Enhancing individual glomerular filtration rate assessment: can we trust the equation? Development and validation of machine learning models to assess the trustworthiness of estimated GFR compared to measured GFR

Abstract Background Creatinine-based estimated glomerular filtration rate (eGFR) equations are widely used in clinical practice but exhibit inherent limitations. On the other side, measuring GFR is time consuming and not available in routine clinical practice. We developed and validated machine lear...

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Главные авторы:	Antoine Lanot, Anna Akesson, Felipe Kenji Nakano, Celine Vens, Jonas Björk, Ulf Nyman, Anders Grubb, Per-Ola Sundin, Björn O. Eriksen, Toralf Melsom, Andrew D. Rule, Ulla Berg, Karin Littmann, Kajsa Åsling-Monemi, Magnus Hansson, Anders Larsson, Marie Courbebaisse, Laurence Dubourg, Lionel Couzi, Francois Gaillard, Cyril Garrouste, Lola Jacquemont, Nassim Kamar, Christophe Legendre, Lionel Rostaing, Natalie Ebert, Elke Schaeffner, Arend Bökenkamp, Christophe Mariat, Hans Pottel, Pierre Delanaye
Формат:	Artigo
Язык:	Inglês
Опубликовано:	BMC 2025-01-01
Серии:	BMC Nephrology
Предметы:	Glomerular filtration rate Chronic kidney disease Creatinine Machine learning Random forest
Online-ссылка:	https://doi.org/10.1186/s12882-025-03972-0
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