TY - JOUR
T1 - BREMSO: A simple score to predict early the natural course of multiple sclerosis
AU - Bergamaschi, Roberto
AU - Montomoli, Cristina
AU - Mallucci, Giulia
AU - Lugaresi, Alessandra
AU - Izquierdo, Guillermo
AU - Grand'Maison, Francois
AU - Duquette, Pierre Pascal
AU - Shaygannejad, Vahid
AU - Alroughani, Raed A
AU - Grammond, Pierre
AU - Boz, Cavit
AU - Iuliano, Gerardo
AU - Zwanikken, Cees
AU - Petersen, Thor
AU - Lechner-Scott, Jeannette
AU - Hupperts, Raymond
AU - Butzkueven, Helmut
AU - Pucci, Eugenio
AU - Oreja-Guevara, Celia
AU - Cristiano, Edgardo
AU - Amato, Maria Pia
AU - Havrdova, Eva
AU - Fernandez-Bolanos, Ricardo
AU - Spelman, Tim
AU - Trojano, Maria
PY - 2015
Y1 - 2015
N2 - Background and purpose: Early prediction of long-term disease evolution is a major challenge in the management of multiple sclerosis (MS). Our aim was to predict the natural course of MS using the Bayesian Risk Estimate for MS at Onset (BREMSO), which gives an individual risk score calculated from demographic and clinical variables collected at disease onset. Methods: An observational study was carried out collecting data from MS patients included in MSBase, an international registry. Disease impact was studied using the Multiple Sclerosis Severity Score (MSSS) and time to secondary progression (SP). To evaluate the natural history of the disease, patients were analysed only if they did not receive immune therapies or only up to the time of starting these therapies. Results: Data from 14 211 patients were analysed. The median BREMSO score was significantly higher in the subgroups of patients whose disease had a major clinical impact (MSSS= third quartile vs. = first quartile, P <0.00001) and who reached SP (P <0.00001). The BREMSO showed good specificity (79 ) as a tool for predicting the clinical impact of MS. Conclusions: BREMSO is a simple tool which can be used in the early stages of MS to predict its evolution, supporting therapeutic decisions in an observational setting.
AB - Background and purpose: Early prediction of long-term disease evolution is a major challenge in the management of multiple sclerosis (MS). Our aim was to predict the natural course of MS using the Bayesian Risk Estimate for MS at Onset (BREMSO), which gives an individual risk score calculated from demographic and clinical variables collected at disease onset. Methods: An observational study was carried out collecting data from MS patients included in MSBase, an international registry. Disease impact was studied using the Multiple Sclerosis Severity Score (MSSS) and time to secondary progression (SP). To evaluate the natural history of the disease, patients were analysed only if they did not receive immune therapies or only up to the time of starting these therapies. Results: Data from 14 211 patients were analysed. The median BREMSO score was significantly higher in the subgroups of patients whose disease had a major clinical impact (MSSS= third quartile vs. = first quartile, P <0.00001) and who reached SP (P <0.00001). The BREMSO showed good specificity (79 ) as a tool for predicting the clinical impact of MS. Conclusions: BREMSO is a simple tool which can be used in the early stages of MS to predict its evolution, supporting therapeutic decisions in an observational setting.
UR - http://onlinelibrary.wiley.com/doi/10.1111/ene.12696/epdf
U2 - 10.1111/ene.12696
DO - 10.1111/ene.12696
M3 - Article
SN - 1351-5101
VL - 22
SP - 981
EP - 989
JO - European Journal of Neurology
JF - European Journal of Neurology
IS - 6
ER -