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.

• Klíčová slova
• Delphi study, de-escalation, escalation, personalized treatment strategies, serum neurofilament light chain,
• MeSH
• algoritmy * MeSH
• delfská metoda MeSH
• dospělí MeSH
• imunologické faktory * terapeutické užití   MeSH
• individualizovaná medicína * metody   MeSH
• klinické rozhodování * metody   MeSH
• konsensus MeSH
• lidé středního věku MeSH
• lidé MeSH
• neurofilamentové proteiny * krev   MeSH
• roztroušená skleróza * krev   farmakoterapie   MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• imunologické faktory * MeSH
• neurofilament protein L MeSH Prohlížeč
• neurofilamentové proteiny * MeSH

Multiple sclerosis (MS) is characterized by a progressive worsening of disability over time. As many regulatory-cleared disease-modifying treatments aiming to slow down this progression are now available, a clear need has arisen for a personalized and data-driven approach to treatment optimization in order to more efficiently slow down disease progression and eventually, progressive disability worsening. This strongly depends on the availability of biomarkers that can detect and differentiate between the different forms of disease worsening, and on predictive models to estimate the disease trajectory for each patient under certain treatment conditions. To this end, we here describe a multicenter, retrospective, observational study, aimed at setting up a harmonized database to allow the development, training, optimization, and validation of such novel biomarkers and AI-based decision models. Additionally, the data will be used to develop the tools required to better monitor this progression and to generate further insights on disease worsening and progression, patient prognosis, treatment decisions and responses, and patient profiles of patients with MS.

• Klíčová slova
• AI model, biomarker, clinical trial, data, disease worsening, multiple sclerosis, observational study, real-world data,
• Publikační typ
• časopisecké články MeSH

Multiple sclerosis (MS) is a devastating immune-mediated disorder of the central nervous system resulting in progressive disability accumulation. As there is no cure available yet for MS, the primary therapeutic objective is to reduce relapses and to slow down disability progression as early as possible during the disease to maintain and/or improve health-related quality of life. However, optimizing treatment for people with MS (pwMS) is complex and challenging due to the many factors involved and in particular, the high degree of clinical and sub-clinical heterogeneity in disease progression among pwMS. In this paper, we discuss these many different challenges complicating treatment optimization for pwMS as well as how a shift towards a more pro-active, data-driven and personalized medicine approach could potentially improve patient outcomes for pwMS. We describe how the 'Clinical Impact through AI-assisted MS Care' (CLAIMS) project serves as a recent example of how to realize such a shift towards personalized treatment optimization for pwMS through the development of a platform that offers a holistic view of all relevant patient data and biomarkers, and then using this data to enable AI-supported prognostic modelling.

• Klíčová slova
• AI, data, diagnosis, disease progression, multiple sclerosis, personalized medicine, prognosis,
• MeSH
• biologické markery MeSH
• individualizovaná medicína * metody   trendy   MeSH
• kvalita života MeSH
• lidé MeSH
• prognóza MeSH
• progrese nemoci MeSH
• roztroušená skleróza * terapie   imunologie   MeSH
• umělá inteligence * trendy   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• biologické markery MeSH