<div class="csl-bib-body">
<div class="csl-entry">Bernardi, M. H., Schmidlin, D., Ristl, R., Heitzinger, C., Schiferer, A., Neugebauer, T., Wrba, T., Hiesmayr, M., Druml, W., & Lassnigg, A. (2016). Serum Creatinine Back-Estimation in Cardiac Surgery Patients : Misclassification of AKI Using Existing Formulae and a Data-Driven Model. <i>Clinical Journal of the American Society of Nephrology</i>, <i>11</i>(3), 395–404. https://doi.org/10.2215/cjn.03560315</div>
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dc.identifier.issn
1555-9041
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/149951
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dc.description.abstract
A knowledge of baseline serum creatinine (bSCr) is mandatory for diagnosing and staging AKI. With often missing values, bSCr is estimated by back-calculation using several equations designed for the estimation of GFR, assuming a “true” GFR of 75 ml/min per 1.73 m2. Using a data set from a large cardiac surgery cohort, we tested the appropriateness of such an approach and compared estimated and measured bSCr. Moreover, we designed a novel data-driven model (estimated serum creatinine [eSCr]) for estimating bSCr. Finally, we analyzed the extent of AKI and mortality rate misclassifications.
Data for 8024 patients (2833 women) in our cardiac surgery center were included from 1997 to 2008. Measured and estimated bSCr were plotted against age for men and women. Patients were classified to AKI stages defined by the Kidney Disease Improving Global Outcomes (KDIGO) group. Results were compared with data from another cardiac surgery center in Zurich, Switzerland.
The Modification of Diet in Renal Disease and the Chronic Kidney Disease Epidemiology Collaboration formulae describe higher estimated bSCr values in younger patients, but lower values in older patients compared with the measured bSCr values in both centers. The Pittsburgh Linear Three Variables formula correctly describes the increasing bSCr with age, however, it underestimates the overall bSCr level, being in the range of the 25% quantile of the measured values. Our eSCr model estimated measured bSCr best. AKI stage 1 classification using all formulae, including our eSCr model, was incorrect in 53%–80% of patients in Vienna and in 74%–91% in Zurich; AKI severity (according to KDIGO stages) and also mortality were overestimated. Mortality rate was higher among patients falsely classified into higher KDIGO stages by estimated bSCr.
bSCr values back-estimated using currently available eGFR formulae are inaccurate and cannot correctly classify AKI stages. Our model eSCr improves the prediction of AKI but to a still inadequate extent.
en
dc.language.iso
en
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dc.relation.ispartof
Clinical Journal of the American Society of Nephrology
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dc.subject
Epidemiology
en
dc.subject
Nephrology
en
dc.subject
Critical Care and Intensive Care Medicine
en
dc.subject
Transplantation
en
dc.title
Serum Creatinine Back-Estimation in Cardiac Surgery Patients : Misclassification of AKI Using Existing Formulae and a Data-Driven Model
en
dc.type
Artikel
de
dc.type
Article
en
dc.contributor.affiliation
Medizinische Universität Wien Zentrum für Medizinische Statistik Informatik und Intelligente Systeme
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dc.contributor.affiliation
University of Vienna, Austria
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dc.description.startpage
395
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dc.description.endpage
404
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dc.type.category
Original Research Article
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tuw.container.volume
11
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tuw.container.issue
3
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tuw.journal.peerreviewed
true
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tuw.peerreviewed
true
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wb.publication.intCoWork
International Co-publication
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tuw.researchTopic.id
C6
-
tuw.researchTopic.name
Modelling and Simulation
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tuw.researchTopic.value
100
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dcterms.isPartOf.title
Clinical Journal of the American Society of Nephrology
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tuw.publication.orgunit
E101-03 - Forschungsbereich Scientific Computing and Modelling
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tuw.publisher.doi
10.2215/cjn.03560315
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dc.identifier.eissn
1555-905X
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dc.description.numberOfPages
10
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tuw.author.orcid
0000-0002-4163-9236
-
wb.sci
true
-
wb.sciencebranch
Mathematik
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wb.sciencebranch.oefos
1010
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wb.facultyfocus
Analysis und Scientific Computing
de
wb.facultyfocus
Analysis and Scientific Computing
en
wb.facultyfocus.faculty
E100
-
item.grantfulltext
none
-
item.languageiso639-1
en
-
item.openairetype
research article
-
item.fulltext
no Fulltext
-
item.cerifentitytype
Publications
-
item.openairecristype
http://purl.org/coar/resource_type/c_2df8fbb1
-
crisitem.author.dept
Medizinische Universität Wien Zentrum für Medizinische Statistik Informatik und Intelligente Systeme
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crisitem.author.dept
E194-06 - Forschungsbereich Machine Learning
-
crisitem.author.dept
University of Vienna, Austria
-
crisitem.author.orcid
0000-0002-4163-9236
-
crisitem.author.orcid
0000-0002-5996-1100
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crisitem.author.parentorg
E194 - Institut für Information Systems Engineering