Kidney Week

Abstract: SA-PO407

A Comparison of Different Equations for Estimating GFR in 29 US Health Care Organizations

Session Information

• CKD: Estimating Equations, Incidence, Prevalence, Special Populations
  November 04, 2017 | Location: Hall H, Morial Convention Center
  Abstract Time: 10:00 AM - 10:00 AM

Category: Chronic Kidney Disease (Non-Dialysis)

• 302 CKD: Estimating Equations, Incidence, Prevalence, Special Populations

Authors

• Stempniewicz, Nikita, AMGA, Alexandria, Virginia, United States

Nikita Stempniewicz,

• Ballew, Shoshana, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States

Shoshana Ballew,

• Ciemins, Elizabeth, AMGA, Alexandria, Virginia, United States

Elizabeth Ciemins,

• Grams, Morgan, Welch Center for Prevention, Epidemiology & Clinical Research, Baltimore, Maryland, United States

Morgan Grams,

• Matsushita, Kunihiro, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States

Kunihiro Matsushita,

• Penso, Jerry, AMGA, Alexandria, Virginia, United States

Jerry Penso,

• Coresh, Josef, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States

Josef Coresh,

Background

The KDIGO 2012 guidelines recommend reporting eGFRcreat in adults using the 2009 CKD-EPI creatinine equation to diagnose and stages CKD with known risk relationships. Some health care organizations use different equations, with common alternatives being the MDRD and Mayo Clinic Quadratic (MCQ). Electronic heath record (EHR) data provide the opportunity to estimate the impact of using these three common equations to estimate GFR to a health care organization and its patient population.

Methods

This study uses EHR data from 29 AMGA member organizations who are using the Optum One population health analytics platform. Data from 2.3 million patients age 18-99, with a history of hyperglycemia, at least 1 ambulatory office visit, and a serum creatinine recorded between 01/01/2013 and 12/31/2016 were included. Estimated GFR was calculated for each patient using the CKD-EPI, MDRD, and MCQ equations.

Results

The 2.3 million patients had mean age 62.3 years, were 52.8% female, 10.7% black race, and 51.1% had a diagnosis of type 2 diabetes (on a claim or the patients problem list in the EHR). The average (standard deviation) eGFR from the CKD-EPI, MDRD, and MCQ equations were 77.4 (25.4), 76.3 (28.9), and 88.8 (25.4) mL/min/1.73 m², respectively. Distributions of eGFR for the three equations differed markedly (Figure 1). In the CKD G3+ range (<60 ml/min/1.73m²) the MCQ estimates gave a much lower prevalence compared to the CKD-EPI and MDRD equations (23.2 and 24.8 vs. 12.7%, summing the prevalence in range below 60 in Figure 1). Only 48.7% of patients were classified in the same CKD GFR categories with all three equations.

Conclusion

Compared to the CKD-EPI equation which is currently recommended and stages CKD with a known relationship to risk, the MDRD equation produces similar results at low eGFR, while the MCQ equation yields a dramatically lower prevalence of CKD.