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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Ngā kaituhi matua:	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
Hōputu:	Artigo
Reo:	Inglês
I whakaputaina:	BMC 2025-01-01
Rangatū:	BMC Nephrology
Ngā marau:	Glomerular filtration rate Chronic kidney disease Creatinine Machine learning Random forest
Urunga tuihono:	https://doi.org/10.1186/s12882-025-03972-0
Ngā Tūtohu:	Tāpirihia he Tūtohu Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!