Development of a machine learning-based prediction model for extremely rapid decline in estimated glomerular filtration rate in patients with chronic kidney disease: a retrospective cohort study using a large data set from a hospital in Japan

OBJECTIVES: Trajectories of estimated glomerular filtration rate (eGFR) decline vary highly among patients with chronic kidney disease (CKD). It is clinically important to identify patients who have high risk for eGFR decline. We aimed to identify clusters of patients with extremely rapid eGFR decli...

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Detalles Bibliográficos
Autores principales: Inaguma, Daijo, Hayashi, Hiroki, Yanagiya, Ryosuke, Koseki, Akira, Iwamori, Toshiya, Kudo, Michiharu, Fukuma, Shingo, Yuzawa, Yukio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2022
Materias:
Renal Medicine
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9185577/
https://www.ncbi.nlm.nih.gov/pubmed/35680264
http://dx.doi.org/10.1136/bmjopen-2021-058833

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9185577/
https://www.ncbi.nlm.nih.gov/pubmed/35680264
http://dx.doi.org/10.1136/bmjopen-2021-058833

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