| アブストラクト | BACKGROUND: Pimavanserin is a new non-dopamine neurotransmitter antipsychotic drug. This study aimed to conduct a post-marketing pharmacovigilance study of pimavanserin, through data mining technology using the FDA Adverse Event Reporting System (FAERS) database. RESEARCH DESIGN AND METHODS: We analyzed adverse event reports for patients using pimavanserin. Data were classified using systematic organ classification (SOC) and preferred term (PT) of the Medical Dictionary for Regular Activities (MedDRA). Four signal algorithms [reporting odds ratio (ROR), proportional reporting ratio (PRR), multi-item gamma poisson shrinker (MGPS), and bayesian confidence propagation neural network (BCPNN)] were used to detect positive signals, and the median time-to-onset was determined. RESULTS: Adverse drug events (ADEs) related to pimavanserin (n = 31,852) were analyzed, exhibiting an annual linear upward trend (p = 0.027). The ADEs involved 27 SOCs, but only 'Psychiatric disorders' simultaneously satisfied four algorithms. Overall, 153 PTs simultaneously satisfied four algorithms. Subgroup analysis of differences in the top 30 signal intensity PTs according to sex yielded significant results for seven PTs (p < 0.05). The median time-to-onset was 97 days, the highest proportion occurred within the first 30 days (31.79%). CONCLUSIONS: Some new PT signals not listed in the label were identified, and some PT signals showed differences according to sex. |
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