![]() ![]() Although recommended, universal adoption of the automated process has been varied with gradual and incomplete implementation across laboratories in the United States (US). That is, to implement software in clinical laboratories that automatically calculates and reports eGFR in real-time alongside each corresponding S cr value. ![]() Since 2002 the K-DOQI, NKDEP and International Society for Nephrology have encouraged laboratories to automate eGFR reporting. This is an older equation that is being slowly phased out in favor of the CKD Epidemiology Collaboration (CKD-EPI) equation, however, real-time calculations in EHRs still use the MDRD equation as do many research groups. One equation is the Modification of Diet in Renal Disease (MDRD) study equation, which derives eGFR from patients’ age, race, and serum creatinine (S cr). The National Kidney Foundation Disease Outcomes Quality Initiative (K-DOQI) and the National Kidney Disease Education Program (NKDEP) recommend that in absence of direct measurement of renal excretory function, eGFR can be calculated from prediction equations based on factors commonly found in the electronic health record (EHR). From a research standpoint, eGFR can be used as selection criteria for entry into observational cohorts or clinical trials, as well as be an exposure, outcome, or covariate of interest. Clinically, eGFR can inform therapeutic strategy, disease prognosis, and is predictive of overall patient survival. In very large datasets these small differences could become significant.Įstimated glomerular filtration rate (eGFR) is a standard metric for assessing renal excretory function and staging chronic kidney disease (CKD) in routine clinical practice and is ubiquitously utilized in research settings. Values of retrospectively calculated eGFR can differ from automated values, but do not always result in a significant classification change. A change in recorded age explained 95.6% ( n = 78,903) of discordant values and 85.02% ( n = 9371) of the discordant stages. The Bland–Altman plot showed differences in the data pairs were centered near zero (mean difference: 0.8 mL/min/1.73m 2) with 95% limits of agreement between − 6.4 and 8.0. The majority of discordant pairs were classified as the same CKD stage ( n = 74,542, 85.93%). Approximately 33.0% ( n = 86,747) of eGFR values differed between automated and retrospectively calculated methods. We evaluated eGFR data pairs from 266,084 patients. 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. We used Bland–Altman plots and percent agreement to assess the difference between the automated and calculated values. Stage of chronic kidney disease (CKD) was defined using both eGFR values. The MDRD equation was applied to retrospectively calculate eGFR. We 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. Whether and to what extent the practice of re-calculating eGFR on retrospective data differs from using the automated values is unknown. Due to the dynamic nature of EHRs, current data may not always match past data. 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). 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. Estimated glomerular filtration rate (eGFR) is the clinical standard for assessing kidney function and staging chronic kidney disease. ![]()
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