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Treatment Response Assessment Tools for Relapsing Multiple Sclerosis

The authors explore available tools for assessing treatment response in relapsing multiple sclerosis and provide practical recommendations for their use in clinical practice.

01/22/2025
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  • References

    1. Amin M, Hersh CM. Updates and advances in multiple sclerosis neurotherapeutics. Neurodegener Dis Manag. 2023;13(1):47-70. doi:10.2217/nmt-2021-0058

    2. Newsome SD, Binns C, Kaunzner UW, Morgan S, Halper J. No evidence of disease activity (NEDA) as a clinical assessment tool for multiple sclerosis: clinician and patient perspectives [narrative review]. Neurol Ther. 2023;12(6):1909-1935. doi:10.1007/s40120-023-00549-7

    2a. Ramo-Tello C, Blanco Y, Brieva L, et al. Recommendations for the diagnosis and treatment of multiple sclerosis relapses. J Pers Med. 2021;12(1):6. doi:10.3390/jpm12010006

    3. Kurtzke JF. Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology. 1983;33(11):1444-1452. doi:10.1212/wnl.33.11.1444

    4. Wattjes MP, Ciccarelli O, Reich DS, et al. 2021 MAGNIMS-CMSC-NAIMS consensus recommendations on the use of MRI in patients with multiple sclerosis. Lancet Neurol. 2021;20(8):653-670. doi:10.1016/S1474-4422(21)00095-8

    4a. Vågberg M, Axelsson M, Birgander R, et al. Guidelines for the use of magnetic resonance imaging in diagnosing and monitoring the treatment of multiple sclerosis: recommendations of the Swedish Multiple Sclerosis Association and the Swedish Neuroradiological Society. Acta Neurol Scand. 2017;135(1):17-24. doi:10.1111/ane.12667

    5. Rovira À, Doniselli FM, Auger C, et al. Use of gadolinium-based contrast agents in multiple sclerosis: a review by the ESMRMB-GREC and ESNR Multiple Sclerosis Working Group. Eur Radiol. 2024;34(3):1726-1735. doi:10.1007/s00330-023-10151-y

    6. Mendelsohn Z, Pemberton HG, Gray J, et al. Commercial volumetric MRI reporting tools in multiple sclerosis: a systematic review of the evidence. Neuroradiology. 2023;65(1):5-24. doi:10.1007/s00234-022-03074-w

    7. Balcer LJ, Miller DH, Reingold SC, Cohen JA. Vision and vision-related outcome measures in multiple sclerosis. Brain. 2015;138(Pt 1):11-27. doi:10.1093/brain/awu335

    8. Kemenyova P, Turcani P, Sutovsky S, Waczulikova I. Optical coherence tomography and its use in optical neuritis and multiple sclerosis. Bratisl Lek Listy. 2014;115(11):723-729. doi:10.4149/bll_2014_140

    9. Kuhle J, Barro C, Andreasson U, et al. Comparison of three analytical platforms for quantification of the neurofilament light chain in blood samples: ELISA, electrochemiluminescence immunoassay and Simoa. Clin Chem Lab Med. 2016;54(10):1655-1661. doi:10.1515/cclm-2015-1195

    10. Freedman MS, Gnanapavan S, Booth RA, et al. Guidance for use of neurofilament light chain as a cerebrospinal fluid and blood biomarker in multiple sclerosis management. EBioMedicine. 2024;101:104970. doi:10.1016/j.ebiom.2024.104970

    11. Sotirchos ES, Hu C, Smith MD, et al. Agreement between published reference resources for neurofilament light chain levels in people with multiple sclerosis. Neurology. 2023;101(23):e2448-e2453. doi:10.1212/WNL.0000000000207957

    12. Sun M, Liu N, Xie Q, et al. A candidate biomarker of glial fibrillary acidic protein in csf and blood in differentiating multiple sclerosis and its subtypes: a systematic review and meta-analysis. Mult Scler Relat Disord. 2021;51:102870. doi:10.1016/j.msard.2021.102870

    13. Chitnis T, Foley J, Ionete C, et al. Clinical validation of a multi-protein, serum-based assay for disease activity assessments in multiple sclerosis. Clin Immunol. 2023;253:109688. doi:10.1016/j.clim.2023.109688

    14. Food & Drug Administration. Patient-reported outcome measures: use in medical product development to support labeling claims. Published 2017. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/patient-reported-outcome-measures-use-medical-product-development-support-labeling-claims

    15. Squitieri L, Bozic KJ, Pusic AL. The role of patient-reported outcome measures in value-based payment reform. Value Health. 2017;20(6):834-836. doi:10.1016/j.jval.2017.02.003

    16. National Institutes of Health. HealthMeasures: NIH Toolbox. Published 2023. https://www.healthmeasures.net/explore-measurement-systems/nih-toolbox

    17. Nowinski CJ, Miller DM, Cella D. Evolution of patient-reported outcomes and their role in multiple sclerosis clinical trials. Neurotherapeutics. 2017;14(4):934-944. doi:10.1007/s13311-017-0571-6

    18. Gashi S, Oldrati P, Moebus M, et al. Modeling multiple sclerosis using mobile and wearable sensor data. NPJ Digit Med. 2024;7(1):64. doi:10.1038/s41746-024-01025-8

    19. Oh J, Capezzuto L, Kriara L, et al. Use of smartphone-based remote assessments of multiple sclerosis in floodlight open, a global, prospective, open-access study. Sci Rep. 2024;14(1):122. doi:10.1038/s41598-023-49299-4

    20. Dalla-Costa G, Radaelli M, Maida S, et al. Smart watch, smarter EDSS: improving disability assessment in multiple sclerosis clinical practice. J Neurol Sci. 2017;383:166-168. doi:10.1016/j.jns.2017.10.043

    21. Pardo G, Coates S, Okuda DT. Outcome measures assisting treatment optimization in multiple sclerosis. J Neurol. 2022;269(3):1282-1297. doi:10.1016/j.jns.2017.10.043

    22. Thebault S, Booth RA, Rush CA, MacLean H, Freedman MS. Serum neurofilament light chain measurement in MS: hurdles to clinical translation. Front Neurosci. 2021;15:654942. doi:10.3389/fnins.2021.654942

    23. Akaishi T, Ishii T, Aoki M, Nakashima I. Calculating and comparing the annualized relapse rate and estimating the confidence interval in relapsing neurological diseases. Front Neurol. 2022;13:875456. doi:10.3389/fneur.2022.875456

    24. Kalinowski A, Cutter G, Bozinov N, et al. The timed 25-foot walk in a large cohort of multiple sclerosis patients. Mult Scler. 2022;28(2):289-299. doi:10.1177/13524585211017013

    25. Feys P, Lamers I, Francis G, et al. The Nine-Hole Peg Test as a manual dexterity performance measure for multiple sclerosis. Mult Scler. 2017;23(5):711-720. doi:10.1177/1352458517690824

    26. Benedict RH, DeLuca J, Phillips G, LaRocca N, Hudson LD, Rudick R. Validity of the symbol digit modalities test as a cognition performance outcome measure for multiple sclerosis. Mult Scler. 2017;23(5):721-733. doi:10.1177/1352458517690821

    27. Rocca MA, Preziosa P, Barkhof F, et al. Current and future role of mri in the diagnosis and prognosis of multiple sclerosis. Lancet Reg Health Eur. 2024;44:100978. doi:10.1016/j.lanepe.2024.100978

  • Disclosures

    The authors report no disclosures

  • Cite this Article

    Mahmoudi F, McCarthy M, Ortega M, et al. Treatment response assessment tools for relapsing multiple sclerosis. Practical Neurology (US). 2025;24(1):18-22.

Recommended
Details
  • References

    1. Amin M, Hersh CM. Updates and advances in multiple sclerosis neurotherapeutics. Neurodegener Dis Manag. 2023;13(1):47-70. doi:10.2217/nmt-2021-0058

    2. Newsome SD, Binns C, Kaunzner UW, Morgan S, Halper J. No evidence of disease activity (NEDA) as a clinical assessment tool for multiple sclerosis: clinician and patient perspectives [narrative review]. Neurol Ther. 2023;12(6):1909-1935. doi:10.1007/s40120-023-00549-7

    2a. Ramo-Tello C, Blanco Y, Brieva L, et al. Recommendations for the diagnosis and treatment of multiple sclerosis relapses. J Pers Med. 2021;12(1):6. doi:10.3390/jpm12010006

    3. Kurtzke JF. Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology. 1983;33(11):1444-1452. doi:10.1212/wnl.33.11.1444

    4. Wattjes MP, Ciccarelli O, Reich DS, et al. 2021 MAGNIMS-CMSC-NAIMS consensus recommendations on the use of MRI in patients with multiple sclerosis. Lancet Neurol. 2021;20(8):653-670. doi:10.1016/S1474-4422(21)00095-8

    4a. Vågberg M, Axelsson M, Birgander R, et al. Guidelines for the use of magnetic resonance imaging in diagnosing and monitoring the treatment of multiple sclerosis: recommendations of the Swedish Multiple Sclerosis Association and the Swedish Neuroradiological Society. Acta Neurol Scand. 2017;135(1):17-24. doi:10.1111/ane.12667

    5. Rovira À, Doniselli FM, Auger C, et al. Use of gadolinium-based contrast agents in multiple sclerosis: a review by the ESMRMB-GREC and ESNR Multiple Sclerosis Working Group. Eur Radiol. 2024;34(3):1726-1735. doi:10.1007/s00330-023-10151-y

    6. Mendelsohn Z, Pemberton HG, Gray J, et al. Commercial volumetric MRI reporting tools in multiple sclerosis: a systematic review of the evidence. Neuroradiology. 2023;65(1):5-24. doi:10.1007/s00234-022-03074-w

    7. Balcer LJ, Miller DH, Reingold SC, Cohen JA. Vision and vision-related outcome measures in multiple sclerosis. Brain. 2015;138(Pt 1):11-27. doi:10.1093/brain/awu335

    8. Kemenyova P, Turcani P, Sutovsky S, Waczulikova I. Optical coherence tomography and its use in optical neuritis and multiple sclerosis. Bratisl Lek Listy. 2014;115(11):723-729. doi:10.4149/bll_2014_140

    9. Kuhle J, Barro C, Andreasson U, et al. Comparison of three analytical platforms for quantification of the neurofilament light chain in blood samples: ELISA, electrochemiluminescence immunoassay and Simoa. Clin Chem Lab Med. 2016;54(10):1655-1661. doi:10.1515/cclm-2015-1195

    10. Freedman MS, Gnanapavan S, Booth RA, et al. Guidance for use of neurofilament light chain as a cerebrospinal fluid and blood biomarker in multiple sclerosis management. EBioMedicine. 2024;101:104970. doi:10.1016/j.ebiom.2024.104970

    11. Sotirchos ES, Hu C, Smith MD, et al. Agreement between published reference resources for neurofilament light chain levels in people with multiple sclerosis. Neurology. 2023;101(23):e2448-e2453. doi:10.1212/WNL.0000000000207957

    12. Sun M, Liu N, Xie Q, et al. A candidate biomarker of glial fibrillary acidic protein in csf and blood in differentiating multiple sclerosis and its subtypes: a systematic review and meta-analysis. Mult Scler Relat Disord. 2021;51:102870. doi:10.1016/j.msard.2021.102870

    13. Chitnis T, Foley J, Ionete C, et al. Clinical validation of a multi-protein, serum-based assay for disease activity assessments in multiple sclerosis. Clin Immunol. 2023;253:109688. doi:10.1016/j.clim.2023.109688

    14. Food & Drug Administration. Patient-reported outcome measures: use in medical product development to support labeling claims. Published 2017. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/patient-reported-outcome-measures-use-medical-product-development-support-labeling-claims

    15. Squitieri L, Bozic KJ, Pusic AL. The role of patient-reported outcome measures in value-based payment reform. Value Health. 2017;20(6):834-836. doi:10.1016/j.jval.2017.02.003

    16. National Institutes of Health. HealthMeasures: NIH Toolbox. Published 2023. https://www.healthmeasures.net/explore-measurement-systems/nih-toolbox

    17. Nowinski CJ, Miller DM, Cella D. Evolution of patient-reported outcomes and their role in multiple sclerosis clinical trials. Neurotherapeutics. 2017;14(4):934-944. doi:10.1007/s13311-017-0571-6

    18. Gashi S, Oldrati P, Moebus M, et al. Modeling multiple sclerosis using mobile and wearable sensor data. NPJ Digit Med. 2024;7(1):64. doi:10.1038/s41746-024-01025-8

    19. Oh J, Capezzuto L, Kriara L, et al. Use of smartphone-based remote assessments of multiple sclerosis in floodlight open, a global, prospective, open-access study. Sci Rep. 2024;14(1):122. doi:10.1038/s41598-023-49299-4

    20. Dalla-Costa G, Radaelli M, Maida S, et al. Smart watch, smarter EDSS: improving disability assessment in multiple sclerosis clinical practice. J Neurol Sci. 2017;383:166-168. doi:10.1016/j.jns.2017.10.043

    21. Pardo G, Coates S, Okuda DT. Outcome measures assisting treatment optimization in multiple sclerosis. J Neurol. 2022;269(3):1282-1297. doi:10.1016/j.jns.2017.10.043

    22. Thebault S, Booth RA, Rush CA, MacLean H, Freedman MS. Serum neurofilament light chain measurement in MS: hurdles to clinical translation. Front Neurosci. 2021;15:654942. doi:10.3389/fnins.2021.654942

    23. Akaishi T, Ishii T, Aoki M, Nakashima I. Calculating and comparing the annualized relapse rate and estimating the confidence interval in relapsing neurological diseases. Front Neurol. 2022;13:875456. doi:10.3389/fneur.2022.875456

    24. Kalinowski A, Cutter G, Bozinov N, et al. The timed 25-foot walk in a large cohort of multiple sclerosis patients. Mult Scler. 2022;28(2):289-299. doi:10.1177/13524585211017013

    25. Feys P, Lamers I, Francis G, et al. The Nine-Hole Peg Test as a manual dexterity performance measure for multiple sclerosis. Mult Scler. 2017;23(5):711-720. doi:10.1177/1352458517690824

    26. Benedict RH, DeLuca J, Phillips G, LaRocca N, Hudson LD, Rudick R. Validity of the symbol digit modalities test as a cognition performance outcome measure for multiple sclerosis. Mult Scler. 2017;23(5):721-733. doi:10.1177/1352458517690821

    27. Rocca MA, Preziosa P, Barkhof F, et al. Current and future role of mri in the diagnosis and prognosis of multiple sclerosis. Lancet Reg Health Eur. 2024;44:100978. doi:10.1016/j.lanepe.2024.100978

  • Disclosures

    The authors report no disclosures

  • Cite this Article

    Mahmoudi F, McCarthy M, Ortega M, et al. Treatment response assessment tools for relapsing multiple sclerosis. Practical Neurology (US). 2025;24(1):18-22.

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