Serum Creatinine Back-Estimation in Cardiac Surgery Patients: Misclassification of AKI Using Existing Formulae and a Data-Driven Model

Background and objectives 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. Design, setting, participants, & measurements 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 AM but to a still inadequate extent.