Personalized treatment decision algorithms for the clinical application of serum neurofilament light chain in multiple sclerosis: A modified Delphi Study
Özgür Yaldizli, Pascal Benkert, Lutz Achtnichts, Amit Bar‐Or, Viviane Bohner-Lang, Claire Bridel, Manuel Comabella, Oliver Findling, Giulio Disanto, Sebastian Finkener, Claudio Gobbi, Cristina Granziera, Marina Herwerth, Robert Hoepner, Dana Horáková, Nicole Kamber, Michael Khalil, Philipp Kunz, Patrice H. Lalive, Ralf A. Linker, Johannes Lorscheider, Stefanie Müller, Johanna Oechtering, Victoria Pettypool, Fredrik Piehl, Caroline Pot, Patrick Roth, Marie Théaudin, Mar Tintoré, Carmen Tur, Denis Uffer, Marjolaine Uginet, Jochen Vehoff, Heinz Wiendl, Tjalf Ziemssen, Chiara Zecca, Anke Salmen, David Leppert, Tobias Derfuss, Ludwig Kappos, Lars G. Hemkens, Perrine Janiaud, Jens Kühle
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.