Data-derived subtypes of delirium during critical illness.

ABSTRACT:

Background

To understand delirium heterogeneity, prior work relied on psychomotor symptoms or risk factors to identify subtypes. Data-driven approaches have used machine learning to identify biologically plausible, treatment-responsive subtypes of other acute illnesses but have not been used to examine delirium.

Methods

We conducted a secondary analysis of a large, multicenter prospective cohort study involving adults in medical or surgical ICUs with respiratory failure or shock who experienced delirium per the Confusion Assessment Method for the ICU. We used data collected before delirium diagnosis in an unsupervised latent class model to identify delirium subtypes and then compared demographics, clinical characteristics, and outcomes between subtypes in the final model.

SUBMITTER: Potter KM 

PROVIDER: S-EPMC10797145 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature