Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

Comparison between the EKFC-equation and machine learning models to predict Glomerular Filtration Rate

Nakano, Felipe Kenji ; Åkesson, Anna LU orcid ; de Boer, Jasper ; Dedja, Klest ; D’hondt, Robbe ; Haredasht, Fateme Nateghi ; Björk, Jonas LU orcid ; Courbebaisse, Marie ; Couzi, Lionel and Ebert, Natalie , et al. (2024) In Scientific Reports 14(1).
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... (More)

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.

(Less)
Please use this url to cite or link to this publication:
author
; ; ; ; ; ; ; ; and , et al. (More)
; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; and (Less)
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Scientific Reports
volume
14
issue
1
article number
26383
publisher
Nature Publishing Group
external identifiers
  • pmid:39487227
  • scopus:85208291511
ISSN
2045-2322
DOI
10.1038/s41598-024-77618-w
language
English
LU publication?
yes
id
4e454f59-a25f-42af-9839-71cd2c6de280
date added to LUP
2025-01-08 13:50:23
date last changed
2025-07-10 05:23:12
@article{4e454f59-a25f-42af-9839-71cd2c6de280,
  abstract     = {{<p>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.</p>}},
  author       = {{Nakano, Felipe Kenji and Åkesson, Anna and de Boer, Jasper and Dedja, Klest and D’hondt, Robbe and Haredasht, Fateme Nateghi and Björk, Jonas and Courbebaisse, Marie and Couzi, Lionel and Ebert, Natalie and Eriksen, Björn O. and Dalton, R. Neil and Derain-Dubourg, Laurence and Gaillard, Francois and Garrouste, Cyril and Grubb, Anders and Jacquemont, Lola and Hansson, Magnus and Kamar, Nassim and Legendre, Christophe and Littmann, Karin and Mariat, Christophe and Melsom, Toralf and Rostaing, Lionel and Rule, Andrew D. and Schaeffner, Elke and Sundin, Per Ola and Bökenkamp, Arend and Berg, Ulla and Åsling-Monemi, Kajsa and Selistre, Luciano and Larsson, Anders and Nyman, Ulf and Lanot, Antoine and Pottel, Hans and Delanaye, Pierre and Vens, Celine}},
  issn         = {{2045-2322}},
  language     = {{eng}},
  number       = {{1}},
  publisher    = {{Nature Publishing Group}},
  series       = {{Scientific Reports}},
  title        = {{Comparison between the EKFC-equation and machine learning models to predict Glomerular Filtration Rate}},
  url          = {{http://dx.doi.org/10.1038/s41598-024-77618-w}},
  doi          = {{10.1038/s41598-024-77618-w}},
  volume       = {{14}},
  year         = {{2024}},
}