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Comparison of automated and retrospectively calculated estimated glomerular filtration rate in electronic health record data

Authors
  • Lynch, Kristine E.1, 2
  • Chang, Ji won1, 2
  • Matheny, Michael E.3, 4
  • Goldfarb, Alexander5, 6
  • Efimova, Olga1, 2
  • Coronado, Gregorio1, 2
  • DuVall, Scott L.1, 2
  • 1 VA Informatics and Computing Infrastructure (VINCI), VA Salt Lake City Health Care System, Salt Lake City, UT, USA , Salt Lake City (United States)
  • 2 University of Utah, Division of Epidemiology, Department of Internal Medicine, Salt Lake City, UT, USA , Salt Lake City (United States)
  • 3 Geriatrics Research Education and Clinical Care Center, Tennessee Valley Healthcare System, Nashville, TN, USA , Nashville (United States)
  • 4 Vanderbilt University Medical Center, Department of Biomedical Informatics, Nashville, TN, USA , Nashville (United States)
  • 5 Beth Israel Deaconess Medical Center, Boston, MA, USA , Boston (United States)
  • 6 Harvard Medical School, Boston, MA, USA , Boston (United States)
Type
Published Article
Journal
BMC Nephrology
Publisher
Springer (Biomed Central Ltd.)
Publication Date
Dec 28, 2018
Volume
19
Issue
1
Identifiers
DOI: 10.1186/s12882-018-1179-8
Source
Springer Nature
License
Green

Abstract

BackgroundEstimated glomerular filtration rate (eGFR) is the clinical standard for assessing kidney function and staging chronic kidney disease. Automated reporting of eGFR using the Modification of Diet in Renal Disease (MDRD) study equation was first implemented within the Department of Veterans Affairs (VA) in 2007 with staggered adoption across laboratories. When automated eGFR are not used or unavailable, values are retrospectively calculated using clinical and demographic data that are currently available in the electronic health record (EHR). Due to the dynamic nature of EHRs, current data may not always match past data. Whether and to what extent the practice of re-calculating eGFR on retrospective data differs from using the automated values is unknown.MethodsWe assessed clinical data for patients enrolled in VA who had their first automated eGFR lab in 2013.We extracted the eGFR value, the corresponding serum creatinine value, and patient race, gender, and date of birth from the EHR. The MDRD equation was applied to retrospectively calculate eGFR. Stage of chronic kidney disease (CKD) was defined using both eGFR values. We used Bland–Altman plots and percent agreement to assess the difference between the automated and calculated values. We developed an algorithm to select the most parsimonious parameter set to explain the difference in values and used chart review on a small subsample of patients to determine if one approach more accurately describes the patient at the time of eGFR measurement.ResultsWe evaluated eGFR data pairs from 266,084 patients. Approximately 33.0% (n = 86,747) of eGFR values differed between automated and retrospectively calculated methods. The majority of discordant pairs were classified as the same CKD stage (n = 74,542, 85.93%). The Bland–Altman plot showed differences in the data pairs were centered near zero (mean difference: 0.8 mL/min/1.73m2) with 95% limits of agreement between − 6.4 and 8.0. A change in recorded age explained 95.6% (n = 78,903) of discordant values and 85.02% (n = 9371) of the discordant stages.ConclusionsValues of retrospectively calculated eGFR can differ from automated values, but do not always result in a significant classification change. In very large datasets these small differences could become significant.

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