GFR estimation: from physiology to public health

Abstract

Estimating glomerular filtration rate (GFR) is essential for clinical practice, research, and public health. Appropriate interpretation of estimated GFR (eGFR) requires understanding the principles of physiology, laboratory medicine, epidemiology, and biostatistics used in the development and validation of GFR estimating equations. Equations developed in diverse populations are less biased at higher GFRs than equations developed in chronic kidney disease (CKD) populations and are more appropriate for general use. Equations that include multiple endogenous filtration markers are more precise than equations including a single filtration marker. The CKD-EPI (CKD Epidemiology Collaboration) equations are the most accurate GFR estimating equations that have been evaluated in large diverse populations and are applicable for general clinical use. The 2009 CKD-EPI creatinine equation is more accurate in estimating GFR and prognosis than the 2006 MDRD (Modification of Diet in Renal Disease) Study equation and provides lower estimates of prevalence of decreased eGFR. It is useful as a "first test" for decreased eGFR and should replace the MDRD Study equation for routine reporting of serum creatinine-based eGFR by clinical laboratories. The 2012 CKD-EPI cystatin C equation is as accurate as the 2009 CKD-EPI creatinine equation in estimating GFR, does not require specification of race, and may be more accurate in patients with decreased muscle mass. The 2012 CKD-EPI creatinine-cystatin C equation is more accurate than the 2009 CKD-EPI creatinine and 2012 CKD-EPI cystatin C equations and is useful as a confirmatory test for decreased eGFR as determined by serum creatinine-based eGFR. Further improvement in GFR estimating equations will require development in more broadly representative populations, including diverse racial and ethnic groups, use of multiple filtration markers, and evaluation using statistical techniques to compare eGFR to "true GFR."

Keywords: Estimated glomerular filtration rate (eGFR); GFR estimating equation; chronic kidney disease; filtration marker; kidney function; public health; renal insufficiency.

Figure 1. Clearance and GFR

A. Clearance (Cl, ml/min) is defined as the amount (A) of solute removed from the plasma per unit of time (mg/min) divided by the average plasma concentration (P, mg/ml) during the interval of observation and can by conceptualized as the as the virtual volume of plasma “cleared” of a solute per unit time. Plasma clearance (Cl_P, ml/min) is the sum of clearance by all mechanisms, generally categorized as renal (urinary, Cl_U, ml/min) and extra-renal (Cl_E, ml/min) clearance (for example, gut and biliary elimination). Plasma clearance can be computed from the amount of the marker administered (mg) divided by the area under the plasma disappearance curve [(mg/ml)*min], assuming a two-compartment model, and does not require knowledge of A, U or E. If plasma clearance of the marker exceeds its urinary clearance, it can be inferred that the marker undergoes extra-renal elimination. B. Relationship of Urinary Clearance of Exogenous Filtration Markers to Clearance. Urinary excretion (UV, mg/min) is the sum of the filtered load (the product of GFR times the plasma concentration times the sieving coefficient) plus tubular secretion (TS, mg/min) minus tubular reabsorption (TR, mg/min). Urinary clearance (Cl_U) is measured as the amount of the marker excreted in the urine (UV) per unit time divided by the plasma concentration (P) of the marker during the urine collection period. For an “ideal” filtration marker, tubular secretion and tubular reabsorption are zero, hence urinary clearance equals GFR. For a marker whose mechanism of excretion is unknown, the comparison of urinary clearance to GFR enables inference about its renal handling. For example, if urinary clearance of the marker is less than GFR, it can be inferred that the marker is not freely filtered or is reabsorbed by the tubule. Conversely, if urinary clearance of the marker is greater than GFR, it can be inferred that the marker is secreted by the tubule. C. Relationship of Plasma Level of Endogenous Filtration Markers to GFR. In the steady state, a constant plasma concentration (P, mg/min) of the filtration marker is maintained because generation (G, mg/min) is equal to the sum of urinary excretion (UV, mg/min) and extrarenal elimination (E, mg/min). Thus GFR is related to the reciprocal of the plasma concentration of the marker (P), but it is also influenced by its non-GFR determinants [generation (G), tubular secretion (TS), tubular reabsorption (TR) and extra-renal elimination (E)]. If the non-GFR determinants are known, the GFR can be estimated from the plasma concentration. In the nonsteady state, the rate and direction of change in the level of the filtration marker and estimated GFR (eGFR) are also affected by the magnitude of change in GFR and the volume of distribution of the filtration marker. Hence, the eGFR reflects the magnitude and direction of the change in GFR but does not accurately reflect the level of GFR. After a fall in GFR, the decline in eGFR is less than the decline in GFR, and eGFR thus exceeds GFR. Conversely, after a rise in GFR, the rise in eGFR is less than the rise in GFR, and eGFR is thus less than GFR. As the plasma level approaches the new steady state, the eGFR approaches the GFR, allowing more accurate estimation of GFR. For more information, see Stevens and Levey.

Figure 2. Distribution of eGFR and Prevalence of eGFR <60 ml/min/1.73 m² in NHANES 1999–2002

Data comprise 8238 adults in whom serum creatinine and cystatin C were assayed. eGFR computed using 2006 MDRD Study equation, 2009 CKD-EPI creatinine equation, 2012 CKD-EPI cystatin C equation; and 2012 CKD-EPI creatinine–cystatin C equation. Prevalence estimates include 95% confidence intervals. Abbreviations: MDRD, Modification of Diet in Renal Disease; CKD-EPI, Chronic Kidney Disease Epidemiology Collaboration; cr, creatinine; cys, cystatin C; eGFR, estimated glomerular filtration rate; NHANES, National Health and Nutrition Survey. Data from Grams et al.

Figure 3. Prognosis of eGFR as a Continuous Variable using the (A–C) 2009 CKD-EPI Creatinine Equation and the 2006 MDRD Study Equation and (D–F) the 2009 CKD-EPI Creatinine Equation, 2012 CKD-EPI Cystatin C Equation and 2012 CKD-EPI Creatinine–Cystatin C Equation in General Population Cohorts

The graphs show associations by plotting the adjusted hazard ratio (HR) versus the reference points, which are indicated by black diamonds (at 95 ml/min/1.73 m2 for death from any cause and death from cardiovascular causes and at 65 ml/min/1.73 m2 for end-stage renal disease). The hazard ratios were calculated using eGFR splines and adjusted for age, sex, race, body-mass index, systolic blood pressure, total cholesterol, presence or absence of a history of cardiovascular disease, smoking status, presence or absence of diabetes (all panels), and level of albuminuria (panel D–F only). In each panel, solid circles indicate that the adjusted hazard ratio at the indicated eGFR level was significant, as compared with the reference point. Thresholds indicate the eGFR below which the risk is significantly higher than the reference point (p <0.05). Statistical significance could not be computed for the threshold for Panel F because the threshold was contained within the spline segment including the reference point. Panels A–C adapted and reproduced from Matsushita et al. with permission of the American Medical Association; panels D–F adapted and reproduced with permission from the Massachusetts Medical Society from Shlipak et al (©2013 Massachusetts Medical Society)..

Figure 4. Prognosis of eGFR Categories using the 2009 CKD-EPI Creatinine Equation vs. the 2006 MDRD Study Equation

Left panel: cross-classification by eGFR categories using both equations; right panel: adjusted hazard ratios (HR) for reclassification using the CKD-EPI creatinine equation. Data are from general population cohorts (940,366 participants in 25 cohorts). In left panel, blue segments indicate the proportion of participants reclassified to a higher eGFR category; red segments indicate the proportion reclassification to a lower eGFR category; yellow segments indicate the proportion not reclassified. In the right panel, HR for all-cause mortality, cardiovascular (CV) mortality, and end-stage renal disease (ESRD) compares participants reclassified to a higher eGFR (blue font) and lower eGFR (red font) to participants not reclassified (reference). HR in bold are statistically significant (p <0.05). HR adjusted for age, sex, race, body-mass index, systolic blood pressure, total cholesterol, presence or absence of a history of cardiovascular disease, smoking status, and presence or absence of diabetes. Net reclassification improvement (NRI) for all-cause mortality (ACM), CV mortality (CVM) and end-stage renal disease (ESRD) from 1.1 million participants (940 366 from 25 general population cohorts, 151 494 from 7 high-risk cohorts, and 38 612 from 13 CKD cohorts). Data from Matsushita et al.

Figure 5. Prognosis of eGFR Categories using the 2009 CKD-EPI Creatinine Equation vs. the 2012 CKD-EPI Creatinine–Cystatin C Equation

Left panel: cross-classification by eGFR categories using both equations; right panel: adjusted hazard ratios (HR) for reclassification using the CKD-EPI creatinine-cystatin C (Cr-Cys) equation. Data are from general population cohorts (90,750 participants in 11 cohorts). In left panel, blue segments indicate the proportion of participants reclassified to a higher eGFR category; red segments indicate the proportion reclassification to a lower eGFR category; yellow segments indicate the proportion not reclassified. In the right panel, HR for all-cause mortality, cardiovascular (CV) mortality, and end-stage renal disease (ESRD) compares participants reclassified to a higher eGFR (blue font) and lower eGFR (red font) to participants not reclassified (reference). HR in bold are statistically significant (p <0.05). HRs adjusted for age, sex, race, body-mass index, systolic blood pressure, total cholesterol, presence or absence of a history of cardiovascular disease, smoking status, presence or absence of diabetes, and level of albuminuria. Net reclassification improvement (NRI) for all-cause mortality (ACM), cardiovascular mortality (CVM) and end-stage renal disease (ESRD) from 93,710 participants (90,750 in 11 general population cohorts and 2960 in 5 CKD cohorts). Data from Shlipak et al.