• search hit 12 of 26
Back to Result List

Detecting ongoing disease activity in mildly affected multiple sclerosis patients under first-line therapies

  • Background: The current range of disease-modifying treatments (DMTs) for relapsing-remitting multiple sclerosis (RRMS) has placed more importance on the accurate monitoring of disease progression for timely and appropriate treatment decisions. With a rising number of measurements for disease progression, it is currently unclear how well these measurements or combinations of them can monitor more mildly affected RRMS patients. Objectives: To investigate several composite measures for monitoring disease activity and their potential relation to the biomarker neurofilament light chain (NfL) in a clearly defined early RRMS patient cohort with a milder disease course. Methods: From a total of 301 RRMS patients, a subset of 46 patients being treated with a continuous first-line therapy was analyzed for loss of no evidence of disease activity (lo-NEDA-3) status, relapse-associated worsening (RAW) and progression independent of relapse activity (PIRA), up to seven years after treatment initialization. Kaplan-Meier estimates wereBackground: The current range of disease-modifying treatments (DMTs) for relapsing-remitting multiple sclerosis (RRMS) has placed more importance on the accurate monitoring of disease progression for timely and appropriate treatment decisions. With a rising number of measurements for disease progression, it is currently unclear how well these measurements or combinations of them can monitor more mildly affected RRMS patients. Objectives: To investigate several composite measures for monitoring disease activity and their potential relation to the biomarker neurofilament light chain (NfL) in a clearly defined early RRMS patient cohort with a milder disease course. Methods: From a total of 301 RRMS patients, a subset of 46 patients being treated with a continuous first-line therapy was analyzed for loss of no evidence of disease activity (lo-NEDA-3) status, relapse-associated worsening (RAW) and progression independent of relapse activity (PIRA), up to seven years after treatment initialization. Kaplan-Meier estimates were used for time-to-event analysis. Additionally, a Cox regression model was used to analyze the effect of NIL levels on outcome measures in this cohort. Results: In this mildly affected cohort, both lo-NEDA-3 and PIRA frequently occurred over a median observational period of 67.2 months and were observed in 39 (84.8%) and 23 (50.0%) patients, respectively. Additionally, 12 out of 26 PIRA manifestations (46.2%) were observed without a corresponding lo-NEDA-3 status. Jointly, either PIRA or lo-NEDA-3 showed disease activity in all patients followed-up for at least the median duration (67.2 months). NfL values demonstrated an association with the occurrence of relapses and RAW. Conclusion: The complementary use of different disease progression measures helps mirror ongoing disease activity in mildly affected early RRMS patients being treated with continuous first-line therapy.show moreshow less

Export metadata

Additional Services

Search Google Scholar Statistics
Metadaten
Author details:Lars MasanneckORCiDGND, Leoni Rolfes, Liesa Regner-Nelke, Alice Willison, Saskia Räuber, Falk Steffen, Stefan BittnerORCiD, Frauke Zipp, Philipp Albrecht, Tobias RuckORCiD, Hans-Peter Hartung, Sven G. Meuth, Marc Pawlitzki
DOI:https://doi.org/10.1016/j.msard.2022.103927
ISSN:2211-0348
ISSN:2211-0356
Pubmed ID:https://pubmed.ncbi.nlm.nih.gov/35700670
Title of parent work (English):Multiple Sclerosis and Related Disorders
Publisher:Elsevier
Place of publishing:Amsterdam
Publication type:Article
Language:English
Date of first publication:2022/05/28
Publication year:2022
Release date:2024/09/13
Tag:NEDA; PIRA; RAW; biomarker; disease activity measurements; early MS; neurofilament light chain; relapsing-remitting multiple sclerosis
Volume:63
Article number:103927
Number of pages:12
Organizational units:Digital Engineering Fakultät / Hasso-Plattner-Institut für Digital Engineering GmbH
DDC classification:6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Peer review:Referiert
Publishing method:Open Access / Hybrid Open-Access
License (German):License LogoCC-BY - Namensnennung 4.0 International
Accept ✔
This website uses technically necessary session cookies. By continuing to use the website, you agree to this. You can find our privacy policy here.