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01.12.2016 | Research | Ausgabe 1/2016 Open Access

Critical Care 1/2016

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

Zeitschrift:
Critical Care > Ausgabe 1/2016
Autoren:
Jochen Metzger, William Mullen, Holger Husi, Angelique Stalmach, Stefan Herget-Rosenthal, Heiner V. Groesdonk, Harald Mischak, Matthias Klingele
Wichtige Hinweise

Electronic supplementary material

The online version of this article (doi:10.​1186/​s13054-016-1344-z) contains supplementary material, which is available to authorized users.

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).

Conclusions

This study gives further proof for the general applicability of our proteomic multimarker model for early and accurate prediction of AKI irrespective of its underlying disease cause.
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