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RALES发表后的高钾血症报告——一项药物警戒研究。

Reports of hyperkalemia after publication of RALES--a pharmacovigilance study.

作者信息

Hauben Manfred, Reich Lester, Gerrits Charles M

机构信息

Risk Management Strategy, Pfizer Inc, New York, NY 10017, USA.

出版信息

Pharmacoepidemiol Drug Saf. 2006 Nov;15(11):775-83. doi: 10.1002/pds.1275.

Abstract

PURPOSE

A population-based study and anecdotal reports have indicated that the publication of the Randomized Aldactone Evaluation Study (RALES) was associated with not merely a broader use of spironolactone in the treatment of heart failure, but also with a coinciding sharp increase in hyperkalemia-associated morbidity/mortality in patients also being treated with ACE-inhibitors. Data mining algorithms (DMAs) are being applied to spontaneous reporting system (SRS) databases in hopes of obtaining early warnings/additional insights into post-licensure safety data. We applied two DMAs (i.e. multi-item gamma Poisson shrinker [MGPS] and proportional reporting ratios [PRRs]) to spontaneous reporting system (SRS) data to determine if these DMAs could have provided an earlier indication of a possible hyperkalemia safety issue.

METHODS

MGPS and PRRs were retrospectively applied to US FDA-AERS, an SRS database. Year-by-year analysis and analysis of increasing cumulative time intervals were performed on cases in which both spironolactone and hyperkalemia and possibly related cardiac events had been reported.

RESULTS

Neither of the DMAs initially provided a compelling signal of disproportionate reporting (SDR) for hyperkalemia after publication of RALES. However, using events consistent with clinical sequelae of hyperkalemia (e.g,. sudden death), SDRs were identified with PRRs.

CONCLUSIONS

The quality and usefulness of data mining analysis is highly situation dependent and may vary with the knowledge and experience of the drug safety reviewer. Our analysis suggests that contemporary DMAs may have significant limitations in detecting increased frequency of labeled events in real-life prospective pharmacovigilance. There is a paucity of research in this area and we recommend further research for new approaches to detecting increased frequency of labeled events.

摘要

目的

一项基于人群的研究及轶事报告表明,随机螺内酯评估研究(RALES)的发表不仅与螺内酯在心力衰竭治疗中的更广泛应用有关,还与同时接受ACE抑制剂治疗的患者中高钾血症相关发病率/死亡率的急剧上升有关。数据挖掘算法(DMA)正被应用于自发报告系统(SRS)数据库,以期获得有关上市后安全性数据的早期预警/更多见解。我们将两种数据挖掘算法(即多项目伽马泊松收缩器[MGPS]和比例报告比[PRR])应用于自发报告系统(SRS)数据,以确定这些算法是否能更早地提示可能存在的高钾血症安全问题。

方法

MGPS和PRR被回顾性应用于美国食品药品监督管理局不良事件报告系统(US FDA - AERS)这一SRS数据库。对报告了螺内酯、高钾血症以及可能相关心脏事件的病例进行逐年分析和累积时间间隔增加的分析。

结果

在RALES发表后,两种数据挖掘算法最初均未提供高钾血症不成比例报告(SDR)的有力信号。然而,使用与高钾血症临床后遗症一致的事件(如猝死),通过PRR识别出了SDR。

结论

数据挖掘分析的质量和实用性高度依赖具体情况,可能因药物安全审查员的知识和经验而异。我们的分析表明,当代数据挖掘算法在实际前瞻性药物警戒中检测标记事件频率增加方面可能存在重大局限性。该领域研究匮乏,我们建议进一步研究检测标记事件频率增加的新方法。

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