Prognostic Factors Poster Presentation

P0420 -  Performance on Symbol Digits Modalities Test preceding conversion to Secondary Progressive Multiple Sclerosis (ID 1254)

Speakers
  • V. Karrenbauer
Authors
  • V. Karrenbauer
  • L. Forsberg
  • J. Hillert
Presentation Number
P0420
Presentation Topic
Prognostic Factors

Abstract

Background

In an era of emerging new therapies for SPMS, neurologist are searching for robust tools to predict and identify conversion to SPMS. Cognitive impairment is prevalent in 40-75% of MS patients and supposedly more common in SPMS as it increases with age. Symbol Digits Modalities Test (SDMT) is a sensitive, easy administrated test of information processing speed and predicts driving capacity and future income in MS populations.

Objectives

In this pilot study we test if SDMT absolute values could be used to predict SPMS conversion, hypothesizing that SPMS patients prior to conversion have lower scores on SDMT compared to age and gender matched RRMS patients.

Methods

This is a Swedish MS Registry-based study. We extracted first SDMT score and age at testing, available for MS patients included in the registry. Then we selected RRMS patients with first SDMT value at least 4 years (mean ± SD, 6.5 ± 1.98) before SPMS onset n=192 (SPMS converters) and gender matched RRMS patients (n=192) that performed their first SDMT at the same age as SPMS converters. We performed a linear regression analysis using SDMT as dependent variable and age, gender, disease course (SP convert) as independent variables.

Results

RRMS patients that later converted to SPMS, had statistically significant lower SDMT values compared to age and gender matched RRMS patients, p=3.43x10-13. Overall, SDMT decreased with age and was lower for men compared to women (p=0.0002 and p=0.024).

Conclusions

Differences in SDMT absolute values correlate with and precede SPMS conversion at the group level. High variation of inter-individual SDMT performance is likely to limit the usefulness of SDMT to predict SPMS by itself, but SDMT could be an integrated component of a more complex prediction algorithm.

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