To analyze the characteristics of patient subgroups and temporal patterns of adverse events (AE) associated with efgartigimod treatment for generalized myasthenia gravis (gMG), based on the U.S. FDA Adverse Event Reporting System (FAERS) database, providing evidence for personalized clinical use and long-term drug safety monitoring.

Individual case safety reports (ICSRs) in which efgartigimod was the primary suspect drug were extracted from the FAERS database covering Q1 2020 to Q4 2023. After deduplication and MedDRA standardization, binary logistic regression was used to assess the impact of age, sex, and body weight on symptom recurrence risk; K-means clustering identified high-risk patient subgroups; and descriptive analysis characterized the distribution of AE onset times (TTO).

A total of 788 ICSRs were included, of which 321 had complete time information. Logistic regression revealed that low body weight (OR=0.97, P=0.021) and younger age (OR=0.96, P=0.014) were risk factors for symptom recurrence. Clustering analysis divided patients into three groups: older and heavier individuals (mainly cardiovascular or procedure related AEs), younger and lighter individuals (primarily immune or hypersensitivity related AEs), and a heterogeneous group. The overall TTO distribution of AEs was right-skewed, with 68% occurring within 30 days. Median TTO for serious AEs (73 days) was longer than for non-serious AEs (54 days), indicating a clear delayed risk (>180 days).

Efgartigimod related AE risks exhibit heterogeneity across populations and time dependency. Younger, lower weight patients require close monitoring for immune related AEs, while older, higher weight patients should be monitored for cardiovascular risks. A comprehensive, long-term monitoring strategy throughout the treatment cycle is warranted clinically. The integrated framework proposed here "risk stratification-subgroup identification-temporal monitoring" can be directly applied to other neonatal Fc receptor (FcRn) targeted drugs, offering an innovative model for post-marketing evaluation of biologics in China.