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.