A data-driven model of disability progression in progressive multiple sclerosis.

This study applies the Gaussian process progression model, a Bayesian data-driven disease progression model, to analyse the evolution of primary progressive multiple sclerosis. Utilizing data from 1521 primary progressive multiple sclerosis participants collected within the International Progressive Multiple Sclerosis Alliance Project, the analysis includes 18 581 longitudinal time-points (average follow-up time: 28.2 months) of disability assessments including the expanded disability status scale, symbol digit modalities, timed 25-foot-walk, 9-hole-peg test and of MRI metrics such as T1 and T2 lesion volume and normalized brain volume. From these data, Gaussian process progression model infers a data-driven description of the progression common to all individuals, alongside scores measuring the individual progression rates relative to the population, spanning ∼50 years of disease duration. Along this timeline, Gaussian process progression model identifies an initial steep worsening of the expanded disability status scale that stabilizes after ∼30 years of disease duration, suggesting its diminished utility in monitoring disease progression beyond this time. Conversely, it underscores the slower evolution of normalized brain volume across the disease duration. The individual progression rates estimated by Gaussian process progression model can be used to identify three distinct sub-groups within the primary progressive multiple sclerosis population: a normative group (76% of the population) and two 'outlier' sub-groups displaying either accelerated (13% of the population) or decelerated (11%) progression compared to the normative one. Notably, fast progressors exhibit older age at symptom onset (38.5 versus 35.0,