中国药物警戒 ›› 2022, Vol. 19 ›› Issue (3): 270-274.
DOI: 10.19803/j.1672-8629.2022.03.08

• 法规与管理研究 • 上一篇    下一篇

乌普萨拉监测中心vigiRank预测模型研究

陈超1, 赵云飞2, 王长之1, 龚立雄1,#, 刘伟2,*   

  1. 1河南省药品评价中心,河南 郑州 450008;
    2郑州大学药学院,河南 郑州 450001
  • 收稿日期:2021-07-16 出版日期:2022-03-15 发布日期:2022-03-16
  • 通讯作者: *刘伟,男,博士,副教授,药物警戒与合理用药。E-mail：liuweiyxy@zzu.edu.cn
  • 作者简介:陈超,男,硕士,工程师,药物警戒。#为共同通信作者。
  • 基金资助:
    河南省科技攻关项目（212102311048）

VigiRank prediction model of Uppsala monitoring center

CHEN Chao1, ZHAO Yunfei2, WANG Changzhi1, GONG Lixiong1,#, LIU Wei2,*   

  1. 1Center for Drug Reevaluation of Henan, Zhengzhou Henan 450008, China;
    2School of Pharmaceutical Sciences, Zhengzhou University, Zhengzhou Henan 450001, China
  • Received:2021-07-16 Online:2022-03-15 Published:2022-03-16

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摘要： 目的 介绍乌普萨拉监测中心（UMC）信号检测方法vigiRank的原理、流程和应用,为完善我国药物警戒信号检测提供参考。方法 检索Scopus、Web of Science、PubMed等数据库进行文献调研,并查阅UMC官网,分析vigiRank预测模型的工作流程及应用。结果 UMC开发的信号检测方法vigiRank,结合了药品不良反应（ADR）报告的多个证据强度,包括信息报告（INF）、叙述（NAR）、去激发试验（DCH）、再激发试验（RCH）、因果关系评估（CAU和CAU+）、发病时间（TTO）、单独报告（SOL）、多个报告元素（MUL）、近期报告（REC）、比例失衡报告（DIS）、地理分布（GEO）、时间趋势（TRE）,通过lasso-logistic模型确定5个变量纳入到vigiRank评分体系,计算出药品-不良反应（drug-ADR）对的vigiRank分数,由专家团队按照分数的降序排列进行初步评估,筛选出需要考量的drug-ADR对,再深入评估并最终决定出风险信号。结论 vigiRank预测模型兼顾ADR报告的数量和质量,以科学的方式识别药物危害的早期信号,对完善我国ADR信号检测方法具有较大的参考价值。

关键词: 药品不良反应, 信号检测, 乌普萨拉监测中心, vigiRank

Abstract: Objective To outline the principles, processes and applications of the vigiRank signal detection method in Uppsala Monitoring Centre (UMC) so as to provide reference for the establishment and improvement of pharmacovigilance signal detection statistics in China. Methods Scopus, Web of Science, PubMed and other databases were searched for the purpose of literature research, and the UMC official website was consulted to analyze the workflow and application of the vigiRank prediction model. Results VigiRank,a signal detection method developed by UMC, combined multiple strength-of-evidence aspects of adverse drug reaction (ADR) reports, including Informative reports (INF), Narrative (NAR), Dechallenge (DCH), Rechallenge (RCH), Causality assessment (CAU and CAU+), Time-to-onset (TTO), Sole reporting (SOL), Multiple reporting elements (MUL), Resultsreporting (REC), Disproportional reporting (DIS), Geographic spread (GEO), and Time trend (TRE). According to the lasso-logistic model, five predictive variables were included in the vigiRank score system and the vigiRank score of a drug-ADR pair was calculated. An expert team conducted a preliminary evaluation in the descending order of scores, and chose drug-ADR pairs that needed to be further analyzed before they conducted an in-depth evaluation and finally screened out risk signals. Conclusion VigiRank has taken into account the number and quality of ADR reports, and is able to identify early signs of harm from drugs, which is of great referential value for improving the detection methods of ADR signals in China.

Key words: adverse drug reaction, signal detection, Uppsala Monitoring Center, vigiRank

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