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Novel biomarkers of AKI: the challenges of progress 'Amid the noise and the haste'
Author(s) -
Sean M. Bagshaw,
Michael Zappitelli,
Lakhmir S. Chawla
Publication year - 2013
Publication title -
nephrology dialysis transplantation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.654
H-Index - 168
eISSN - 1460-2385
pISSN - 0931-0509
DOI - 10.1093/ndt/gfs595
Subject(s) - medicine , acute kidney injury , intensive care medicine , biomarker , dialysis , kidney transplantation , nephrology , narrative review , value (mathematics) , transplantation , computer science , biochemistry , chemistry , machine learning
The clinical integration of novel biomarkers specific for kidney damage have brought the promise of a new era in our understanding of and care for those patients susceptible to or suffering from acute kidney injury (AKI) and has consistently been viewed as a top research priority. The expectations are clearly high; however, as with many promises, there are often accompanying challenges and a degree of pessimism. In this issue of Nephrology Dialysis Transplantation, Van Massenhove et al. offer their 'Devil's advocacy' view in a narrative review focused on the state of novel biomarkers for the diagnosis of AKI. While AKI biomarkers would appear to clearly have value, in particular for informing on the pathobiology of AKI, the question of how to optimally utilize them remains unresolved. Their performance is influenced by patient case-mix, comorbid illness, inciting kidney injury event, timing of measurement, the specific biomarker being investigated and the selected thresholds for diagnosis, not to mention factors related to study design, methodology and how to best translate to the bedside. The challenge as the field moves forward is to fully and appropriately utilize and interpret information from AKI biomarker studies in order to understand and evaluate how to optimally utilize these novel biomarkers (or panel of biomarkers) in the susceptible patient across a spectrum of clinical settings to improve and better inform our clinical decision-making.

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