Pre-transplant Evaluation of Donor Urinary Biomarkers can Predict Reduced Graft Function After Deceased Donor Kidney Transplantation. Issue 11 (March 2016)

• Record Type: Journal Article
• Authors: Koo, Tai Yeon; Jeong, Jong Cheol; Lee, Yonggu; Ko, Kwang-Pil; Lee, Kyoung-Bun; Lee, Sik; Park, Suk Joo; Park, Jae Berm; Han, Miyeon; Lim, Hye Jin; Ahn, Curie; Yang, Jaeseok
• Editors: Chhabra., Darshika
• Is Part Of: Medicine. Volume 95:Issue 11(2016)
• Page Start: e3076
• Publication Date: 2016-03
• Subjects: Medicine -- Periodicals; Medicine -- Periodicals; Médecine -- Périodiques; Geneeskunde; Medicine; Periodicals; Periodicals; 610.5
• DOI: 10.1097/MD.0000000000003076
• Languages: English
• ISSNs: 0025-7974

Abstract

Abstract : Supplemental Digital Content is available in the text Abstract : Abstract: 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.