Authors' objectives: 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. 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. Chronic kidney disease (CKD) is commonly identified using estimation of glomerular filtration rate (GFR) and/or detection of albuminuria [urinary albumin-to-creatinine ratio (ACR)]. Ideally, GFR is measured using reference procedures, but these are cumbersome and impractical for clinical practice. Estimation of GFR using equations based on serum creatinine with adjustments for age, gender and black ethnicity has been widely used. In the UK, the Modification of Diet in Renal Disease (MDRD) study equation and more recently the Chronic Kidney Disease Epidemiology Collaboration creatinine (CKD-EPIcreatinine) equation have been recommended. Other more recently published equations, including CKD-EPI cystatin C-containing equations (CKD-EPIcystatin, CKD-EPIcreatinine-cystatin), the Berlin Initiative Study equations, the Caucasian, Asian, Pediatric and Adult equation, the Lund–Malmö revised equation, the full age spectrum equation, the European Kidney Function Consortium equation and the 2021 revisions of the CKD-EPI equations, have not yet gained widespread acceptance in clinical practice. In addition to the accurate identification of CKD, the ability of tests to identify which individuals with CKD have higher risk of progressive or mortal disease is a crucial issue. Many people with stage 3 CKD are not at increased risk of CKD progression and there are concerns that CKD detection using creatinine-based approaches may identify some individuals who are at low risk and unlikely to benefit from active management. Equations utilising serum cystatin C, an alternative marker of GFR, instead of, or in addition to, creatinine have been proposed. Given the higher unit cost of cystatin C compared to creatinine, its diagnostic accuracy and clinical utility should be validated ahead of widespread introduction into the NHS. Primary objectives The comparative performance of GFR-estimating equations in assessing and monitoring measured glomerular filtration rate (mGFR) in people with stage 3 CKD (GFR 30–59 ml/minute/1.73 m2) was evaluated. The aims of the study were to: estimate and compare the accuracy of the MDRD and three CKD-EPI equations estimate the accuracy of the GFR-estimating equations according to ethnic group (particularly Caucasian, South Asian and African-Caribbean), baseline diabetes, albuminuria and other characteristics evaluate and compare how accurately these GFR-estimating equations track and detect change in mGFR over 3 years establish the biological variability of mGFR and estimated glomerular filtration rate (eGFR) estimate which GFR-estimating equation, together with ACR, or ACR alone, most accurately predicts mortality and CKD progression estimate and model disease progression (decline in GFR or increase in ACR) and differences in progression between ethnic groups (Caucasian, South Asian and African-Caribbean), baseline diabetes and albuminuria status and other potential risk factors explore the comparative cost-effectiveness of monitoring strategies for identifying people who have CKD progression utilising different GFR-estimating equations.

Authors' results and conclusions: Accuracy (P30) of all equations was ≥ 89.5%: the combined creatinine–cystatin equation (94.9%) was superior (p 

Authors' methods: A longitudinal, prospective study was designed to: (1) assess accuracy of glomerular filtration rate equations at baseline (n = 1167) and their ability to detect change over 3 years (n = 875); (2) model disease progression predictors in 278 individuals who received additional measurements; (3) quantify glomerular filtration rate variability components (n = 20); and (4) develop a measurement model analysis to compare different monitoring strategy costs (n = 875). Primary, secondary and tertiary care. Adults (≥ 18 years) with stage 3 chronic kidney disease. Estimated glomerular filtration rate using the Chronic Kidney Disease Epidemiology Collaboration and Modification of Diet in Renal Disease equations. 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. Recruitment of people from South Asian and African-Caribbean backgrounds was below the study target. Main study. A 3-year prospective longitudinal cohort study using 6-monthly GFR estimates and baseline and final mGFR values was undertaken to assess and compare the accuracy of each estimate of GFR and change in GFR. Substudy of disease progression. Predictors of progression of GFR in a subset of the cohort who received annual GFR measurements were modelled. Substudy of biological variation. Components of variability in mGFR and eGFR were quantified. An economic evaluation tested the consequences of implementing creatinine- and/or cystatin C-based eGFR for monitoring subjects who are initially stage 3 CKD. Glomerular filtration rate was measured using iohexol clearance. Iohexol was measured by ID-MS. Creatinine was measured by a commercial enzymatic assay and by ID-MS. Cystatin C was measured by a commercial immunoassay. Both creatinine and cystatin C methods were internationally standardised. Primary, secondary and tertiary care. Recruitment occurred across six centres in England. Adults (≥ 18 years) with stage 3 CKD proportionally enriched to include people more likely to have progressive kidney disease (i.e. those with proteinuria and/or diabetes) and including South Asian and African-Caribbean people. Estimated GFR using the MDRD and three CKD-EPI equations, using either creatinine or cystatin C or a combination of both, in addition to urinary ACR. Other GFR-estimating equations were also studied. Measured GFR was the reference test against which GFR-estimating equations were compared. Accuracy of GFR-estimating equations was expressed as P30, the percentage of estimated values within 30% of mGFR, with P30 ≥ 90% considered acceptable. P30 incorporates elements of bias and imprecision. The ability of eGFR equations to both track and detect change in mGFR over time gave an estimate of temporal error. For each individual, the average change per year in eGFR and mGFR was derived and error, the difference between the annual change in mGFR and eGFR, calculated. Large error was accepted as ≥ 3 ml/minute/1.73 m2/year, or > 5%/year difference between mGFR and eGFR. Ability of equations to detect change was studied based on whether or not eGFR detected overall change, or decline only, in mGFR over 3 years against threshold changes variously defined as (1) > 10 ml/minute/1.73 m2; (2) > reference change value (RCV) (a > 21.5% increase or a > 17.7% decrease); (3) > 25% change; and (4) > 25% change and a change in disease stage. Sensitivity and specificity of eGFRs to identify progressive disease were evaluated. Estimated GFRs, in addition to urinary ACR, were also tested as predictors of progression and mortality. In the substudy of disease progression the change in mGFR, and the difference between mGFRs and eGFRs (bias), assessed every 12 months, were modelled over time using a longitudinal linear random coefficients regression model, to estimate average and variability in disease progression and bias. A model of disease progression based on mGFR was developed and differences in progression for risk factors estimated. In the biological variation substudy, analytical (CVA) and individual (CVI) components of variation were calculated and used to derive the RCV for significant changes in serial results for both mGFR and eGFR. Results from the main study informed a measurement model analysis. The trajectory of participants mGFR and eGFR over 10 years was used to estimate the proportion meeting the National Institute for Health and Care Excellence (NICE) definition of accelerated progression or of progression to CKD stage G4, assuming an annual testing schedule, and the number of participants expected to be incorrectly managed at each of the evaluated monitoring time points using different estimating equations. Based on the findings, the comparative costs of monitoring with GFR-estimating equations were calculated.

Project Status: Completed

URL for project: https://www.journalslibrary.nihr.ac.uk/programmes/hta/11/103/01

Year Published: 2024

URL for published report: https://www.journalslibrary.nihr.ac.uk/hta/HYHN1078

URL for additional information: English

English language abstract: An English language summary is available

Publication Type: Full HTA

Country: England, United Kingdom

DOI: 10.3310/HYHN1078

Organisation Name: NIHR Health Technology Assessment programme

Contact Address: NIHR Journals Library, National Institute for Health and Care Research, Evaluation, Trials and Studies Coordinating Centre, Alpha House, University of Southampton Science Park, Southampton SO16 7NS, UK

Contact Name: journals.library@nihr.ac.uk

Contact Email: journals.library@nihr.ac.uk