Latent cluster analysis of ALS phenotypes identifies prognostically differing groups

Ganesalingam, Jeban, Stahl, Daniel, Wijesekera, Lokesh, Galtrey, Clare, Shaw, Christopher E, Leigh, Nigel and Al-Chalabi, Ammar (2009) Latent cluster analysis of ALS phenotypes identifies prognostically differing groups. PloS ONE, 4 (9). e7107. ISSN 1932-6203

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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 generated five distinct phenotypic classes that strongly predicted survival (p<0.0001). Eight variables were used for the latent class analysis, but a good estimate of the classification could be obtained using just two variables: site of first symptoms (bulbar or limb) and time from symptom onset to diagnosis (p<0.00001).

CONCLUSION

The five phenotypic classes identified using latent cluster analysis can predict prognosis. They could be used to stratify patients recruited into clinical trials and generating more homogeneous disease groups for genetic, proteomic and risk factor research.

Item Type: Article
Schools and Departments: Brighton and Sussex Medical School > Clinical Medicine
Subjects: R Medicine > RC Internal medicine > RC0321 Neurosciences. Biological psychiatry. Neuropsychiatry > RC0346 Neurology. Diseases of the nervous system Including speech disorders
Depositing User: Patricia Butler
Date Deposited: 09 Nov 2012 15:55
Last Modified: 07 Mar 2017 05:49
URI: http://sro.sussex.ac.uk/id/eprint/42229

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