# Efgartigimod non-responders after the first treatment cycle in generalized myasthenia gravis: a retrospective analysis of predictive factors

**Authors:** Zhenyu Niu, Jianchun Wang, Jingru Ren, Ran Liu, Jing Guo, Nan Zhang, Yiming Zheng, Hongjun Hao, Feng Gao, Haiqiang Jin

PMC · DOI: 10.3389/fneur.2025.1715486 · 2025-11-06

## TL;DR

This study identifies factors that predict poor response to efgartigimod in patients with generalized myasthenia gravis.

## Contribution

The study provides predictive factors for suboptimal response to efgartigimod in gMG patients using clinical and serological data.

## Key findings

- Non-responders had higher baseline gross motor and respiratory sub-scores.
- Thymoma, tumors, thyroid disease, and autoimmune disorders were more common in non-responders.
- Combinations of these factors predicted poor response with up to 97.9% probability.

## Abstract

This study aimed to identify predictors of suboptimal response to efgartigimod in patients with generalized myasthenia gravis (gMG).

In this single-center retrospective study, 35 gMG patients treated with efgartigimod were categorized into responders (n = 25) and non-responders (n = 10). Responders were defined by a reduction of >2 points in MG-ADL or >3 points in QMG score after one cycle, whereas non-responders showed improvement below these thresholds and subsequently responded to eculizumab. Demographic, clinical, and serological features were compared using univariate and multivariate analyses.

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%).

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.

## Linked entities

- **Diseases:** thymoma (MONDO:0006456), thyroid disease (MONDO:0003240)

## Full-text entities

- **Diseases:** thymoma (MESH:D013945), thyroid disease (MESH:D013959), autoimmune diseases (MESH:D001327), gMG (MESH:D009157), tumors (MESH:D009369)
- **Chemicals:** Efgartigimod (MESH:C000718373), eculizumab (MESH:C481642)
- **Species:** Homo sapiens (human, species) [taxon 9606]

## Figures

3 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12631618/full.md

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Source: https://tomesphere.com/paper/PMC12631618