Personalizing Relapsing-Remitting Multiple Sclerosis Monitoring: Patient Acceptance of Serum Neurofilament Light Chain and the Role of Disease Knowledge

复发缓解型多发性硬化症监测的个性化:患者对血清神经丝轻链检测的接受度及疾病知识的作用

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Abstract

Background: Serum neurofilament light chain (sNfL) is an established biomarker of neuroaxonal damage in multiple sclerosis (MS). Despite its prognostic utility, patient awareness of its clinical application remains poorly characterized. The objective of this study was to assess the acceptance of sNfL monitoring among patients with early-stage relapsing-remitting MS (RRMS) and identify factors predicting their willingness to adopt this tool. Methods: This non-interventional, cross-sectional study was conducted across 16 neuroimmunology clinics. We included RRMS patients with a disease duration of ≤3 years receiving disease-modifying therapy. Acceptance was assessed following a standardized educational tutorial. Multivariable logistic regression was employed to identify predictors of patient acceptance. Results: The study included 144 patients (mean age 37.6 [SD 10.3] years, 69.4% female). Only 19.4% (n = 28) had prior awareness of sNfL. However, after the tutorial, 84.0% (n = 121) expressed willingness to adopt sNfL testing. Furthermore, 62.5% (n = 90) indicated that normal sNfL levels would provide emotional reassurance between clinical visits. Patients willing to undergo testing showed higher disease knowledge, less treatment regret, and better physical quality of life and cognitive performance. In the multivariable analysis, higher disease knowledge (OR = 1.52, 95%CI 1.16-1.99; p = 0.002) and lower symptom burden (OR = 0.96, 95%CI 0.93-0.99; p = 0.038) were associated with greater acceptance. Conclusions: Patients demonstrate high receptivity to sNfL monitoring when provided with adequate clinical context. Because disease knowledge is a primary driver of acceptance, personalized educational initiatives may be a complementary strategy to facilitate the integration of precision biomarkers into MS management.

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