目的 基于JADER数据库挖掘佩玛贝特不良事件信号，为临床安全用药提供参考。方法 从JADER数据库提取佩玛贝特自上市至2025年3月的药品不良事件报告，联合使用报告比值比法、比例报告比值法、综合标准法和贝叶斯置信度递进神经网络法对其不良事件进行挖掘，同时评估分析不良事件的临床优先级及发生时间规律。结果 挖掘到佩玛贝特为主要怀疑药品的不良事件信号15个，其中6个信号为新的不良事件，包括胆管消失综合征、急性胆囊炎、胆囊炎、高钾血症、胰腺炎和步态障碍。发现1个高临床优先级的不良事件横纹肌溶解。不良事件中位发生时间为70 d，韦伯分布提示早期失败型曲线。结论 除药品说明书收录的不良事件外，本研究发现了佩玛贝特潜在新的不良事件。用药开始即应加强监护，保障患者的用药安全。

Objective To mine adverse event signals associated with pemafibrate based on the JADER database, providing reference for clinical medication safety. Methods Adverse event reports for pemafibrate from its market launch to March 2025 were extracted from the JADER database. A combination of reporting odds ratio method, proportional reporting ratio method, combined standard method, and Bayesian confidence propagation neural network method was used to mine its adverse events while assessing the clinical priority and occurrence timing of these events. Results A total of 15 adverse reaction signals were identified for pemafibrate as the primary suspected drug. Among these, 6 signals were new adverse events, including disappearing bile duct syndrome, acute cholecystitis, cholecystitis, hyperkalemia, pancreatitis, and gait disorders. One high clinical priority adverse event, rhabdomyolysis, was also identified. The median time to onset of adverse events was 70 d, with a Weibull distribution suggesting an early failure-type curve. Conclusion In addition to the adverse reactions listed in the medication instructions, this study identified potential new adverse events associated with pemafibrate. Enhanced monitoring should be implemented from the start of treatment to ensure patient safety.

R972