• Nattachai Srisawat, Xiaoyan Wen, Minjae Lee, Lan Kong, Michele Elder, Melinda Carter, Mark Unruh, Kevin Finkel, Anitha Vijayan, Mohan Ramkumar, Emil Paganini, Kai Singbartl, Paul M Palevsky, and John A Kellum.
• 604 Scaife Hall, Department of Critical Care Medicine, University of Pittsburgh, 3550 Terrace Street, Pittsburgh, PA 15261, USA.
• Clin J Am Soc Nephrol. 2011 Aug 1;6(8):1815-23.

Background And ObjectivesDespite significant advances in the epidemiology of acute kidney injury (AKI), prognostication remains a major clinical challenge. Unfortunately, no reliable method to predict renal recovery exists. The discovery of biomarkers to aid in clinical risk prediction for recovery after AKI would represent a significant advance over current practice.Design, Setting, Participants, & MeasurementsWe conducted the Biological Markers of Recovery for the Kidney study as an ancillary to the Acute Renal Failure Trial Network study. Urine samples were collected on days 1, 7, and 14 from 76 patients who developed AKI and received renal replacement therapy (RRT) in the intensive care unit. We explored whether levels of urinary neutrophil gelatinase-associated lipocalin (uNGAL), urinary hepatocyte growth factor (uHGF), urinary cystatin C (uCystatin C), IL-18, neutrophil gelatinase-associated lipocalin/matrix metalloproteinase-9, and urine creatinine could predict subsequent renal recovery.ResultsWe defined renal recovery as alive and free of dialysis at 60 days from the start of RRT. Patients who recovered had higher uCystatin C on day 1 (7.27 versus 6.60 ng/mg·creatinine) and lower uHGF on days 7 and 14 (2.97 versus 3.48 ng/mg·creatinine; 2.24 versus 3.40 ng/mg·creatinine). For predicting recovery, decreasing uNGAL and uHGF in the first 14 days was associated with greater odds of renal recovery. The most predictive model combined relative changes in biomarkers with clinical variables and resulted in an area under the receiver-operator characteristic curve of 0.94.ConclusionsWe showed that a panel of urine biomarkers can augment clinical risk prediction for recovery after AKI.

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