Latent cluster analysis of ALS phenotypes identifies prognostically differing groups.

ABSTRACT:

Background

Amyotrophic lateral sclerosis (ALS) is a degenerative disease predominantly affecting motor neurons and manifesting as several different phenotypes. Whether these phenotypes correspond to different underlying disease processes is unknown. We used latent cluster analysis to identify groupings of clinical variables in an objective and unbiased way to improve phenotyping for clinical and research purposes.

Methods

Latent class cluster analysis was applied to a large database consisting of 1467 records of people with ALS, using discrete variables which can be readily determined at the first clinic appointment. The model was tested for clinical relevance by survival analysis of the phenotypic groupings using the Kaplan-Meier method.

Results

The best model genera

SUBMITTER: Ganesalingam J 

PROVIDER: S-EPMC2741575 | biostudies-literature | 2009 Sep

REPOSITORIES: biostudies-literature