Machine learning in secondary progressive multiple sclerosis: an improved predictive model for short-term disability progression

Background: Enhanced prediction of progression in secondary progressive multiple sclerosis (SPMS) could improve clinical trial design. Machine learning (ML) algorithms are methods for training predictive models with minimal human intervention.

Objective: To evaluate individual and ensemble model performance built using decision tree (DT)-based algorithms compared to logistic regression (LR) and support vector machines (SVMs) for predicting SPMS disability progression.

Methods: SPMS participants (n = 485) enrolled in a 2-year placebo-controlled (negative) trial assessing the efficacy of MBP8298 were classified as progressors if a 6-month sustained increase in Expanded Disability Status Scale (EDSS) (≥1.0 or ≥0.5 for a baseline of ≤5.5 or ≥6.0 respectively) was observed. Variables included EDSS, Multiple Sclerosis Functional Composite component scores, T2 lesion volume, brain parenchymal fraction, disease duration, age, and sex. Area under the receiver operating characteristic curve (AUC) was the primary outcome for model evaluation.

Results: Three DT-based models had greater AUCs (61.8%, 60.7%, and 60.2%) than independent and ensemble SVM (52.4% and 51.0%) and LR (49.5% and 51.1%).

Conclusion: SPMS disability progression was best predicted by non-parametric ML. If confirmed, ML could select those with highest progression risk for inclusion in SPMS trial cohorts and reduce the number of low-risk individuals exposed to experimental therapies.