A single measurement of urinary NGAL helps to distinguish acute injury from normal function, prerenal azotemia, and chronic kidney disease and predicts poor inpatient outcomes.

Objectives This study aimed to determine the diagnostic and prognostic value of urinary biomarkers of intrinsic acute kidney injury (AKI) when patients were triaged in the emergency department. Background Intrinsic AKI is associated with nephron injury and results in poor clinical outcomes. Several urinary biomarkers have been proposed to detect and measure intrinsic AKI. Methods In a multicenter prospective cohort study, 5 urinary biomarkers (urinary neutrophil gelatinase–associated lipocalin, kidney injury molecule-1, urinary liver-type fatty acid binding protein, urinary interleukin-18, and cystatin C) were measured in 1,635 unselected emergency department patients at the time of hospital admission. We determined whether the biomarkers diagnosed intrinsic AKI and predicted adverse outcomes during hospitalization. Results All biomarkers were elevated in intrinsic AKI, but urinary neutrophil gelatinase-associated lipocalin was most useful (81% specificity, 68% sensitivity at a 104-ng/ml cutoff) and predictive of the severity and duration of AKI. Intrinsic AKI was strongly associated with adverse in-hospital outcomes. Urinary neutrophil gelatinase-associated lipocalin and urinary kidney injury molecule 1 predicted a composite outcome of dialysis initiation or death during hospitalization, and both improved the net risk classification compared with conventional assessments. These biomarkers also identified a substantial subpopulation with low serum creatinine at hospital admission, but who were at risk of adverse events. Conclusion Urinary biomarkers of nephron damage enable prospective diagnostic and prognostic stratification in the emergency department.

Timely access to a provider is a critical dimension of ED quality performance. In an environment in which EDs are often understaffed, analyses of arrival patterns and the use of queueing models can be extremely useful in identifying the most effective allocation of staff.