Scoring the 10-year risk of ambulatory disability in multiple sclerosis: the RoAD score.
clinical score
disability
multiple sclerosis
prognosis
Journal
European journal of neurology
ISSN: 1468-1331
Titre abrégé: Eur J Neurol
Pays: England
ID NLM: 9506311
Informations de publication
Date de publication:
08 2021
08 2021
Historique:
revised:
04
03
2021
received:
08
01
2021
accepted:
25
03
2021
pubmed:
1
4
2021
medline:
13
8
2021
entrez:
31
3
2021
Statut:
ppublish
Résumé
Both baseline prognostic factors and short-term predictors of treatment response can influence the long-term risk of disability accumulation in patients with relapsing-remitting multiple sclerosis (RRMS). The objective was to develop and validate a scoring system combining baseline prognostic factors and 1-year variables of treatment response into a single numeric score predicting the long-term risk of disability. We analysed two independent datasets of patients with RRMS who started interferon beta or glatiramer acetate, had an Expanded Disability Status Scale (EDSS) score <4.0 at treatment start and were followed for at least 10 years. The first dataset ('training set') included patients attending three MS centres in Italy and served as a framework to create the so-called RoAD score (Risk of Ambulatory Disability). The second ('validation set') included a cohort of patients followed in Barcelona, Spain, to explore the performance of the RoAD score in predicting the risk of reaching an EDSS score ≥6.0. The RoAD score (ranging from 0 to 8) derived from the training set (n = 1225), was based on demographic (age), clinical baseline prognostic factors (disease duration, EDSS) and 1-year predictors of treatment response (number of relapses, presence of gadolinium enhancement and new T2 lesions). The best cut-off score for discriminating patients at higher risk of reaching the disability milestone was ≥4. When applied to the validation set (n = 296), patients with a RoAD score ≥4 had an approximately 4-fold increased risk for reaching the disability milestone (p < 0.001). The RoAD score is proposed as an useful tool to predict individual prognosis and optimize treatment strategy of patients with RRMS.
Sections du résumé
BACKGROUND AND PURPOSE
Both baseline prognostic factors and short-term predictors of treatment response can influence the long-term risk of disability accumulation in patients with relapsing-remitting multiple sclerosis (RRMS). The objective was to develop and validate a scoring system combining baseline prognostic factors and 1-year variables of treatment response into a single numeric score predicting the long-term risk of disability.
METHODS
We analysed two independent datasets of patients with RRMS who started interferon beta or glatiramer acetate, had an Expanded Disability Status Scale (EDSS) score <4.0 at treatment start and were followed for at least 10 years. The first dataset ('training set') included patients attending three MS centres in Italy and served as a framework to create the so-called RoAD score (Risk of Ambulatory Disability). The second ('validation set') included a cohort of patients followed in Barcelona, Spain, to explore the performance of the RoAD score in predicting the risk of reaching an EDSS score ≥6.0.
RESULTS
The RoAD score (ranging from 0 to 8) derived from the training set (n = 1225), was based on demographic (age), clinical baseline prognostic factors (disease duration, EDSS) and 1-year predictors of treatment response (number of relapses, presence of gadolinium enhancement and new T2 lesions). The best cut-off score for discriminating patients at higher risk of reaching the disability milestone was ≥4. When applied to the validation set (n = 296), patients with a RoAD score ≥4 had an approximately 4-fold increased risk for reaching the disability milestone (p < 0.001).
DISCUSSION
The RoAD score is proposed as an useful tool to predict individual prognosis and optimize treatment strategy of patients with RRMS.
Substances chimiques
Contrast Media
0
Glatiramer Acetate
5M691HL4BO
Gadolinium
AU0V1LM3JT
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2533-2542Informations de copyright
© 2021 European Academy of Neurology.
Références
Rotstein D, Montalban X. Reaching an evidence-based prognosis for personalized treatment of multiple sclerosis. Nat Rev Neurol. 2019;1:287-300.
Degenhardt A, Ramagopalan SV, Scalfari A, Ebers GC. Clinical prognostic factors in multiple sclerosis: a natural history review. Nat Rev Neurol. 2009;5(12):672-682.
Tintore M, Rovira À, Río J, et al. Defining high, medium and low impact prognostic factors for developing multiple sclerosis. Brain J Neurol. 2015;138(Pt 7):1863-1874.
Tintore M, Otero-Romero S, Río J, et al. Contribution of the symptomatic lesion in establishing MS diagnosis and prognosis. Neurology. 2016;87(13):1368-1374.
Tintore M, Rovira A, Arrambide G, et al. Brainstem lesions in clinically isolated syndromes. Neurology. 2010;75(21):1933-1938.
Arrambide G, Rovira A, Sastre-Garriga J, et al. Spinal cord lesions: a modest contributor to diagnosis in clinically isolated syndromes but a relevant prognostic factor. Mult Scler. 2018;24(3):301-312.
Trojano M, Pellegrini F, Fuiani A, et al. New natural history of interferon-beta-treated relapsing multiple sclerosis. Ann Neurol. 2007;61(4):300-306.
Brown JWL, Coles A, Horakova D, et al. Association of initial disease-modifying therapy with later conversion to secondary progressive multiple sclerosis. JAMA. 2019;321(2):175-187.
Gasperini C, Prosperini L, Tintoré M, et al. Unraveling treatment response in multiple sclerosis: a clinical and MRI challenge. Neurology. 2019;92(4):180-192.
Sormani MP, De Stefano N. Defining and scoring response to IFN-β in multiple sclerosis. Nat Rev Neurol. 2013;9(9):504-512.
Bergamaschi R, Berzuini C, Romani A, Cosi V. Predicting secondary progression in relapsing-remitting multiple sclerosis: a Bayesian analysis. J Neurol Sci. 2001;189(1-2):13-21.
Bergamaschi R, Quaglini S, Trojano M, et al. Early prediction of the long term evolution of multiple sclerosis: the Bayesian Risk Estimate for Multiple Sclerosis (BREMS) score. J Neurol Neurosurg Psychiatry. 2007;78(7):757-759.
Bergamaschi R, Montomoli C, Mallucci G, et al. BREMSO: a simple score to predict early the natural course of multiple sclerosis. Eur J Neurol. 2015;22(6):981-989.
Río J, Castilló J, Rovira A, et al. Measures in the first year of therapy predict the response to interferon beta in MS. Mult Scler. 2009;15(7):848-853.
Sormani M, Signori A, Stromillo M, De Stefano N. Refining response to treatment as defined by the Modified Rio Score. Mult Scler. 2013;19(9):1246-1247.
Sormani MP, Gasperini C, Romeo M, et al. Assessing response to interferon-β in a multicenter dataset of patients with MS. Neurology. 2016;87(2):134-140.
Zhang Z, Zhang H, Khanal MK. Development of scoring system for risk stratification in clinical medicine: a step-by-step tutorial. Ann Transl Med. 2017;5(21):436.
Kurtzke JF. Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology. 1983;33(11):1444-1452.
Lublin FD, Reingold SC, Cohen JA, et al. Defining the clinical course of multiple sclerosis: the 2013 revisions. Neurology. 2014;83(3):278-286.
Wattjes MP, Rovira À, Miller D, et al. Evidence-based guidelines: MAGNIMS consensus guidelines on the use of MRI in multiple sclerosis-establishing disease prognosis and monitoring patients. Nat Rev Neurol. 2015;11(10):597-606.
Río J, Nos C, Tintoré M, et al. Defining the response to interferon-beta in relapsing-remitting multiple sclerosis patients. Ann Neurol. 2006;59(2):344-352.
Thompson AJ, Banwell BL, Barkhof F, et al. Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol. 2018;17(2):162-173.
Altay EE, Fisher E, Jones SE, et al. Reliability of classifying multiple sclerosis disease activity using magnetic resonance imaging in a multiple sclerosis clinic. JAMA Neurol. 2013;70(3):338-344.
Prosperini L, Mancinelli C, Haggiag S, et al. Minimal evidence of disease activity (MEDA) in relapsing-remitting multiple sclerosis. J Neurol Neurosurg Psychiatry. 2020;91(3):271-277.
Mehta HB, Mehta V, Girman CJ, Adhikari D, Johnson ML. Regression coefficient-based scoring system should be used to assign weights to the risk index. J Clin Epidemiol. 2016;79:22-28.
Fisniku LK, Brex PA, Altmann DR, et al. Disability and T2 MRI lesions: a 20-year follow-up of patients with relapse onset of multiple sclerosis. Brain. 2008;131(Pt 3):808-817.
Ransohoff RM, Hafler DA, Lucchinetti CF. Multiple sclerosis-a quiet revolution. Nat Rev Neurol. 2015;11(3):134-142.
Harding K, Williams O, Willis M, et al. Clinical outcomes of escalation vs early intensive disease-modifying therapy in patients with multiple sclerosis. JAMA Neurol. 2019;76(5):536-541.
Río J, Comabella M, Montalban X. Predicting responders to therapies for multiple sclerosis. Nat Rev Neurol. 2009;5(10):553-560.
Goodkin DE, Cookfair D, Wende K, et al. Inter- and intrarater scoring agreement using grades 1.0 to 3.5 of the Kurtzke Expanded Disability Status Scale (EDSS). Multiple Sclerosis Collaborative Research Group. Neurology. 1992;42(4):859-863.
Sormani MP, Rio J, Tintorè M, et al. Scoring treatment response in patients with relapsing multiple sclerosis. Mult Scler. 2013;19(5):605-612.
Rush CA, MacLean HJ, Freedman MS. Aggressive multiple sclerosis: proposed definition and treatment algorithm. Nat Rev Neurol. 2015;11(7):379-389.
Fogarty E, Schmitz S, Tubridy N, Walsh C, Barry M. Comparative efficacy of disease-modifying therapies for patients with relapsing remitting multiple sclerosis: systematic review and network meta-analysis. Mult Scler Relat Disord. 2016;9:23-30.
Kalincik T, Sormani MP. Reporting treatment outcomes in observational data: a fine balance. Mult. Scler. 2017;23(1):21-22.
Rudick RA, Polman CH. Current approaches to the identification and management of breakthrough disease in patients with multiple sclerosis. Lancet Neurol. 2009;8(6):545-559.
Tintoré M, Rovira A, Río J, et al. Do oligoclonal bands add information to MRI in first attacks of multiple sclerosis? Neurology. 2008;70(13 Pt 2):1079-1083.