• Shina Menon, Stuart L Goldstein, Theresa Mottes, Lin Fei, Ahmad Kaddourah, Tara Terrell, Patricia Arnold, Michael R Bennett, and Rajit K Basu.
• Center for Acute Care Nephrology, Cincinnati Children's Hospital and Medical Center, University of Cincinnati, Cincinnati, OH, USA.
• Nephrol. Dial. Transplant. 2016 Apr 1; 31 (4): 586-94.

BackgroundThe inconsistent ability of novel biomarkers to predict acute kidney injury (AKI) across heterogeneous patients and illnesses limits integration into routine practice. We previously retrospectively validated the ability of the renal angina index (RAI) to risk-stratify patients and provide context for confirmatory serum biomarker testing for the prediction of severe AKI.MethodsWe conducted this first prospective study of renal angina to determine whether the RAI on the day of admission (Day0) risk-stratified critically ill children for 'persistent, severe AKI' on Day 3 (Day3-AKI: KDIGO Stage 2-3) and whether incorporation of urinary biomarkers in the RAI model optimized AKI prediction.ResultsA total of 184 consecutive patients (52.7% male) were included. Day0 renal angina was present (RAI ≥8) in 60 (32.6%) patients and was associated with longer duration of mechanical ventilation (P = 0.04), higher number of organ failure days (P = 0.003) and increased mortality (P < 0.001) than in patients with absence of renal angina. Day3-AKI was present in 15/156 (9.6%) patients; 12/15 (80%) fulfilled Day0 renal angina. Incorporation of urinary biomarkers into the RAI model increased the specificity and positive likelihood, and demonstrated net reclassification improvement (P < 0.001) for the prediction of Day3-AKI. Inclusion of urinary neutrophil gelatinase-associated lipocalin increased the area under the curve receiver-operating characteristic of RAI for Day3-AKI from 0.80 [95% confidence interval (CI): 0.58, 1.00] to 0.97 (95% CI: 0.93, 1.00).ConclusionsWe have now prospectively validated the RAI as a functional risk stratification methodology in a heterogeneous group of critically ill patients, providing context to direct measurement of novel urinary biomarkers and improving the prediction of severe persistent AKI.© The Author 2016. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.

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