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dc.contributor.authorProwle, John Richarden
dc.contributor.authorCalzavacca, Paoloen
dc.contributor.authorLicari, Elisaen
dc.contributor.authorLigabo, E Valentinaen
dc.contributor.authorEcheverri, Jorge Een
dc.contributor.authorBagshaw, Sean Men
dc.contributor.authorHaase-Fielitz, Anjaen
dc.contributor.authorHaase, Michaelen
dc.contributor.authorOstland, Vaughnen
dc.contributor.authorNoiri, Eiseien
dc.contributor.authorWesterman, Marken
dc.contributor.authorDevarajan, Prasaden
dc.contributor.authorBellomo, Rinaldoen
dc.date.accessioned2015-05-16T02:17:32Z
dc.date.available2015-05-16T02:17:32Z
dc.date.issued2015-01-14en
dc.identifier.citationRenal Failure 2015; 37(3): 408-16en
dc.identifier.govdoc25585949en
dc.identifier.otherPUBMEDen
dc.identifier.urihttp://ahro.austin.org.au/austinjspui/handle/1/12576en
dc.description.abstractNovel acute kidney injury (AKI) biomarkers offer promise of earlier diagnosis and risk stratification, but have yet to find widespread clinical application. We measured urinary α and π glutathione S-transferases (α-GST and π-GST), urinary l-type fatty acid-binding protein (l-FABP), urinary neutrophil gelatinase-associated lipocalin (NGAL), urinary hepcidin and serum cystatin c (CysC) before surgery, post-operatively and at 24 h after surgery in 93 high risk patient undergoing cardiopulmonary bypass (CPB) and assessed the ability of these biomarkers alone and in combination to predict RIFLE-R defined AKI in the first 5 post-operative days. Twenty-five patients developed AKI. π-GST (ROCAUC = 0.75), lower urine Hepcidin:Creatine ratio at 24 h (0.77), greater urine NGAL:Cr ratio post-op (0.73) and greater serum CysC at 24 h (0.72) best predicted AKI. Linear combinations with significant improvement in AUC were: Hepcidin:Cr 24 h + post-operative π-GST (AUC = 0.86, p = 0.01), Hepcidin:Cr 24 h + NGAL:Cr post-op (0.84, p = 0.03) and CysC 24 h + post-operative π-GST (0.83, p = 0.03), notably these significant biomarkers combinations all involved a tubular injury and a glomerular filtration biomarker. Despite statistical significance in receiver-operator characteristic (ROC) analysis, when assessed by ability to define patients to two groups at high and low risk of AKI, combinations failed to significantly improve classification of risk compared to the best single biomarkers. In an alternative approach using Classification and Regression Tree (CART) analysis a model involving NGAL:Cr measurement post-op followed by Hepcidin:Cr at 24 h was developed which identified high, intermediate and low risk groups for AKI. Regression tree analysis has the potential produce models with greater clinical utility than single combined scores.en
dc.language.isoenen
dc.subject.otherAcute kidney injuryen
dc.subject.otherbiomarkersen
dc.subject.othercardiac bypassen
dc.subject.otherglutathione S-transferaseen
dc.subject.otherhepcidinen
dc.subject.otherliver fatty acid binding proteinen
dc.subject.otherneutrophil gelatinase associated lipocalinen
dc.titleCombination of biomarkers for diagnosis of acute kidney injury after cardiopulmonary bypass.en
dc.typeJournal Articleen
dc.identifier.journaltitleRenal failureen
dc.identifier.affiliationDepartment of Intensive Care, Austin Hospital , Melbourne , Australia .en
dc.identifier.doi10.3109/0886022X.2014.1001303en
dc.description.pages408-16en
dc.relation.urlhttp://www.ncbi.nlm.nih.gov/pubmed/25585949en
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