Personalized treatment decision algorithms for the clinical application of serum neurofilament light chain in multiple sclerosis: A modified Delphi Study
Year: 2025
Type: article
Abstract: Background: Serum neurofilament light (sNfL) chain levels, a sensitive measure of disease activity in multiple sclerosis (MS), are increasingly considered for individual therapy optimization yet without consensus on their use for clinical application. Objective: We here propose treatment decision algorithms incorporating sNfL levels to adapt disease-modifying therapies (DMTs). Methods: We conducted a modified Delphi study to reach consensus on algorithms using sNfL within typical clinical scenarios. sNfL levels were defined as “high” (>90th percentile) vs “normal” (<80th percentile), based on normative values of control persons. In three rounds, 10 international and 18 Swiss MS experts, and 3 patient consultants rated their agreement on treatment algorithms. Consensus thresholds were defined as moderate (50%–79%), broad (80%–94%), strong (≥95%), and full (100%). Results: The Delphi provided 9 escalation algorithms (e.g. initiating treatment based on high sNfL), 11 horizontal switch (e.g. switching natalizumab to another high-efficacy DMT based on high sNfL), and 3 de-escalation (e.g. stopping DMT or extending intervals in B-cell depleting therapies). Conclusion: The consensus reached on typical clinical scenarios provides the basis for using sNfL to inform treatment decisions in a randomized pragmatic trial, an important step to gather robust evidence for using sNfL to inform personalized treatment decisions in clinical practice.
DOI: 10.5167/uzh-284543
Authors: Yaldizli, Özgür, Benkert, Pascal, Achtnichts, Lutz, Bar-Or, Amit, Bohner-Lang, Viviane, Bridel, Claire, Comabella, Manuel, Findling, Oliver, Disanto, Giulio, Finkener, Sebastian, Gobbi, Claudio, Granziera, Cristina, Herwerth, Marina, Hoepner, Robert, Horakova, Dana, Kamber, Nicole, Khalil, Michael, Kunz, Philipp, Lalive, Patrice, Linker, Ralf, Lorscheider, Johannes, Müller, Stefanie, Oechtering, Johanna, Pettypool, Victoria, Piehl, Fredrik, Pot, Caroline, Roth, Patrick, Théaudin, Marie, Tintore, Mar, Tur, Carmen, et al
Language: English
Subfield: Pathology and Forensic Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7127636512
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