首页> 外文期刊>European journal of clinical pharmacology >Postmarketing surveillance of potentially fatal reactions to oncology drugs: potential utility of two signal-detection algorithms.
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Postmarketing surveillance of potentially fatal reactions to oncology drugs: potential utility of two signal-detection algorithms.

机译:肿瘤药物潜在致命反应的上市后监视:两种信号检测算法的潜在用途。

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PURPOSE: Several data mining algorithms (DMAs) are being studied in hopes of enhancing screening of large post-marketing safety databases for signals of novel adverse events (AEs). The objective of this study was to apply two DMAs to the United States FDA Adverse Event Reporting System (AERS) database to see whether signals of potentially fatal AEs with cancer drugs might have been identified earlier than with traditional methods. METHODS: Screening algorithms used for analysis were the multi-item gamma Poisson shrinker (MGPS) and proportional reporting ratios (PRRs). Data mining was performed on data from the FDA AERS database. When a signal was identified, it was compared with that in the year in which the event was added to package insert and/or the year a "case series" was published. A recent publication summarizing the time of dissemination of information on potentially fatal AEs to cancer drugs provided the data set for analysis. RESULTS: The peer-reviewed published analysis contained 21 drugs and 26 drug-event combinations (DECs) that were considered sufficiently specific for data mining. Twenty-four of the DECs generated a signal of disproportionate reporting with PRRs (6 at 1 year and 16 from 2 years to 18 years prior to either a published "case series" or a package insert change) and 20 with MGPS (3 at 1 year and 11 from 2 years to 16 years prior to either a published "case series" or a package insert change). Two DECs did not signal with either DMA. CONCLUSION: At least one commonly cited DMA generated a signal of disproportionate reporting for 24 of 26 DECs for selected cancer drugs. For 16 DECs, one could conclude that a signal was generated well in advance (> or =2 years) of standard techniques in use with at least one DMA. DMAs might be useful in supplementing traditional surveillance strategies with oncology drugs and other drugs with similar features. (i.e., drugs that may be approved on an accelerated basis, are known to have serious toxicity, are administered to patients with substantial and complicated comorbid illness, are not available to the general medical community, and may have a high frequency of "off-label" use).
机译:目的:正在研究几种数据挖掘算法(DMA),以期加强对大型上市后安全数据库中新型不良事件(AE)信号的筛选。这项研究的目的是将两个DMA应用于美国FDA不良事件报告系统(AERS)数据库,以查看是否可以比传统方法更早地发现癌症药物潜在致命性AE的信号。方法:用于分析的筛选算法是多项目伽马泊松收缩器(MGPS)和比例报告比率(PRR)。数据挖掘是对FDA AERS数据库中的数据进行的。识别出信号后,将其与将事件添加到包装插页中的年份和/或发布“案例系列”的年份进行比较。最近的出版物概述了将有关致命致命不良事件的信息传播到癌症药物的时间,为分析提供了数据集。结果:同行评审发表的分析包含21种药物和26种药物事件组合(DECs),它们被认为足够专门用于数据挖掘。 24个DEC使用PRR生成了不成比例的报告信号(在发布的“案例系列”或包装变更之前为1年为6个,从2年到18年为16个,从2年到18年为16),而MGPS为20个(3为1从发布的“案例系列”或包装插页更改之前的2年到16年的第11年和第11年)。两个DEC均未使用任一DMA发出信号。结论:至少一个通常被引用的DMA产生了针对特定癌症药物的26个DEC中的24个DEC不相称报告的信号。对于16个DEC,可以得出结论,至少在一个DMA使用的标准技术之前(>或= 2年)就已经很好地产生了信号。 DMA在用肿瘤药物和其他具有类似功能的药物补充传统监测策略方面可能会很有用。 (即,可能在加速的基础上批准的,已知具有严重毒性的药物,用于患有严重和复杂的合并症的患者,普通医学界无法使用的药物以及可能出现“非处方药”的频率很高标签”)。

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