Latent variable modeling improves AKI risk factor identification and AKI prediction compared to traditional methods

BACKGROUND: Acute kidney injury (AKI) is diagnosed based on postoperative serum creatinine change, but AKI models have not consistently performed well, in part due to the omission of clinically important but practically unmeasurable variables that affect creatinine. We hypothesized that a latent var...

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Detalles Bibliográficos
Autores principales: Smith, Loren E., Smith, Derek K., Blume, Jeffrey D., Siew, Edward D., Billings, Frederic T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5299779/
https://www.ncbi.nlm.nih.gov/pubmed/28178929
http://dx.doi.org/10.1186/s12882-017-0465-1

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5299779/
https://www.ncbi.nlm.nih.gov/pubmed/28178929
http://dx.doi.org/10.1186/s12882-017-0465-1

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