Recommendations for European laboratories based on the KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease.
[en] The 2024 Kidney Disease: Improving Global Outcomes (KDIGO) guidelines for chronic kidney disease (CKD) evaluation and management bring important updates, particularly for European laboratories. These guidelines emphasize the need for harmonization in CKD testing, promoting the use of regional equations. In Europe, the European Kidney Function Consortium (EKFC) equation is particularly suited for European populations, particularly compared to the CKD-EPI 2021 race-free equation. A significant focus is placed on the combined use of creatinine and cystatin C to estimate glomerular filtration rate (eGFRcr-cys), improving diagnostic accuracy. In situations where eGFR may be inaccurate or clinically insufficient, the guidelines encourage the use of measured GFR (mGFR) through exogenous markers like iohexol. These guidelines emphasize the need to standardize creatinine and cystatin C measurements, ensure traceability to international reference materials, and adopt harmonized reporting practices. The recommendations also highlight the importance of incorporating risk prediction models, such as the Kidney Failure Risk Equation (KFRE), into routine clinical practice to better tailor patient care. This article provides a European perspective on how these KDIGO updates should be implemented in clinical laboratories to enhance CKD diagnosis and management, ensuring consistency across the continent.
Disciplines :
Laboratory medicine & medical technology Urology & nephrology
Author, co-author :
Cavalier, Etienne ; Université de Liège - ULiège > Département de pharmacie > Chimie médicale
Zima, Tomáš; Institute of Medical Biochemistry and Laboratory Diagnostics, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
Datta, Pradip; Siemens Healthineers Diagnostics, Newark, DE, USA
Makris, Konstantinos ; Clinical Biochemistry Department, KAT General Hospital, Kifissia, Athens, Greece
Schaeffner, Elke; Division of Nephrology and Intensive Care Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
Langlois, Michel; Department of Laboratory Medicine, AZ St. Jan Hospital, Bruges, Belgium
Plebani, Mario ; Honorary Professor of Clinical Biochemistry and Clinical Molecular Biology, University of Padova, Padova, Italy ; Department of Pathology, University of Texas, Galveston, TX, USA
Delanaye, Pierre ; Université de Liège - ULiège > Département des sciences cliniques ; Department of Nephrology-Dialysis-Apheresis, Hôpital Universitaire Carémeau, Nîmes, France
EFLM Task Group on Chronic Kidney Disease
Language :
English
Title :
Recommendations for European laboratories based on the KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease.
KDIGO 2024. Clinical Practice Guideline for the evaluation and management of chronic kidney disease. Kidney Int Suppl 2024;105: S1–314.
KDIGO 2012. Clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int Suppl 2013;3:1–150.
Grams ME, Coresh J, Matsushita K, Ballew SH, Sang Y, Surapaneni A, et al. Estimated glomerular filtration rate, albuminuria, and adverse outcomes: an individual-participant data meta-analysis. JAMA; 2023, 330:1266–77 pp.
Delanaye P, Jager KJ, Bökenkamp A, Christensson A, Dubourg L, Eriksen BO, et al. CKD: a call for an age-adapted definition. J Am Soc Nephrol 2019;30:1785–805.
Jonsson AJ, Lund SH, Eriksen BO, Palsson R, Indridason OS. The prevalence of chronic kidney disease in Iceland according to KDIGO criteria and age-adapted estimated glomerular filtration rate thresholds. Kidney Int. Elsevier Inc 2020;98:1286–95.
Liu P, Quinn RR, Lam NN, Elliott MJ, Xu Y, James MT, et al. Accounting for age in the definition of chronic kidney disease. JAMA Intern Med 2021; 181:1359–66.
Delanaye P, Cavalier E. Staging chronic kidney disease and estimating glomerular filtration rate: an opinion paper about the new international recommendations. Clin Chem Lab Med 2013;51:1911–7.
Delanaye P. Too much nephrology? The CKD epidemic is real and concerning. A CON view. Nephrol Dial Transpl 2019;34:581–4.
Gansevoort RT. Too much nephrology? The CKD epidemic is real and concerning. A PRO view. Nephrol Dial Transpl 2019;34:577–80.
Zoccali C, Santoro A, Plebani M. Age, stage and biomarkers for the definition of CKD: a construction in progress. Clin Chem Lab Med 2013; 51:1–5.
Eriksen BO, Palsson R, Ebert N, Melsom T, van der Giet M, Gudnason V, et al. GFR in healthy aging: an individual participant data meta-analysis of iohexol clearance in european population-based cohorts. J Am Soc Nephrol 2020;31:1602–15.
Delanaye P, Glassock RJ, De Broe ME. Epidemiology of chronic kidney disease: think (at least) twice. Clin Kidney J 2017;10:370–4.
Benghanem Gharbi M, Elseviers M, Zamd M, Belghiti Alaoui A, Benahadi N, Trabelssi EH, et al. Chronic kidney disease, hypertension, diabetes, and obesity in the adult population of Morocco: how to avoid “over”- and “under”-diagnosis of CKD. Kidney Int 2016;89:1363–71.
Fu EL, Levey AS, Coresh J, Elinder C, Rotmans JI, Dekker FW, et al. Accuracy of GFR estimating equations in patients with discordances between creatinine and cystatin C-based estimations. J Am Soc Nephrol 2023;34:1241–51.
Pottel H, Björk J, Rule AD, Ebert N, Eriksen BO, Dubourg L, et al. Cystatin C–based equation to estimate GFR without the inclusion of race and sex. N Engl J Med 2023;388:333–43.
Pottel H, Björk J, Courbebaisse M, Couzi L, Ebert N, Eriksen BO, et al. Development and validation of a modified full age spectrum creatinine-based equation to estimate glomerular filtration rate. A cross-sectional analysis of pooled data. Ann Intern Med 2021;174: 183–91.
Inker LA, Eneanya ND, Coresh J, Tighiouart H, Wang D, Sang Y, et al. New creatinine- and cystatin C–based equations to estimate GFR without race. N Engl J Med 2021;385:1737–49.
Inker LA, Schmid CH, Tighiouart H, Eckfeldt JH, Feldman HI, Greene T, et al. Estimating glomerular filtration rate from serum creatinine and cystatin C. N Engl J Med 2012;367:20–9.
Björk J, Bäck SE, Ebert N, Evans M, Grubb A, Hansson M, et al. GFR estimation based on standardized creatinine and cystatin C: a European multicenter analysis in older adults. Clin Chem Lab Med 2018;56:422–35.
Cavalier E, Makris K, Portakal O, Nikler A, Datta P, Zima T, et al. Assessing the status of European laboratories in evaluating biomarkers for chronic kidney diseases (CKD) and recommendations for improvement: insights from the 2022 EFLM Task Group on CKD survey. Clin Chem Lab Med 2024;62:253–61.
Stehlé T, Ouamri Y, Morel A, Vidal-Petiot E, Fellahi S, Segaux L, et al. Development and validation of a new equation based on plasma creatinine and muscle mass assessed by CT-scan to estimate glomerular filtration rate: a cross-sectional study. Clin Kidney J 2023;16: 1265–77.
Adingwupu OM, Barbosa ER, Palevsky PM, Vassalotti JA, Levey AS, Inker LA. Cystatin C as a GFR estimation marker in acute and chronic illness: a systematic review. Kidney Med. 2023;5. https://doi.org/10.1016/j.xkme.2023.100727.
Inker LA, Schmid CH, Greene T, Li L, Beck GJ, Joffe MM, et al. Factors other than glomerular filtration rate affect serum cystatin C levels. Kidney Int 2009;75:652–60.
Delanaye P, Pottel H, Cavalier E, Flamant M, Stehlé T, Mariat C. Diagnostic standard: assessing glomerular filtration rate. Nephrol Dial Transpl 2024;39:1088–96.
Ebert N, Bevc S, Bökenkamp A, Gaillard F, Hornum M, Jager KJ, et al. Assessment of kidney function: clinical indications for measured GFR. Clin Kidney J 2021;14:1861–70.
Agarwal R, Delanaye P. Glomerular filtration rate: when to measure and in which patients? Nephrol Dial Transpl 2019;34:2001–7.
Nyssen L, Delanaye P, Le Goff C, Peeters S, Cavalier É. A simple LC-MS method for the determination of iohexol and iothalamate in serum, using ioversol as an internal standard. Clin Chim Acta 2016;463:96–102.
Gaspari F, Thakar S, Carrara F, Perna A, Trillini M, Aparicio MC, et al. Safety of iohexol administration to measure glomerular filtration rate in different patient populations: a 25-year experience. Nephron 2018; 140:1–8.
Nordin G, Ekvall S, Kristoffersson C, Jonsson A-S, Bäck S-E, Rollborn N, et al. Accuracy of determination of the glomerular filtration marker iohexol by European laboratories as monitored by external quality assessment. Clin Chem Lab Med 2019;57:1006–11.
Ebert N, Schaeffner E, Seegmiller JC, Londen M, Bökenkamp A, Cavalier E, et al. Iohexol plasma clearance measurement protocol standardization for adults – a consensus paper of the European kidney function consortium. Kidney Int 2024;106:583–96.
Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AFIII, Feldman HI, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med 2009;150:604–12.
Eneanya ND, Boulware LE, Tsai J, Bruce MA, Ford CL, Harris C, et al. Health inequities and the inappropriate use of race in nephrology. Nat Rev Nephrol 2022;18:84–94.
Delanaye P, Pottel H, Glassock RJ. Americentrism in estimation of GFR equations. Kidney Int 2022;101:856–8.
Delanaye P, Mariat C, Cavalier E, Glassock RJ, Gemenne F, Pottel H. The « race» correction in estimating glomerular filtration rate. Curr Opin Nephrol Hypertens 2021;30:525–30.
Flamant M, Vidal-Petiot E, Metzger M, Haymann JP, Letavernier E, Delatour V, et al. Performance of GFR estimating equations in African Europeans: basis for a lower race-ethnicity factor than in African Americans. Am J Kidney Dis 2013;62:182–4.
Delanaye P, Vidal-Petiot E, Björk J, Ebert N, Eriksen B, Dubourg L, et al. Performance of creatinine-based equations to estimate glomerular filtration rate in White and Black populations in Europe, Brazil, and Africa. Nephrol Dial Transpl 2023;38:106–18.
Bukabau JB, Yayo E, Gnionsahé A, Monnet D, Pottel H, Cavalier E, et al. Performance of creatinine- or cystatin C–based equations to estimate glomerular filtration rate in sub-Saharan African populations. Kidney Int 2019;95:1181–9.
Fabian J, Kalyesubula R, Mkandawire J, Hansen CH, Nitsch D, Musenge E, et al. Measurement of kidney function in Malawi, South Africa, and Uganda: a multicentre cohort study. Lancet Glob Health 2022;10:e1159–69.
Gansevoort RT, Anders H-J, Cozzolino M, Fliser D, Ortiz A, Soler MJ, et al. What should European nephrology do with the new CKD-EPI equation? Nephrol Dial Transpl 2022;38:1–6.
Delanaye P, Schaeffner E, Cozzolino M, Langlois M, Plebani M, Ozben T, et al. The new, race-free, chronic kidney disease epidemiology consortium (CKD-EPI) equation to estimate glomerular filtration rate: is it applicable in Europe? A position statement by the European Federation of clinical chemistry and laboratory medicine (EFLM). Clin Chem Lab Med 2023;61:44–7.
Fu EL, Levey AS, Coresh J, Grams ME, Faucon A-L, Elinder C-G, et al. Accuracy of GFR estimating equations based on creatinine, cystatin C or both in routine care. Nephrol Dial Transpl 2024;39: 694–706.
Delanaye P, Rule AD, Schaeffner ES, Cavalier E, Shi J, Hoofnagle AN, et al. Performance of the European kidney function consortium (EKFC) creatinine-based equation in American cohorts. Kidney Int 2024;105: 629–37.
Ma Y, Wei L, Yong Z, Yu Y, Chen Y, Zhu B, et al. Validation of the European Kidney Function Consortium (EKFC) equation in Chinese adult population: an equation standing on the shoulders of predecessors. Nephron 2023. In process. https://doi.org/10.1159/ 000531030.
Delanaye P, Cavalier E, Stehlé T, Pottel H. Glomerular filtration rate estimation in adults: myths and promises. Nephron 2024;148:408–14.
Delanaye P, Cavalier E, Pottel H, Stehlé T. New and old GFR equations: a European perspective. Clin Kidney J. 2023;16:1375–83.
Matsushita K, van der Velde M, Astor BC, Woodward M, Levey AS, de Jong PE, et al. Association of estimated glomerular filtration rate and albuminuria with all-cause and cardiovascular mortality in general population cohorts: a collaborative meta-analysis. Lancet 2010;375: 2073–81.
Miller WG, Bachmann LM, Fleming JK, Delanghe JR, Parsa A, Narva AS, et al. Recommendations for reporting low and high values for urine albumin and total protein. Clin Chem 2019;65:349–50.
Seegmiller JC, Bachmann LM. Urine albumin measurements in clinical diagnostics. Clin Chem 2024;70:382–91.
Miller WG, Bachmann LM, Budd J, Beasley-Green A, Phinney KW, Tan HT, et al. Extent of equivalence of results for urine albumin among 3 candidate mass spectrometry reference measurement procedures. Clin Chem 2024:hvae122.
Shaikh A, Seegmiller JC, Borland TM, Burns BE, Ladwig PM, Singh RJ, et al. Comparison between immunoturbidimetry, size-exclusion chromatography, and LC-MS to quantify urinary albumin. Clin Chem 2008;54:1504–10.
Mosca A, Paleari R, Ceriotti F, Lapolla A, Fedele D, Group LS. Biological variability of albumin excretion rate and albumin-to-creatinine ratio in hypertensive type 2 diabetic patients. Clin Chem Lab Med 2003;41: 1229–33.
Kouri TT, Hofmann W, Falbo R, Oyaert M, Schubert S, Gertsen JB, et al. The EFLM European urinalysis guideline 2023. Clin Chem Lab Med 2024;62:1653–786.
Naresh CN, Hayen A, Weening A, Craig JC, Chadban SJ. Day-to-day variability in spot urine albumin-creatinine ratio. Am J Kidney Dis 2013; 62:1095–101.
Norris KC, Smoyer KE, Rolland C, Van der Vaart J, Grubb EB. Albuminuria, serum creatinine, and estimated glomerular filtration rate as predictors of cardio-renal outcomes in patients with type 2 diabetes mellitus and kidney disease: a systematic literature review. BMC Nephrol 2018;19:36.
Berhane AM, Weil EJ, Knowler WC, Nelson RG, Hanson RL. Albuminuria and estimated glomerular filtration rate as predictors of diabetic end-stage renal disease and death. Clin J Am Soc Nephrol 2011;6:2444–51.
Lambers Heerspink HJ, Gansevoort RT. Albuminuria is an appropriate therapeutic target in patients with CKD: the pro view. Clin J Am Soc Nephrol 2015;10:1079–88.
Hingwala J, Wojciechowski P, Hiebert B, Bueti J, Rigatto C, Komenda P, et al. Risk-based triage for nephrology referrals using the kidney failure risk equation. Can J Kidney Health Dis 2017;4. https://doi.org/10.1177/ 2054358117722782.
Che M, Iliescu E, Thanabalasingam S, Day AG, White CA. Death and dialysis following discharge from chronic kidney disease clinic: a retrospective cohort study. Can J Kidney Health Dis. 2022;9. https://doi.org/10.1177/20543581221118434.
Bhachu HK, Cockwell P, Subramanian A, Adderley NJ, Gokhale K, Fenton A, et al. Impact of using risk-based stratification on referral of patients with chronic kidney disease from primary care to specialist care in the United Kingdom. Kidney Int Rep 2021;6:2189–99.
Smekal MD, Tam-Tham H, Finlay J, Donald M, Thomas C, Weaver RG, et al. Patient and provider experience and perspectives of a risk-based approach to multidisciplinary chronic kidney disease care: a mixed methods study. BMC Nephrol 2019;20:110.
Lundström UH, Ramspek CL, Dekker FW, van Diepen M, Carrero JJ, Hedin U, et al. Clinical impact of the kidney failure risk equation for vascular access planning. Nephrol Dial Transpl 2024. Online ahead of print. https://doi.org/10.1093/ndt/gfae064.
Atiquzzaman M, Zhu B, Romann A, Er L, Djurdjev O, Bevilacqua M, et al. Kidney Failure Risk Equation in vascular access planning: a population-based study supporting value in decision making. Clin Kidney J 2024;17: sfae008.
Schulz C, Messikh Z, Reboul P, Cariou S, Ahmadpoor P, Pambrun E, et al. Characteristics of outpatients referred for a first consultation with a nephrologist: impact of different guidelines. J Nephrol 2022;35: 1375–85.
Kuningas K, Stringer S, Cockwell P, Khawaja A, Inston N. Is there a role of the kidney failure risk equation in optimizing timing of vascular access creation in pre-dialysis patients? J Vasc Access 2023;24: 1305–13.
Grams ME, Brunskill NJ, Ballew SH, Sang Y, Coresh J, Matsushita K, et al. Development and validation of prediction models of adverse kidney outcomes in the population with and without diabetes. Diabetes Care 2022;45:2055–63.
Ferguson T, Ravani P, Sood MM, Clarke A, Komenda P, Rigatto C, et al. Development and external validation of a machine learning model for progression of CKD. Kidney Int Rep 2022;7:1772–81.
Chan L, Nadkarni GN, Fleming F, McCullough JR, Connolly P, Mosoyan G, et al. Derivation and validation of a machine learning risk score using biomarker and electronic patient data to predict progression of diabetic kidney disease. Diabetologia 2021;64: 1504–15.
Tangri N, Ferguson TW, Bamforth RJ, Leon SJ, Arnott C, Mahaffey KW, et al. Machine learning for prediction of chronic kidney disease progression: validation of the Klinrisk model in the CANVAS Program and CREDENCE trial. Diabetes Obes Metab 2024. Online ahead of print. https://doi.org/10.1111/dom.15678.
Grams ME, Brunskill NJ, Ballew SH, Sang Y, Coresh J, Matsushita K, et al. The kidney failure risk equation: evaluation of novel input variables including eGFR estimated using the CKD-EPI 2021 equation in 59 cohorts. J Am Soc Nephrol 2023;34:482–94.
Tio MC, Butler J, Zhu X, Obi Y, Yen TE, Kalantar-Zadeh K, et al. Individualized risk of CKD progression among US adults. J Am Soc Nephrol 2024. Online ahead of print. https://doi.org/10.1681/asn. 0000000000000377.
Berthoux F, Mohey H, Laurent B, Mariat C, Afiani A, Thibaudin L. Predicting the risk for dialysis or death in IgA nephropathy. J Am Soc Nephrol 2011;22:752–61.
Barbour SJ, Coppo R, Zhang H, Liu Z-H, Suzuki Y, Matsuzaki K, et al. Evaluating a new international risk-prediction tool in IgA nephropathy. JAMA Intern Med 2019;179:942–52.
Cornec-Le Gall E, Audrézet M-P, Rousseau A, Hourmant M, Renaudineau E, Charasse C, et al. The PROPKD score: a new algorithm to predict renal survival in autosomal dominant polycystic kidney disease. J Am Soc Nephrol 2016;27:942–51.
Tangri N, Stevens LA, Griffith J, Tighiouart H, Djurdjev O, Naimark D, et al. A predictive model for progression of chronic kidney disease to kidney failure. JAMA 2011;305:1553–9.
Zacharias HU, Altenbuchinger M, Schultheiss UT, Raffler J, Kotsis F, Ghasemi S, et al. A predictive model for progression of CKD to kidney failure based on routine laboratory tests. Am J Kidney Dis 2022;79: 217–30.e1.
Schroeder EB, Yang X, Thorp ML, Arnold BM, Tabano DC, Petrik AF, et al. Predicting 5-year risk of RRT in stage 3 or 4 CKD: development and external validation. Clin J Am Soc Nephrol 2017;12:87–94.
Landray MJ, Thambyrajah J, McGlynn FJ, Jones HJ, Baigent C, Kendall MJ, et al. Epidemiological evaluation of known and suspected cardiovascular risk factors in chronic renal impairment. Am J Kidney Dis 2001;38:537–46.
Major RW, Shepherd D, Medcalf JF, Xu G, Gray LJ, Brunskill NJ. The Kidney Failure Risk Equation for prediction of end stage renal disease in UK primary care: an external validation and clinical impact projection cohort study. PLoS Med 2019;16:e1002955.
Ramspek CL, de Jong Y, Dekker FW, van Diepen M. Towards the best kidney failure prediction tool: a systematic review and selection aid. Nephrol Dial Transpl 2020;35:1527–38.
Tangri N, Kitsios GD, Inker LA, Griffith J, Naimark DM, Walker S, et al. Risk prediction models for patients with chronic kidney disease: a systematic review. Ann Intern Med 2013;158:596–603.
Oliva-Damaso N, Delanaye P, Oliva-Damaso E, Payan J, Glassock RJ. Risk-based versus GFR threshold criteria for nephrology referral in chronic kidney disease. Clin Kidney J 2022;15:1996–2005.