title: Serum Creatinine Back-Estimation in Cardiac Surgery Patients: Misclassification of AKI Using Existing Formulae and a Data-Driven Model.
abstract: OBJECTIVE
  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 m(2). 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.
  
  
  METHODS
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: *Department of Cardiothoracic and Vascular Anesthesia and Intensive Care Medicine, martin.bernardi@meduniwien.ac.at.
authors.forename: Martin Hermann
authors.initials: MH
authors.lastname: Bernardi
authors.affiliation: Department of Anesthesiology and Intensive Care Medicine, Klinik Im Park, Zurich, Switzerland; and.
authors.forename: Daniel
authors.initials: D
authors.lastname: Schmidlin
authors.affiliation: Center for Medical Statistics, Informatics and Intelligent Systems, and.
authors.forename: Robin
authors.initials: R
authors.lastname: Ristl
authors.affiliation: Institute for Analysis and Scientific Computing, Technical University of Vienna, Vienna, Austria.
authors.forename: Clemens
authors.initials: C
authors.lastname: Heitzinger
authors.affiliation: *Department of Cardiothoracic and Vascular Anesthesia and Intensive Care Medicine.
authors.forename: Arno
authors.initials: A
authors.lastname: Schiferer
authors.affiliation: *Department of Cardiothoracic and Vascular Anesthesia and Intensive Care Medicine.
authors.forename: Thomas
authors.initials: T
authors.lastname: Neugebauer
authors.affiliation: Center for Medical Statistics, Informatics and Intelligent Systems, and.
authors.forename: Thomas
authors.initials: T
authors.lastname: Wrba
authors.affiliation: *Department of Cardiothoracic and Vascular Anesthesia and Intensive Care Medicine.
authors.forename: Michael
authors.initials: M
authors.lastname: Hiesmayr
authors.affiliation: *Department of Cardiothoracic and Vascular Anesthesia and Intensive Care Medicine.
authors.forename: Wilfred
authors.initials: W
authors.lastname: Druml
authors.affiliation: *Department of Cardiothoracic and Vascular Anesthesia and Intensive Care Medicine.
authors.forename: Andrea
authors.initials: A
authors.lastname: Lassnigg
mesh_terms: D058186:Acute Kidney Injury / Q000097:blood / Q000175:diagnosis* / Q000401:mortality / Q000503:physiopathology; D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D001317:Austria; D015415:Biomarkers / Q000097:blood; D006348:Cardiac Surgical Procedures / Q000009:adverse effects* / Q000401:mortality; D003404:Creatinine / Q000097:blood*; D005260:Female; D005919:Glomerular Filtration Rate; D006801:Humans; D007668:Kidney / Q000503:physiopathology*; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D018570:Risk Assessment; D012307:Risk Factors; D012720:Severity of Illness Index; D012737:Sex Factors; D013557:Switzerland; D055815:Young Adult
chemical_list: D015415:Biomarkers; D003404:Creatinine
keywords: Serum creatinine; acute kidney injury; cardiac surgery; estimation methods; glomerular filtration rate; humans; kidney diseases; kidney function tests; renal insufficiency, chronic