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Effectiveness of Plasma and Urine Neutrophil Gelatinase-Associated Lipocalin for Predicting Acute Kidney Injury in High-Risk Patients
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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Korean Society for Laboratory Medicine
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7443531/ https://www.ncbi.nlm.nih.gov/pubmed/32829580 http://dx.doi.org/10.3343/alm.2021.41.1.60 |
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author | Yi, Ahram Lee, Chang-Hoon Yun, Yeo-Min Kim, Hanah Moon, Hee-Won Hur, Mina |
author_facet | Yi, Ahram Lee, Chang-Hoon Yun, Yeo-Min Kim, Hanah Moon, Hee-Won Hur, Mina |
author_sort | Yi, Ahram |
collection | PubMed |
description | 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 (R(2)=0.435, both) than Model 0, including S-Cr only (R(2)=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. |
format | Online Article Text |
id | pubmed-7443531 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Korean Society for Laboratory Medicine |
record_format | MEDLINE/PubMed |
spelling | pubmed-74435312021-01-01 Effectiveness of Plasma and Urine Neutrophil Gelatinase-Associated Lipocalin for Predicting Acute Kidney Injury in High-Risk Patients Yi, Ahram Lee, Chang-Hoon Yun, Yeo-Min Kim, Hanah Moon, Hee-Won Hur, Mina Ann Lab Med Original Article 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 (R(2)=0.435, both) than Model 0, including S-Cr only (R(2)=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. Korean Society for Laboratory Medicine 2021-01 2021-01-01 /pmc/articles/PMC7443531/ /pubmed/32829580 http://dx.doi.org/10.3343/alm.2021.41.1.60 Text en Copyright © Korean Society for Laboratory Medicine This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Article Yi, Ahram Lee, Chang-Hoon Yun, Yeo-Min Kim, Hanah Moon, Hee-Won Hur, Mina Effectiveness of Plasma and Urine Neutrophil Gelatinase-Associated Lipocalin for Predicting Acute Kidney Injury in High-Risk Patients |
title | Effectiveness of Plasma and Urine Neutrophil Gelatinase-Associated Lipocalin for Predicting Acute Kidney Injury in High-Risk Patients |
title_full | Effectiveness of Plasma and Urine Neutrophil Gelatinase-Associated Lipocalin for Predicting Acute Kidney Injury in High-Risk Patients |
title_fullStr | Effectiveness of Plasma and Urine Neutrophil Gelatinase-Associated Lipocalin for Predicting Acute Kidney Injury in High-Risk Patients |
title_full_unstemmed | Effectiveness of Plasma and Urine Neutrophil Gelatinase-Associated Lipocalin for Predicting Acute Kidney Injury in High-Risk Patients |
title_short | Effectiveness of Plasma and Urine Neutrophil Gelatinase-Associated Lipocalin for Predicting Acute Kidney Injury in High-Risk Patients |
title_sort | effectiveness of plasma and urine neutrophil gelatinase-associated lipocalin for predicting acute kidney injury in high-risk patients |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7443531/ https://www.ncbi.nlm.nih.gov/pubmed/32829580 http://dx.doi.org/10.3343/alm.2021.41.1.60 |
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