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

Overview of attention for article published in Critical Care, January 2016
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  • Good Attention Score compared to outputs of the same age (71st percentile)

Mentioned by

7 tweeters


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44 Mendeley
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Acute kidney injury prediction in cardiac surgery patients by a urinary peptide pattern: a case-control validation study
Published in
Critical Care, January 2016
DOI 10.1186/s13054-016-1344-z
Pubmed ID

Jochen Metzger, William Mullen, Holger Husi, Angelique Stalmach, Stefan Herget-Rosenthal, Heiner V. Groesdonk, Harald Mischak, Matthias Klingele, Metzger, Jochen, Mullen, William, Husi, Holger, Stalmach, Angelique, Herget-Rosenthal, Stefan, Groesdonk, Heiner V, Mischak, Harald, Klingele, Matthias


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. 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. 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). 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.

Twitter Demographics

Twitter Demographics

The data shown below were collected from the profiles of 7 tweeters who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 44 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Italy 1 2%
Unknown 43 98%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 10 23%
Student > Ph. D. Student 7 16%
Researcher 6 14%
Professor > Associate Professor 4 9%
Student > Doctoral Student 3 7%
Other 5 11%
Unknown 9 20%
Readers by discipline Count As %
Medicine and Dentistry 23 52%
Biochemistry, Genetics and Molecular Biology 5 11%
Nursing and Health Professions 3 7%
Agricultural and Biological Sciences 2 5%
Economics, Econometrics and Finance 1 2%
Other 1 2%
Unknown 9 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 15 June 2016.
All research outputs
of 23,577,761 outputs
Outputs from Critical Care
of 6,194 outputs
Outputs of similar age
of 397,071 outputs
Outputs of similar age from Critical Care
of 575 outputs
Altmetric has tracked 23,577,761 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 6,194 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.1. This one is in the 36th percentile – i.e., 36% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 397,071 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 575 others from the same source and published within six weeks on either side of this one. This one is in the 19th percentile – i.e., 19% of its contemporaries scored the same or lower than it.