A real-world pharmacovigilance analysis of uveitis from systemic medications
Abstract
OBJECTIVE: To identify U.S. Food and Drug Administration (FDA)-approved systemic drugs disproportionately associated with reported uveitis events using real-world pharmacovigilance data.
DESIGN: A retrospective pharmacovigilance study of a large spontaneous adverse event reporting database.
PARTICIPANTS: All uveitis cases reported to the FDA Adverse Event Reporting System (FAERS) between the fourth quarter of 2003 and the third quarter of 2024.
METHODS: FAERS data were analyzed using OpenVigil 2.1 data-mining software. Reports identifying FDA-approved drugs as primary suspects in uveitis cases were included. Disproportionality analyses were conducted by comparing drug-specific uveitis reports with the background reporting rate across all other drugs in the database. Reporting odds ratios (RORs) with 95% confidence intervals (CIs) were calculated. Bayesian confidence propagation neural network algorithms were applied to reduce false-positive associations and strengthen signal detection.
RESULTS: A total of 7 301 uveitis events were identified. The strongest safety signals were observed for cidofovir (ROR = 103.8; 95% CI 53.0-203.2) and rifabutin (ROR = 77.4; 95% CI 50.5-118.7). Additional signals were detected for cobimetinib (ROR = 30.5), foscarnet (ROR = 29.0), infliximab (ROR = 18.1), dabrafenib (ROR = 16.4), vemurafenib (ROR = 16.2), trametinib (ROR = 15.5), brodalumab (ROR = 14.9), binimetinib (ROR = 11.3), encorafenib (ROR = 11.2), ipilimumab (ROR = 11.0), and…
How strong is the evidence?
Evidence Hub reads this as a cohort study, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence.
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