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KMID : 1100720210410010001
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Annals of Laboratory Medicine
2021 Volume.41 No. 1 p.1 ~ p.15
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Biomarker-Guided Risk Assessment for Acute Kidney Injury: Time for Clinical Implementation?
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Albert Christian
Haase Michael
Albert Annemarie
Zapf Antonia
Braun-Dullaeus Rudiger Christian
Haase-Fielitz Anja
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Abstract
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Acute kidney injury (AKI) is a common and serious complication in hospitalized patients, which continues to pose a clinical challenge for treating physicians. The most recent Kidney Disease Improving Global Outcomes practice guidelines for AKI have restated the importance of earliest possible detection of AKI and adjusting treatment accordingly. Since the emergence of initial studies examining the use of neutrophil gelatinase-associated lipocalin (NGAL) and cycle arrest biomarkers, tissue inhibitor metalloproteinase-2 (TIMP-2) and insulin-like growth factor-binding protein (IGFBP7), for early diagnosis of AKI, a vast number of studies have investigated the accuracy and additional clinical benefits of these biomarkers. As proposed by the Acute Dialysis Quality Initiative, new AKI diagnostic criteria should equally utilize glomerular function and tubular injury markers for AKI diagnosis. In addition to refining our capabilities in kidney risk prediction with kidney injury biomarkers, structural disorder phenotypes referred to as “preclinical-” and “subclinical AKI” have been described and are increasingly recognized. Additionally, positive biomarker test findings were found to provide prognostic information regardless of an acute decline in renal function (positive serum creatinine criteria). We summarize and discuss the recent findings focusing on two of the most promising and clinically available kidney injury biomarkers, NGAL and cell cycle arrest markers, in the context of AKI phenotypes. Finally, we draw conclusions regarding the clinical implications for kidney risk prediction.
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KEYWORD
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Acute kidney injury, AKI phenotypes, Neutrophil gelatinase-associated lipocalin, Subclinical AKI, Preclinical AKI, Kidney biomarker, Serum creatinine, Kidney risk prediction, Cell cycle arrest biomarker
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