Patient-reported outcomes as predictors of disability evolution in Multiple Sclerosis: An interpretable machine learning approach.

BACKGROUND: Disability accrual in multiple sclerosis (MS) is highly variable and challenging to predict, complicating personalised care. Integrating machine learning (ML) with patient-reported outcomes (PROs) and clinician-assessed outcomes (CAOs) may support tailored interventions. OBJECTIVES: To develop and validate an interpretable ML model for predicting disability accrual trajectories in MS. METHODS: A multicentre data set of 1,176 MS patients with up to 8 years of follow-up was used. A random forest model was trained to predict disability accrual at 2, 3, 4, and 5 years, using baseline clinical variables, PROs and initial risk class as predictors. Model performance was assessed using accuracy, area under the curve, and survival analysis. RESULTS: 437 patients composed final cohort. The model predicted disability changes with an accuracy of 0.82 (95% CI: 0.77-0.86) at 2 years and 0.73 (95% CI: 0.66-0.80) at 5 years. Initial risk class and baseline Expanded Disability Status Scale (EDSS) were the most influential predictors. Survival analysis confirmed model's ability to effectively capture the time-dependent patterns of disability accrual events at the population level (log rank p > .05). CONCLUSIONS: The model offers robust and interpretable predictions that may support clinical decision-making using routine clinical data.