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Indexed neutrophil gelatinase associated lipocalin: a novel biomarker for the assessment of acute kidney injury.

Journal of Nephrology 2023 November 3
INTRODUCTION: Acute kidney injury (AKI) is a clinically relevant and common complication among patients with acute coronary syndrome. Neutrophil gelatinase-associated lipocalin (NGAL), secreted from different cells including renal tubules, has been widely studied as an early marker for kidney injury. However, chronic kidney disease (CKD) could impact NGAL levels and alter their predictive performance. Some studies attempted to address this issue by setting different cutoff values for patients with CKD, with limited success to date. Our aim was to evaluate a novel estimated glomerular filtration rate (eGFR)-adjusted "indexed NGAL" and its ability to predict in-hospital AKI among patients with ST elevation myocardial infarction.

METHODS: We performed a prospective, observational, single center study involving patients with ST elevation myocardial infarction admitted to the coronary intensive care unit. Serum samples for baseline NGAL were collected within 24 h following hospital admission. The eGFR was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation. In-hospital AKI was determined as occurring after ≥ 24 h from admission. To perform an individualized adjustment, we used the result of 24 h NGAL divided by the eGFR measured upon admission to the hospital (Indexed-NGAL; I-NGAL).

RESULTS: Our cohort includes 311 patients, of whom 123 (40%) had CKD, and 66 (21%) suffered in-hospital AKI. NGAL levels as well as I-NGAL levels were significantly higher in patients with AKI (136 vs. 86, p < 0.01 and 3.13 VS. 1.06, p < 0.01, respectively). Multivariate analysis revealed I-NGAL to be independently associated with AKI (OR 1.34 (1.10-1.58), p < 0.01). I-NGAL had a higher predictive ability than simple NGAL results (AUC-ROC of 0.858 vs. 0.778, p < 0.001).

CONCLUSION: Adjusting NGAL values according to eGFR yields a new indexed NGAL value that enables better prediction of AKI regardless of baseline kidney function.

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