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⁻) and GFAP (p = 1.4 × 10) 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.