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Multiple sclerosis clinical decision support system based on projection to reference datasets

Authors :
Chadia Ed‐driouch
Florent Chéneau
Françoise Simon
Guillaume Pasquier
Benoit Combès
Anne Kerbrat
Emmanuelle Le Page
Sophie Limou
Nicolas Vince
David‐Axel Laplaud
Franck Mars
Cédric Dumas
Gilles Edan
Pierre‐Antoine Gourraud
Source :
Annals of Clinical and Translational Neurology, Vol 9, Iss 12, Pp 1863-1873 (2022)
Publication Year :
2022
Publisher :
Wiley, 2022.

Abstract

Abstract Objective Multiple sclerosis (MS) is a multifactorial disease with increasingly complicated management. Our objective is to use on‐demand computational power to address the challenges of dynamically managing MS. Methods A phase 3 clinical trial data (NCT00906399) were used to contextualize the medication efficacy of peg‐interferon beta‐1a vs placebo on patients with relapsing–remitting MS (RRMS). Using a set of reference patients (PORs), selected based on adequate features similar to those of an individual patient, we visualize disease activity by measuring the percentage of relapses, accumulation of new T2 lesions on MRI, and worsening EDSS during the clinical trial. Results We developed MS Vista, a functional prototype of clinical decision support system (CDSS), with a user‐centered design and distributed infrastructure. MS Vista shows the medication efficacy of peginterferon beta‐1a versus placebo for each individual patient with RRMS. In addition, MS Vista initiated the integration of a longitudinal magnetic resonance imaging (MRI) viewer and interactive dual physician‐patient data display to facilitate communication. Interpretation The pioneer use of PORs for each individual patient enables personalized analytics sustaining the dialog between neurologists, patients and caregivers with quantified evidence.

Details

Language :
English
ISSN :
23289503
Volume :
9
Issue :
12
Database :
Directory of Open Access Journals
Journal :
Annals of Clinical and Translational Neurology
Publication Type :
Academic Journal
Accession number :
edsdoj.3aa3aa89d1a642b69c94ddff9b8613b1
Document Type :
article
Full Text :
https://doi.org/10.1002/acn3.51649