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Original ArticlesChronic Kidney Disease
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Implications of the CKD-EPI GFR Estimation Equation in Clinical Practice

Jesse D. Schold, Sankar D. Navaneethan, Stacey E. Jolly, Emilio D. Poggio, Susana Arrigain, Welf Saupe, Anil Jain, John W. Sharp, James F. Simon, Martin J. Schreiber and Joseph V. Nally
CJASN March 2011, 6 (3) 497-504; DOI: https://doi.org/10.2215/CJN.04240510
Jesse D. Schold
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Sankar D. Navaneethan
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Stacey E. Jolly
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Emilio D. Poggio
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Susana Arrigain
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Welf Saupe
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Anil Jain
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John W. Sharp
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James F. Simon
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Martin J. Schreiber Jr
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Joseph V. Nally Jr
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  • Figure 1.
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    Figure 1.

    Age distribution of stage 3 to 5 CKD population on the basis of estimating equations. (A) Population classified with CKD by both CKD-EPI and MDRD (n = 46,985). (B) Population classified with CKD only by MDRD (n = 5951). (C) Population classified with CKD only by CKD-EPI (n = 823).

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    Figure 2.

    (A) Time to classification of CKD by age on the basis of the MDRD equation. (B) Time to classification of CKD by age on the basis of the CKD-EPI equation. *Extrapolated time assuming a Weibull survival distribution.

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    Table 1.

    Demographic characteristics of patients classified with CKD on the basis of the MDRD and the CKD-EPI equation

    Patient CharacteristicsMethod of CKD ClassificationPercentage of Change in CKD Patients on the Basis of the CKD-EPI Equation
    Both MDRD and CKD-EPI EquationsMDRD Equation OnlyCKD-EPI Equation Only
    Age 18 to 59 (n = 11,413)65%35%0%−35%
    Age 60 to 69 (n = 12,301)89%11%0%b−11%
    Age 70 to 79 (n = 16,286)96%4%1%−3%
    Age 80 to 89 (n = 12,033)96%0%4%4%
    Age 90+ (n = 1726)91%0%9%10%
    African American (n = 6251)93%3%4%2%
    Non-African American (n = 47,508)87%12%1%−11%
    Diabetes present (n = 11,004)91%7%1%−6%
    Diabetes absent (n = 42,755)86%12%2%−11%
    Male gender (n = 23,779)89%9%2%−7%
    Female gender (n = 29,980)86%13%1%−12%
    BMI <20 kg/m2 (n = 1813)88%9%3%−6%
    BMI 20 to 24 kg/m2 (n = 11,052)87%10%2%−8%
    BMI 25 to 29 kg/m2 (n = 17,719)88%10%2%−9%
    BMI 30 to 34 kg/m2 (n = 10,858)87%12%1%−11%
    BMI 35+ kg/m2 (n = 8113)85%15%0%b−14%
    BMI missing (n = 4204)90%8%2%−7%
    Primary insurance: Medicare (n = 29,223)94%4%2%−2%
    Primary insurance: Medicaid (n = 1012)82%18%0%b−18%
    Primary insurance: other (n = 22,283)79%20%1%−19%
    Primary insurance: unknown (n = 1241)79%20%1%−20%
    Year of initial GFR: 2005 (n = 28,275)a92%7%1%−6%
    Year of initial GFR: 2006 (n = 10,586)a85%13%2%−11%
    Year of initial GFR: 2007 (n = 7393)a81%17%2%−15%
    Year of initial GFR: 2008 (n = 5166)a79%18%3%−16%
    Year of initial GFR: 2009 (n = 2339)a78%19%3%−16%
    Overall n (%)46,985 (87%)5951 (11%)823 (2%)−10%
    • The study excluded 3,877 patients identified with CKD on the basis of diagnosis codes alone. The row percentages may not sum to 100 because of rounding.

    • ↵a Minimum date of eGFR <60 ml/min per 1.73 m2 on the basis of either the MDRD or CKD-EPI equation.

    • ↵b The values are <0.5%.

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    Table 2.

    Proportion of study population with ICD-9 diagnostic codes indicative of kidney disease

    Patient GroupICD-9 Diagnosis ConditionsAny Listed Condition (%)
    CKD (%)Hypertensive Nephrosclerosis (%)Diabetic Nephropathy (%)GN (%)PKD (%)
    Age 18 to 5915.94.44.31.71.218.8
    Age 60 to 6912.62.53.30.50.314.6
    Age 70 to 7911.42.42.50.30.113.1
    Age 80 to 898.71.91.10.10.010.0
    Age 90+6.51.00.50.00.07.2
    All patients11.7 (n = 6266)2.6 (n = 1406)2.6 (n = 1400)0.6 (n = 297)0.3 (n = 185)13.8 (n = 7266)
    • The study population consisted of patients with CKD as determined by either the CKD-EPI or MDRD GFR estimating equations. The ICD-9 diagnostic codes used were: CKD = 585, 585.1, 585.2, 585.3, 585.4, 585.5, 585.6, 585.9; Diabetic nephropathy = 250.4, 250.40, 250.41, 250.42, 250.43; glomerulonephritis = 580, 580.0, 580.4, 580.8, 580.81, 580.89, 580.9, 582, 582.1, 582.2, 582.4, 582.8, 582.81, 582.89, 582.9; Polycystic kidney disease = 753.12, 753.13, 753.14; Hypertensive nephrosclerosis = 403, 403.0, 403.00, 403.01, 403.1, 403.10, 403.9, 403.90, 403.91. GN, glomerulonephritis; PKD, polycystic kidney disease.

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    Table 3.

    Difference in estimated GFR by the MDRD and CKD-EPI at the time of CKD

    Patient CharacteristicsAverage Difference (± SD) in eGFR in ml/min per 1.73 m2 (CKD-EPI − MDRD)aPercentage of Patients with eGFR on the Basis of CKD-EPI ± 5 ml/min per 1.73 m2 Different from eGFR on the Basis of MDRDPercent of Patients with eGFR on the Basis of CKD-EPI ≥10% Different from eGFR on the Basis of MDRD
    Age 18 to 59 (n = 7379)2.84 ± 1.848.85.8
    Age 60 to 69 (n = 10,982)1.43 ± 1.3400.6
    Age 70 to 79 (n = 15,688)−0.33 ± 1.1401.1
    Age 80 to 89 (n = 12,033)−1.89 ± 1.071.34.4
    Age 90+ (n = 1726)−3.60 ± 1.179.426.7
    African American (n = 6095)−1.51 ± 2.105.314.3
    Non-African American (n = 41,713)0.28 ± 2.112.02.4
    Diabetes present (n = 10,213)0.09 ± 2.071.83.2
    Diabetes absent (n = 37,595)0.04 ± 2.232.64.0
    Male gender (n = 21,745)−0.42 ± 2.071.64.0
    Female gender (n = 26,063)0.45 ± 2.223.13.7
    BMI <20 kg/m2 (n = 1656)−0.33 ± 2.464.78.5
    BMI 20 to 24 kg/m2 (n = 9935)−0.36 ± 2.273.05.3
    BMI 25 to 29 kg/m2 (n = 15,905)−0.12 ± 2.102.03.1
    BMI 30 to 34 kg/m2 (n = 9528)0.37 ± 2.072.12.6
    BMI 35 + kg/m2 (n = 6930)1.08 ± 2.022.52.3
    BMI missing (n = 3854)−0.60 ± 2.152.56.5
    Primary insurance: Medicare (n = 28,165)−0.50 ± 1.901.34.0
    Primary insurance: Medicaid (n = 832)1.59 ± 2.317.17.6
    Primary insurance: other/unknown (18,811)0.81 ± 2.343.63.4
    Overall (n = 47,808)0.05 ± 2.202.43.8
    • The classification of CKD was on the basis of the CKD-EPI equation.

    • ↵a Negative values indicate lower GFR with the CKD-EPI equation.

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Clinical Journal of the American Society of Nephrology: 6 (3)
Clinical Journal of the American Society of Nephrology
Vol. 6, Issue 3
1 Mar 2011
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Implications of the CKD-EPI GFR Estimation Equation in Clinical Practice
Jesse D. Schold, Sankar D. Navaneethan, Stacey E. Jolly, Emilio D. Poggio, Susana Arrigain, Welf Saupe, Anil Jain, John W. Sharp, James F. Simon, Martin J. Schreiber, Joseph V. Nally
CJASN Mar 2011, 6 (3) 497-504; DOI: 10.2215/CJN.04240510

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Implications of the CKD-EPI GFR Estimation Equation in Clinical Practice
Jesse D. Schold, Sankar D. Navaneethan, Stacey E. Jolly, Emilio D. Poggio, Susana Arrigain, Welf Saupe, Anil Jain, John W. Sharp, James F. Simon, Martin J. Schreiber, Joseph V. Nally
CJASN Mar 2011, 6 (3) 497-504; DOI: 10.2215/CJN.04240510
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