Tom 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 José Sá, 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 Simó, Vahid Shaygannejad, Yolanda Blanco, Eduardo Aguera-Morales, Justin Garber, Claudio Solaro, Neil Shuey, Dheeraj Khurana, D. Decoo, Abdorreza Naser Moghadasi, Katherine Buzzard, Olga Skibina, Nevin John, Thor Petersen, Bianca Weinstock-Guttman
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.