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Predicting disease progression in multiple sclerosis with clinically accessible information and technology
Journal article   Open access   Peer reviewed

Predicting disease progression in multiple sclerosis with clinically accessible information and technology

Tom A N Fuchs, Menno M Schoonheim, Eva M M Strijbis, Julia R Jelgerhuis, Dana Horakova, Eva K Havrdova, Tomas Uher, Robert Zivadinov, Serkan Ozakbas, Marc Girard, …
Journal of neurology, Vol.273(5), 281
2026
DOI: https://doi.org/10.1007/s00415-026-13802-4
PMID: 42002655
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Published (Version of Record) Open Access CC BY V4.0

Abstract

Multiple sclerosis Decision support tools Prediction Clinical Disease progression Secondary progressive multiple sclerosis
Background Predicting disease progression at the individual level is essential for personalized medicine. We previously developed machine-learning tools to estimate 5-year progression risk in people with multiple sclerosis (PwMS). Such models should account for disease-modifying therapy (DMT) and objective outcome definitions. Methods In a retrospective multicenter case–control study, we evaluated adults with relapsing–remitting multiple sclerosis (RRMS) at baseline. Using machine-learning, we developed two complementary tools for individualized 5-year risk estimation: DAAE-M, optimized for transparency, software-neutral use, and mitigation of indication bias, and ELIE, optimized for dynamic landmark-based modeling, complex treatment histories, and mitigation of immortal-time bias. Disease progression was defined using both a clinical outcome (RRMS-to-progressive MS) and an objective outcome (late-stage confirmed progression independent of relapse activity). Results Among 34,510 people with RRMS (72.6% female, mean age = 37.1, mean disease duration = 5.8), 9.8% and 21% met clinical and objective progression criteria, respectively, over five years. Both models demonstrated good calibration across risk-groups (Brier scores 0.06–0.16). DAAE-M provided patient-level risk estimates with monotonic risk escalation across risk-groups for clinical (3.1%/11.2%/22.6%/33.0%) and objective (8.4%/14.5%/23.3%/38.8%) progression. For DAAE-M, high-efficacy DMT was associated with approximately half the progression risk compared with low-efficacy DMT (risk-ratios: 0.42–0.59; p < 0.01). ELIE also showed good calibration across risk deciles with increasing incidence for both clinical (0.3%/1.2%/1.7%/2.5%/3.7%/5.5%/7.2%/10.2%/14.3%/21.5%) and objective (0.9%/1.6%/2.5%/4.0%/5.8%/7.8%/10.2%/15.3%/20.9%/32.5%) outcomes. Conclusion We developed two well-calibrated machine-learning-based tools for individualized 5-year prediction of clinically- and objectively-defined MS progression, each with distinct strengths in usability, bias handling, and treatment modeling. These findings support future tool use in personalized risk stratification and secondary prevention.

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UN Sustainable Development Goals (SDGs)

This output has contributed to the advancement of the following goals:

#3 Good Health and Well-Being

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