Classification of neurological diseases using multi-dimensional CSF analysis

BRAIN(2021)

Cited 16|Views5
No score
Abstract
By investigating patients with neuroinflammatory, -degenerative, and -vascular disorders, Gross et al. identify cellular parameters in blood and CSF that can improve the differential diagnosis of neurological diseases, and that provide insights into shared and distinct pathophysiological processes. Although CSF analysis routinely enables the diagnosis of neurological diseases, it is mainly used for the gross distinction between infectious, autoimmune inflammatory, and degenerative disorders of the CNS. To investigate, whether a multi-dimensional cellular blood and CSF characterization can support the diagnosis of clinically similar neurological diseases, we analysed 546 patients with autoimmune neuroinflammatory, degenerative, or vascular conditions in a cross-sectional retrospective study. By combining feature selection with dimensionality reduction and machine learning approaches we identified pan-disease parameters that were altered across all autoimmune neuroinflammatory CNS diseases and differentiated them from other neurological conditions and inter-autoimmunity classifiers that subdifferentiate variants of CNS-directed autoimmunity. Pan-disease as well as diseases-specific changes formed a continuum, reflecting clinical disease evolution. A validation cohort of 231 independent patients confirmed that combining multiple parameters into composite scores can assist the classification of neurological patients. Overall, we showed that the integrated analysis of blood and CSF parameters improves the differential diagnosis of neurological diseases, thereby facilitating early treatment decisions.
More
Translated text
Key words
CNS autoimmunity, multiple sclerosis, differential diagnosis, CSF, immune profile
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined