Literature DB >> 31243138

Using biomarkers to predict clinical outcomes in multiple sclerosis.

Daniel Castle1,2, Ray Wynford-Thomas1,2, Sam Loveless1,2, Emily Bentley2, Owain W Howell3, Emma C Tallantyre4,2.   

Abstract

Long-term outcomes in multiple sclerosis (MS) are highly varied and treatment with disease-modifying therapies carries significant risks. Finding tissue biomarkers that can predict clinical outcomes would be valuable in individualising treatment decisions for people with MS. Several candidate biomarkers-reflecting inflammation, neurodegeneration and glial pathophysiology-show promise for predicting outcomes. However, many candidates still require validation in cohorts with long-term follow-up and evaluation for their independent contribution in predicting outcome when models are adjusted for known demographic, clinical and radiological predictors. Given the complexity of MS pathophysiology, heterogeneous panels comprising a combination of biomarkers that encompass the various aspects of neurodegenerative, glial and immune pathology seen in MS, may enhance future predictions of outcome. © Author(s) (or their employer(s)) 2019. No commercial re-use. See rights and permissions. Published by BMJ.

Entities:  

Keywords:  biomarkers; multiple sclerosis (ms); neurofilament; outcome; pathophysiology

Year:  2019        PMID: 31243138     DOI: 10.1136/practneurol-2018-002000

Source DB:  PubMed          Journal:  Pract Neurol        ISSN: 1474-7758


  1 in total

1.  Personalized prediction of rehabilitation outcomes in multiple sclerosis: a proof-of-concept using clinical data, digital health metrics, and machine learning.

Authors:  Christoph M Kanzler; Ilse Lamers; Peter Feys; Roger Gassert; Olivier Lambercy
Journal:  Med Biol Eng Comput       Date:  2021-11-25       Impact factor: 2.602

  1 in total

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