Journal Article

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

Courbebaisse, Marie, Ebert, Natalie, Åkesson, Anna, D’hondt, Robbe, Derain-Dubourg, Laurence, Bökenkamp, Arend, Mariat, Christophe, Larsson, Anders, Littmann, Karin, Rostaing, Lionel, Schaeffner, Elke, Gaillard, Francois, Rule, Andrew D., Dedja, Klest, Dalton, R. Neil, Björk, Jonas, Jacquemont, Lola, Hansson, Magnus, Sundin, Per-Ola, de Boer, Jasper, Couzi, Lionel, Eriksen, Björn O., Melsom, Toralf, Nakano, Felipe Kenji, Grubb, Anders, Garrouste, Cyril, Nyman, Ulf, Lanot, Antoine, Delanaye, Pierre, Berg, Ulla, Legendre, Christophe, Pottel, Hans, Kamar, Nassim, Åsling-Monemi, Kajsa, Haredasht, Fateme Nateghi, Vens, Celine, Selistre, Luciano

2024

Overview

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.

• Publisher: Nature Publishing Group UK,Nature Publishing Group,Springer Science and Business Media LLC,Nature Portfolio
• Subject: 639/705/1046 / 639/705/117 / 639/705/794 / 692/4022/1585/104 / Adult / Aged / Biomarkers / Clinical Laboratory Medicine / Clinical Medicine / Creatinine / Creatinine - blood / Cystatin C / Cystatin C - blood / Datasets / Female / Glomerular Filtration Rate / Hemodialysis / Hospitals / Human health sciences / Humanities and Social Sciences / Humans / Hypertension / Kidney diseases / Kidney Function Tests / Kidney Function Tests - methods / Kidneys / Klinisk laboratoriemedicin / Klinisk medicin / Learning algorithms / Life Sciences / Machine Learning / Male / Medical and Health Sciences / Medicin och hälsovetenskap / Medicine / Middle Aged / multidisciplinary / Nephrology / Public health / Science / Science (multidisciplinary) / Sciences de la santé humaine / Urologie & néphrologie / Urology & nephrology