Alessio Signori1, Guillermo Izquierdo2, Alessandra Lugaresi3, Raymond Hupperts4, Francois Grand'Maison5, Patrizia Sola6, Dana Horakova7, Eva Havrdova7, Alexandre Prat8, Marc Girard8, Pierre Duquette8, Cavit Boz9, Pierre Grammond10, Murat Terzi11, Bhim Singhal12, Raed Alroughani13, Thor Petersen14, Cristina Ramo15, Celia Oreja-Guevara16, Daniele Spitaleri17, Vahid Shaygannejad18, Helmut Butzkueven19, Tomas Kalincik20, Vilija Jokubaitis20, Mark Slee21, Ricardo Fernandez Bolaños22, Jose Luis Sanchez-Menoyo23, Eugenio Pucci24, Franco Granella25, Jeannette Lechner-Scott26, Gerardo Iuliano27, Stella Hughes28, Roberto Bergamaschi29, Bruce Taylor30, Freek Verheul31, Maria Edite Rio32, Maria Pia Amato33, Seyed Aidin Sajedi34, Nastaran Majdinasab35, Vincent Van Pesch36, Maria Pia Sormani1, Maria Trojano37. 1. Department of Health Sciences (DISSAL), Section of Biostatistics, University of Genoa, Genova, Italy. 2. Hospital Universitario Virgen Macarena, Sevilla, Spain. 3. Department of Biomedical and Neuromotor Sciences(DIBINEM), Alma Mater Studiorum, University of Bologna, Italy/IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy. 4. Zuyderland Ziekenhuis, Sittard, The Netherlands. 5. Clinique Neuro Rive-Sud, Greenfield Park, QC, Canada. 6. Nuovo Ospedale Civile S. Agostino-Estense, Modena, Italy. 7. Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic. 8. Hôpital Notre-Dame, Montreal, QC, Canada. 9. KTU Medical Faculty Farabi Hospital, Trabzon, Turkey. 10. Centre de Réadaptation En Déficience Physique Chaudière-Appalache, Levis, QC, Canada. 11. Medical Faculty, Ondokuz Mayis University, Samsun, Turkey. 12. Bombay Hospital Institute of Medical Sciences (BHIMS), Mumbai, India. 13. Amiri Hospital, Kuwait City, Kuwait. 14. Kommunehospitalet, Aarhus, Denmark. 15. Hospital Germans Trias i Pujol, Badalona, Spain. 16. Hospital Clinico San Carlos, Madrid, Spain. 17. Azienda Ospedaliera di Rilievo Nazionale, San Giuseppe Moscati, Avellino, Italy. 18. Isfahan University of Medical Sciences, Isfahan, Iran. 19. Box Hill Hospital, Melbourne, VIC, Australia/Department of Medicine, The University of Melbourne, Melbourne, VIC, Australia. 20. Department of Medicine, The University of Melbourne, Melbourne, VIC, Australia. 21. Flinders University and Medical Centre, Adelaide, SA, Australia. 22. Hospital Universitario Virgen de Valme, Seville, Spain. 23. Hospital de Galdakao-Usansolo, Galdakao, Spain. 24. UOC Neurologia, Azienda Sanitaria Unica Regionale Marche, Macerata, Italy. 25. University of Parma, Parma, Italy. 26. Hunter Medical Research Institute, The University of Newcastle, Newcastle, NSW, Australia. 27. Ospedali Riuniti di Salerno, Salerno, Italy. 28. Craigavon Area Hospital, Craigavon, UK. 29. C. Mondino National Neurological Institute, Pavia, Italy. 30. Royal Hobart Hospital, Hobart, TAS, Australia. 31. Groene Hart Ziekenhuis, Gouda, The Netherlands. 32. Hospital São João, Porto, Portugal. 33. Department NEUROFARBA, Section Neuroscience, University of Florence, Florence, Italy. 34. Department of Neurology, Golestan University of Medical Sciences, Gorgan, Iran/Golestan Hospital, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran. 35. Golestan Hospital, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran. 36. Cliniques Universitaires Saint-Luc, Brussels, Belgium. 37. Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari, Bari, Italy.
Abstract
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.
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.
Authors: Jordana Hughes; Vilija Jokubaitis; Alessandra Lugaresi; Raymond Hupperts; Guillermo Izquierdo; Alexandre Prat; Marc Girard; Pierre Duquette; Francois Grand'Maison; Pierre Grammond; Patrizia Sola; Diana Ferraro; Cristina Ramo-Tello; Maria Trojano; Mark Slee; Vahid Shaygannejad; Cavit Boz; Jeanette Lechner-Scott; Vincent Van Pesch; Eugenio Pucci; Claudio Solaro; Freek Verheul; Murat Terzi; Franco Granella; Daniele Spitaleri; Raed Alroughani; Jae-Kwan Jun; Adam Fambiatos; Anneke Van der Walt; Helmut Butzkueven; Tomas Kalincik Journal: JAMA Neurol Date: 2018-11-01 Impact factor: 18.302
Authors: Daniel Stoessel; Jan-Patrick Stellmann; Anne Willing; Birte Behrens; Sina C Rosenkranz; Sibylle C Hodecker; Klarissa H Stürner; Stefanie Reinhardt; Sabine Fleischer; Christian Deuschle; Walter Maetzler; Daniela Berg; Christoph Heesen; Dirk Walther; Nicolas Schauer; Manuel A Friese; Ole Pless Journal: Front Hum Neurosci Date: 2018-06-04 Impact factor: 3.169
Authors: Ana Railka de Souza Oliveira-Kumakura; Larissa Maria Bezutti; Juliany Lino Gomes Silva; Renata Cristina Gasparino Journal: Rev Lat Am Enfermagem Date: 2019-10-07
Authors: Cynthia R Rovnaghi; Joseph Rigdon; Jean-Michel Roué; Monica O Ruiz; Victor G Carrion; Kanwaljeet J S Anand Journal: Front Pediatr Date: 2021-10-11 Impact factor: 3.418