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New Multiple Sclerosis Disease Severity Scale Predicts Future Accumulation of Disability
The search for the genetic foundation of multiple sclerosis (MS) severity remains elusive. It is, in fact, controversial whether MS severity is a stable feature that predicts future disability progression. If MS severity is not stable, it is unlikely that genotype decisively determines disability pr...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5686060/ https://www.ncbi.nlm.nih.gov/pubmed/29176958 http://dx.doi.org/10.3389/fneur.2017.00598 |
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author | Weideman, Ann Marie Barbour, Christopher Tapia-Maltos, Marco Aurelio Tran, Tan Jackson, Kayla Kosa, Peter Komori, Mika Wichman, Alison Johnson, Kory Greenwood, Mark Bielekova, Bibiana |
author_facet | Weideman, Ann Marie Barbour, Christopher Tapia-Maltos, Marco Aurelio Tran, Tan Jackson, Kayla Kosa, Peter Komori, Mika Wichman, Alison Johnson, Kory Greenwood, Mark Bielekova, Bibiana |
author_sort | Weideman, Ann Marie |
collection | PubMed |
description | The search for the genetic foundation of multiple sclerosis (MS) severity remains elusive. It is, in fact, controversial whether MS severity is a stable feature that predicts future disability progression. If MS severity is not stable, it is unlikely that genotype decisively determines disability progression. An alternative explanation tested here is that the apparent instability of MS severity is caused by inaccuracies of its current measurement. We applied statistical learning techniques to a 902 patient-years longitudinal cohort of MS patients, divided into training (n = 133) and validation (n = 68) sub-cohorts, to test four hypotheses: (1) there is intra-individual stability in the rate of accumulation of MS-related disability, which is also influenced by extrinsic factors. (2) Previous results from observational studies are negatively affected by the insensitive nature of the Expanded Disability Status Scale (EDSS). The EDSS-based MS Severity Score (MSSS) is further disadvantaged by the inability to reliably measure MS onset and, consequently, disease duration (DD). (3) Replacing EDSS with a sensitive scale, i.e., Combinatorial Weight-Adjusted Disability Score (CombiWISE), and substituting age for DD will significantly improve predictions of future accumulation of disability. (4) Adjusting measured disability for the efficacy of administered therapies and other relevant external features will further strengthen predictions of future MS course. The result is a MS disease severity scale (MS-DSS) derived by conceptual advancements of MSSS and a statistical learning method called gradient boosting machines (GBM). MS-DSS greatly outperforms MSSS and the recently developed Age Related MS Severity Score in predicting future disability progression. In an independent validation cohort, MS-DSS measured at the first clinic visit correlated significantly with subsequent therapy-adjusted progression slopes (r = 0.5448, p = 1.56e−06) measured by CombiWISE. To facilitate widespread use of MS-DSS, we developed a free, interactive web application that calculates all aspects of MS-DSS and its contributing scales from user-provided raw data. MS-DSS represents a much-needed tool for genotype-phenotype correlations, for identifying biological processes that underlie MS progression, and for aiding therapeutic decisions. |
format | Online Article Text |
id | pubmed-5686060 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-56860602017-11-24 New Multiple Sclerosis Disease Severity Scale Predicts Future Accumulation of Disability Weideman, Ann Marie Barbour, Christopher Tapia-Maltos, Marco Aurelio Tran, Tan Jackson, Kayla Kosa, Peter Komori, Mika Wichman, Alison Johnson, Kory Greenwood, Mark Bielekova, Bibiana Front Neurol Neuroscience The search for the genetic foundation of multiple sclerosis (MS) severity remains elusive. It is, in fact, controversial whether MS severity is a stable feature that predicts future disability progression. If MS severity is not stable, it is unlikely that genotype decisively determines disability progression. An alternative explanation tested here is that the apparent instability of MS severity is caused by inaccuracies of its current measurement. We applied statistical learning techniques to a 902 patient-years longitudinal cohort of MS patients, divided into training (n = 133) and validation (n = 68) sub-cohorts, to test four hypotheses: (1) there is intra-individual stability in the rate of accumulation of MS-related disability, which is also influenced by extrinsic factors. (2) Previous results from observational studies are negatively affected by the insensitive nature of the Expanded Disability Status Scale (EDSS). The EDSS-based MS Severity Score (MSSS) is further disadvantaged by the inability to reliably measure MS onset and, consequently, disease duration (DD). (3) Replacing EDSS with a sensitive scale, i.e., Combinatorial Weight-Adjusted Disability Score (CombiWISE), and substituting age for DD will significantly improve predictions of future accumulation of disability. (4) Adjusting measured disability for the efficacy of administered therapies and other relevant external features will further strengthen predictions of future MS course. The result is a MS disease severity scale (MS-DSS) derived by conceptual advancements of MSSS and a statistical learning method called gradient boosting machines (GBM). MS-DSS greatly outperforms MSSS and the recently developed Age Related MS Severity Score in predicting future disability progression. In an independent validation cohort, MS-DSS measured at the first clinic visit correlated significantly with subsequent therapy-adjusted progression slopes (r = 0.5448, p = 1.56e−06) measured by CombiWISE. To facilitate widespread use of MS-DSS, we developed a free, interactive web application that calculates all aspects of MS-DSS and its contributing scales from user-provided raw data. MS-DSS represents a much-needed tool for genotype-phenotype correlations, for identifying biological processes that underlie MS progression, and for aiding therapeutic decisions. Frontiers Media S.A. 2017-11-10 /pmc/articles/PMC5686060/ /pubmed/29176958 http://dx.doi.org/10.3389/fneur.2017.00598 Text en Copyright © 2017 Weideman, Barbour, Tapia-Maltos, Tran, Jackson, Kosa, Komori, Wichman, Johnson, Greenwood and Bielekova. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Weideman, Ann Marie Barbour, Christopher Tapia-Maltos, Marco Aurelio Tran, Tan Jackson, Kayla Kosa, Peter Komori, Mika Wichman, Alison Johnson, Kory Greenwood, Mark Bielekova, Bibiana New Multiple Sclerosis Disease Severity Scale Predicts Future Accumulation of Disability |
title | New Multiple Sclerosis Disease Severity Scale Predicts Future Accumulation of Disability |
title_full | New Multiple Sclerosis Disease Severity Scale Predicts Future Accumulation of Disability |
title_fullStr | New Multiple Sclerosis Disease Severity Scale Predicts Future Accumulation of Disability |
title_full_unstemmed | New Multiple Sclerosis Disease Severity Scale Predicts Future Accumulation of Disability |
title_short | New Multiple Sclerosis Disease Severity Scale Predicts Future Accumulation of Disability |
title_sort | new multiple sclerosis disease severity scale predicts future accumulation of disability |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5686060/ https://www.ncbi.nlm.nih.gov/pubmed/29176958 http://dx.doi.org/10.3389/fneur.2017.00598 |
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