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

Clinical Snapshot

Key Findings

• P Value: p < 0.01 for treatment effect

• Effect Size: High-efficacy DMT associated with approximately half the progression risk

• Primary Outcome: 5-year disease progression (clinical: 9.8%, objective: 21%)

• Nnt Or Sensitivity: DAAE-M risk stratification: 3.1%/11.2%/22.6%/33.0% for clinical progression across risk groups

• Confidence Interval: Risk ratios: 0.42-0.59 for high vs low-efficacy DMT

Clinical Application

High feasibility using clinically accessible information, though requires implementation of machine learning tools in clinical practice Highly relevant for Australian MS care, particularly for PBS authority applications for high-cost DMTs and specialist referral decisions. Aligns with RACGP chronic disease management principles and could inform TGA post-market surveillance Adults with relapsing-remitting multiple sclerosis requiring risk stratification for treatment decisions

Abstract

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.

References

1. 1.Fuchs, T. A., Schoonheim, M. M., Strijbis, E. M., Jelgerhuis, J. R., Horakova, D., Havrdova, E. K., Uher, T., Zivadinov, R., Ozakbas, S., Girard, M., Alroughani, R., Grammond, P., Lugaresi, A., Tomassini, V., Kalincik, T., Roos, I., Gerlach, O., van der Walt, A., Khoury, S. J., van Pesch, V., Surcinelli, A., Foschi, M., Sa, M. J., D'amico, E., Kuhle, J., Cartechini, E., Maimone, D., Karabudak, R., Soysal, A., Spitaleri, D., Laureys, G., Taylor, B., D'hooghe, M., Ampapa, R., Castillo-Triviño, T., Altintas, A., Gray, O., Gouider, R., Meca-Lallana, J. E., Kermode, A. G., Fabis-Pedrini, M., Carroll, W. M., de Gans, K., Sanchez-Menoyo, J. L., Etemadifar, M., Al-Asmi, A., McCombe, P., Simu, M., Yetkin, M. F., Al-Harbi, T., Csepany, T., Lalive, P., Hardy, T. A., Ramanathan, S., Willekens, B., Sempere, A. P., Cárdenas-Robledo, S., Habek, M., Singhal, B., Grigoriadis, N., Simo, M., Shaygannejad, V., Blanco, Y., Aguera-Morales, E., Garber, J., Solaro, C., Shuey, N., Khurana, D., Decoo, D., Moghadasi, A. N., Buzzard, K., Skibina, O., John, N., Petersen, T., & Weinstock-Guttman, B. (2026). Predicting disease progression in multiple sclerosis with clinically accessible information and technology. Journal of Neurology. https://doi.org/10.1002/ana.26913