A systematic review and meta-analysis of real-world data predictors for conversion to progressive multiple sclerosis.

BACKGROUND

Available fluid biomarkers, neuroimaging techniques, and clinical assessments may enhance prediction of conversion to secondary progressive multiple sclerosis (SPMS).

OBJECTIVES

To identify robust real-world predictors of MS progression in the literature.

METHODS

A systematic review was conducted in EMBASE. A total of 20,338 MS patients from 64 eligible studies were included. A p-value meta-analysis and a tipping point analysis were performed to assess associations with MS progression and EDSS.

RESULTS

Among CSF biomarkers, GFAP (p = 6.2 × 10⁻¹⁰) and CHI3L1 (p = 1.7 × 10⁻¹¹) demonstrated consistent aggregated associations with disease progression. In serum, NfL (p = 2.5 × 10⁻13) and GFAP (p = 1.4 × 10-8) showed strong association with concurrent EDSS and disease progression. Spinal cord lesions (p = 9.4 × 10⁻¹¹) and iron rim lesions (p = 4 × 10⁻⁶) were the strongest radiological predictors. Among clinical assessment tools, significant associations were found with concurrent EDSS, but prediction of progression was limited.

CONCLUSION

Fluid biomarkers, particularly CSF GFAP, CHI3L1, and serum NfL, along with spinal cord lesions and iron rim lesions on MRI, provide consistent evidence for predicting MS progression. There is a need for harmonized and accessible outcome measures in real-world datasets.