Classification of neurological diseases using multi-dimensional cerebrospinal fluid analysis.

Although cerebrospinal fluid (CSF) analysis routinely enables diagnosis of neurological diseases, it is mainly used for gross distinction between infectious, autoimmune inflammatory, and degenerative central nervous system (CNS) disorders. To investigate, whether a multi-dimensional cellular blood and CSF characterization can support the diagnosis of clinically similar neurological diseases, we analyzed 546 patients with autoimmune neuro-inflammatory, 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 altered across all autoimmune neuro-inflammatory CNS-diseases and differentiating them from other neurological conditions and inter-autoimmunity classifiers sub-differentiating 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 classification of neurological patients. Overall, we show that an integrated analysis of blood and CSF parameters improves differential diagnosis of neurological diseases, thereby facilitating early treatment decisions.