不良事件报告系统
药物警戒
医学
不利影响
优势比
置信区间
药物流行病学
数据库
数据挖掘
急诊医学
内科学
药理学
计算机科学
药方
作者
Dehua Zhao,Xiaoqing Long,Jisheng Wang
标识
DOI:10.1080/14740338.2024.2338250
摘要
BACKGROUND: To data, there is insufficient large-scale data on the adverse events (AEs) of pemigatinib. Consequently, we conducted a pharmacovigilance study utilizing the Food and Drug Administration Adverse Event Reporting System (FAERS) database to investigate these AEs. RESEARCH DESIGN AND METHODS: The OpenVigil 2.1 was used to extract AE data from the FAERS database. Proportional reporting ratio (PRR), reporting odds ratios (ROR), and bayesian analysis confidence propagation neural network (BCPNN) were used to assess the association between pemigatinib and AEs. The clinical importance of AE signals were prioritized using a rating scale. RESULTS: A total of 848 AE reports were retrieved from the FAERS database, and 421 AE reports were identified after the data cleaning process. After accounting for indication bias and removal of medication errors, 59 positive signals were finally included. The 59 positive signals emerged in 11 system organ classes (SOCs). Besides, 19 positive AEs were classified as moderate clinical priority, while 40 were deemed weak in terms of priority. 9 positive AEs were not included in the drug label. CONCLUSIONS: This study provided valuable evidence for clinicians to mitigate the risk of pemigatinib-related toxicities in the real world.
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