Effectiveness of Plasma and Urine Neutrophil Gelatinase-Associated Lipocalin for Predicting Acute Kidney Injury in High-Risk Patients
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Background: Neutrophil gelatinase-associated lipocalin (NGAL) is a useful biomarker for acute kidney injury (AKI) prediction. However, studies on whether using both plasma NGAL (PNGAL) and urine NGAL (UNGAL) can improve AKI prediction are limited. We investigated the best approach to predict AKI in high-risk patients when using PNGAL and UNGAL together. Methods: We enrolled 151 AKI suspected patients with one or more AKI risk factors. We assessed the diagnostic performance of PNGAL and UNGAL for predicting AKI according to chronic kidney disease (CKD) status by determining the areas under the receiver operating curve (AuROC). Independent predictors of AKI were assessed using univariate and multivariate logistic regression analyses. Results: In the multivariate logistic regression analysis for all patients (N=151), Model 2 and 3, including PNGAL (P=0.012) with initial serum creatinine (S-Cr), showed a better AKI prediction power (R2=0.435, both) than Model 0, including S-Cr only (R2=0.390). In the non-CKD group (N=135), the AuROC of PNGAL for AKI prediction was larger than that of UNGAL (0.79 vs 0.66, P=0.010), whereas in the CKD group (N=16), the opposite was true (0.94 vs 0.76, P=0.049). Conclusions: PNGAL may serve as a useful biomarker for AKI prediction in high-risk patients. However, UNGAL predicted AKI better than PNGAL in CKD patients. Our findings provide guidance for selecting appropriate specimens for NGAL testing according to the presence of CKD in AKI high-risk patients.
Abstract
Background: Neutrophil gelatinase-associated lipocalin (NGAL) is a useful biomarker for acute kidney injury (AKI) prediction. However, studies on whether using both plasma NGAL (PNGAL) and urine NGAL (UNGAL) can improve AKI prediction are limited. We investigated the best approach to predict AKI in high-risk patients when using PNGAL and UNGAL together. Methods: We enrolled 151 AKI suspected patients with one or more AKI risk factors. We assessed the diagnostic performance of PNGAL and UNGAL for predicting AKI according to chronic kidney disease (CKD) status by determining the areas under the receiver operating curve (AuROC). Independent predictors of AKI were assessed using univariate and multivariate logistic regression analyses. Results: In the multivariate logistic regression analysis for all patients (N=151), Model 2 and 3, including PNGAL (P=0.012) with initial serum creatinine (S-Cr), showed a better AKI prediction power (R2=0.435, both) than Model 0, including S-Cr only (R2=0.390). In the non-CKD group (N=135), the AuROC of PNGAL for AKI prediction was larger than that of UNGAL (0.79 vs 0.66, P=0.010), whereas in the CKD group (N=16), the opposite was true (0.94 vs 0.76, P=0.049). Conclusions: PNGAL may serve as a useful biomarker for AKI prediction in high-risk patients. However, UNGAL predicted AKI better than PNGAL in CKD patients. Our findings provide guidance for selecting appropriate specimens for NGAL testing according to the presence of CKD in AKI high-risk patients.
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