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Long-term disability trajectories in primary progressive MS patients: A latent class growth analysis

A. Signori, G. Izquierdo, A. Lugaresi, R. Hupperts, F. Grand'Maison, P. Sola, D. Horakova, E. Havrdova, A. Prat, M. Girard, P. Duquette, C. Boz, P. Grammond, M. Terzi, B. Singhal, R. Alroughani, T. Petersen, C. Ramo, C. Oreja-Guevara, D....

. 2018 ; 24 (5) : 642-652. [pub] 20170406

Jazyk angličtina Země Velká Británie

Typ dokumentu časopisecké články, práce podpořená grantem

Perzistentní odkaz   https://www.medvik.cz/link/bmc19045615

BACKGROUND: Several natural history studies on primary progressive multiple sclerosis (PPMS) patients detected a consistent heterogeneity in the rate of disability accumulation. OBJECTIVES: To identify subgroups of PPMS patients with similar longitudinal trajectories of Expanded Disability Status Scale (EDSS) over time. METHODS: All PPMS patients collected within the MSBase registry, who had their first EDSS assessment within 5 years from onset, were included in the analysis. Longitudinal EDSS scores were modeled by a latent class mixed model (LCMM), using a nonlinear function of time from onset. LCMM is an advanced statistical approach that models heterogeneity between patients by classifying them into unobserved groups showing similar characteristics. RESULTS: A total of 853 PPMS (51.7% females) from 24 countries with a mean age at onset of 42.4 years (standard deviation (SD): 10.8 years), a median baseline EDSS of 4 (interquartile range (IQR): 2.5-5.5), and 2.4 years of disease duration (SD: 1.5 years) were included. LCMM detected three different subgroups of patients with a mild ( n = 143; 16.8%), moderate ( n = 378; 44.3%), or severe ( n = 332; 38.9%) disability trajectory. The probability of reaching EDSS 6 at 10 years was 0%, 46.4%, and 81.9% respectively. CONCLUSION: Applying an LCMM modeling approach to long-term EDSS data, it is possible to identify groups of PPMS patients with different prognosis.

Amiri Hospital Kuwait City Kuwait

Azienda Ospedaliera di Rilievo Nazionale San Giuseppe Moscati Avellino Italy

Bombay Hospital Institute of Medical Sciences Mumbai India

Box Hill Hospital Melbourne VIC Australia Department of Medicine The University of Melbourne Melbourne VIC Australia

C Mondino National Neurological Institute Pavia Italy

Centre de Réadaptation En Déficience Physique Chaudière Appalache Levis QC Canada

Clinique Neuro Rive Sud Greenfield Park QC Canada

Cliniques Universitaires Saint Luc Brussels Belgium

Craigavon Area Hospital Craigavon UK

Department NEUROFARBA Section Neuroscience University of Florence Florence Italy

Department of Basic Medical Sciences Neuroscience and Sense Organs University of Bari Bari Italy

Department of Biomedical and Neuromotor Sciences Alma Mater Studiorum University of Bologna Italy IRCCS Istituto delle Scienze Neurologiche di Bologna Bologna Italy

Department of Health Sciences Section of Biostatistics University of Genoa Genova Italy

Department of Medicine The University of Melbourne Melbourne VIC Australia

Department of Neurology and Center of Clinical Neuroscience 1st Faculty of Medicine Charles University and General University Hospital Prague Prague Czech Republic

Department of Neurology Golestan University of Medical Sciences Gorgan Iran Golestan Hospital Ahvaz Jundishapur University of Medical Sciences Ahvaz Iran

Flinders University and Medical Centre Adelaide SA Australia

Golestan Hospital Ahvaz Jundishapur University of Medical Sciences Ahvaz Iran

Groene Hart Ziekenhuis Gouda The Netherlands

Hôpital Notre Dame Montreal QC Canada

Hospital Clinico San Carlos Madrid Spain

Hospital de Galdakao Usansolo Galdakao Spain

Hospital Germans Trias i Pujol Badalona Spain

Hospital São João Porto Portugal

Hospital Universitario Virgen de Valme Seville Spain

Hospital Universitario Virgen Macarena Sevilla Spain

Hunter Medical Research Institute The University of Newcastle Newcastle NSW Australia

Isfahan University of Medical Sciences Isfahan Iran

Kommunehospitalet Aarhus Denmark

KTU Medical Faculty Farabi Hospital Trabzon Turkey

Medical Faculty Ondokuz Mayis University Samsun Turkey

Nuovo Ospedale Civile S Agostino Estense Modena Italy

Ospedali Riuniti di Salerno Salerno Italy

Royal Hobart Hospital Hobart TAS Australia

University of Parma Parma Italy

UOC Neurologia Azienda Sanitaria Unica Regionale Marche Macerata Italy

Zuyderland Ziekenhuis Sittard The Netherlands

Citace poskytuje Crossref.org

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$a BACKGROUND: Several natural history studies on primary progressive multiple sclerosis (PPMS) patients detected a consistent heterogeneity in the rate of disability accumulation. OBJECTIVES: To identify subgroups of PPMS patients with similar longitudinal trajectories of Expanded Disability Status Scale (EDSS) over time. METHODS: All PPMS patients collected within the MSBase registry, who had their first EDSS assessment within 5 years from onset, were included in the analysis. Longitudinal EDSS scores were modeled by a latent class mixed model (LCMM), using a nonlinear function of time from onset. LCMM is an advanced statistical approach that models heterogeneity between patients by classifying them into unobserved groups showing similar characteristics. RESULTS: A total of 853 PPMS (51.7% females) from 24 countries with a mean age at onset of 42.4 years (standard deviation (SD): 10.8 years), a median baseline EDSS of 4 (interquartile range (IQR): 2.5-5.5), and 2.4 years of disease duration (SD: 1.5 years) were included. LCMM detected three different subgroups of patients with a mild ( n = 143; 16.8%), moderate ( n = 378; 44.3%), or severe ( n = 332; 38.9%) disability trajectory. The probability of reaching EDSS 6 at 10 years was 0%, 46.4%, and 81.9% respectively. CONCLUSION: Applying an LCMM modeling approach to long-term EDSS data, it is possible to identify groups of PPMS patients with different prognosis.
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