Pietro Iaffaldano1, Giuseppe Lucisano2, Helmut Butzkueven3, Jan Hillert4, Robert Hyde5, Nils Koch-Henriksen6, Melinda Magyari6, Fabio Pellegrini5, Tim Spelman7, Per Soelberg Sørensen6, Sandra Vukusic8, Maria Trojano1. 1. Department of Basic Medical Sciences, Neurosciences and Sense Organs, University of Bari Aldo Moro, Bari, Italy. 2. Department of Basic Medical Sciences, Neurosciences and Sense Organs, University of Bari Aldo Moro, Bari, Italy/Center for Outcomes Research and Clinical Epidemiology (CORESEARCH), Pescara, Italy. 3. Department of Neurology, Box Hill Hospital, Monash University, Melbourne, VIC, Australia. 4. Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden. 5. Biogen International GmbH, Zug, Switzerland. 6. Department of Neurology, The Danish Multiple Sclerosis Registry, Rigshospitalet, Copenhagen, Denmark. 7. Department of Neurology, Box Hill Hospital, Monash University, Melbourne, VIC, Australia/Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden. 8. Neurology, Multiple Sclerosis, Myelin Disorders and Neuroinflammation, Pierre Wertheimer Neurological Hospital, Hospices Civils de Lyon, Lyon, France/Observatoire Français de la Sclérose en Plaques (OFSEP), Lyon, France.
Abstract
BACKGROUND: The optimal timing of treatment starts for achieving the best control on the long-term disability accumulation in multiple sclerosis (MS) is still to be defined. OBJECTIVE: The aim of this study was to estimate the optimal time to start disease-modifying therapies (DMTs) to prevent the long-term disability accumulation in MS, using a pooled dataset from the Big Multiple Sclerosis Data (BMSD) network. METHODS: Multivariable Cox regression models adjusted for the time to first treatment start from disease onset (in quintiles) were used. To mitigate the impact of potential biases, a set of pairwise propensity score (PS)-matched analyses were performed. The first quintile, including patients treated within 1.2 years from onset, was used as reference. RESULTS: A cohort of 11,871 patients (median follow-up after treatment start: 13.2 years) was analyzed. A 3- and 12-month confirmed disability worsening event and irreversible Expanded Disability Status Scale (EDSS) 4.0 and 6.0 scores were reached by 7062 (59.5%), 4138 (34.9%), 3209 (31.1%), and 1909 (16.5%) patients, respectively. The risk of reaching all the disability outcomes was significantly lower (p < 0.0004) for the first quintile patients' group. CONCLUSION: Real-world data from the BMSD demonstrate that DMTs should be commenced within 1.2 years from the disease onset to reduce the risk of disability accumulation over the long term.
BACKGROUND: The optimal timing of treatment starts for achieving the best control on the long-term disability accumulation in multiple sclerosis (MS) is still to be defined. OBJECTIVE: The aim of this study was to estimate the optimal time to start disease-modifying therapies (DMTs) to prevent the long-term disability accumulation in MS, using a pooled dataset from the Big Multiple Sclerosis Data (BMSD) network. METHODS: Multivariable Cox regression models adjusted for the time to first treatment start from disease onset (in quintiles) were used. To mitigate the impact of potential biases, a set of pairwise propensity score (PS)-matched analyses were performed. The first quintile, including patients treated within 1.2 years from onset, was used as reference. RESULTS: A cohort of 11,871 patients (median follow-up after treatment start: 13.2 years) was analyzed. A 3- and 12-month confirmed disability worsening event and irreversible Expanded Disability Status Scale (EDSS) 4.0 and 6.0 scores were reached by 7062 (59.5%), 4138 (34.9%), 3209 (31.1%), and 1909 (16.5%) patients, respectively. The risk of reaching all the disability outcomes was significantly lower (p < 0.0004) for the first quintile patients' group. CONCLUSION: Real-world data from the BMSD demonstrate that DMTs should be commenced within 1.2 years from the disease onset to reduce the risk of disability accumulation over the long term.
Entities:
Keywords:
EDSS; Multiple sclerosis; big data; early treatment
Authors: David Ellenberger; Tina Parciak; Waldemar Brola; Jan Hillert; Rod Middleton; Alexander Stahmann; Christoph Thalheim; Peter Flachenecker Journal: Mult Scler J Exp Transl Clin Date: 2022-04-27
Authors: Corey C Ford; Jeffrey A Cohen; Andrew D Goodman; John W Lindsey; Robert P Lisak; Christopher Luzzio; Amy Pruitt; John Rose; Horea Rus; Jerry S Wolinsky; Shaul E Kadosh; Emily Bernstein-Hanlon; Yafit Stark; Jessica K Alexander Journal: Mult Scler Date: 2022-06-29 Impact factor: 5.855