SÉMINAIRES DE L’UNF
|Présentateur:||Arman Eshaghi, MD., Ph. D.|
|Titre:||Subtyping and Staging Multiple Sclerosis for Precision Clinical Trials|
|Endroit:||CRIUGM Room E1910 (http://www.criugm.qc.ca/en/contact.html)|
|Date:||Vendredi 13 décembre, 11h00-12h00|
*La conférence sera présentée en anglais
Dr Eshaghi is currently a research associate at the Department of Neuroinflammation at University College London (UCL). His main research interests are the application of model-based machine learning, Bayesian, and causal inference methods to understand the underlying mechanism of progressive multiple sclerosis. He is currently working as part of the international network of Progressive MS Alliance led by Prof Douglas Arnold (Montreal Neurological Institute, Montreal, Canada), Prof Olga Ciccarelli, and Dr Declan Chard (UCL, UK). He is a member of the Progression of Neurodegenerative Disorders (POND) Team at the Centre for Medical Image Computing at the Department of Computer Science at UCL, working closely with Prof Danny Alexander. He obtained his medical doctorate degree (M.D) in 2013 from Tehran University of Medical Sciences in Iran and has been awarded a PhD in Neuroscience from University College London in the UK in 2018. He was awarded the Young Investigator of Year in 2016 by the European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS). He has been awarded the Jacqueline du Pré Grant in 2011, ECTRIMS-MAGNIMS Fellowship in 2015, and the Multiple Sclerosis International Federation’s Ian McDonald Fellowship in 2016.
There are 4 courses of multiple sclerosis (MS): clinically-isolated syndrome, relapsing-remitting MS, primary-progressive MS and secondary-progressive MS. We aimed to achieve a further sophistication in the definition of MS phenotypes by identifying patient subgroups who accumulate MRI abnormalities with similar patterns. In a retrospective study, we assessed whether a data-driven subtyping algorithm (called SuStaIn) predicted clinical outcome and response to experimental treatments. We included longitudinal data from 8,545 people with MS who had 31,451 visits from 14 double-blind randomised controlled trials and three observational cohorts. We also included cross-sectional data from 14,928 healthy volunteers. We identified three data-driven subtypes with a distinct neuroanatomical spread of abnormality. Data-driven subtyping and staging, but not clinical classifications or expanded disability status scale (EDSS) at baseline, was associated with time to EDSS progression ( Subtype=0.04 and stage=-0.06, p<0.01 for all). Interpretation: Data-driven MS subtypes and stages, when compared with clinical classification or baseline EDSS better predicts the subsequent clinical course and treatment response. Data-driven subtyping has the potential to prospectively enrich clinical trials with patients who are more likely to respond to treatments.