Machine learning for the prediction of volume responsiveness in patients with oliguric acute kidney injury in critical care

BACKGROUND AND OBJECTIVES: Excess fluid balance in acute kidney injury (AKI) may be harmful, and conversely, some patients may respond to fluid challenges. This study aimed to develop a prediction model that can be used to differentiate between volume-responsive (VR) and volume-unresponsive (VU) AKI...

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Detalles Bibliográficos
Autores principales: Zhang, Zhongheng, Ho, Kwok M., Hong, Yucai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6454725/
https://www.ncbi.nlm.nih.gov/pubmed/30961662
http://dx.doi.org/10.1186/s13054-019-2411-z

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6454725/
https://www.ncbi.nlm.nih.gov/pubmed/30961662
http://dx.doi.org/10.1186/s13054-019-2411-z

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