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Bremso: A Simple Score to Predict Early the Natural Course of Multiple Sclerosis Publisher Pubmed



Bergamaschi R1 ; Montomoli C2 ; Mallucci G1 ; Lugaresi A3 ; Izquierdo G4 ; Grandmaison F5 ; Duquette P6 ; Shaygannejad V7 ; Alroughani R8 ; Grammond P9 ; Boz C10 ; Iuliano G11 ; Zwanikken C12 ; Petersen T13 Show All Authors
Authors
  1. Bergamaschi R1
  2. Montomoli C2
  3. Mallucci G1
  4. Lugaresi A3
  5. Izquierdo G4
  6. Grandmaison F5
  7. Duquette P6
  8. Shaygannejad V7
  9. Alroughani R8
  10. Grammond P9
  11. Boz C10
  12. Iuliano G11
  13. Zwanikken C12
  14. Petersen T13
  15. Lechnerscott J14
  16. Hupperts R15
  17. Butzkueven H16
  18. Pucci E17
  19. Orejaguevara C18
  20. Cristiano E19
  21. Pia Amato MP20
  22. Havrdova E21
  23. Fernandezbolanos R22
  24. Spelman T23
  25. Trojano M24

Source: European Journal of Neurology Published:2015


Abstract

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. © 2015 EAN.
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