External validation of a deep-learning model to predict severe acute kidney injury based on urine output changes in critically ill patients

OBJECTIVES: The purpose of this study was to externally validate algorithms (previously developed and trained in two United States populations) aimed at early detection of severe oliguric AKI (stage 2/3 KDIGO) in intensive care units patients. METHODS: The independent cohort was composed of 10'...

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Detalles Bibliográficos
Autores principales: Alfieri, Francesca, Ancona, Andrea, Tripepi, Giovanni, Randazzo, Vincenzo, Paviglianiti, Annunziata, Pasero, Eros, Vecchi, Luigi, Politi, Cristina, Cauda, Valentina, Fagugli, Riccardo Maria
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2022
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9585008/
https://www.ncbi.nlm.nih.gov/pubmed/35554875
http://dx.doi.org/10.1007/s40620-022-01335-8

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9585008/
https://www.ncbi.nlm.nih.gov/pubmed/35554875
http://dx.doi.org/10.1007/s40620-022-01335-8

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