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A modular, prospective, semi-automated drug safety monitoring system for use in a distributed data environment

机译:模块化,前瞻性,半自动化的药物安全监控系统,用于分布式数据环境

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Purpose: The aim of this study was to develop and test a semi-automated process for conducting routine active safety monitoring for new drugs in a network of electronic healthcare databases. Methods: We built a modular program that semi-automatically performs cohort identification, confounding adjustment, diagnostic checks, aggregation and effect estimation across multiple databases, and application of a sequential alerting algorithm. During beta-testing, we applied the system to five databases to evaluate nine examples emulating prospective monitoring with retrospective data (five pairs for which we expected signals, two negative controls, and two examples for which it was uncertain whether a signal would be expected): cerivastatin versus atorvastatin and rhabdomyolysis; paroxetine versus tricyclic antidepressants and gastrointestinal bleed; lisinopril versus angiotensin receptor blockers and angioedema; ciprofloxacin versus macrolide antibiotics and Achilles tendon rupture; rofecoxib versus non-selective non-steroidal anti-inflammatory drugs (ns-NSAIDs) and myocardial infarction; telithromycin versus azithromycin and hepatotoxicity; rosuvastatin versus atorvastatin and diabetes and rhabdomyolysis; and celecoxib versus ns-NSAIDs and myocardial infarction. Results: We describe the program, the necessary inputs, and the assumed data environment. In beta-testing, the system generated four alerts, all among positive control examples (i.e., lisinopril and angioedema; rofecoxib and myocardial infarction; ciprofloxacin and tendon rupture; and cerivastatin and rhabdomyolysis). Sequential effect estimates for each example were consistent in direction and magnitude with existing literature. Conclusions: Beta-testing across nine drug-outcome examples demonstrated the feasibility of the proposed semi-automated prospective monitoring approach. In retrospective assessments, the system identified an increased risk of myocardial infarction with rofecoxib and an increased risk of rhabdomyolysis with cerivastatin years before these drugs were withdrawn from the market.
机译:目的:本研究的目的是开发和测试一种半自动化过程,用于在电子医疗数据库网络中对新药进行常规的主动安全性监测。方法:我们构建了一个模块化程序,该程序可以半自动执行队列识别,混淆调整,诊断检查,跨多个数据库的汇总和效果估计以及顺序警报算法的应用。在进行beta测试期间,我们将系统应用于五个数据库,以评估九个使用回顾性数据模拟前瞻性监测的示例(五对预期信号,两个阴性对照,以及两个不确定是否预期信号的示例) :西立伐他汀与阿托伐他汀和横纹肌溶解症的比较;帕罗西汀与三环类抗抑郁药和胃肠道出血;赖诺普利与血管紧张素受体阻滞剂和血管性水肿;环丙沙星与大环内酯类抗生素和跟腱断裂;罗非昔布与非选择性非甾体类抗炎药(ns-NSAIDs)和心肌梗塞;泰利霉素与阿奇霉素和肝毒性;罗苏伐他汀与阿托伐他汀和糖尿病及横纹肌溶解症的关系;塞来昔布与ns-NSAIDs和心肌梗塞的关系。结果:我们描述了程序,必要的输入和假定的数据环境。在beta测试中,系统产生了四个警报,所有警报均来自阳性对照例(即赖诺普利和血管性水肿;罗非昔布和心肌梗塞;环丙沙星和肌腱破裂;西立伐他汀和横纹肌溶解症)。每个例子的顺序效应估计在方向和大小上与现有文献一致。结论:在九个药物结果实例中进行Beta测试证明了所提出的半自动化前瞻性监测方法的可行性。在回顾性评估中,该系统发现罗非昔布导致心肌梗死的风险增加,而西立伐他汀引起的横纹肌溶解的风险增加,这些药物被撤出市场已有数年。

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