Unsupervised clustering reveals phenotypes of AKI in ICU COVID-19 patients.

ABSTRACT:

Background

Acute Kidney Injury (AKI) is a very frequent condition, occurring in about one in three patients admitted to an intensive care unit (ICU). AKI is a syndrome defined as a sudden decrease in glomerular filtration rate. However, this unified definition does not reflect the various mechanisms involved in AKI pathophysiology, each with its own characteristics and sensitivity to therapy. In this study, we aimed at developing an innovative machine learning based method able to subphenotype AKI according to its pattern of risk factors.

Methods

We adopted a three-step pipeline of analyses. First, we looked for factors associated with AKI using a generalized additive model. Second, we calculated the importance of each identified AKI related factor in the estimated AKI risk

SUBMITTER: Legouis D 

PROVIDER: S-EPMC9579431 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature