ArticleFrontiers in neurology2025
Efgartigimod non-responders after the first treatment cycle in generalized myasthenia gravis: a retrospective analysis of predictive factors.
Article in Frontiers in neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- An Immune Model Incorporating NK Cells and IL-12 to Predict the Efficacy of the Initial Treatment Cycle of Efgartigimod in Generalized Myasthenia Gravis.Drug design, development and therapy · 2026Observational
- Real-world efficacy of eculizumab in generalized myasthenia gravis patients with poor early response to efgartigimod: a prospective cohort study.Frontiers in immunology · 2026Article
- Efgartigimod for generalized myasthenia gravis: a comprehensive review of clinical evidence and future perspectives.Frontiers in neurologyReview
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: This study aimed to identify predictors of suboptimal response to efgartigimod in patients with generalized myasthenia gravis (gMG). Methods: In this single-center retrospective study, 35 gMG patients treated with efgartigimod were categorized into responders ( Results: Non-responders had higher baseline gross motor and respiratory sub-scores. Univariate analysis revealed that thymoma, non-thymoma tumors, thyroid disease, and other autoimmune diseases were more common in non-responders. Multivariate analysis indicated that combinations of these factors were associated with a high predicted probability of poor response (up to 97.9%). Interpretation: Comorbidities including thymoma, other tumors, thyroid disease, and additional autoimmune disorders may predict reduced response to efgartigimod in gMG patients. Systematic evaluation of these factors could help optimize treatment selection.
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