| アブストラクト | Background: Pediatric cataracts are a main cause of irreversible vision loss and a significant public health challenge. This study aimed to identify pharmacovigilance signals by analyzing large-scale data from the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS). Methods: Using real-world data from FAERS (Q1 2004 to Q3 2025), we investigated associations between medications and pediatric cataracts. Following data standardization, signal detection was performed using multiple disproportionality analyses, including the Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-item Gamma Poisson Shrinker (MGPS). The time to onset was also evaluated. Results: Among 690,374 reports for individuals aged 0-17 years, 671 reports involving 232 drugs were reported with cataracts. Disproportionality analysis identified 24 drugs with significant signals, predominantly glucocorticoids (11/24), followed by immunosuppressants, monoclonal antibodies, cystic fibrosis transmembrane conductance regulator (CFTR) modulators, antineoplastic agents, an antiepileptic drug, and a colony-stimulating factor. Difluprednate showed the highest pharmacovigilance signal (ROR: 963.67; 95% CI: 316.27-2936.31; n = 4). Notably, CFTR modulators exhibited striking signals: ivacaftor (ROR: 30.75; 95% CI: 18.06-52.37; n = 14), elexacaftor-ivacaftor-tezacaftor (ROR: 15.58; 95% CI: 9.86-24.63; n = 19), and ivacaftor-lumacaftor (ROR: 13.2; 95% CI: 7.9-22.07; n = 15). Conclusions: This study provides a comprehensive large-scale pharmacovigilance profile of drug-induced pediatric cataracts, identifying agents with high-risk pharmacovigilance signals and underscoring the need for proactive ocular monitoring. These findings can inform clinical decision making and prevention strategies and guide future mechanistic research. |
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