Gagne Joshua J, Wang Shirley V, Rassen Jeremy A, Schneeweiss Sebastian
Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02120, USA.
Pharmacoepidemiol Drug Saf. 2014 Jun;23(6):619-27. doi: 10.1002/pds.3616. Epub 2014 Apr 30.
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.
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.
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.
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infarction with rofecoxib and an increased risk of rhabdomyolysis with cerivastatin years before these drugs were withdrawn from the market.
本研究旨在开发并测试一种用于在电子医疗数据库网络中对新药进行常规主动安全性监测的半自动流程。
我们构建了一个模块化程序，该程序可半自动地进行队列识别、混杂因素调整、诊断检查、跨多个数据库的汇总及效应估计，以及应用序贯警报算法。在β测试期间，我们将该系统应用于五个数据库，以评估九个模拟前瞻性监测的回顾性数据示例（五对我们预期会有信号的示例、两个阴性对照，以及两个不确定是否会预期有信号的示例）：西立伐他汀与阿托伐他汀及横纹肌溶解症；帕罗西汀与三环类抗抑郁药及胃肠道出血；赖诺普利与血管紧张素受体阻滞剂及血管性水肿；环丙沙星与大环内酯类抗生素及跟腱断裂；罗非昔布与非选择性非甾体抗炎药（ns-NSAIDs）及心肌梗死；泰利霉素与阿奇霉素及肝毒性；瑞舒伐他汀与阿托伐他汀及糖尿病和横纹肌溶解症；塞来昔布与ns-NSAIDs及心肌梗死。
我们描述了该程序、所需输入以及假定的数据环境。在β测试中，该系统生成了四个警报，均出现在阳性对照示例中（即赖诺普利与血管性水肿；罗非昔布与心肌梗死；环丙沙星与肌腱断裂；以及西立伐他汀与横纹肌溶解症）。每个示例的序贯效应估计在方向和幅度上与现有文献一致。
对九个药物-结局示例进行的β测试证明了所提出的半自动前瞻性监测方法的可行性。在回顾性评估中，该系统在罗非昔布和西立伐他汀退市前数年就识别出了罗非昔布导致心肌梗死风险增加以及西立伐他汀导致横纹肌溶解症风险增加。
