated with HLA incompatibility. Articles not using the terms death censored and/or providing only Kaplan-Meier curves were not selected. Among the 13 studies, HLA incompatibility was defined by crossmatch results (n = 2),192,200 preformed IgG DSA (n = 10),197,220,231,233,236,244,261,271,284,298 and molecular mismatches (n = 2).175,231 Two articles studied complement activating DSA but measured those only in IgG DSA-positive participants.231,236

Table 5 presents the population characteristics, immune risk predictor definitions, study conduct, analysis, and risk estimates from the studies reporting on HRs of DCGF. Of these articles, 1 study also conducted analysis while considering only the subset of graft failure events attributable to immune-mediated injury.298 Most of the articles reporting on HRs for DCGF considered time at risk commencing at transplantation. Yet, some articles conducted landmark analysis, with time at risk commencing at various time points following transplantation upon diagnosis of biopsy-proven rejection.231,261

TABLE 5. - Illustration of the heterogeneity in the published literature on prognostic implications of HLA incompatibility using the example of death-censored graft failure
Author and year Population Predictor Analysis Immunosuppression
Study selection criteria Center(s) Cohort years Sample size (analytic) Donor HLA loci and typing method Screening for HLA Abs SAB verification strategy MFI threshold Follow-up Exposure group Univariable HR (95% CI) Multivariable HR (95% CI) Covariates (multivariable model) Induction Maintenance
Kamburova et al, 2018 197 KTR with negative T-cell CDC XM and without historic cytotoxic HLA Abs a Multicenter (Netherlands) 1995–2005 4724 HLA-A, B, DR, DQ
Typing method not reported
Lifecodes LifeScreen Deluxe (Immucor) Lifecodes SAB assay Class I/II kits (Immucor) on LABScan 100 flow analyzer (One Lambda) 750 Up to 10 y DSA vs no DSA b 1.77 (1.51-2.08) Recipient age, donor age, donor type, cold ischemia time, time on dialysis, induction with IL-2Ri IL-2Ri, lymphocyte depleting CSA, mTORi, TAC, steroid, other
Louis et al, 2019 220 Adult KTR with negative CDC XM with biopsy samples for IHC a Single-center (France) 2010–2015 248 HLA-A, B, C, DR, DQ, and DP
Typing method not reported
SAB Assay on Luminex platform (One Lambda) 500 c Immunodominant DSA with highest MFI Mean: 4.3 y DSA vs no DSA 1.2 (-) Proteinuria at time of biopsy, i, MI (g + ptc), cg, ci, and EndMT d Lymphocyte depleting NR
Michielsen et al, 2019 233 KTRs with negative T-cell CDC XM a Multicenter (Netherlands) 1995–2005 115 HLA-A, B, DR, DQ
HLA-C and -DP typing not routinely performed
Typing method not reported
Lifecodes LifeScreen (Immucor) Lifecodes LSA Class I/II kit, (Immucor)
HLA-C, DP Abs classified as nDSA
750 Up to 10 y DSA vs no HLA 2.75 (1.61-4.68) 2.94 (1.69-5.1) Recipient age, cold ischemia time, calcineurin inhibitor, induction therapy e IL-2Ri, lymphocyte depleting CSA, MPA, TAC, steroid
nDSA vs no HLA 1.26 (0.84-1.90) 1.10 (0.73-1.68)
Meneghini et al, 2018 231 Consecutive adult living donor KTR a KTR who were HLA-identical, multiorgan recipients, or with early graft loss (surgical complication) excluded Two centers (Spain) 2000–2013 330 HLA-A, B, DR,-DQ
Molecular typing (SSP) of donors and recipients were converted to allele-level types using local frequency table of sequence-based typing
Lifecodes SAB (Immucor) 1500 c Considered eplets for DSA assignment Mean: 67 ± 29 mo DSA vs no DSA 4.42
(1.69-11.52)
2.23 (0.5-18.38) d,f Recipient age, transplant number, desensitization therapy, acute rejection, DCD PRA>20%, T- or B-cell Flow XM+, DSA C3d+, DSA MFI>6190 d,f IL-2Ri, lymphocyte depleting CSA, MPA, TAC, steroid, mTORi
Molina et al, 2017 236 KTR with negative T- and B-cell XM a Single-center (Spain) 1995–2009 389 HLA-A, B, DRB1/3/4/5, DQB1
Molecular typing (SSO) of donors only
HLA-DQB1 Locus type by Innolipa typing kit (Innogenetics)
LABScreen Mixed I/II (One Lambda) LABScreen SAB (One Lambda) 1000 c Up to 7 y DSA vs no DSA 2.01 (1.31-3.09) 2.13
(1.38-3.3)
Donor age, cold ischemia time, HLA-DR MM Lymphocyte depleting MPA, CSA, azathioprine, mTORi, steroid, TAC
Passamonti et al, 2019 244 KTR with negative T-cell CDC XM a Single-center (Italy) 1996–2005 573 HLA-A, B, C, DRB, DQB, DPB
Typing method not reported
LABScreen Mixed Kit (One Lambda) LABScreenSAB (One Lambda) 1000 Median: 10 y DSA vs no DSA 2.31 (1.48-3.61) 1.6 (0.92-2.79) f HLA-A MM, HLA-B MM, HLA-DR MM, donor age, recipient age, sex f IL-2Ri, lymphocyte depleting, steroids Azathioprine, CSA, MPA, steroid, TAC
Senev et al, 2019 261 Consecutive adult KTR with negative B- and T-cell CDC XM a Single-center (Belgium) 2004–2013 935 HLA-A, B, C, DRB1/3/4/5, DQA1/B1, DPA1/B1
Molecular typing (NGS) of donors and recipients
Lifecodes LifeScreen deluxe (Immucor) Lifecodes SAB (LSA) kits (Immucor) 500
Immunodominant DSA with highest MFI
Median: 7.13 (IQR 4.5) y DSA MFI ≥500–<1400 vs <500 1.48 (0.5-4.7) 1.3 (0.4-4.2) AMR, Retransplant, delayed graft function, induction therapy d IL-2Ri, lymphocyte depleting, other (alefacept) MPA, TAC, steroid
DSA MFI ≥1400 vs <500 5.09 (3.3-8.3) 3.22 (1.7-6.1)
Uffing et al, 2019 271 KTR with negative T-cell CDC XM a KTR with Class I DSA excluded Single-center (USA) 2007–2015 179 HLA-DR, DQ but no DP
Serological typing
LABScreen Mixed Kit
(One Lambda)
LABScreen SAB Class I/Class II (One Lambda) 1000 Mean: 5.5 y Class II DSA vs no Class II DSA 1.45 (0.62-3.36)
1.1 (0.41-2.97) Age, prior transplants, sex IL-2Ri, lymphocyte depleting MPA, TAC, steroid
Wehmeier et al, 2017 284 KTR with negative T- and B-cell CDC XM a Single-center (Switzerland) 2005–2014 527 HLA-A, B, DRB1/3/4/5, DQB1
Typing method not reported
FlowPRA SA (< Dec 2006) and Luminex SA (≥ Dec 2006), One Lambda) g 500 c Median: 5.7 (IQR: 5.1–8.1) y DSA vs no DSA 3.58
(1.34-9.68)
cPRA last, cPRA peak, repeat transplantation, pregnancies, HLA-A/B/DRB1 mismatch, deceased donor f IL-2Ri, lymphocyte depleting, other IVIG MPA,
TAC, steroid,
mTORi other
Zecher et al, 2017 298 KTR with negative T- and B-cell CDC XM, receiving IL-2Ri and triple agent immunosuppression a KTR receiving lymphocyte depleting and mTORi excluded Single-center (Germany) 2005–2012 174 HLA-A, B, DR but HLA-C and DQ partially done
Typing method not reported
LSM12 (One Lambda) on LABScan 100 flow analyzer (One Lambda) LABScreen SAB Class I/II 500 Median: 6 y (DSA) vs 5.3 y (no DSA) DSA vs no DSA 4.84
(1.83-12.79)
1.85
(0.5-6.84) f
Class I and II DSA, MFImax > 10 000, AMRand donor type f IL-2Ri CSA, MPA, steroid, TAC, other (staurosporine)
Johnson et al, 2016 192 Adult KTR; excluded DSA+ Single-center (USA) 2005–2009 508 HLA-A, B, Cw, DRB1, DQB1
HLA-DPB1 from January 2008
HLA-DQA1 from mid 2009
Typing method not reported
FlowPRA (One Lambda)
LABScreen PRA (One Lambda)
LabScreen (One Lambda) 1000 Median: 7.1 (range 5–10) y Flow XM positive vs negative 1.6
(0.75-3.39)
1.27
(0.58-2.76)
Recipient age (threshold 60 y), deceased vs living donor, sex, 0 mismatch transplant (vs all others), induction, with lymphocyte depleting agent IL-2Ri, lymphocyte depleting MPA, TAC, steroid
Khovanova et al, 2015 200 HLAi KTR Single-center (United Kingdom) 2003–2012 80 Pan-IgG HLA class I and class II-specific antibodies were identified before transplant using microbead assays manufactured by One Lambda Inc and analyzed on a Luminex platform (XMap 200) SABs used to retrospectively confirm Ab specificities (no company specified) 1000 Median: 5 (IQR 3–6.7) y CDC XM positive vs negative 1.46 (0.36-5.86) Previous transplant, single highest pan-IgG DSA MFI, IgG1, DSA IgG2 DSA, IgG3 DSA, IgG4 DSA, DGF IL-2Ri, steroids MPA, TAC, steroid
Meneghini et al, 2018 231 Consecutive adult living donor KTR a KTRs who were HLA-identical, multiorgan recipients, or with early graft loss (surgical complication) excluded Two centers (Spain) 2000–2013 330 HLA-A, B, DR,-DQ
Molecular typing (SSP) of donors and recipients
were converted to allele-level types using local frequency table of
sequence-based typing
HLAMatchmaker (4ABCEpletMatchingVs02protoype.xlsb and
DRDQDPEpletMatchingVs02protoype.xlsb)
Mean: 67 ± 29 mo Class I (A,B) eplet mismatch 1.03 (0.95-1.11) IL-2Ri, lymphocyte depleting CSA, MPA, TAC, steroid, mTORi
Class II: DR eplet mismatch 1.01 (0.93-1.09)
Class II: DQ eplet mismatch 1.01 (0.93-1.09)
Geneugelijk et al, 2018 175 KTR excluding >5/6 Ag MMs; nonstandard protocol (Eurotransplant); 0/10 Ag mismatches; 0-1 PIRCHE-II mismatches Multicenter (Netherlands) 1995–2005 2504 first kidney transplants HLA-A, B, C and DRB1, DQB1 Ag-level typing used to impute allele-level typing Median: 11 y Eplets 1.01 (1-1.01)
aRetransplant recipients included.
bIn 64 KTRs, only broad-level typing was available, and not considered as DSA. Sensitivity analysis assigning them as DSA yielded similar results.
cMean fluorescence intensity.
dMultivariable models adjusted for posttransplant variables.
eRecipient and transplant characteristics adjusted for as donors were matched on.
fMultivariable models adjusted for multiple potentially correlated variables.
gVirtual crossmatch considering current and remote anti-HLA antibodies.
Ab, antibody; Ag, antigen; AMR, antibody-mediated rejection; CDC XM, complement-dependent cytotoxicity crossmatch; cg, glomerular basement membrane double contours; CI, confidence interval; ci, interstitial fibrosis; CNI, calcineurin inhibitor; cPRA, calculated panel-reactive antibody; CSA, cyclosporine A; DCD, donation after cardiocirculatory death; Dec, December; DGF, delayed graft function; DSA, donor-specific antibody; EndMT, endothelial-to-mesenchymal transition; g, glomerulitis; HR, hazard ratio; i, interstitial inflammation; IgG, immunoglobulin G; IHC, immunohistochemistry; IL-2Ri, interleukin-2 receptor inhibitor; IQR, interquartile range; KTR, kidney transplant recipient; MFI, median fluorescence intensity; MI, microcirculatory inflammation; MM, mismatch; MMF, mycophenolate mofetil; MPA, mycophenolic acid; mTORi, mammalian target of rapamycin inhibitor; nDSA, anti-HLA antibodies present but no donor-specific antibodies; NGS, next-generation sequencing; NR, not reported; PIRCHE-II, Predicted Indirectly ReCognizable HLA Epitopes; PRA, panel-reactive antibody; ptc, peritubular capillaritis; SAB, single-antigen bead; SAFB, single-antigen flow bead; SSO, sequence-specific oligonucleotide; SSP, sequence-specific primer; TAC, tacrolimus; WBC, white blood cell.

Risk of bias and applicability assessment of the studies reporting on HRs of DCGF by the PROBAST checklist revealed that for the participant domain, several studies reported on populations comprised primarily of White patients, limiting generalizability of findings to more diverse populations. Also, studies did not separately analyze recipients of first transplant events and retransplant recipients, in whom HLA incompatibility may differently inform immunological risk as a consequence of memory response and protracted exposure to immunosuppression. When evaluating the predictor domain, DSA was assigned based on remote and pretransplant sera, sera collected up to several weeks pretransplant, and on the day of transplant in 1, 2, and 10 studies, respectively. Seven studies conducted screening by Flow PRA (phenotype beads) followed by single-antigen bead assays (Table 5). Sequential testing might introduce measurement bias, should Flow PRA give rise to false-negative results, for example. Most articles modeled DSA as a dichotomous variable (ie, presence or absence) but 1 study divided the “no DSA” category to (1) preformed nondonor-specific anti-HLA antibodies present and (2) preformed anti-HLA antibodies absent, with the latter serving as the reference category.233 When evaluating the analysis domain, studies did not always report on missing data and how they were handled; in addition to noticeable heterogeneity across studies with regard to the covariates considered in multivariable models, risk of bias was deemed high as studies frequently relied on univariable analysis for predictor selection. Also, multivariable models adjusted for multiple potentially correlated variables and/or both primary predictors and mediators of risk on the pathway between HLA incompatibility and graft failure (ie, preformed DSA and AMR occurring at various time points posttransplant220,261). Finally, some models were vulnerable to overfitting given a small number of events. As most studies focused on hypothesis testing rather than development of prediction models, model performance and external validation were infrequently reported on. Tabular presentation of risk of bias by a study using the PROBAST tool is provided in Table S3, SDC, https://links.lww.com/TP/C493. When assessing the applicability to the study question, as a consequence of the selection process, all studies were deemed to be of “low concern.”

DISCUSSION

Striving to improve long-term outcomes of transplant recipients, the international transplantation community has sought to estimate immunological risk with the objective of mitigating immune injury. In the absence of a standard definition for HLA-incompatible transplants, we systematically reviewed for the first time how HLA incompatibility was defined in contemporary peer-reviewed publications. Articles primarily relied on retrospectively collected data and reported on potential immunological risk related to antigen, allele, and molecular mismatches alongside evident memory response demonstrated by preformed DSAs detected by solid-phase and/or cell-based assays. Our review identified gaps in reporting on a predefined set of variables pertaining to HLA incompatibility, therapies, and outcomes. In addition to heterogeneity in definitions of HLA incompatibility and rejection outcomes, some studies were underpowered and vulnerable to bias in the participant, predictor, and analysis domains.

The significant polymorphism of HLA genes makes the vast majority of kidney transplant recipients incompatible with their donors at some level. Laboratory tools estimating HLA incompatibility can inform where each patient (or donor-recipient pair) may be placed across a continuum of immunological risk, spanning from molecular HLA incompatibility (informed, preferably, by unambiguous allele-level HLA typing), through preformed DSAs, to transplantation in the context of positive cell-based flow or cytotoxic crossmatch. Guided by each patient’s preferences and likelihood of access to transplantation, this assessment can inform decisions to accept or refuse donor offers, guide individualized monitoring needs, and justify personalized immunosuppression regimens. Although optimization of transplant outcomes depends on accurate assessment of potential immunological risk related to HLA incompatibility,305-309 it is often a combination of diagnostic tools that must be used as no single test can measure HLA antibodies with perfect sensitivity and specificity,310 accurately estimate likelihood of a memory response, or prognosticate on the development of new DSA and immune injury posttransplant.

Despite the evident need, we found gaps in reporting on a predefined set of variables pertaining to HLA incompatibility. Relying on retrospectively collected data, approximately a third of the articles primarily relied on antigen mismatches to inform on potential immunological risk, whereas only 9.8% reported on molecular mismatches (primarily using the HLAMatchmaker and PIRCHE-II algorithms). When molecular genotyping was done, it provided a partial account of HLA loci at low or intermediate resolution. Such incomplete and ambiguous HLA genotypes compromise the capacity to precisely estimate molecular mismatch and accurately confirm (or rule out) presence of preformed DSAs.305-307

We also observed gaps in reporting on evident memory response measured by solid-phase311 and cell-based assays. Despite the fundamental role solid-phase assays play in clinical practice, we found that there was insufficient detail on the technical aspects of assay performance and interpretation. Also, there was significant heterogeneity in technique, analysis (including threshold values), and loci considered for DSA ascertainment. For example, some laboratories considered MFI of immunodominant antibody,220,298 whereas others added MFI across multiple beads as a measure of antibody strength.199,224,241,286,312 These observations, together with the wide variability in the techniques used by laboratories to handle inaccurate IgG specificity arising, among others, from reactivity to denatured antigen and bead saturation, have made interpretation of solid-phase assays challenging. Additional complexity is introduced when considering if and how to integrate complement-dependent assays313 and IgM-based assays.

It is noteworthy that virtual crossmatches, comparing the molecular HLA type of a donor against the candidate’s HLA antibody profile, were infrequently discussed in the eligible articles. Decisions to proceed with transplantation were primarily informed by negative pretransplant T- and B-cell CDC crossmatches and, in a third of the articles, by flow cytometric techniques. Crossmatches are known to be susceptible to suboptimal specificity and sensitivity (Clifford Sullivan et al314). Yet, strategies to recognize and minimize false-positive DSA results, including serum pretreatment with EDTA to overcome interferences, are increasingly recognized. Detailed testing procedures and interpretation, however, were often missing (Table 2).

Future efforts to assess the prognostic significance of various strategies for immunological risk assessment depend on the tests applied adhering to certain quality standards. Detailed reporting on a predefined set of variables related to HLA incompatibility will help explain differences in baseline immunological risk (and consequently outcomes) observed across studies and evaluate generalizability of observations. As HLA laboratories strive to improve diagnostic performance, only greater consistency in the way testing is performed, analyzed, interpreted,315 and reported will prevent this variability from impeding the ability to fully understand the significance of DSA to long-term transplant outcomes. Thus, the Banff AMI-WG supports efforts by the American Society of Histocompatibility and Immunogenetics, European Federation for Immunogenetics, and the Sensitization in Transplantation: Assessment of Risk group308,309 to harmonize practices.

We believe that the incorporation of markers of immunological risk in clinical care should hinge upon their predictive and prognostic value against standardized outcomes. The mandate of the Banff Foundation is to promote international consensus and standardization of outcomes, among them—immune-mediated injuries, in an iterative fashion. We observed significant heterogeneity in rejection diagnoses because of biennial revisions to the Banff classification. To overcome this challenge, future studies could collect, in addition to Banff diagnoses, a minimal set of variables, including Banff Lesion Scores, additional diagnostic parameters (eg, prior evidence of DSA) and category 6 diagnoses.316 Datasets including findings on allograft biopsies in addition to detailed HLA laboratory test results may be more amenable to reassessment of immunological risk with evolving classifications.

To overcome challenges of inconsistent case ascertainment, we illustrated how various methods for HLA incompatibility assessment informed immunological risk by evaluating their association with DCGF. Because greater immunological risk calls for more potent immunosuppression, and consequently risk of infections and cancer, future analyses should explore death as a competing risk for graft failure.317 Given the latency of DCGF and reliance on retrospectively collected data, we found that studies reporting on immunological risk were somewhat outdated. With the growing interest in noninvasive biomarkers (eg, molecular HLA incompatibility by HLAMatchmaker,170,254,287,290,318-322 EMMA,323 and PIRCHE-II algorithms175,324-326; amino acid sequence and physicochemical disparities327,328; as well as donor-derived cell-free DNA and gene expression profiles) and rapidly evolving technologies, it is important for the transplantation community to outline a consensus process for study design, prospective data and biospecimen collection, and analysis to determine the diagnostic and prognostic value of emerging assays (beyond the already available tools). Authors are encouraged to follow protocol development and reporting guidelines as outlined on the EQUATOR Network website (www.equator-network.org) to ensure research papers are of high quality, accurate, complete, and reproducible. Adhering to these guidelines is also expected to facilitate knowledge synthesis. By pooling aggregate data, one can overcome limitations related to sample size when new biomarkers are assessed by high-quality albeit small single-center studies.

Relying on retrospective cohorts, a sizable proportion of the eligible articles reported on desensitization protocols for incompatible transplants. Future knowledge synthesis efforts will be able to assess whether this practice has diminished in the era of programs promoting living donor paired exchange and organ sharing to facilitate access to transplantation of highly sensitized patients. Notably, <5% of the studies used an interventional design, highlighting the fact that the field of transplantation relies primarily on observational data. In addition to the case-mix (or characteristics of study participants), we encourage observational studies on HLA incompatibility to describe (among the standard set of variables) how exposure to immunosuppression varied over time. Reporting on prevalent practices could help promote awareness and greater international collaboration ensuring future clinical trials will have sufficient power to evaluate the efficacy of interventions.

We conducted a comprehensive review of contemporary literature on HLA incompatibility, independent of language of publication, and with inputs from a multidisciplinary team involved in transplantation diagnostics (immunogenetics and pathology) and therapeutics (clinicians and surgeons). The 2 independent search strategies conducted by our team outline a selection of keywords for the classification of articles discussing incompatible transplants and facilitate future updates on this topic. Despite these strengths, we must acknowledge that the heterogeneity observed across publications, among others, in predictor ascertainment and analysis, was not conducive to conducting a meta-analysis to provide pooled estimates of outcomes, including DCGF risk, by various HLA incompatibility strategies.

In conclusion, we conducted a systematic review of the literature to assess how HLA incompatibility was defined in recent peer-reviewed publications and its prognostic implications to transplant outcomes. Our review highlights gaps in reporting and heterogeneity in the conduct of immunogenetic assays and in outcome definitions. As optimization of transplant outcomes and personalized care depend on accurate immunological risk assessment, we summarize contemporary definitions of HLA-incompatible transplants (Figure 5) based on ascending immunological risk and call for a consensus to be achieved on a predefined set of variables pertaining to HLA incompatibility, patient characteristics, therapy, and outcomes that should be reported on in future publications. These efforts are expected to enhance the assessment of generalizability of research findings and knowledge synthesis and facilitate future international collaboration in clinical trials.

F5
FIGURE 5.:
Potential immunological risk and evident memory response associated with HLA-incompatible transplants can be informed by a combination of laboratory tools. To assess potential immunological risk, verify donors and recipients’ unambiguous allele-level genotypes of 11 HLA loci by molecular methods. If imputation was used, describe the method applied and its performance in reference to molecular allele-level genotyping. Reporting on molecular HLA mismatch should indicate the algorithm used (eg, HLAMatchmaker, PIRCHE, etc), the version, and software used. In addition to molecular mismatch load, the specific repertoire of molecular mismatches observed in the cohort should be provided. Evident immunological risk may be assessed by solid-phase and cell-based assays. Solid-phase assays are considered sensitive methods for HLA antibody detection. Yet, they are vulnerable to false-negative and false-positive results. Consequently, the less sensitive cell-based (cytotoxicity and flow crossmatch) assays are used alongside to determine the clinical relevance of detected HLA antibodies. Reporting on solid-phase SAB assay should include information on serum treatment, reagent vendor and lot number, MFI threshold used to determine positive DSA (by locus and/or by bead or sum across several beads, when relevant), if MFI normalization was used, (ie, raw or background correction), assay timing, transience vs persistence, assay context (surveillance or for cause), and whether CREGs or molecular mismatch algorithms were considered. For complement-based assays, report on how DSA positive sera were assessed and how discrepancies between complement-binding assays and SAB were handled. Reporting on cell-based mismatch by flow cytometry should include the threshold used to define a positive flow crossmatch and the timing it was performed. *HLA incompatibility at the antigen level (eg, A-B-DR) is deemed outdated. **Verify donor genotype was included within solid phase allele-specific antibody repertoire. ***Supplement information on DSA by SAB if used towards interpretation of clinical relevance. £See also Valenzuela et al, 2018.311 See Roufosse et al, 2018.316 &Adapted from Wolff RF et al, 2019.63 CDC, complement-dependent cytotoxicity; CREG, cross reactive group; DSA, donor-specific antibody; MFI, median fluorescence intensity; PIRCHE, Predicted Indirectly ReCognizable HLA Epitopes; SAB, single-antigen bead; XM, crossmatch.

ACKNOWLEDGMENTS

We are grateful to Zhi Song for his assistance with data management. The authors also acknowledge the work of Taline Ekmekjian, MLIS, Librarian at the McGill University Health Center, in designing the preliminary literature search.

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