Prediction of long-term disability in multiple sclerosis

Area of Science:

• Neuroscience
• Neurology
• Clinical Neurophysiology

Background:

• The predictive value of neurophysiological measures for the long-term prognosis of multiple sclerosis (MS) remains largely unknown.
• Understanding long-term disability progression is crucial for managing MS.

Purpose of the Study:

• To prospectively assess if combined visual evoked potentials (VEPs) and motor evoked potentials (MEPs) can predict disability over a 14-year period in MS patients.
• To explore the utility of neurophysiological tests in forecasting the long-term course of MS.

Main Methods:

• 30 patients with relapsing-remitting and secondary progressive MS underwent VEPs, MEPs, and Expanded Disability Status Scale (EDSS) assessments at baseline (T0) and at 6, 12, and 24 months.
• Cranial MRI scans were performed at baseline. EDSS was reassessed at 14 years (T4).
• Spearman's rank correlation analyzed associations between evoked potentials (EPs), MRI data, and EDSS. Multivariable linear regression predicted EDSS(T4) using z-transformed EP latencies(T0).

Main Results:

• A significant correlation was found between EDSS values at 14 years (EDSS(T4)) and the sum of z-transformed EP latencies at baseline (T0) (rho = 0.68, p < 0.0001).
• No significant correlation was observed between EDSS(T4) and baseline MRI parameters.
• A predictive model using baseline EP latencies (P100 and CMCT) showed strong correlation with observed EDSS(T4) values (rho = 0.69, p < 0.0001).

Conclusions:

• Combined evoked potentials (EPs) demonstrate the ability to predict long-term disability in patients with multiple sclerosis.
• These findings have significant implications for selecting appropriate monitoring strategies in MS clinical trials and guiding clinical practice decisions.