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Brain atrophy and clinical characteristics predicting SDMT performance in multiple sclerosis: A 10-year follow-up study

OBJECTIVES: To identify Magnetic Resonance Imaging (MRI), clinical and demographic biomarkers predictive of worsening information processing speed (IPS) as measured by Symbol Digit Modalities Test (SDMT). METHODS: Demographic, clinical data and 1.5 T MRI scans were collected in 76 patients at time o...

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Autores principales: Jacobsen, Cecilie, Zivadinov, Robert, Myhr, Kjell-Morten, Dalaker, Turi O, Dalen, Ingvild, Benedict, Ralph HB, Bergsland, Niels, Farbu, Elisabeth
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7876764/
https://www.ncbi.nlm.nih.gov/pubmed/33623706
http://dx.doi.org/10.1177/2055217321992394
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author Jacobsen, Cecilie
Zivadinov, Robert
Myhr, Kjell-Morten
Dalaker, Turi O
Dalen, Ingvild
Benedict, Ralph HB
Bergsland, Niels
Farbu, Elisabeth
author_facet Jacobsen, Cecilie
Zivadinov, Robert
Myhr, Kjell-Morten
Dalaker, Turi O
Dalen, Ingvild
Benedict, Ralph HB
Bergsland, Niels
Farbu, Elisabeth
author_sort Jacobsen, Cecilie
collection PubMed
description OBJECTIVES: To identify Magnetic Resonance Imaging (MRI), clinical and demographic biomarkers predictive of worsening information processing speed (IPS) as measured by Symbol Digit Modalities Test (SDMT). METHODS: Demographic, clinical data and 1.5 T MRI scans were collected in 76 patients at time of inclusion, and after 5 and 10 years. Global and tissue-specific volumes were calculated at each time point. For the primary outcome of analysis, SDMT was used. RESULTS: Worsening SDMT at 5-year follow-up was predicted by baseline age, Expanded Disability Status Scale (EDSS), SDMT, whole brain volume (WBV) and T2 lesion volume (LV), explaining 30.2% of the variance of SDMT. At 10-year follow-up, age, EDSS, grey matter volume (GMV) and T1 LV explained 39.4% of the variance of SDMT change. CONCLUSION: This longitudinal study shows that baseline MRI-markers, demographic and clinical data can help predict worsening IPS. Identification of patients at risk of IPS decline is of importance as follow-up, treatment and rehabilitation can be optimized.
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spelling pubmed-78767642021-02-22 Brain atrophy and clinical characteristics predicting SDMT performance in multiple sclerosis: A 10-year follow-up study Jacobsen, Cecilie Zivadinov, Robert Myhr, Kjell-Morten Dalaker, Turi O Dalen, Ingvild Benedict, Ralph HB Bergsland, Niels Farbu, Elisabeth Mult Scler J Exp Transl Clin Original Research Paper OBJECTIVES: To identify Magnetic Resonance Imaging (MRI), clinical and demographic biomarkers predictive of worsening information processing speed (IPS) as measured by Symbol Digit Modalities Test (SDMT). METHODS: Demographic, clinical data and 1.5 T MRI scans were collected in 76 patients at time of inclusion, and after 5 and 10 years. Global and tissue-specific volumes were calculated at each time point. For the primary outcome of analysis, SDMT was used. RESULTS: Worsening SDMT at 5-year follow-up was predicted by baseline age, Expanded Disability Status Scale (EDSS), SDMT, whole brain volume (WBV) and T2 lesion volume (LV), explaining 30.2% of the variance of SDMT. At 10-year follow-up, age, EDSS, grey matter volume (GMV) and T1 LV explained 39.4% of the variance of SDMT change. CONCLUSION: This longitudinal study shows that baseline MRI-markers, demographic and clinical data can help predict worsening IPS. Identification of patients at risk of IPS decline is of importance as follow-up, treatment and rehabilitation can be optimized. SAGE Publications 2021-02-08 /pmc/articles/PMC7876764/ /pubmed/33623706 http://dx.doi.org/10.1177/2055217321992394 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/ Creative Commons CC-BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Original Research Paper
Jacobsen, Cecilie
Zivadinov, Robert
Myhr, Kjell-Morten
Dalaker, Turi O
Dalen, Ingvild
Benedict, Ralph HB
Bergsland, Niels
Farbu, Elisabeth
Brain atrophy and clinical characteristics predicting SDMT performance in multiple sclerosis: A 10-year follow-up study
title Brain atrophy and clinical characteristics predicting SDMT performance in multiple sclerosis: A 10-year follow-up study
title_full Brain atrophy and clinical characteristics predicting SDMT performance in multiple sclerosis: A 10-year follow-up study
title_fullStr Brain atrophy and clinical characteristics predicting SDMT performance in multiple sclerosis: A 10-year follow-up study
title_full_unstemmed Brain atrophy and clinical characteristics predicting SDMT performance in multiple sclerosis: A 10-year follow-up study
title_short Brain atrophy and clinical characteristics predicting SDMT performance in multiple sclerosis: A 10-year follow-up study
title_sort brain atrophy and clinical characteristics predicting sdmt performance in multiple sclerosis: a 10-year follow-up study
topic Original Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7876764/
https://www.ncbi.nlm.nih.gov/pubmed/33623706
http://dx.doi.org/10.1177/2055217321992394
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