1. Early clinical markers of aggressive multiple sclerosis
- Author
-
Tomas Kalincik, Marc Girard, Guillermo Izquierdo, Cristina Ramo-Tello, Francois Grand'Maison, Ali Manouchehrinia, Youssef Sidhom, Charles B Malpas, Alexandre Prat, Maria Trojano, Franco Granella, Pamela A. McCombe, Rana Karabudak, Murat Terzi, Eugenio Pucci, Raymond Hupperts, Vincent Van Pesch, Pierre Grammond, Sifat Sharmin, Richard A L Macdonell, Ayse Altintas, Riadh Gouider, Recai Turkoglu, Diana Ferraro, Alessandra Lugaresi, Pierre Duquette, Helmut Butzkueven, Serkan Ozakbas, Sara Eichau, Izanne Roos, Daniele Spitaleri, Yara Dadalti Fragoso, Mark Slee, Roberto Bergamaschi, Jan Hillert, Edgardo Cristiano, Thor Petersen, Eva Havrdova, Javier Olascoaga, Cavit Boz, Patrizia Sola, Freek Verheul, Aysun Soysal, Tomas Olsson, Dana Horakova, Ondokuz Mayıs Üniversitesi, Malpas C.B., Manouchehrinia A., Sharmin S., Roos I., Horakova D., Havrdova E.K., Trojano M., Izquierdo G., Eichau S., Bergamaschi R., Sola P., Ferraro D., Lugaresi A., Prat A., Girard M., Duquette P., Grammond P., Grand'Maison F., Ozakbas S., van Pesch V., Granella F., Hupperts R., Pucci E., Boz C., Sidhom Y., Gouider R., Spitaleri D., Soysal A., Petersen T., Verheul F., Karabudak R., Turkoglu R., Ramo-Tello C., Terzi M., Cristiano E., Slee M., McCombe P., Macdonell R., Fragoso Y., Olascoaga J., Altintas A., Olsson T., Butzkueven H., Hillert J., Kalincik T., Altıntaş, Ayşe (ORCID 0000-0002-8524-5087 & YÖK ID 11611), School of Medicine, UCL - SSS/IONS/CEMO - Pôle Cellulaire et moléculaire, and UCL - (SLuc) Service de neurologie
- Subjects
Male ,PROGRESSION ,Disease ,multiple sclerosis ,01 natural sciences ,THERAPY ,Severity of Illness Index ,Aggressive disease ,010104 statistics & probability ,Disability Evaluation ,0302 clinical medicine ,PROGNOSTIC-FACTORS ,Multiple Sclerosi ,Disability ,Multiple sclerosis ,Precision medicine ,Prediction ,R PACKAGE ,Age of Onset ,AcademicSubjects/SCI01870 ,Neurosciences and neurology ,IMPAIRMENT ,Cohort ,Disease Progression ,RELAPSE RATE ,Female ,Cohort study ,Human ,Adult ,medicine.medical_specialty ,Multiple Sclerosis ,precision medicine ,BRAIN MRI ,Sensitivity and Specificity ,03 medical and health sciences ,Internal medicine ,Severity of illness ,medicine ,Humans ,0101 mathematics ,Letters to the Editor ,Expanded Disability Status Scale ,business.industry ,NATURAL-HISTORY ,prediction ,medicine.disease ,PREDICTIVE-VALUE ,Confidence interval ,disability ,AcademicSubjects/MED00310 ,Neurology (clinical) ,Age of onset ,business ,aggressive disease ,030217 neurology & neurosurgery ,LONG-TERM DISABILITY - Abstract
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., National Health and Medical Research Council of Australia; Roche; Merck; Biogen; Novartis; Bayer Schering; Sanofi Genzyme; Teva
- Published
- 2020