Multiple Sclerosis Severity Score (MSSS) improves the accuracy of individualized prediction in MS

T Kalincik; I Kister; TE Bacon; CB Malpas; S Sharmin; D Horakova; E Kubala-Havrdova; F Patti; G Izquierdo; S Eichau; S Ozakbas; M Onofrj; A Lugaresi; A Prat; M Girard; P Duquette; P Grammond; P Sola; D Ferraro; R Alroughani; M Terzi; C Boz; F Grand’Maison; R Bergamaschi; O Gerlach; MJ Sa; L Kappos; E Cartechini; J Lechner-Scott; V van Pesch; V Shaygannejad; F Granella; D Spitaleri; G Iuliano; D Maimone; J Prevost; A Soysal; R Turkoglu; R Ampapa; H Butzkueven; G Cutter

Journal article

Multiple Sclerosis Severity Score (MSSS) improves the accuracy of individualized prediction in MS

T Kalincik, I Kister, TE Bacon, CB Malpas, S Sharmin, D Horakova, E Kubala-Havrdova, F Patti, G Izquierdo, S Eichau, S Ozakbas, M Onofrj, A Lugaresi, A Prat, M Girard, P Duquette, P Grammond, P Sola, D Ferraro, R Alroughani Show all

Multiple Sclerosis Journal | SAGE PUBLICATIONS LTD | Published : 2022

DOI: 10.1177/13524585221084577

Abstract

Background: The MSBase prediction model of treatment response leverages multiple demographic and clinical characteristics to estimate hazards of relapses, confirmed disability accumulation (CDA), and confirmed disability improvement (CDI). The model did not include Multiple Sclerosis Severity Score (MSSS), a disease duration-adjusted ranked score of disability. Objective: To incorporate MSSS into the MSBase prediction model and compare model accuracy with and without MSSS. Methods: The associations between MSSS and relapse, CDA, and CDI were evaluated with marginal proportional hazards models adjusted for three principal components representative of patients’ demographic and clinical charact..

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University of Melbourne Researchers

Tomas Kalincik Author

Charles Malpas Author

Sifat Sharmin Author

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Grants

Awarded by Bristol-Myers Squibb

Funding Acknowledgements

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by an unrestricted investigator-initiated grant from Genentech. MSBase would like to acknowledge financial contributions to support the MSBase Registry from Biogen, Novartis, Merck, Roche, and Sanofi Genzyme. Authors acknowledge funding from NHMRC (1140766, 1129189) to develop the original prediction model.