Revisiting Traditional Risk Factors for Rejection and Graft Loss After Kidney Transplantation

Dunn, Ty B.; Noreen, Harriet; Gillingham, K.; Maurer, David; Öztürk, Özlem; Pruett, T.; Bray, Robert A.; Gebel, Howard M.; Matas, A. J.

doi:10.1111/j.1600-6143.2011.03640.x

Cited by 187 publications

(174 citation statements)

References 34 publications

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“…On the other hand, rejections might have been missed or not registered. Others have shown that in living donor transplants, DSAs was an important predictor of antibody‐mediated rejection, while this was not the case for graft failure 31. Using the limited rejection data that we had, we observed that patients with pretransplant DSAs had a higher incidence of rejection in living as well as deceased donor transplants (data not shown).…”

Section: Discussionmentioning

confidence: 54%

Differential effects of donor-specific HLA antibodies in living versus deceased donor transplant

Kamburova

Wisse

Joosten

et al. 2018

American Journal of Transplantation

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The presence of donor‐specific anti‐HLA antibodies (DSAs) is associated with increased risk of graft failure after kidney transplant. We hypothesized that DSAs against HLA class I, class II, or both classes indicate a different risk for graft loss between deceased and living donor transplant. In this study, we investigated the impact of pretransplant DSAs, by using single antigen bead assays, on long‐term graft survival in 3237 deceased and 1487 living donor kidney transplants with a negative complement‐dependent crossmatch. In living donor transplants, we found a limited effect on graft survival of DSAs against class I or II antigens after transplant. Class I and II DSAs combined resulted in decreased 10‐year graft survival (84% to 75%). In contrast, after deceased donor transplant, patients with class I or class II DSAs had a 10‐year graft survival of 59% and 60%, respectively, both significantly lower than the survival for patients without DSAs (76%). The combination of class I and II DSAs resulted in a 10‐year survival of 54% in deceased donor transplants. In conclusion, class I and II DSAs are a clear risk factor for graft loss in deceased donor transplants, while in living donor transplants, class I and II DSAs seem to be associated with an increased risk for graft failure, but this could not be assessed due to their low prevalence.

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Section: Discussionmentioning

confidence: 54%

Differential effects of donor-specific HLA antibodies in living versus deceased donor transplant

Kamburova

Wisse

Joosten

et al. 2018

American Journal of Transplantation

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“…Studies from numerous investigators reported of AMR and/or graft loss in patients who's donor-specific antibody (DSA) were undetectable by complement dependent cytotoxicity assays but present by multiplex solid phase assays (36)(37)(38)(39)(40). Thus, the preferred donor lacks the HLA antigen(s) corresponding to antibodies identified by solid phase technology.…”

Section: Theory Versus Realitymentioning

confidence: 99%

HLA Antibody Detection With Solid Phase Assays: Great Expectations or Expectations Too Great?

Gebel

Bray

2014

American Journal of Transplantation

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Alloantibodies directed against HLA antigens, are a barrier to long-term solid organ allograft survival. The clinical impact of preformed, donor-directed HLA alloantibodies range from acceptable risk to unequivocal contraindication for organ transplantation. HLA antibodies are key factors that limit patient access to donor organs. Serological methods were once the only approach to identify HLA antigens and antibodies. Limitations in these technologies led to the development of solid phase approaches. In the early 1990s, the development of the polymerase chain reaction enabled DNA-based HLA antigen testing to be performed. By the mid-1990s, microparticle-based technology that utilized flow cytometry for analysis was developed to detect both classes I and II HLA antibodies. These methodologies revolutionized clinical histocompatibility testing. The strengths and weaknesses of these assays are described in detail in this review.

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“…The identification of risk factors for AR events may therefore contribute to optimization of therapy. Previous studies have identified histocompatibility measures and certain donor information to be amongst such factors [15,16]. However, owing to differences in the studied populations, observation periods, treatment protocols and methods of analysis, the significance of such factors may not be generalizable to other study populations, making it necessary to investigate to what extent previous findings can be confirmed in the new population.…”

Section: Introductionmentioning

confidence: 99%

A time‐to‐event model for acute rejections in paediatric renal transplant recipients treated with ciclosporin A

Frobel

Karlsson

Backman

et al. 2013

Brit J Clinical Pharma

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AIMSCiclosporin A (CsA) dosing in immunosuppression after paediatric kidney transplantation remains challenging, and appropriate target CsA exposures (AUCs) are controversial. This study aimed to develop a time-to-first-acute rejection (AR) model and to explore predictive factors for therapy outcome. METHODSPatient records at the Children's Hospital in Helsinki, Finland, were analysed. A parametric survival model in NONMEM was used to describe the time to first AR. The influences of AUC and other covariates were explored using stepwise covariate modelling, bootstrap-stepwise covariate modelling and cross-validated stepwise covariate modelling. The clinical relevance of the effects was assessed with the time at which 90% of the patients were AR free (t90). RESULTSData from 87 patients (0.7-19.8 years old, 54 experiencing an AR) were analysed. The baseline hazard was described with a function changing in steps over time. No statistically significant covariate effects were identified, a finding substantiated by all methods used. Thus, within the observed AUC range (90% interval 1.13-8.40 h mg l -1 ), a rise in AUC was not found to increase protection from AR. Dialysis time, sex and baseline weight were potential covariates, but the predicted clinical relevance of their effects was low. For the strongest covariate, dialysis time, median t90 was 5.8 days (90% confidence interval 5.1-6.8) for long dialysis times (90th percentile) and 7.4 days (6.4-11.7) for short dialysis times (10th percentile). CONCLUSIONSA survival model with discrete time-varying hazards described the data. Within the observed range, AUC was not identified as a covariate. This feedback on clinical practice may help to avoid unnecessarily high CsA dosing in children.

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Revisiting Traditional Risk Factors for Rejection and Graft Loss After Kidney Transplantation

Cited by 187 publications

References 34 publications

Differential effects of donor-specific HLA antibodies in living versus deceased donor transplant

Differential effects of donor-specific HLA antibodies in living versus deceased donor transplant

HLA Antibody Detection With Solid Phase Assays: Great Expectations or Expectations Too Great?

A time‐to‐event model for acute rejections in paediatric renal transplant recipients treated with ciclosporin A

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