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

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.