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Background <p>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.</p> Methods <p>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).</p> Results <p>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; <i>p</i> &lt; 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.</p> Conclusion <p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月19日

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  • 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,
  • Raed Alroughani,
  • Pierre Grammond,
  • Alessandra Lugaresi,
  • Valentina Tomassini,
  • Tomas Kalincik,
  • Izanne Roos,
  • Oliver Gerlach,
  • Anneke van der Walt,
  • Samia J. Khoury,
  • Vincent van Pesch,
  • Andrea Surcinelli,
  • Matteo Foschi,
  • Maria Jose Sa,
  • Emanuelle D’amico,
  • Jens Kuhle,
  • Elisabetta Cartechini,
  • Davide Maimone,
  • Rana Karabudak,
  • Aysun Soysal,
  • Daniele Spitaleri,
  • Guy Laureys,
  • Bruce Taylor,
  • Marie D’hooghe,
  • Radek Ampapa,
  • Tamara Castillo-Triviño,
  • Ayse Altintas,
  • Orla Gray,
  • Riadh Gouider,
  • Jose E. Meca-Lallana,
  • Allan G. Kermode,
  • Marzena Fabis-Pedrini,
  • William M. Carroll,
  • Koen de Gans,
  • Jose Luis Sanchez-Menoyo,
  • Masoud Etemadifar,
  • Abdullah Al-Asmi,
  • Pamela McCombe,
  • Mihaela Simu,
  • Mehmet Fatih Yetkin,
  • Talal Al-Harbi,
  • Tunde Csepany,
  • Patrice Lalive,
  • Todd A. Hardy,
  • Sudarshini Ramanathan,
  • Barbara Willekens,
  • Angel Perez Sempere,
  • Simón Cárdenas-Robledo,
  • Mario Habek,
  • Bhim Singhal,
  • Nikolaos Grigoriadis,
  • Magdolna Simo,
  • Vahid Shaygannejad,
  • Yolanda Blanco,
  • Eduardo Aguera-Morales,
  • Justin Garber,
  • Claudio Solaro,
  • Neil Shuey,
  • Dheeraj Khurana,
  • Danny Decoo,
  • Abdorreza Naser Moghadasi,
  • Katherine Buzzard,
  • Olga Skibina,
  • Nevin John,
  • Thor Petersen,
  • Bianca Weinstock-Guttman

摘要

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.