| アブストラクト | BACKGROUND: Anti-beta-amyloid monoclonal antibodies provide a disease-modifying treatment approach for Alzheimer's disease, but post-marketing safety concerns remain, particularly amyloid-related imaging abnormalities (ARIA). Real-world studies have mainly described adverse event spectra and disproportionality signals, whereas report-level amyloid-related imaging abnormalities with edema or effusion (ARIA-E) risk stratification remains less well characterized. OBJECTIVES: To compare post-marketing adverse event profiles of lecanemab (LEC) and donanemab (DON), identify report-level factors associated with ARIA-E, and develop a machine learning model for ARIA-E risk stratification. DESIGN: Retrospective pharmacovigilance study using spontaneous reporting databases. METHODS: Adverse event reports for lecanemab and donanemab were analyzed using the Food and Drug Administration Adverse Event Reporting System (FAERS) and WHO-VigiAccess. FAERS disproportionality analyses were performed at the Preferred Term and System Organ Class levels using frequency-based and Bayesian methods. Time-to-onset and serious-outcome patterns were further explored. Multivariable logistic regression was used to examine factors associated with report-level ARIA-E. An extreme gradient boosting (XGBoost) model was developed using temporally split FAERS datasets with nested cross-validation and temporal external validation. RESULTS: FAERS included 2961 lecanemab and 1542 donanemab primary suspect reports; WHO-VigiAccess included 2525 lecanemab- and 1450 donanemab-related reports. Across databases, adverse event profiles were dominated by neurological events, with ARIA-related events representing the central safety signals. Serious outcomes clustered mainly in the early treatment period and attenuated over time. In complete-case regression, no overall difference in ARIA-E reporting odds was observed between donanemab and lecanemab, whereas a significant donanemab-by-body weight interaction was identified. The XGBoost model showed modest discrimination, with area under the receiver operating characteristic curve values of 0.677 in internal validation and 0.630 in temporal external validation. Calibration was suboptimal, and decision curve analysis suggested limited net benefit mainly within low-threshold ranges. CONCLUSION: ARIA-related and other neurological events remain the principal post-marketing safety concern for lecanemab and donanemab. The XGBoost model may support report-level risk prioritization, particularly during early treatment, but should not be used as a diagnostic substitute or a source of precise individualized risk estimates. |
|---|
| ジャーナル名 | Therapeutic advances in drug safety |
|---|
| Pubmed追加日 | 2026/8/20 |
|---|
| 投稿者 | Xue, Wei-Yang; Li, Xi; Li, Wen-Ting; Qin, Bao-Zhi; Wang, Cong-Zhi; Liu, Tao; Wang, Min |
|---|
| 組織名 | Department of Pharmacy, Hainan Affiliated Hospital of Hainan Medical University;(Hainan General Hospital), Haikou, China.;School of Pharmacy, Hainan Medical University, Haikou, China.;Department of Neurology, Hainan General Hospital (Hainan Affiliated Hospital of;Hainan Medical University), Haikou, China.;Department of Geriatric, Hainan General Hospital (Hainan Affiliated Hospital of;School of Nursing, Wannan Medical College, Wuhu 241002, China.;Hainan Medical University), Haikou 570311, China.;Department of Pharmacy, Hainan General Hospital (Hainan Affiliated Hospital of |
|---|
| Pubmed リンク | https://www.ncbi.nlm.nih.gov/pubmed/42621237/ |
|---|
