What does this research mean for the field?

A dual-biomarker immune model incorporating baseline NK cell counts and IL-12p70 levels can accurately predict the clinical response to the initial treatment cycle of Efgartigimod in patients with acetylcholine receptor antibody-positive generalized myasthenia gravis. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The aim is to investigate how baseline immune characteristics relate to the response to Efgartigimod in AChR+ gMG.

May 20, 2026Open Access

An Immune Model Incorporating NK Cells and IL-12 to Predict the Efficacy of the Initial Treatment Cycle of Efgartigimod in Generalized Myasthenia Gravis

Key Points

The aim is to investigate how baseline immune characteristics relate to the response to Efgartigimod in AChR+ gMG.
Multicenter retrospective observational study with 35 AChR+ gMG patients receiving Efgartigimod.
Constructed models (Clinical, Immune, Integrated) from a subset of 26 patients using multivariable regression and machine learning algorithms.
Developed a clinical risk score and an online prediction calculator.
25 out of 35 patients (68.6%) responded to treatment.
Higher baseline NK cell counts (p = 0.013) and lower IL-12p70 levels (p = 0.095) were observed in non-responders.
The Immune Model (AUC = 0.861) outperformed the Clinical Model (AUC = 0.625, p = 0.021).

Abstract

Background: Efgartigimod (EFG), a neonatal Fc receptor (FcRn) antagonist for generalized myasthenia gravis (gMG), exhibits variable treatment responses. This study aimed to investigate the associations of baseline clinical characteristics and multidimensional immune profiles with the clinical response to EFG in patients with acetylcholine receptor antibody-positive (AChR+) gMG, and develop a predictive model. Methods: This multicenter retrospective observational study enrolled 35 AChR+ gMG patients who received at least one cycle of EFG. Responders were defined as those achieving a Myasthenia Gravis Activities of Daily Living (MG-ADL) score reduction of ≥ 2 points that was sustained for ≥ 1 month. After univariable screening, three models (Clinical, Immune, Integrated) were constructed using a complete-case modeling subset of 26 patients via multivariable logistic regression, least absolute shrinkage and selection operator (LASSO), and random forest algorithms. The optimal model was selected via leave-one-out cross-validation (LOOCV) and translated into a clinical risk score and an online calculator. Results: Among 35 patients, 25 responded (68.6%). Non-responders had significantly higher baseline NK cell counts ( p = 0.013) and showed a trend toward lower IL-12p70 levels ( p = 0.095). Both NK cell (OR = 0.985) and IL-12p70 (OR = 11.657) were independent predictors of EFG response. The Immune Model (NK cells + IL-12p70) demonstrated robust discrimination (AUC = 0.861), outperforming the Clinical Model (AUC = 0.625, p = 0.021) and the Integrated Model (AUC = 0.847, p = 0.782). It showed good calibration and clinical utility. Bootstrap validation confirmed robustness (corrected AUC = 0.872, optimism=0.017). A derived clinical risk score stratified patients into high (100%), moderate (80.0%), and low (33.3%) response probability groups. An online prediction calculator was developed. Conclusion: Baseline NK cell counts and IL-12p70 levels may predict EFG response in AChR+ gMG. The dual-biomarker Immune Model demonstrates robust performance, and has been translated into an online tool but yet requires validation in prospective, larger cohorts. Keywords: myasthenia gravis, natural killer cells, interleukin-12, biomarkers, predictive model

Bookmark

View Full Paper

Bookmark

View Full Paper

An Immune Model Incorporating NK Cells and IL-12 to Predict the Efficacy of the Initial Treatment Cycle of Efgartigimod in Generalized Myasthenia Gravis

Key Points

Abstract

Cite This Study