Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Womens Hospital, Harvard Medical School, Boston, MA 02120, USA.
Drug Saf. 2012 May 1;35(5):407-16. doi: 10.2165/11594770-000000000-00000.
Several efforts are under way to develop and test methods for prospective drug safety monitoring using large, electronic claims databases. Prospective monitoring systems must incorporate signalling algorithms and techniques to mitigate confounding in order to minimize false positive and false negative signals due to chance and bias.
The aim of the study was to describe a prototypical targeted active safety monitoring system and apply the framework to three empirical examples.
We performed sequential, targeted safety monitoring in three known drug/adverse event (AE) pairs: (i) paroxetine/upper gastrointestinal (UGI) bleed; (ii) lisinopril/angioedema; (iii) ciprofloxacin/Achilles tendon rupture (ATR). Data on new users of the drugs of interest were extracted from the HealthCore Integrated Research Database. New users were matched by propensity score to new users of comparator drugs in each example. Analyses were conducted sequentially to emulate prospective monitoring. Two signalling rules--a maximum sequential probability ratio test and an effect estimate-based approach--were applied to sequential, matched cohorts to identify signals within the system.
Signals were identified for all three examples: paroxetine/UGI bleed in the seventh monitoring cycle, within 2 calendar years of sequential data; lisinopril/angioedema in the second cycle, within the first monitoring year; ciprofloxacin/ATR in the tenth cycle, within the fifth year.
In this proof of concept, our targeted, active monitoring system provides an alternative to systems currently in the literature. Our system employs a sequential, propensity score-matched framework and signalling rules for prospective drug safety monitoring and identified signals for all three adverse drug reactions evaluated.
目前有多项工作正在进行中，旨在利用大型电子索赔数据库开发和测试前瞻性药物安全性监测方法。前瞻性监测系统必须纳入信号算法和技术，以减轻混杂因素，从而最大限度地减少因机会和偏倚而导致的假阳性和假阴性信号。
本研究旨在描述一个原型靶向主动安全性监测系统，并将该框架应用于三个实证示例。
我们在三个已知的药物/不良事件（AE）对中进行了连续的靶向安全性监测：（i）帕罗西汀/上消化道（UGI）出血；（ii）赖诺普利/血管性水肿；（iii）环丙沙星/跟腱断裂（ATR）。有关感兴趣药物的新使用者的数据从 HealthCore 综合研究数据库中提取。在每个示例中，新使用者通过倾向评分与比较药物的新使用者相匹配。分析是按顺序进行的，以模拟前瞻性监测。两种信号规则——最大序贯概率比检验和基于效应估计的方法——应用于序贯、匹配队列中，以在系统内识别信号。
所有三个示例均发现了信号：帕罗西汀/UGI 出血发生在第七个监测周期内，即在序贯数据的两年内；赖诺普利/血管性水肿发生在第二个周期内，在第一年监测内；环丙沙星/ATR 发生在第十个周期内，在第五年内。
在这个概念验证中，我们的靶向主动监测系统为目前文献中的系统提供了替代方案。我们的系统采用了序贯、倾向评分匹配框架和信号规则，用于前瞻性药物安全性监测，并确定了评估的所有三种药物不良反应的信号。
