IntroductionMultiple sclerosis (MS) is an incurable autoimmune disease marked by heterogeneous progression and a lack of reliable biomarkers, complicating prognosis and individualized care. This study introduces a novel trajectory-based statistical approach designed to identify patterns in patient histories within MS populations.MethodsUsing longitudinal clinical data from a real-world cohort of 1,025 MS patients (median follow-up: 6.75 years), two complementary analyses were conducted based on patient trajectory analysis.
In the first analysis, the technique is applied to the complete dataset after removal of missing values (n = 985; 11,048 events) to uncover latent progressive trajectories. The second analysis evaluated the techniques’ performance on a smaller, limited-sample cohort (n = 83; 282 events).ResultsAcross both analyses, the approach revealed previously unrecognized progression patterns, giving rise to new hypotheses, including an effect of Alemtuzumab on the bowel/bladder function (p<0.01, RR = 2.83) and glatiramer acetate on the occurrence of relapses (p<0.01, RR = 1.49).