Sage Journals: Discover world-class research

Abstract

Objective:

The objective of this paper is to identify clinical or magnetic resonance imaging (MRI) predictors of long-term clinical progression in a large cohort of multiple sclerosis (MS) patients.

Methods:

A total of 241 relapsing–remitting (RR) MS patients were included in a nine-year follow-up (FU) study. The reference MRIs were acquired at baseline (BL) as part of a multicenter, cross-sectional, clinical-MRI study. Volumetric MRI metrics were measured by a fully automated, operator-independent, multi-parametric segmentation method. Clinical progression was evaluated as defined by: conversion from RR to secondary progressive (SP) disease course; progression of Expanded Disability Status Scale (EDSS); achievement and time to reach EDSS 4.

Results:

We concluded that conversion from RR to SP (OR 0.79; CI 0.7–0.9), progression of EDSS (OR 0.85; CI 0.77–0.93), achievement of EDSS 4 (OR 0.8; CI 0.7–0.9), and time to reach EDSS 4 (HR 0.88; CI 0.82–0.94) were all predicted by BL gray matter (GM) volume and, except for progression of EDSS, by BL EDSS (respectively: (OR 2.88; CI 1.9–4.36), (OR 2.7; CI 1.7–4.2), (HR 3.86; CI 1.94–7.70)).

Conclusions:

BL GM volume and EDSS are the best long-term predictors of disease progression in RRMS patients with a relatively long and mild disease.

Keywords

Magnetic resonance imaging follow-up multiple sclerosis clinical predictors gray matter atrophy

Get full access to this article

View all access options for this article.

References

Lassmann

Bruck

Lucchinetti

The immunopathology of multiple sclerosis: An overview. Brain Pathol 2007; 17: 210–218.

Weinshenker

The natural history of multiple sclerosis. Neurol Clin 1995; 13: 119–146.

Confavreux

Vukusic

Natural history of multiple sclerosis: A unifying concept. Brain 2006; 129: 606–616.

Kurtzke

Rating neurologic impairment in multiple sclerosis: An expanded disability status scale (EDSS). Neurology 1983; 33: 1444–1452.

Confavreux

Aimard

Devic

Course and prognosis of multiple sclerosis assessed by the computerized data processing of 349 patients. Brain 1980; 103: 281–300.

Runmarker

Andersen

Prognostic factors in a multiple sclerosis incidence cohort with twenty-five years of follow-up. Brain 1993; 116: 117–134.

Confavreux

Vukusic

Adeleine

Early clinical predictors and progression of irreversible disability in multiple sclerosis: An amnesic process. Brain 2003; 126: 770–782.

Pittock

McClelland

Mayr

. Clinical implications of benign multiple sclerosis: A 20-year population-based follow-up study. Ann Neurol 2004; 56: 303–306.

Pittock

Mayr

McClelland

. Change in MS-related disability in a population-based cohort: A 10-year follow-up study. Neurology 2004; 62: 51–59.

10.

Weinshenker

Rice

Noseworthy

. The natural history of multiple sclerosis: A geographically based study. 3. Multivariate analysis of predictive factors and models of outcome. Brain 1991; 114: 1045–1056.

11.

Renoux

Natural history of multiple sclerosis: Long-term prognostic factors. Neurol Clin 2011; 29: 293–308.

12.

Kappos

Moeri

Radue

. Predictive value of gadolinium-enhanced magnetic resonance imaging for relapse rate and changes in disability or impairment in multiple sclerosis: A meta-analysis. Lancet 1999; 353: 964–969.

13.

Fisniku

Brex

Altmann

. Disability and T2 MRI lesions: A 20-year follow-up of patients with relapse onset of multiple sclerosis. Brain 2008; 131: 808–817.

14.

Di Filippo

Anderson

Altmann

. Brain atrophy and lesion load measures over 1 year relate to clinical status after 6 years in patients with clinically isolated syndromes. J Neurol Neurosurg Psychiatry 2010; 81: 204–208.

15.

Sailer

Losseff

Wang

. T1 lesion load and cerebral atrophy as a marker for clinical progression in patients with multiple sclerosis. A prospective 18 months follow-up study. Eur J Neurol 2001; 8: 37–42.

16.

Rudick

Lee

Simon

. Significance of T2 lesions in multiple sclerosis: A 13-year longitudinal study. Ann Neurol 2006; 60: 236–242.

17.

Tiberio

Chard

Altman

. Gray and white matter volume changes in early RRMS. A 2-year longitudinal study. Neurology 2005; 64: 1001–1007.

18.

Fisher

Rudick

Simon

. Eight-year follow-up study of brain atrophy in patients with MS. Neurology 2002; 59: 1412–1420.

19.

Fisniku

Chard

Jackson

. Gray matter atrophy is related to long-term disability in multiple sclerosis. Ann Neurol 2008: 64: 247–254.

20.

Tedeschi

Lavorgna

Russo

. Brain atrophy and lesion load in a large population of patients with multiple sclerosis. Neurology 2005; 65: 280–285.

21.

Horakova

Dwyer

Havrdova

. Gray matter atrophy and disability progression in patients with early relapsing–remitting multiple sclerosis: A 5-year longitudinal study. J Neurol Sci 2009; 282: 112–119.

22.

Vaneckova

Seidl

Krasensky

. Patients’ stratification and correlation of brain magnetic resonance imaging parameters with disability progression in multiple sclerosis. Eur Neurol 2009; 6: 278–284.

23.

Alfano

Brunetti

Covelli

. Unsupervised, automated segmentation of the normal brain using a multispectral relaxometric magnetic resonance approach. Magn Reson Med 1997; 37: 84–93.

24.

Alfano

Brunetti

Larobina

. Automated segmentation and measurement of global white matter lesion volume in patients with multiple sclerosis. J Magn Reson Imaging 2000; 12: 799–807.

25.

Freedman

Selchen

Arnold

. Treatment optimization in MS: Canadian MS Working Group updated recommendations. Can J Neurol Sci 2013; 40: 307–323.

26.

Held

Petkau

. MRI T2 lesion burden in multiple sclerosis: A plateauing relationship with clinical disability. Neurology 2006; 66: 1384–1389.

27.

Filippi

Paty

Kappos

. Correlations between changes in disability and T2-weighted brain MRI activity in multiple sclerosis: A follow-up study. Neurology 2005; 45: 255–260.

28.

Truyen

van Waesberghe

van Walderveen

. Accumulation of hypointense lesions (“black holes”) on T1 spin-echo MRI correlates with disease progression in multiple sclerosis. Neurology 1996; 47: 1469–1476.

29.

Koudriatseva

Thompson

Fiorelli

. Gadolinium-enhanced MRI predicts clinical and MRI disease activity in relapsing–remitting multiple sclerosis. J Neurol Neurosurg Psychiatry 1997; 67: 285–287.

30.

Roosendaal

Bendfeldt

Vrenken

. Grey matter volume in a large cohort of MS patients: Relation to MRI parameters and disability. Mult Scler 2011; 17: 1098–1106.

31.

Geurts

Calabrese

Fisher

. Measurement and clinical effect of grey matter pathology in multiple sclerosis. Lancet Neurol 2012; 11: 1082–1092.

32.

Mostert

Koch

Steen

. T2 lesions and rate of progression of disability in multiple sclerosis. Eur J Neurol 2010; 17: 1471–1475.

33.

Rudick

Lee

Cutter

. Disability progression in a clinical trial of relapsing–remitting multiple sclerosis: Eight-year follow-up. Arch Neurol 2010; 67: 1329–1335.

34.

Noseworthy

Vandervoort

Wong

. Interrater variability with the Expanded Disability Status Scale (EDSS) and functional systems (FS) in a multiple sclerosis clinical trial. Neurology 1990; 40: 971–975.

35.

Goodkin

Cookfair

Wende

. Inter-intrarater scoring agreement using grades 1.0 to 3 5 of the Kurtzke Expanded Disability Status Scale (EDSS). Neurology 1992; 42: 859–863.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.03 MB

Clinical and magnetic resonance imaging predictors of disease progression in multiple sclerosis: a nine-year follow-up study

Abstract

Objective:

Methods:

Results:

Conclusions:

Keywords

Get full access to this article

References

Supplementary Material