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