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Updated: Nov 11, 2025

The Multiple Sclerosis Performance Test MSPT: An iPad-Based Disability Assessment Tool
Published on: June 30, 2014
Scoring the 10-year risk of ambulatory disability in multiple sclerosis: the RoAD score
Claudio Gasperini1, Luca Prosperini1, Àlex Rovira2
1Department of Neurosciences, San Camillo-Forlanini Hospital, Rome, Italy.
Background And Purpose:
Both baseline prognostic factors and short-term predictors of treatment response can influence the long-term risk of disability accumulation in patients with relapsing-remitting multiple sclerosis (RRMS). The objective was to develop and validate a scoring system combining baseline prognostic factors and 1-year variables of treatment response into a single numeric score predicting the long-term risk of disability.
Methods:
We analysed two independent datasets of patients with RRMS who started interferon beta or glatiramer acetate, had an Expanded Disability Status Scale (EDSS) score <4.0 at treatment start and were followed for at least 10 years. The first dataset ('training set') included patients attending three MS centres in Italy and served as a framework to create the so-called RoAD score (Risk of Ambulatory Disability). The second ('validation set') included a cohort of patients followed in Barcelona, Spain, to explore the performance of the RoAD score in predicting the risk of reaching an EDSS score ≥6.0.
Results:
The RoAD score (ranging from 0 to 8) derived from the training set (n = 1225), was based on demographic (age), clinical baseline prognostic factors (disease duration, EDSS) and 1-year predictors of treatment response (number of relapses, presence of gadolinium enhancement and new T2 lesions). The best cut-off score for discriminating patients at higher risk of reaching the disability milestone was ≥4. When applied to the validation set (n = 296), patients with a RoAD score ≥4 had an approximately 4-fold increased risk for reaching the disability milestone (p < 0.001).
Discussion:
The RoAD score is proposed as an useful tool to predict individual prognosis and optimize treatment strategy of patients with RRMS.

