Accuracy of glomerular filtration rate estimation using creatinine and cystatin C for identifying and monitoring moderate chronic kidney disease: the eGFR-C study.

Background: Estimation of glomerular filtration rate using equations based on creatinine is widely used to manage chronic kidney disease. In the UK, the Chronic Kidney Disease Epidemiology Collaboration creatinine equation is recommended. Other published equations using cystatin C, an alternative marker of kidney function, have not gained widespread clinical acceptance. Given higher cost of cystatin C, its clinical utility should be validated before widespread introduction into the NHS.

Objectives: Primary objectives were to: (1) compare accuracy of glomerular filtration rate equations at baseline and longitudinally in people with stage 3 chronic kidney disease, and test whether accuracy is affected by ethnicity, diabetes, albuminuria and other characteristics; (2) establish the reference change value for significant glomerular filtration rate changes; (3) model disease progression; and (4) explore comparative cost-effectiveness of kidney disease monitoring strategies.

Design: A longitudinal, prospective study was designed to: (1) assess accuracy of glomerular filtration rate equations at baseline ( = 1167) and their ability to detect change over 3 years ( = 875); (2) model disease progression predictors in 278 individuals who received additional measurements; (3) quantify glomerular filtration rate variability components ( = 20); and (4) develop a measurement model analysis to compare different monitoring strategy costs ( = 875).

Setting: Primary, secondary and tertiary care.

Participants: Adults (≥ 18 years) with stage 3 chronic kidney disease.

Interventions: Estimated glomerular filtration rate using the Chronic Kidney Disease Epidemiology Collaboration and Modification of Diet in Renal Disease equations.

Main Outcome Measures: Measured glomerular filtration rate was the reference against which estimating equations were compared with accuracy being expressed as P30 (percentage of values within 30% of reference) and progression (variously defined) studied as sensitivity/specificity. A regression model of disease progression was developed and differences for risk factors estimated. Biological variation components were measured and the reference change value calculated. Comparative costs of monitoring with different estimating equations modelled over 10 years were calculated.

Results: Accuracy (P30) of all equations was ≥ 89.5%: the combined creatinine-cystatin equation (94.9%) was superior ( < 0.001) to other equations. Within each equation, no differences in P30 were seen across categories of age, gender, diabetes, albuminuria, body mass index, kidney function level and ethnicity. All equations showed poor (< 63%) sensitivity for detecting patients showing kidney function decline crossing clinically significant thresholds (e.g. a 25% decline in function). Consequently, the additional cost of monitoring kidney function annually using a cystatin C-based equation could not be justified (incremental cost per patient over 10 years = £43.32). Modelling data showed association between higher albuminuria and faster decline in measured and creatinine-estimated glomerular filtration rate. Reference change values for measured glomerular filtration rate (%, positive/negative) were 21.5/-17.7, with lower reference change values for estimated glomerular filtration rate.

Limitations: Recruitment of people from South Asian and African-Caribbean backgrounds was below the study target.

Future Work: Prospective studies of the value of cystatin C as a risk marker in chronic kidney disease should be undertaken.

Conclusions: Inclusion of cystatin C in glomerular filtration rate-estimating equations marginally improved accuracy but not detection of disease progression. Our data do not support cystatin C use for monitoring of glomerular filtration rate in stage 3 chronic kidney disease.

Trial Registration: This trial is registered as ISRCTN42955626.

Funding: This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: 11/103/01) and is published in full in ; Vol. 28, No. 35. See the NIHR Funding and Awards website for further award information.