Lependu Paea, Liu Yi, Iyer Srinivasan, Udell Madeleine R, Shah Nigam H
Stanford University, Stanford, CA.
AMIA Jt Summits Transl Sci Proc. 2012;2012:63-70. Epub 2012 Mar 19.
Doctors prescribe drugs for indications that are not FDA approved. Research indicates that 21% of prescriptions filled are for off-label indications. Of those, more than 73% lack supporting scientific evidence. Traditional drug safety alerts may not cover usages that are not FDA approved. Therefore, analyzing patterns of off-label drug usage in the clinical setting is an important step toward reducing the incidence of adverse events and for improving patient safety. We applied term extraction tools on the clinical notes of a million patients to compile a database of statistically significant patterns of drug use. We validated some of the usage patterns learned from the data against sources of known on-label and off-label use. Given our ability to quantify adverse event risks using the clinical notes, this will enable us to address patient safety because we can now rank-order off-label drug use and prioritize the search for their adverse event profiles.
医生会为未获美国食品药品监督管理局(FDA)批准的适应症开具药物。研究表明,所开具的处方中有21%是用于非标签适应症的。其中,超过73%缺乏科学证据支持。传统的药物安全警示可能并不涵盖未获FDA批准的用法。因此,分析临床环境中非标签药物使用模式是降低不良事件发生率和提高患者安全性的重要一步。我们将术语提取工具应用于100万名患者的临床记录,以编制一个具有统计学意义的药物使用模式数据库。我们根据已知的标签内和标签外使用来源,对从数据中学到的一些使用模式进行了验证。鉴于我们有能力利用临床记录量化不良事件风险,这将使我们能够解决患者安全问题,因为我们现在可以对非标签药物使用进行排序,并优先查找其不良事件特征。