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Development of a MALDI MS‐based platform for early detection of acute kidney injury
Author(s) -
Carrick Emma,
Vanmassenhove Jill,
Glorieux Griet,
Metzger Jochen,
Dakna Mohammed,
Pejchinovski Martin,
Jankowski Vera,
Mansoorian Bahareh,
Husi Holger,
Mullen William,
Mischak Harald,
Vanholder Raymond,
Biesen Wim
Publication year - 2016
Publication title -
proteomics – clinical applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.948
H-Index - 54
eISSN - 1862-8354
pISSN - 1862-8346
DOI - 10.1002/prca.201500117
Subject(s) - medicine , acute kidney injury , receiver operating characteristic , sepsis , urine , urinary system , renal function , creatinine , population , area under the curve , environmental health
Purpose Septic acute kidney injury (AKI) is associated with poor outcome. This can partly be attributed to delayed diagnosis and incomplete understanding of the underlying pathophysiology. Our aim was to develop an early predictive test for AKI based on the analysis of urinary peptide biomarkers by MALDI‐MS. Experimental design Urine samples from 95 patients with sepsis were analyzed by MALDI‐MS. Marker search and multimarker model establishment were performed using the peptide profiles from 17 patients with existing or within the next 5 days developing AKI and 17 with no change in renal function. Replicates of urine sample pools from the AKI and non‐AKI patient groups and normal controls were also included to select the analytically most robust AKI markers. Results Thirty‐nine urinary peptides were selected by cross‐validated variable selection to generate a support vector machine multidimensional AKI classifier. Prognostic performance of the AKI classifier on an independent validation set including the remaining 61 patients of the study population (17 controls and 44 cases) was good with an area under the receiver operating characteristics curve of 0.82 and a sensitivity and specificity of 86% and 76%, respectively. Conclusion and clinical relevance A urinary peptide marker model detects onset of AKI with acceptable accuracy in septic patients. Such a platform can eventually be transferred to the clinic as fast MALDI‐MS test format.