Optimal cut points of plasma and urine neutrophil gelatinase-associated lipocalin for the prediction of acute kidney injury among critically ill adults: retrospective determination and clinical validation of a prospective multicentre study.

ABSTRACT:

Objectives

To determine the optimal threshold of blood and urine neutrophil gelatinase-associated lipocalin (NGAL) to predict moderate to severe acute kidney injury (AKI) and persistent moderate to severe AKI lasting at least 48 consecutive hours, as defined by an adjudication panel.

Methods

A multicentre prospective observational study enrolled intensive care unit (ICU) patients and recorded daily ethylenediaminetetraacetic acid (EDTA) plasma, heparin plasma and urine NGAL. We used natural log-transformed NGAL in a logistic regression model to predict stage 2/3 AKI (defined by Kidney Disease International Global Organization). We performed the same analysis using the NGAL value at the start of persistent stage 2/3 AKI.

Results

Of 245 subjects, 33 (13.5%) developed stage 2/3 AKI and 25 (10.2%) developed persistent stage 2/3 AKI. Predicting stage 2/3 AKI revealed the optimal NGAL cutoffs in EDTA plasma (142.0?ng/mL), heparin plasma (148.3?ng/mL) and urine (78.0?ng/mL) and yielded the following decision statistics: sensitivity (SN)=78.8%, specificity (SP)=73.0%, positive predictive value (PPV)=31.3%, negative predictive value (NPV)=95.7%, diagnostic accuracy (DA)=73.8% (EDTA plasma); SN=72.7%, SP=73.8%, PPV=30.4%, NPV=94.5%, DA=73.7% (heparin plasma); SN=69.7%, SP=76.8%, PPV=32.9%, NPV=94%, DA=75.8% (urine). The optimal NGAL cutoffs to predict persistent stage 2/3 AKI were similar: 148.3?ng/mL (EDTA plasma), 169.6?ng/mL (heparin plasma) and 79.0?ng/mL (urine) yielding: SN=84.0%, SP=73.5%, PPV=26.6%, NPV=97.6, DA=74.6% (EDTA plasma), SN=84%, SP=76.1%, PPV=26.8%, NPV=96.5%, DA=76.1% (heparin plasma) and SN=75%, SP=75.8%, PPV=26.1, NPV=96.4%, DA=75.7% (urine).

Conclusion

Blood and urine NGAL predicted stage 2/3 AKI, as well as persistent 2/3 AKI in the ICU with acceptable decision statistics using a single cut point in each type of specimen.

SUBMITTER: Tecson KM 

PROVIDER: S-EPMC5726065 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature