Measuring treatment response to advance precision medicine for multiple sclerosis
Abstract Objective To assess the independent contributions of clinical measures (relapses, Expanded Disability Status Scale [EDSS] scores, and neuroperformance measures) and nonclinical measures (new brain magnetic resonance imaging [MRI] activity and serum neurofilament light chain [sNfL] levels) f...
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| Autores principales: | , , , , , , , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Wiley
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/fa352fd8745148f48a725597ae0f7a77 |
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| Sumario: | Abstract Objective To assess the independent contributions of clinical measures (relapses, Expanded Disability Status Scale [EDSS] scores, and neuroperformance measures) and nonclinical measures (new brain magnetic resonance imaging [MRI] activity and serum neurofilament light chain [sNfL] levels) for distinguishing natalizumab‐treated from placebo‐treated patients. Methods We conducted post hoc analyses using data from the AFFIRM trial of natalizumab for multiple sclerosis. We used multivariable regression analyses with predictors (EDSS progression, no relapse, new or enlarging MRI activity, brain atrophy, sNfL levels, and neuroperformance worsening) to identify measures that independently discriminated between treatment groups. Results The multivariable model that best distinguished natalizumab from placebo was no new or enlarging T2 or gadolinium‐enhancing activity on MRI (odds ratio; 95% confidence interval: 7.2; 4.7–10.9), year 2 sNfL levels <97.5th percentile (4.1; 2.6–6.2), and no relapses in years 0–2 (2.1; 1.5–3.0). The next best‐fitting model was a two‐component model that included no MRI activity and sNfL levels <97.5th percentile at year 2. There was little difference between the three‐ and two‐component models. Interpretation Nonclinical measures (new MRI activity and sNfL levels) discriminate between treatment and placebo groups similarly to or better than clinical outcomes composites and have implications for patient monitoring. |
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