Acute kidney injury prediction in cardiac surgery patients by a urinary peptide pattern: a case-control validation study

Metzger, J., Mullen, W. , Husi, H., Stalmach, A. , Herget-Rosenthal, S., Groesdonk, H., Mischak, H. and Klingele, M. (2016) Acute kidney injury prediction in cardiac surgery patients by a urinary peptide pattern: a case-control validation study. Critical Care, 20, 157. (doi: 10.1186/s13054-016-1344-z) (PMID:27230659) (PMCID:PMC4882859)

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Abstract

Background Acute kidney injury (AKI) is a prominent problem in hospitalized patients and associated with increased morbidity and mortality. Clinical medicine is currently hampered by the lack of accurate and early biomarkers for diagnosis of AKI and the evaluation of the severity of the disease. In 2010, we established a multivariate peptide marker pattern consisting of 20 naturally occurring urinary peptides to screen patients for early signs of renal failure. The current study now aims to evaluate if, in a different study population and potentially various AKI causes, AKI can be detected early and accurately by proteome analysis. Methods Urine samples from 60 patients who developed AKI after cardiac surgery were analyzed by capillary electrophoresis-mass spectrometry (CE-MS). The obtained peptide profiles were screened by the AKI peptide marker panel for early signs of AKI. Accuracy of the proteomic model in this patient collective was compared to that based on urinary neutrophil gelatinase-associated lipocalin (NGAL) and kidney injury molecule-1 (KIM-1) ELISA levels. Sixty patients who did not develop AKI served as negative controls. Results From the 120 patients, 110 were successfully analyzed by CE-MS (59 with AKI, 51 controls). Application of the AKI panel demonstrated an AUC in receiver operating characteristics (ROC) analysis of 0.81 (95 % confidence interval: 0.72–0.88). Compared to the proteomic model, ROC analysis revealed poorer classification accuracy of NGAL and KIM-1 with the respective AUC values being outside the statistical significant range (0.63 for NGAL and 0.57 for KIM-1).

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Stalmach, Dr Angelique and Mullen, Dr Bill and Husi, Dr Holger and Mischak, Professor Harald
Authors: Metzger, J., Mullen, W., Husi, H., Stalmach, A., Herget-Rosenthal, S., Groesdonk, H., Mischak, H., and Klingele, M.
College/School:College of Medical Veterinary and Life Sciences > Institute of Cardiovascular and Medical Sciences
Journal Name:Critical Care
Publisher:BioMed Central Ltd.
ISSN:1364-8535
ISSN (Online):1466-609X
Published Online:26 May 2016
Copyright Holders:Copyright © 2016 The Authors
First Published:First published in Critical Care 20:157
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
546181Development of an efficient, robust MS-based platform for early detection of acute kidney injuryHarald MischakMedical Research Council (MRC)G1000791RI CARDIOVASCULAR & MEDICAL SCIENCES
546182Development of an efficient, robust MS-based platform for early detection of acute kidney injuryHarald MischakMedical Research Council (MRC)G1000791RI CARDIOVASCULAR & MEDICAL SCIENCES