Features of asthma which provide meaningful insights for understanding the disease heterogeneity.

ABSTRACT:

Background

Data-driven methods such as hierarchical clustering (HC) and principal component analysis (PCA) have been used to identify asthma subtypes, with inconsistent results.

Objective

To develop a framework for the discovery of stable and clinically meaningful asthma subtypes.

Methods

We performed HC in a rich data set from 613 asthmatic children, using 45 clinical variables (Model 1), and after PCA dimensionality reduction (Model 2). Clinical experts then identified a set of asthma features/domains which informed clusters in the two analyses. In Model 3, we reclustered the data using these features to ascertain whether this improved the discovery process.

Results

Cluster stability was poor in Models 1 and 2. Clinical experts highlighted four asthma feature

SUBMITTER: Deliu M 

PROVIDER: S-EPMC5763358 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature