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Early clinical markers of aggressive multiple sclerosis

CB. Malpas, A. Manouchehrinia, S. Sharmin, I. Roos, D. Horakova, EK. Havrdova, M. Trojano, G. Izquierdo, S. Eichau, R. Bergamaschi, P. Sola, D. Ferraro, A. Lugaresi, A. Prat, M. Girard, P. Duquette, P. Grammond, F. Grand'Maison, S. Ozakbas, V....

. 2020 ; 143 (5) : 1400-1413. [pub] 20200501

Jazyk angličtina Země Velká Británie

Typ dokumentu časopisecké články, pozorovací studie, práce podpořená grantem

Perzistentní odkaz   https://www.medvik.cz/link/bmc20028197

Patients with the 'aggressive' form of multiple sclerosis accrue disability at an accelerated rate, typically reaching Expanded Disability Status Score (EDSS) ≥ 6 within 10 years of symptom onset. Several clinicodemographic factors have been associated with aggressive multiple sclerosis, but less research has focused on clinical markers that are present in the first year of disease. The development of early predictive models of aggressive multiple sclerosis is essential to optimize treatment in this multiple sclerosis subtype. We evaluated whether patients who will develop aggressive multiple sclerosis can be identified based on early clinical markers. We then replicated this analysis in an independent cohort. Patient data were obtained from the MSBase observational study. Inclusion criteria were (i) first recorded disability score (EDSS) within 12 months of symptom onset; (ii) at least two recorded EDSS scores; and (iii) at least 10 years of observation time, based on time of last recorded EDSS score. Patients were classified as having 'aggressive multiple sclerosis' if all of the following criteria were met: (i) EDSS ≥ 6 reached within 10 years of symptom onset; (ii) EDSS ≥ 6 confirmed and sustained over ≥6 months; and (iii) EDSS ≥ 6 sustained until the end of follow-up. Clinical predictors included patient variables (sex, age at onset, baseline EDSS, disease duration at first visit) and recorded relapses in the first 12 months since disease onset (count, pyramidal signs, bowel-bladder symptoms, cerebellar signs, incomplete relapse recovery, steroid administration, hospitalization). Predictors were evaluated using Bayesian model averaging. Independent validation was performed using data from the Swedish Multiple Sclerosis Registry. Of the 2403 patients identified, 145 were classified as having aggressive multiple sclerosis (6%). Bayesian model averaging identified three statistical predictors: age > 35 at symptom onset, EDSS ≥ 3 in the first year, and the presence of pyramidal signs in the first year. This model significantly predicted aggressive multiple sclerosis [area under the curve (AUC) = 0.80, 95% confidence intervals (CIs): 0.75, 0.84, positive predictive value = 0.15, negative predictive value = 0.98]. The presence of all three signs was strongly predictive, with 32% of such patients meeting aggressive disease criteria. The absence of all three signs was associated with a 1.4% risk. Of the 556 eligible patients in the Swedish Multiple Sclerosis Registry cohort, 34 (6%) met criteria for aggressive multiple sclerosis. The combination of all three signs was also predictive in this cohort (AUC = 0.75, 95% CIs: 0.66, 0.84, positive predictive value = 0.15, negative predictive value = 0.97). Taken together, these findings suggest that older age at symptom onset, greater disability during the first year, and pyramidal signs in the first year are early indicators of aggressive multiple sclerosis.

Austin Health Melbourne Australia

Azienda Ospedaliera di Rilievo Nazionale San Giuseppe Moscati Avellino Avellino Italy

Bakirkoy Education and Research Hospital for Psychiatric and Neurological Diseases Istanbul Turkey

Central Clinical School Monash University Melbourne Australia Department of Neurology The Alfred Hospital Melbourne Australia Department of Neurology Box Hill Hospital Monash University Melbourne Australia

Centre for Molecular Medicine Karolinska University Hospital Stockholm Sweden

CHUM and Universite de Montreal Montreal Canada

CISSS de Chaudière Appalaches Levis Canada

Cliniques Universitaires Saint Luc Brussels Belgium Université Catholique de Louvain Brussels Belgium

CORe Unit Department of Medicine University of Melbourne Melbourne Australia Department of Neurology Royal Melbourne Hospital Melbourne Australia

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

Department of Biomedical and Neuromotor Science University of Bologna Bologna Italy IRCCS Istituto delle Scienze Neurologiche di Bologna Bologna Italy

Department of Clinical Neuroscience Karolinska Institutet Sweden

Department of Medicine and Surgery University of Parma Parma Italy

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

Department of Neurology Razi Hospital LR 18SP03 Clinical Investigation Center Neurosciences and Mental Health Faculty of Medicine University Tunis El Manar Tunis Tunisia

Department of Neurology Razi Hospital Manouba Tunisia

Department of Neuroscience Azienda Ospedaliera Universitaria Modena Italy

Department of Neuroscience Azienda Ospedaliera Universitaria Modena Italy Department of Biomedical Metabolic and Neurosciences University of Modena and Reggio Emilia Modena Italy

Dokuz Eylul University Konak Izmir Turkey

Flinders University Adelaide Australia

Groene Hart Ziekenhuis Gouda The Netherlands

Hacettepe University Ankara Turkey

Haydarpasa Numune Training and Research Hospital Istanbul Turkey

Hospital Germans Trias i Pujol Badalona Spain

Hospital Italiano Buenos Aires Argentina

Hospital Universitario Virgen Macarena Sevilla Spain

Instituto de Investigación Sanitaria Biodonostia Hospital Universitario Donostia San Sebastián Spain

IRCCS Mondino Foundation Pavia Italy

Koc University School of Medicine Department of Neurology Istanbul Turkey

Kommunehospitalet Arhus C Denmark

KTU Medical Faculty Farabi Hospital Trabzon Turkey

Medical Faculty 19 Mayis University Samsun Turkey

Neuro Rive Sud Quebec Canada

Universidade Metropolitana de Santos Santos Brazil

University of Queensland Brisbane Australia Royal Brisbane and Women's Hospital Brisbane Australia

UOC Neurologia Azienda Sanitaria Unica Regionale Marche AV3 Macerata Italy

Zuyderland Ziekenhuis Sittard The Netherlands

Citace poskytuje Crossref.org

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$a Patients with the 'aggressive' form of multiple sclerosis accrue disability at an accelerated rate, typically reaching Expanded Disability Status Score (EDSS) ≥ 6 within 10 years of symptom onset. Several clinicodemographic factors have been associated with aggressive multiple sclerosis, but less research has focused on clinical markers that are present in the first year of disease. The development of early predictive models of aggressive multiple sclerosis is essential to optimize treatment in this multiple sclerosis subtype. We evaluated whether patients who will develop aggressive multiple sclerosis can be identified based on early clinical markers. We then replicated this analysis in an independent cohort. Patient data were obtained from the MSBase observational study. Inclusion criteria were (i) first recorded disability score (EDSS) within 12 months of symptom onset; (ii) at least two recorded EDSS scores; and (iii) at least 10 years of observation time, based on time of last recorded EDSS score. Patients were classified as having 'aggressive multiple sclerosis' if all of the following criteria were met: (i) EDSS ≥ 6 reached within 10 years of symptom onset; (ii) EDSS ≥ 6 confirmed and sustained over ≥6 months; and (iii) EDSS ≥ 6 sustained until the end of follow-up. Clinical predictors included patient variables (sex, age at onset, baseline EDSS, disease duration at first visit) and recorded relapses in the first 12 months since disease onset (count, pyramidal signs, bowel-bladder symptoms, cerebellar signs, incomplete relapse recovery, steroid administration, hospitalization). Predictors were evaluated using Bayesian model averaging. Independent validation was performed using data from the Swedish Multiple Sclerosis Registry. Of the 2403 patients identified, 145 were classified as having aggressive multiple sclerosis (6%). Bayesian model averaging identified three statistical predictors: age > 35 at symptom onset, EDSS ≥ 3 in the first year, and the presence of pyramidal signs in the first year. This model significantly predicted aggressive multiple sclerosis [area under the curve (AUC) = 0.80, 95% confidence intervals (CIs): 0.75, 0.84, positive predictive value = 0.15, negative predictive value = 0.98]. The presence of all three signs was strongly predictive, with 32% of such patients meeting aggressive disease criteria. The absence of all three signs was associated with a 1.4% risk. Of the 556 eligible patients in the Swedish Multiple Sclerosis Registry cohort, 34 (6%) met criteria for aggressive multiple sclerosis. The combination of all three signs was also predictive in this cohort (AUC = 0.75, 95% CIs: 0.66, 0.84, positive predictive value = 0.15, negative predictive value = 0.97). Taken together, these findings suggest that older age at symptom onset, greater disability during the first year, and pyramidal signs in the first year are early indicators of aggressive multiple sclerosis.
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