Dataset Information

Development and validation of a prognostic model for kidney function 1 year after combined pancreas and kidney transplantation using pre-transplant donor and recipient variables.

ABSTRACT:

Purpose

The widening gap between demand and supply of organs for transplantation provides extraordinary challenges for ethical donor organ allocation rules. The transplant community is forced to define favorable recipient/donor combinations for simultaneous kidney-pancreas transplantation. The aim of this study is the development of a prognostic model for the prediction of kidney function 1 year after simultaneous pancreas and kidney transplantation using pre-transplant donor and recipient variables with subsequent internal and external validation.

Methods

Included were patients with end-stage renal failure due to diabetic nephropathy. Multivariable logistic regression modeling was applied for prognostic model design with retrospective data from Hannover Medical School, Germany (01.01.2000-31.12.2011) followed by prospective internal validation (01 Jan. 2012-31 Dec. 2015). Retrospective data from another German transplant center in Kiel was retrieved for external model validation via the initially derived logit link function.

Results

The developed prognostic model is able to predict kidney graft function 1 year after transplantation ? KDIGO stage III with high areas under the receiver operating characteristic curve in the development cohort (0.943) as well as the internal (0.807) and external validation cohorts (0.784).

Conclusion

The proposed validated model is a valuable tool to optimize present allocation rules with the goal to prevent transplant futility. It might be used to support donor organ acceptance decisions for individual recipients.

SUBMITTER: Zorn KS

PROVIDER: S-EPMC6244698 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Publications

Development and validation of a prognostic model for kidney function 1 year after combined pancreas and kidney transplantation using pre-transplant donor and recipient variables.

Zorn Katharina S KS Littbarski Simon S Schwager Ysabell Y Kaltenborn Alexander A Beneke Jan J Gwiasda Jill J Becker Thomas T Braun Felix F Reichert Benedikt B Klempnauer Jürgen J Schrem Harald H

Langenbeck's archives of surgery 20181018 7

<h4>Purpose</h4>The widening gap between demand and supply of organs for transplantation provides extraordinary challenges for ethical donor organ allocation rules. The transplant community is forced to define favorable recipient/donor combinations for simultaneous kidney-pancreas transplantation. The aim of this study is the development of a prognostic model for the prediction of kidney function 1 year after simultaneous pancreas and kidney transplantation using pre-transplant donor and recipie ...[more]

PMID: 30338375

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Project description:Several recipient biomarkers are reported to predict graft dysfunction, but these are not useful in decision making for the acceptance or allocation of deceased donor kidneys; thus, it is necessary to develop donor biomarkers predictive of graft dysfunction. To address this issue, we prospectively enrolled 94 deceased donors and their 109 recipients who underwent transplantation between 2010 and 2013 at 4 Korean transplantation centers. We investigated the predictive values of donor urinary neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule-1 (KIM-1), and L-type fatty acid binding protein (L-FABP) for reduced graft function (RGF). We also developed a prediction model of RGF using these donor biomarkers. RGF was defined as delayed or slow graft function. Multiple logistic regression analysis was used to generate a prediction model, which was internally validated using a bootstrapping method. Multiple linear regression analysis was used to assess the association of biomarkers with 1-year graft function. Notably, donor urinary NGAL levels were associated with donor AKI (P = 0.014), and donor urinary NGAL and L-FABP were predictive for RGF, with area under the receiver-operating characteristic curves (AUROC) of 0.758 and 0.704 for NGAL and L-FABP, respectively. The best-fit model including donor urinary NGAL, L-FABP, and serum creatinine conveyed a better predictive value for RGF than donor serum creatinine alone (P = 0.02). In addition, we generated a scoring method to predict RGF based on donor urinary NGAL, L-FABP, and serum creatinine levels. Diagnostic performance of the RGF prediction score (AUROC 0.808) was significantly better than that of the DGF calculator (AUROC 0.627) and the kidney donor profile index (AUROC 0.606). Donor urinary L-FABP levels were also predictive of 1-year graft function (P = 0.005). Collectively, these findings suggest donor urinary NGAL and L-FABP to be useful biomarkers for RGF, and support the use of a new scoring system based on donor biomarkers to facilitate decision-making in acceptance and allocation of deceased donor kidneys and contribute to maximal organ utilization.

Project description:ImportanceThe proportion of living donor kidney transplants from donors unrelated to their recipients is increasing in the US.ObjectiveTo examine the association between donor-recipient biological relationship and allograft survival after living donor kidney transplant.Design, setting, and participantsThis retrospective cohort study used Organ Procurement and Transplantation Network data on US adult living donor kidney transplants (n = 86 154) performed from January 1, 2000, to December 31, 2014, excluding cases in which recipients previously received a kidney transplant (n = 10 342) or key data were missing (n = 2832). Last follow-up was March 20, 2020.ExposuresDonor-recipient biological relationship.Main outcomes and measuresThe primary outcome was death-censored allograft failure. Univariate and multivariable time-to-event analyses were performed for death-censored allograft failure for the overall cohort, then separately for recipients with and without primary diagnoses of cystic kidney disease and for transplants from African American and non-African American donors.ResultsAmong the 72 980 transplant donor and recipients included in the study (median donor age, 41 years; interquartile range [IQR], 32-50 years; 43 990 [60%] female; 50 014 [69%] White), 43 174 (59%) donors and recipients were biologically related and 29 806 (41%) were unrelated. Donors related to their recipients were younger (median [IQR] age, 39 [31-48] vs 44 [35-52] years) and less likely to be female (24 848 [58%] vs 19 142 [64%]) or White (26 933 [62%] vs 23 081 [77%]). Recipients related to their donors were younger (median [IQR] age, 48 [34-58] vs 50 [40-58] years), more likely to be female (18 035 [42%] vs 10 530 [35%]), and less likely to have cystic kidney disease (2530 [6%] vs 4600 [15%]). Related pairs had fewer HLA mismatches overall (median [IQR], 3 [2-3] vs 5 [4-5]). After adjustment for HLA mismatches, donor and recipient characteristics, and transplant era, donor-recipient biological relationship was associated with higher death-censored allograft failure (hazard ratio, 1.05; 95% CI, 1.01-1.10; P = .03). When stratified by primary disease, this association persisted only for recipients without cystic kidney disease. When stratified by donor race, this association persisted only for transplants from African American donors.Conclusions and relevanceIn this cohort study, living donor kidney transplants from donors biologically related to their recipients had higher rates of allograft failure than transplants from donors unrelated to their recipients after HLA matching was accounted for. Further study is needed to determine which genetic or socioenvironmental factors are associated with this finding.

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Dataset Information

Development and validation of a prognostic model for kidney function 1 year after combined pancreas and kidney transplantation using pre-transplant donor and recipient variables.

Purpose

Methods

Results

Conclusion

Publications

Development and validation of a prognostic model for kidney function 1 year after combined pancreas and kidney transplantation using pre-transplant donor and recipient variables.

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