Abstract
We 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.
Highlights
Acute kidney injury (AKI) is reported in 52.9–57.3% of critically ill patients and strongly associated with increased morbidity and mortality[1,2,3]
An increased risk of Major adverse kidney events at 365 days (MAKE365) was associated with increasing urinary neutrophil gelatinase-associated lipocalin (uNGAL) concentration, increasing age, an intensive care unit (ICU) admission, AKI of stage 3 versus stages 1 and 2, sepsis, malignancy, ischemic heart disease, persistent AKI and chronic liver disease
The area under the receiver operating characteristic curve (AROC) was 0.77 (95%CI 0.74–0.80), which was significantly better than the AROC with uNGAL alone (0.70 (95%CI 0.67–0.74); P < 0.001) (Fig. 5A, Table 4)
Summary
Acute kidney injury (AKI) is reported in 52.9–57.3% of critically ill patients and strongly associated with increased morbidity and mortality[1,2,3]. High uNGAL can be used to predict AKI14–17, discriminate intrinsic AKI from pre-renal AKI18,19, predict renal non-recovery, in-hospital mortality[20,21,22], long-term. Previous predictive models have utilized uNGAL to predict AKI, in-hospital renal replacement therapy (RRT), or death[21,24,25]. No studies have assessed the clinical utility of uNGAL for assessment of overall spectrum of outcomes ranging from in-hospital mortality, renal replacement therapy (RRT), and renal non-recovery, to survival and renal function after hospital discharge. We aimed to assess and evaluate, using multivariable logistic regression, the usefulness of uNGAL measurement in combination with standard clinical covariates for prediction of 30-day and 365-day major adverse kidney events in a heterogeneous adult population
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