Scientific Reports (Nov 2024)
Comparison between the EKFC-equation and machine learning models to predict Glomerular Filtration Rate
- Felipe Kenji Nakano,
- Anna Åkesson,
- Jasper de Boer,
- Klest Dedja,
- Robbe D’hondt,
- Fateme Nateghi Haredasht,
- Jonas Björk,
- Marie Courbebaisse,
- Lionel Couzi,
- Natalie Ebert,
- Björn O. Eriksen,
- R. Neil Dalton,
- Laurence Derain-Dubourg,
- Francois Gaillard,
- Cyril Garrouste,
- Anders Grubb,
- Lola Jacquemont,
- Magnus Hansson,
- Nassim Kamar,
- Christophe Legendre,
- Karin Littmann,
- Christophe Mariat,
- Toralf Melsom,
- Lionel Rostaing,
- Andrew D. Rule,
- Elke Schaeffner,
- Per-Ola Sundin,
- Arend Bökenkamp,
- Ulla Berg,
- Kajsa Åsling-Monemi,
- Luciano Selistre,
- Anders Larsson,
- Ulf Nyman,
- Antoine Lanot,
- Hans Pottel,
- Pierre Delanaye,
- Celine Vens
Affiliations
- Felipe Kenji Nakano
- Department of Public Health and Primary Care, KU Leuven Campus Kulak Kortrijk
- Anna Åkesson
- Division of Occupational and Environmental Medicine, Lund University
- Jasper de Boer
- Department of Public Health and Primary Care, KU Leuven Campus Kulak Kortrijk
- Klest Dedja
- Department of Public Health and Primary Care, KU Leuven Campus Kulak Kortrijk
- Robbe D’hondt
- Department of Public Health and Primary Care, KU Leuven Campus Kulak Kortrijk
- Fateme Nateghi Haredasht
- Department of Public Health and Primary Care, KU Leuven Campus Kulak Kortrijk
- Jonas Björk
- Division of Occupational and Environmental Medicine, Lund University
- Marie Courbebaisse
- Physiology Department, Georges Pompidou European Hospital, Assistance Publique Hôpitaux de Paris, INSERM U1151-CNRS UMR8253, Paris Descartes University
- Lionel Couzi
- CNRS-UMR 5164 Immuno ConcEpT, CHU de Bordeaux, Nephrologie-Transplantation-Dialyse, Université de Bordeaux
- Natalie Ebert
- Institute of Public Health, Charité Universitätsmedizin Berlin
- Björn O. Eriksen
- Metabolic and Renal Research Group, UiT the Arctic University of Norway
- R. Neil Dalton
- The Wellchild Laboratory, Evelina London Children’s Hospital
- Laurence Derain-Dubourg
- Néphrologie, Dialyse, Hypertension et Exploration Fonctionnelle Rénale, Hôpital Edouard Herriot
- Francois Gaillard
- Renal Transplantation Department, Assistance Publique–Hôpitaux de Paris (AP-HP)
- Cyril Garrouste
- Department of Nephrology, Clermont-Ferrand University Hospital
- Anders Grubb
- Department of Clinical Chemistry, Skåne University Hospital, Lund University
- Lola Jacquemont
- Renal Transplantation Department, CHU Nantes, Nantes University
- Magnus Hansson
- Function Area Clinical Chemistry, Karolinska University Laboratory, Karolinska Institute, Karolinska University Hospital Huddinge and Department of Laboratory Medicine
- Nassim Kamar
- Department of Nephrology, Dialysis and Organ Transplantation, CHU Rangueil, INSERM U1043, IFR–BMT, University Paul Sabatier
- Christophe Legendre
- Hôpital Necker, AP-HP and Université Paris Descartes
- Karin Littmann
- Institute om Medicine Huddinge (Med H), Karolinska Institute
- Christophe Mariat
- Service de Néphrologie, Dialyse et Transplantation Rénale, Hôpital Nord, CHU de Saint-Etienne
- Toralf Melsom
- Metabolic and Renal Research Group, UiT the Arctic University of Norway
- Lionel Rostaing
- Service de Néphrologie, Hémodialyse, Aphérèses et Transplantation Rénale, Hôpital Michallon, CHU Grenoble-Alpes
- Andrew D. Rule
- Division of Nephrology and Hypertension, Mayo Clinic
- Elke Schaeffner
- Institute of Public Health, Charité Universitätsmedizin Berlin
- Per-Ola Sundin
- Karla Healthcare Centre, Faculty of Medicine and Health, Örebro University
- Arend Bökenkamp
- Department of Paediatric Nephrology, Emma Children’s Hospital, Amsterdam UMC, Vrije Universiteit Amsterdam
- Ulla Berg
- Department of Clinical Science, Intervention and Technology, Division of Pediatrics, Karolinska Institutet, Karolinska University Hospital Huddinge
- Kajsa Åsling-Monemi
- Department of Clinical Science, Intervention and Technology, Division of Pediatrics, Karolinska Institutet, Karolinska University Hospital Huddinge
- Luciano Selistre
- Mestrado Em Ciências da Saúde-Universidade Caxias do Sul Foundation CAPES
- Anders Larsson
- Department of Medical Sciences, Clinical Chemistry, Uppsala University
- Ulf Nyman
- Department of Translational Medicine, Division of Medical Radiology, Lund University
- Antoine Lanot
- Normandie Université, Unicaen, CHU de Caen Normandie, Néphrologie
- Hans Pottel
- Department of Public Health and Primary Care, KU Leuven Campus Kulak Kortrijk
- Pierre Delanaye
- Department of Nephrology-Dialysis-Transplantation, University of Liège (ULg CHU), CHU Sart Tilman
- Celine Vens
- Department of Public Health and Primary Care, KU Leuven Campus Kulak Kortrijk
- DOI
- https://doi.org/10.1038/s41598-024-77618-w
- Journal volume & issue
-
Vol. 14,
no. 1
pp. 1 – 9
Abstract
Abstract In clinical practice, the glomerular filtration rate (GFR), a measurement of kidney functioning, is normally calculated using equations, such as the European Kidney Function Consortium (EKFC) equation. Despite being the most general equation, EKFC, just like previously proposed approaches, can still struggle to achieve satisfactory performance, limiting its clinical applicability. As a possible solution, recently machine learning (ML) has been investigated to improve GFR prediction, nonetheless the literature still lacks a general and multi-center study. Using a dataset with 19,629 patients from 13 cohorts, we investigate if ML can improve GFR prediction in comparison to EKFC. More specifically, we compare diverse ML methods, which were allowed to use age, sex, serum creatinine, cystatin C, height, weight and BMI as features, in internal and external cohorts against EKFC. The results show that the most performing ML method, random forest (RF), and EKFC are very competitive where RF and EKFC achieved respectively P10 and P30 values of 0.45 (95% CI 0.44;0.46) and 0.89 (95% CI 0.88;0.90), whereas EKFC yielded 0.44 (95% CI 0.43; 0.44) and 0.89 (95% CI 0.88; 0.90), considering the entire cohort. Small differences were, however, observed in patients younger than 12 years where RF slightly outperformed EKFC.