Neutrophil Gelatinase-Associated Lipocalin for Assessment of Acute Kidney Injury in Cirrhosis: A Prospective Study.

Kidney biomarkers appear to be useful in differential diagnosis between acute tubular necrosis (ATN) and other types of acute kidney injury (AKI) in cirrhosis, particularly hepatorenal syndrome (HRS-AKI). Distinction is important because treatment is different. However, kidney biomarkers are still not used in clinical practice. The aim of the current study was to investigate the accuracy of several biomarkers in differential diagnosis of AKI and in predicting kidney outcome and patient survival. This was a prospective study of 320 consecutive cases of AKI in patients hospitalized for decompensated cirrhosis. Evaluation of AKI was made with a diagnostic algorithm that included identification and removal/treatment of precipitating factors and albumin administration (1 g/kg for 2 days) to patients with AKI stage 1B or greater. Urinary neutrophil gelatinase-associated lipocalin (NGAL), monomeric NGAL (mNGAL), interleukin-18, and standard biomarkers were measured at diagnosis and on days 3, 7, and 14. Of the 320 cases, 153 were hypovolemia-induced AKI (48%), 93 were HRS-AKI (29%), 39 were ATN (12%), and 35 were due to miscellaneous causes (11%). Among all biomarkers, urinary NGAL measured at day 3 had the greatest accuracy for differential diagnosis between ATN and other types of AKI (area under the receiver operating characteristic curve, 0.87; 95% confidence interval, 0.78-0.95). The cutoff with the best predictive accuracy for ATN diagnosis was 220 µg/g creatinine. Progression of AKI during hospitalization was associated with persistently high NGAL levels, and NGAL was an independent predictive factor of AKI progression. Likewise, NGAL was also an independent predictive factor of 28-day mortality together with Model for End-Stage Liver Disease score.

Conclusion

These results support the use of NGAL in clinical practice within the context of a diagnostic algorithm for differential diagnosis of AKI and outcome prediction in cirrhosis.