Clinical review: Predictive value of neutrophil gelatinase-associated lipocalin for acute kidney injury in intensive care patients.

  • Hjortrup P
  • Haase N
  • Wetterslev M
 et al. 
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Neutrophil gelatinase-associated lipocalin (NGAL) may be an early marker of acute kidney injury (AKI), but elevated NGAL occurs in a wide range of systemic diseases. Because intensive care patients have high levels of comorbidity, our objective was to conduct a systematic review of the literature to evaluate the value of plasma and urinary NGAL to predict AKI in these patients. We conducted a systematic electronic literature search of MEDLINE through PubMed, EMBASE, and Cochrane Library for all English language research publications evaluating the predictive value of plasma or urinary NGAL (or both) for AKI in adult intensive care patients. Two authors independently extracted data by using a standardized extraction sheet including study characteristics, type of NGAL measurements, and type of outcome measures. The primary summary measure was area under receiver operating characteristic curve (AuROC) for NGAL to predict study outcomes. Eleven studies with a total of 2,875 (range of 20 to 632) participants were included: seven studies assessed urinary NGAL and six assessed plasma NGAL. The included studies varied in design, including observation period from NGAL sampling to AKI follow-up (range of 12 hours to 7 days), definition of baseline creatinine value, and urinary NGAL quantification method (normalizing to urinary creatinine or absolute concentration). AuROC values for the prediction of AKI ranged from 0.54 to 0.98. Five studies reported AuROC for use of renal replacement therapy ranging from 0.73 to 0.89, and four studies reported AuROC for mortality ranging from 0.58 to 0.83. There were no differences in the predictive values of urinary and plasma NGAL. The heterogeneity in study design and results made it difficult to evaluate the value of NGAL to predict AKI in intensive care patients. NGAL seems to have reasonable value in predicting use of renal replacement therapy but not mortality.

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  • Peter B Hjortrup

  • Nicolai Haase

  • Mik Wetterslev

  • Anders Perner

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