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Urinary L-FABP predicts poor outcomes in critically ill patients with early acute kidney injury
Author(s) -
Sharidan K. Parr,
Amanda J. Clark,
Aihua Bian,
Ayumi Shintani,
Nancy Wickersham,
Lorraine B. Ware,
T. Alp İkizler,
Edward D. Siew
Publication year - 2014
Publication title -
kidney international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.499
H-Index - 276
eISSN - 1523-1755
pISSN - 0085-2538
DOI - 10.1038/ki.2014.301
Subject(s) - acute kidney injury , medicine , critically ill , intensive care medicine , urinary system
Biomarker studies for early detection of acute kidney injury (AKI) have been limited by nonselective testing and uncertainties in using small changes in serum creatinine as a reference standard. Here we examine the ability of urine L-type fatty acid-binding protein (L-FABP), neutrophil gelatinase-associated lipocalin (NGAL), interleukin-18 (IL-18), and kidney injury molecule-1 (KIM-1) to predict injury progression, dialysis, or death within 7 days in critically ill adults with early AKI. Of 152 patients with known baseline creatinine examined, 36 experienced the composite outcome. Urine L-FABP demonstrated an area under the receiver-operating characteristic curve (AUC-ROC) of 0.79 (95% confidence interval 0.70-0.86), which improved to 0.82 (95% confidence interval 0.75-0.90) when added to the clinical model (AUC-ROC of 0.74). Urine NGAL, IL-18, and KIM-1 had AUC-ROCs of 0.65, 0.64, and 0.62, respectively, but did not significantly improve discrimination of the clinical model. The category-free net reclassification index improved with urine L-FABP (total net reclassification index for nonevents 31.0%) and urine NGAL (total net reclassification index for events 33.3%). However, only urine L-FABP significantly improved the integrated discrimination index. Thus, modest early changes in serum creatinine can help target biomarker measurement for determining prognosis with urine L-FABP, providing independent and additive prognostic information when combined with clinical predictors.

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