Urine Neutrophil Gelatinase-associated Lipocalin (NGAL) for Prediction of Persistent AKI and Major Adverse Kidney Events

Abstract

AbstractWe aimed to determine whether urinary neutrophil gelatinase-associated lipocalin (uNGAL) can accurately predict persistent AKI, major adverse kidney events at 30 days (MAKE30) and 365 days (MAKE365) in hospitalized AKI patients. This is a retrospective study of adult patients who were admitted at King Chulalongkorn Memorial Hospital. We performed multivariable logistic regression for persistent AKI, MAKE30, and MAKE365. We developed equations for predicting MAKE30 and MAKE365 and divided the dataset into derivation and validation cohorts. uNGAL performance and predictive models were assessed using the area under the receiver operating characteristic curve (AROC). Among 1,322 patients with AKI, 76.9%, 45.1%, and 61.7% had persistent AKI, MAKE30, and MAKE365. The AROC were 0.75 (95% confidence interval[CI] 0.70–0.80), 0.66 (95%CI 0.61–0.71), and 0.64 (95%CI 0.59–0.70) for prediction of persistent AKI, MAKE30, and MAKE365 by uNGAL. The AROC in the validation dataset combining uNGAL with clinical covariates were 0.74 (95%CI 0.69–0.79) and 0.72 (95%CI 0.67–0.77) for MAKE30 and MAKE365. We demonstrated an association between uNGAL and persistent AKI, MAKE30, and MAKE365. Prediction models combining uNGAL can modestly predict MAKE30 and MAKE365. Therefore, uNGAL is a useful tool for improving AKI risk stratification.