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Multiple Sclerosis Severity Score (Msss) Improves the Accuracy of Individualized Prediction in Ms Publisher Pubmed



Kalincik T1, 39 ; Kister I2 ; Bacon TE2 ; Malpas CB1, 39 ; Sharmin S1, 39 ; Horakova D3 ; Kubalahavrdova E3 ; Patti F4 ; Izquierdo G5 ; Eichau S5 ; Ozakbas S6 ; Onofrj M7 ; Lugaresi A8, 38 ; Prat A9 Show All Authors
Authors
  1. Kalincik T1, 39
  2. Kister I2
  3. Bacon TE2
  4. Malpas CB1, 39
  5. Sharmin S1, 39
  6. Horakova D3
  7. Kubalahavrdova E3
  8. Patti F4
  9. Izquierdo G5
  10. Eichau S5
  11. Ozakbas S6
  12. Onofrj M7
  13. Lugaresi A8, 38
  14. Prat A9
  15. Girard M9
  16. Duquette P9
  17. Grammond P10
  18. Sola P11, 12
  19. Ferraro D11, 12, 35
  20. Alroughani R13
  21. Terzi M14
  22. Boz C15
  23. Grandmaison F16
  24. Bergamaschi R17
  25. Gerlach O18, 36
  26. Sa MJ19, 37
  27. Kappos L20
  28. Cartechini E21
  29. Lechnerscott J22
  30. Van Pesch V23
  31. Shaygannejad V24
  32. Granella F25
  33. Spitaleri D26
  34. Iuliano G27
  35. Maimone D28
  36. Prevost J29
  37. Soysal A30
  38. Turkoglu R31
  39. Ampapa R32
  40. Butzkueven H33
  41. Cutter G34

Source: Multiple Sclerosis Journal Published:2022


Abstract

Background: The MSBase prediction model of treatment response leverages multiple demographic and clinical characteristics to estimate hazards of relapses, confirmed disability accumulation (CDA), and confirmed disability improvement (CDI). The model did not include Multiple Sclerosis Severity Score (MSSS), a disease duration-adjusted ranked score of disability. Objective: To incorporate MSSS into the MSBase prediction model and compare model accuracy with and without MSSS. Methods: The associations between MSSS and relapse, CDA, and CDI were evaluated with marginal proportional hazards models adjusted for three principal components representative of patients’ demographic and clinical characteristics. The model fit with and without MSSS was assessed with penalized r2 and Harrell C. Results: A total of 5866 MS patients were started on disease-modifying therapy during prospective follow-up (age 38.4 ± 10.6 years; 72% female; disease duration 8.5 ± 7.7 years). Including MSSS into the model improved the accuracy of individual prediction of relapses by 31%, of CDA by 23%, and of CDI by 24% (Harrell C) and increased the amount of variance explained for relapses by 49%, for CDI by 11%, and for CDA by 10% as compared with the original model. Conclusion: Addition of a single, readily available metric, MSSS, to the comprehensive MSBase prediction model considerably improved the individual accuracy of prognostics in MS. © The Author(s), 2022.
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