Cognitive processing speed in multiple sclerosis clinical practice: association with patient-reported outcomes, employment and magnetic resonance imaging metrics

G Macaron; L E Baldassari; K Nakamura; S M Rao; M P McGinley; B P Moss; H Li; D M Miller; S E Jones; R A Bermel; J A Cohen; D Ontaneda; D S Conway

doi:10.1111/ene.14239

Cognitive processing speed in multiple sclerosis clinical practice: association with patient-reported outcomes, employment and magnetic resonance imaging metrics

Eur J Neurol. 2020 Jul;27(7):1238-1249. doi: 10.1111/ene.14239. Epub 2020 May 1.

Authors

G Macaron^{1

2}, L E Baldassari¹, K Nakamura³, S M Rao⁴, M P McGinley¹, B P Moss¹, H Li⁵, D M Miller¹, S E Jones⁶, R A Bermel¹, J A Cohen¹, D Ontaneda¹, D S Conway¹

Affiliations

¹ Mellen Center for Multiple Sclerosis, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA.
² Faculty of Medicine, Université Saint Joseph de Beyrouth, Beirut, Lebanon.
³ Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA.
⁴ Schey Center for Cognitive Neuroimaging, Lou Ruvo Center for Brain Health, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA.
⁵ Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, USA.
⁶ Neuroradiology Department, Imaging Institute, Cleveland Clinic, Cleveland, OH, USA.

PMID: 32222019
DOI: 10.1111/ene.14239

Abstract

Background and purpose: To analyze the relationship between cognitive processing speed, patient-reported outcome measures (PROMs), employment and magnetic resonance imaging (MRI) metrics in a large multiple sclerosis cohort.

Methods: Cross-sectional clinical data, PROMs, employment and MRI studies within 90 days of completion of the Processing Speed Test (PST), a technology-enabled adaptation of the Symbol Digit Modalities Test, were collected. MRI was analyzed using semi-automated methods. Correlations of PST score with PROMs and MRI metrics were examined using Spearman's rho. Wilcoxon rank sum testing compared MRI metrics across PST score quartiles and linear regression models identified predictors of PST performance. Effects of employment and depression were also investigated.

Results: In 721 patients (mean age 47.6 ± 11.4 years), PST scores were significantly correlated with all MRI metrics, including cord atrophy and deep gray matter volumes. Linear regression demonstrated self-reported physical disability, cognitive function, fatigue and social domains (adjusted R² = 0.44, P < 0.001) as the strongest clinical predictors of PST score, whereas that of MRI variables included T2 lesion volume, whole-brain fraction and cord atrophy (adjusted R² = 0.42, P < 0.001). An inclusive model identified T2 lesion volume, whole-brain fraction, self-reported upper extremity function, cognition and social participation as the strongest predictors of PST score (adjusted R² = 0.51, P < 0.001). There was significant effect modification by depression on the relationship between self-reported cognition and PST performance. Employment status was associated with PST scores independent of age and physical disability.

Conclusion: The PST score correlates with PROMs, MRI measures of focal and diffuse brain injury, and employment. The PST score is a feasible and meaningful measure for routine multiple sclerosis care.

Keywords: cognitive dysfunction; employment; magnetic resonance imaging metrics; multiple sclerosis; patient-reported outcomes; processing speed.

Publication types

Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Adult
Atrophy / pathology
Benchmarking
Brain / pathology
Cognition
Cross-Sectional Studies
Employment
Humans
Magnetic Resonance Imaging
Middle Aged
Multiple Sclerosis* / diagnostic imaging
Multiple Sclerosis* / pathology
Patient Reported Outcome Measures

Abstract

Publication types

MeSH terms

Grants and funding