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Evaluation of 32 urine biomarkers to predict the progression of acute kidney injury after cardiac surgery

Biomarkers for acute kidney injury (AKI) have been used to predict the progression of AKI but a systematic comparison of the prognostic ability of each biomarkers alone or in combination has not been performed. In order to assess this, we measured the concentration of 32 candidate biomarkers in the...

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Detalles Bibliográficos
Autores principales: Arthur, John M., Hill, Elizabeth G., Alge, Joseph L., Lewis, Evelyn C., Neely, Benjamin A., Janech, Michael G, Tumlin, James A., Chawla, Lakhmir S., Shaw, Andrew D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3880389/
https://www.ncbi.nlm.nih.gov/pubmed/24005224
http://dx.doi.org/10.1038/ki.2013.333
Descripción
Sumario:Biomarkers for acute kidney injury (AKI) have been used to predict the progression of AKI but a systematic comparison of the prognostic ability of each biomarkers alone or in combination has not been performed. In order to assess this, we measured the concentration of 32 candidate biomarkers in the urine of 95 patients with AKIN stage 1 after cardiac surgery. Urine markers were divided into eight groups based on the putative pathophysiologic mechanism they reflect. We then compared the ability of the markers alone or in combination to predict the primary outcome of worsening AKI or death (23 patients) and the secondary outcome of AKIN stage 3 or death (13 patients). IL-18 was the best predictor of both outcomes (AUC of 0.74 and 0.89). L-FABP (AUC of 0.67 and 0.85), NGAL (AUC of 0.72 and 0.83) and KIM-1 (AUC of 0.73 and 0.81) were also good predictors. Correlation between most of the markers was generally related to their predictive ability but KIM-1 had a relatively weak correlation with other markers. The combination of IL-18 and KIM-1 had a very good predictive value with an AUC of 0.93 to predict AKIN 3 or death. Thus, combination of IL-18 and KIM-1 would result in improved identification of high risk patients for enrollment in clinical trials.