Article(id=1321425610526798569, tenantId=1146029695717560320, journalId=1235980733773295621, issueId=1321425569611362746, articleNumber=null, orderNo=null, doi=10.20251/j.cnki.1003-3734.2026.07.014, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=null, revisedDateStr=null, acceptedDate=1760025600000, acceptedDateStr=2025-10-10, onlineDate=1792982016449, onlineDateStr=2026-10-26, pubDate=1776182400000, pubDateStr=2026-04-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792982016449, onlineIssueDateStr=2026-10-26, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792982016449, creator=13701087609, updateTime=1792982016449, updator=13701087609, issue=Issue{id=1321425569611362746, tenantId=1146029695717560320, journalId=1235980733773295621, year='2026', volume='35', issue='7', pageStart='673', pageEnd='784', issueExtLink='null', onlineDate='null', pubDate='1776182400000', pubDateStr='2026-04-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1792982006695, creator='13701087609', updateTime=1792983136833, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1321430309837587328, tenantId=1146029695717560320, journalId=1235980733773295621, issueId=1321425569611362746, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1321430309837587329, tenantId=1146029695717560320, journalId=1235980733773295621, issueId=1321425569611362746, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=772, endPage=779, ext={EN=ArticleExt(id=1321425610740708074, articleId=1321425610526798569, tenantId=1146029695717560320, journalId=1235980733773295621, language=EN, title=Adverse event signal mining and serious adverse event influencing factor analysis of avacopan based on FAERS database, columnId=null, journalTitle=Chinese Journal of New Drugs, columnName=null, runingTitle=null, highlight=null, articleAbstract=
Objective: To explore post-marketing adverse drug events (ADE) of avacopan and explore the risk factors for severe ADE, providing a reference for clinical safe and rational drug use.
Methods: Retrieved adverse event report information associated with avacopan from the U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database were extracted, covering a total of 12 quarters from the fourth quarter of 2021 to the third quarter of 2024. Suspicious AEs were screened using the reporting odds ratio (ROR), proportional reporting ratio (PRR), bayesian confidence propagation neural network (BCPNN), multiple item empirical bayesian gamma poisson shrinker (MGPS) based on disproportionality analysis. Univariate and multivariate logistic regression analyses were conducted on severe ADE to explore the risk factors associated with avacopan-induced severe ADE according to their severity.
Results: A total of 2 886 ADE reports associated with avacopan were obtained, including 1 489 reports of severe AEs. Using the disproportionality analysis methods, a total of 69 positive ADE signals were identified for avacopan, involving 14 systems and organs. The ADE associated with avacopan were sorted by frequency of occurrence, with hospitalisation (272 cases) having the highest number of reports. By signal strength, antineutrophil cytoplasmic antibody increased showed the strongest signal (ROR=290.83, 95%CI=114.09~741.35). The results of the Logistic regression analysis showed that gender (OR=1.27, 95%CI=1.02~1.58, P<0.05), age (65~80 years: OR=2.20, 95%CI=1.61~2.99, P<0.001；≥80 years: OR=4.61, 95%CI=2.89~7.34, P<0.001) and multiple medications (<5 prescribed drugs: OR=1.32, 95%CI=1.05~1.66, P<0.05; ≥5 prescribed drugs: OR=12.22, 95%CI=6.83~21.87, P<0.001) were the influencing factors for occurrence of severe ADE associated with avacopan.
Conclusion: Clinicians and pharmacists are advised topay greater attention to severe and new ADE signals not included in drug labeling. They should enhance management and pharmaceutical care for elderly, female, and polypharmacy patients to prevent serious ADE and ensure the patient medication safety.
, authors=Di LI, Xiao-chun YE, Lu-lu LI, Shao-hui ZHANG
*, authorsList=Di LI, Xiao-chun YE, Lu-lu LI, Shao-hui ZHANG, authorCompany=null, correspAuthors=Shao-hui ZHANG, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1321425610975589099, articleId=1321425610526798569, tenantId=1146029695717560320, journalId=1235980733773295621, language=CN, title=基于FAERS数据库的阿伐可泮不良事件信号挖掘与严重不良事件危险因素分析, columnId=1236331163573342670, journalTitle=中国新药杂志, columnName=药物安全与合理应用, runingTitle=null, highlight=null, articleAbstract=
目的： 挖掘阿伐可泮上市后不良事件（adverse drug event, ADE）并探究严重ADE的危险因素，为临床安全合理用药提供参考。
方法： 提取2021年第4季度至2024年第3季度共12个季度美国FDA不良事件报告系统（The FDA Adverse Event Reporting System, FAERS）数据库中阿伐可泮相关ADE报告，采用报告比值比法（reporting odds ratio, ROR）、比例报告比法（proportional reporting ratio, PRR）、贝叶斯置信度递进神经网络法（bayesian confidence propagation neural network, BCPNN）以及多项经验贝叶斯伽马泊松分布缩减法（multiple item empirical bayesian gamma poisson shrinker, MGPS）对ADE信号进行挖掘分析；根据严重程度分组，通过单因素及多因素Logistic回归模型分析阿伐可泮致严重ADE的危险因素。
结果： 共获取阿伐可泮为首要怀疑药物的ADE报告2 886份，其中严重ADE报告1 489份，挖掘阳性信号69个，涉及14个系统器官分类（system organ class, SOC），按发生频次排序，住院治疗报告数最多（272例次），按信号强度排序，抗中性粒细胞胞质抗体增加信号最强（ROR值为290.83，95%CI=114.09~741.35）。对严重ADE报告进行Logistic回归分析显示性别[比值比（odds ratio, OR）=1.27，95%CI=1.02~1.58，P<0.05]、年龄65~80岁：OR=2.20，95%CI=1.61~2.99，P<0.001；80岁以上：OR=4.61，95%CI=2.89~7.34，P<0.001以及合并用药（<5种：OR=1.32，95%CI=1.05~1.66，P<0.05；5种以上：OR=12.22，95%CI=6.83~21.87，P<0.001）是阿伐可泮发生严重ADE的影响因素。
结论： 建议临床医师与药师高度关注药品说明书中未收载的严重的、新的ADE信号，加强对老年、女性及多重用药患者的管理及药学监护，避免严重ADE的发生，以保证患者用药安全。
, authors=李笛, 叶小春, 李璐璐, 张韶辉
*, authorsList=李笛, 叶小春, 李璐璐, 张韶辉, authorCompany=null, correspAuthors=张韶辉, authorNote=
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李笛，女，博士研究生，主要从事临床药学及药物警戒方面研究。联系电话：（027）85332011，E-mail: lidi_hust@163.com。
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李笛，女，博士研究生，主要从事临床药学及药物警戒方面研究。联系电话：（027）85332011，E-mail: lidi_hust@163.com。
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