Personalized treatment decision algorithms for the clinical application of serum neurofilament light chain in multiple sclerosis: A modified Delphi Study

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Institut Català de la Salut

[Yaldizli Ö] Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland. Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel, University of Basel, Basel, Switzerland Neurologic Clinic and Policlinic, MS Centre, University Hospital Basel, Basel, Switzerland. Translational Imaging in Neurology Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel, University of Basel, Basel, Switzerland. [Benkert P] Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland. [Achtnichts L] Neurozentrum Oberaargau, Langenthal, Switzerland. [Bar-Or A] Center for Neuroinflammation and Experimental Therapeutics and Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. [Bohner-Lang V] Patient Consultant, Basel, Switzerland. [Bridel C] Translational Biomarker Research Group, Department of Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland. [Comabella M, Tintore M, Tur C] Centre d’Esclerosi Múltiple de Catalunya (CEMCAT), Barcelona, Spain. Vall d’Hebron Hospital Universitari, Barcelona, Spain

Vall d'Hebron Barcelona Hospital Campus

Data de publicació

2025-09-19T11:55:18Z

2025-09-19T11:55:18Z

2025-07



Resum

De-escalation; Personalized treatment strategies; Serum neurofilament light chain


Desescalada; Estrategias de tratamiento personalizadas; Cadena ligera de neurofilamentos sérica


Desescalada; Estratègies de tractament personalitzades; Cadena lleugera de neurofilaments sèrics


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.


This study is part of the MultiSCRIPT trial supported by the Swiss National Science Foundation as part of the Investigator Initiated Clinical Trial program (grant no. 33IC30_205806/1).

Tipus de document

Article


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Llengua

Anglès

Publicat per

SAGE Publications

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Multiple Sclerosis Journal;31(8)

https://doi.org/10.1177/13524585251335466

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Attribution 4.0 International

http://creativecommons.org/licenses/by/4.0/

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