Suppr超能文献

一种基于美国食品药品监督管理局监管行动和上市后不良事件报告的药物警戒信号系统。

A Pharmacovigilance Signaling System Based on FDA Regulatory Action and Post-Marketing Adverse Event Reports.

作者信息

Hoffman Keith B, Dimbil Mo, Tatonetti Nicholas P, Kyle Robert F

机构信息

Advera Health Analytics, Inc., 3663 N. Laughlin Road, Suite 102, Santa Rosa, CA, 95403, USA.

Department of Biomedical Informatics, Columbia University, New York, USA.

出版信息

Drug Saf. 2016 Jun;39(6):561-75. doi: 10.1007/s40264-016-0409-x.

Abstract

INTRODUCTION

Many serious drug adverse events (AEs) only manifest well after regulatory approval. Therefore, the development of signaling methods to use with post-approval AE databases appears vital to comprehensively assess real-world drug safety. However, with millions of potential drug-AE pairs to analyze, the issue of focus is daunting.

OBJECTIVE

Our objective was to develop a signaling platform that focuses on AEs with historically demonstrated regulatory interest and to analyze such AEs with a disproportional reporting method that offers broad signal detection and acceptable false-positive rates.

METHODS

We analyzed over 1500 US FDA regulatory actions (safety communications and drug label changes) from 2008 to 2015 to construct a list of eligible signal AEs. The FDA Adverse Event Reporting System (FAERS) was used to evaluate disproportional reporting rates, constrained by minimum case counts and confidence interval limits, of these selected AEs for 109 training drugs. This step led to 45 AEs that appeared to have a low likelihood of being added to a label by FDA, so they were removed from the signal eligible list. We measured disproportional reporting for the final group of eligible AEs on a test group of 29 drugs that were not used in either the eligible list construction or the training steps.

RESULTS

In a group of 29 test drugs, our model reduced the number of potential drug-AE signals from 41,834 to 97 and predicted 73 % of individual drug label changes. The model also predicted at least one AE-drug pair label change in 66 % of all the label changes for the test drugs.

CONCLUSIONS

By concentrating on AE types with already demonstrated interest to FDA, we constructed a signaling system that provided focus regarding drug-AE pairs and suitable accuracy with regard to the issuance of FDA labeling changes. We suggest that focus on historical regulatory actions may increase the utility of pharmacovigilance signaling systems.

摘要

引言

许多严重的药物不良事件(AE)仅在监管批准后才充分显现。因此,开发适用于批准后AE数据库的信号检测方法对于全面评估实际用药安全性显得至关重要。然而,要分析数百万种潜在的药物 - AE组合,重点关注的问题令人望而生畏。

目的

我们的目标是开发一个信号检测平台,该平台专注于历史上已证明具有监管关注的AE,并使用一种能提供广泛信号检测且假阳性率可接受的不成比例报告方法来分析此类AE。

方法

我们分析了2008年至2015年期间美国食品药品监督管理局(FDA)的1500多项监管行动(安全通信和药品标签变更),以构建一份符合条件的信号AE清单。FDA不良事件报告系统(FAERS)用于评估109种受试药物中这些选定AE的不成比例报告率,并受最小病例数和置信区间限制。这一步骤导致45种AE似乎被FDA添加到标签中的可能性较低,因此它们被从符合信号条件的清单中删除。我们在一组29种未用于构建符合条件清单或受试步骤的药物上,对最终符合条件的AE组进行了不成比例报告测量。

结果

在一组29种受试药物中,我们的模型将潜在的药物 - AE信号数量从41,834个减少到97个,并预测了73%的个别药物标签变更。该模型还在受试药物所有标签变更的66%中预测了至少一对AE - 药物标签变更。

结论

通过专注于FDA已表现出关注的AE类型,我们构建了一个信号检测系统,该系统提供了有关药物 - AE组合的重点关注,并在FDA标签变更发布方面具有适当的准确性。我们建议关注历史监管行动可能会提高药物警戒信号检测系统的效用。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验