Interpretable clinical phenotypes among patients hospitalized with COVID-19 using cluster analysis.

ABSTRACT:

Background

Multiple clinical phenotypes have been proposed for coronavirus disease (COVID-19), but few have used multimodal data. Using clinical and imaging data, we aimed to identify distinct clinical phenotypes in patients admitted with COVID-19 and to assess their clinical outcomes. Our secondary objective was to demonstrate the clinical applicability of this method by developing an interpretable model for phenotype assignment.

Methods

We analyzed data from 547 patients hospitalized with COVID-19 at a Canadian academic hospital. We processed the data by applying a factor analysis of mixed data (FAMD) and compared four clustering algorithms: k-means, partitioning around medoids (PAM), and divisive and agglomerative hierarchical clustering. We used imaging data and 34 clini

SUBMITTER: Yamga E 

PROVIDER: S-EPMC10128042 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature