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Publication Information

PubMed ID
Public Release Type
Journal
Publication Year
2024
Affiliation
1Department of Nephrology-Dialysis-Transplantation, University of Liège (ULg CHU), CHU Sart Tilman, Liège, Belgium. 2Department of Nephrology-Dialysis-Apheresis, Hopital Universitaire Caremeau, Nimes, France 3Néphrologie, Dialyse, Hypertension et Exploration Fonctionnelle Rénale, Hôpital Edouard Herriot, Hospices Civils de Lyon, France. 4Division of Occupational and Environmental Medicine, Lund University, Lund, Sweden 5Clinical Studies Sweden, Forum South, Skåne University Hospital, Lund, Sweden 6Physiology Department, Georges Pompidou European Hospital, Assistance Publique Hôpitaux de Paris, Paris Cité University, INSERM U1151-CNRS UMR8253, Paris, France 7CHU de Bordeaux, Nephrologie - Transplantation - Dialyse, Université de Bordeaux, CNRS-UMR 5164 Immuno ConcEpT, France. 8 AURAL, Association pour l’utilisation du rein artificiel dans la région lyonnaise, Lyon, France. 9Department of Nephrology, Clermont-Ferrand University Hospital, Clermont-Ferrand, France. 10Department of Clinical Chemistry, Skåne University Hospital, Lund, Lund University, Sweden. 11Renal Transplantation Department, CHU Nantes, Nantes University, Nantes, France. 12Function area Clinical Chemistry, Karolinska University Laboratory, Karolinska University Hospital Huddinge and Department of Laboratory Medicine, Karolinska Institute, Stockholm, Sweden. 13Department of Nephrology, Dialysis and Organ Transplantation, CHU Rangueil, INSERM U1043, IFR –BMT, University Paul Sabatier, Toulouse, France. 14Hôpital Necker, AP-HP & Université Paris Descartes, Paris, France. 15Division of Clinical Chemistry, Department of Laboratory Medicine, Karolinska Institute, Huddinge, Sweden. 16Service de Néphrologie, Dialyse et Transplantation Rénale, Hôpital Nord, CHU de Saint-Etienne, France. 17Service de Néphrologie, Hémodialyse, Aphérèses et Transplantation Rénale, Hôpital Michallon, CHU Grenoble-Alpes, France. 18Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, USA. 19Karla Healthcare Cantre, Faculty of Medicine and Health, Örebro University, Örebro, Sweden. 20Department of Paediatric Nephrology, Emma Children’s Hospital, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands 21Department of Clinical Science, Intervention and Technology, Division of Pediatrics, Karolinska Institutet, Karolinska University Hospital Huddinge, Stockholm, Sweden 22Clinical Studies Sweden, Forum South, Skåne University Hospital, Lund, Sweden 23Department of Medical Sciences, Clinical Chemistry, Uppsala University, Uppsala, Sweden 24Department of Translational Medicine, Division of Medical Radiology, Lund University, Malmö, Sweden 25Department of Public Health and Primary Care, KU Leuven Campus Kulak Kortrijk, Kortrijk, Belgium.
Authors
Åkesson A, Åsling-Monemi K, Berg U, Björk J, Bökenkamp A, Courbebaisse M, Couzi L, Delanaye P, Derain-Dubourg L, Gaillard F, Garrouste C, Grubb A, Hansson M, Jacquemont L, Kamar N, Larsson A, Legendre C, Littmann K, Mariat C, Nyman U, Pottel H, Rostaing L, Rule AD, Sundin P

Abstract

Background. Creatinine-based equations are the most used to estimate glomerular filtration rate( eGFR) . The Chronic Kidney Disease Epidemiology Collaboration( CKD-EPI) , the re-expressed Lund-MalmöRevised( r-LMR) and the European Kidney Function Consortium( EKFC) equations are the most validated. The EKFC and r-LMR equations have been suggested to have better performances in young adults, but this is debated. Methods. We collected data( GFR) measured by clearance of an exogenous marker( reference method) , serum creatinine, age and sex from 2366 young adults( aged between 18 and 25 years) both from Europe and the USA. Results. In the European cohorts( n = 1892) , the bias( in mL/min/1.73 m²) was systematically better for the EKFC and r-LMR equations compared with the CKD-EPI equation [2.28, 95% confidence interval( 1.59; 2.91) , –2.50( –3.85; –1.76) , 17.41 ( 16.49; 18.47) , respectively]. The percentage of estimated GFR within 30% of measured GFR( P30) was also better for EKFC and r-LMR equations compared with the CKD-EPI equation [84.4%( 82.8; 86.0) , 87.2%( 85.7; 88.7) and 65.4%( 63.3; 67.6) , respectively]. In the US cohorts( n = 474) , the bias for the EKFC and r-LMR equations was better than for the CKD-EPI equation in the non-Black population [0.97( –1.69; 3.06) , –2.62( –5.14; –1.43) and 7.74( 5.97; 9.63) , respectively], whereas the bias was similar in Black US individuals. P30 results were not different between the three equations in US cohorts. Analyses in sub-populations confirmed these results, except in individuals with high GFR levels( GFR ≥120 mL/min/1.73 m²) for whom the CKD-EPI equation might have a lower bias. Conclusions. We demonstrated that both the EKFC and r-LMR creatinine-based equations have a better performance than the CKD-EPI equation in a young population. The only exception might be in patients with hyperfiltration.