目的 基于JADER數(shù)據(jù)庫(kù)挖掘佩瑪貝特不良事件信號(hào)，為臨床安全用藥提供參考。方法 從JADER數(shù)據(jù)庫(kù)提取佩瑪貝特自上市至2025年3月的藥品不良事件報(bào)告，聯(lián)合使用報(bào)告比值比法、比例報(bào)告比值法、綜合標(biāo)準(zhǔn)法和貝葉斯置信度遞進(jìn)神經(jīng)網(wǎng)絡(luò)法對(duì)其不良事件進(jìn)行挖掘，同時(shí)評(píng)估分析不良事件的臨床優(yōu)先級(jí)及發(fā)生時(shí)間規(guī)律。結(jié)果 挖掘到佩瑪貝特為主要懷疑藥品的不良事件信號(hào)15個(gè)，其中6個(gè)信號(hào)為新的不良事件，包括膽管消失綜合征、急性膽囊炎、膽囊炎、高鉀血癥、胰腺炎和步態(tài)障礙。發(fā)現(xiàn)1個(gè)高臨床優(yōu)先級(jí)的不良事件橫紋肌溶解。不良事件中位發(fā)生時(shí)間為70 d，韋伯分布提示早期失敗型曲線(xiàn)。結(jié)論 除藥品說(shuō)明書(shū)收錄的不良事件外，本研究發(fā)現(xiàn)了佩瑪貝特潛在新的不良事件。用藥開(kāi)始即應(yīng)加強(qiáng)監(jiān)護(hù)，保障患者的用藥安全。

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