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Data Mining and Analysis of Adverse Events of Vedolizumab Based on the FAERS Database

Qinyun Xu,Jing Zhang, Weihong Tang, Minhong Zhou,Xiaoling Zhang, Pu Yuan

Scientific reports(2025)SCI 3区

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Abstract
This study aims to mine and analyze adverse events (AEs) of Vedolizumab based on the FAERS database to better understand its safety and potential risks in the real world. Data from the second quarter of 2014 to the third quarter of 2023 were collected, employing various signal mining methods such as Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Empirical Bayesian Geometric Mean (EBGM). The study gathered 14,753,012 reports of AEs, of which 46,726 were related to Vedolizumab. Signal mining identified 401 Preferred Terms (PTs) involving 27 System Organ Classes (SOCs). There was an increasing trend in the number of reports, with a slightly higher proportion of reports from women compared to men, and the primary reporting group was adults, especially those aged between 18 and 65 years. New potential AE signals were identified, such as a higher incidence of Pregnancy, Haematochezia, and Clostridium difficile infection. Although less frequent, strong signals were noted for Incisional hernia, Intestinal fistula infection, Anastomotic complication, Drug metabolising enzyme increased, Gingival graft, Intestinal anastomosis complication, Anorectal infection, Perineal rash, and Abdominal hernia obstructive. Despite the positive prospects of Vedolizumab in the treatment of inflammatory bowel diseases, the AEs related to its use identified in this study, particularly the newly identified potential risks, suggest that even targeted therapies can have systemic effects beyond expectations.
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Vedolizumab,Inflammatory bowel disease,Adverse events,FAERS Database,Drug safety
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要点】:本研究基于FAERS数据库对Vedolizumab的不良事件进行挖掘和分析,发现新的潜在风险,以更好地理解其实际应用中的安全性和潜在风险。

方法】:采用报告比值比(ROR)、比例报告比值(PRR)、贝叶斯置信传播神经网络(BCPNN)和经验贝叶斯几何均值(EBGM)等信号挖掘方法。

实验】:收集2014年第二季度至2023年第三季度的数据,共获取14,753,012份不良事件报告,其中46,726份与Vedolizumab相关。实验数据来源于FAERS数据库,识别出401个首选术语(PTs)涉及27个系统器官分类(SOCs),发现新的潜在不良事件信号。