Deceased-donor kidney perfusate and urine biomarkers for kidney allograft outcomes: a systematic review

肾移植预后与已故供体肾脏灌注液和尿液生物标志物的关系:系统评价

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Abstract

BACKGROUND: Accurate and reliable assessment of kidney quality before transplantation is needed to predict recipient outcomes and to optimize management and allocation of the allograft. The aim of this study was to systematically review the published literature on biomarkers in two mediums (the perfusate from deceased-donor kidneys receiving machine perfusion and deceased-donor urine) that were evaluated for their possible association with outcomes after kidney transplantation. METHODS: We searched the Ovid Medline and Scopus databases using broad keywords related to deceased-donor biomarkers in kidney transplantation (limited to humans and the English language). Studies were included if they involved deceased-donor kidneys, measured perfusate or urine biomarkers and studied a possible relationship between biomarker concentrations and kidney allograft outcomes. Each included article was assessed for methodological quality. RESULTS: Of 1430 abstracts screened, 29 studies met the inclusion criteria. Of these, 23 were studies of perfusate (16 biomarkers examined) and 6 were studies of urine (18 biomarkers examined). Only 3 studies (two perfusate) met the criteria of 'good' quality and only 12 were published since 2000. Perfusate lactate dehydrogenase, glutathione-S-transferase (GST) and aspartate transaminase were all found to be significantly associated with delayed graft function in a majority of their respective studies (6/9, 4/6 and 2/2 studies, respectively). Urine neutrophil gelatinase-associated lipocalin, GST, Trolox-equivalent antioxidant capacity and kidney injury molecule-1 were found to be significantly associated with allograft outcomes in single studies that examined diverse end points. CONCLUSION: Higher quality studies are needed to investigate modern kidney injury biomarkers, to validate novel biomarkers in larger donor populations and to determine the incremental predictive value of biomarkers over traditional clinical variables.

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