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从药物警戒到临床护理优化。

From Pharmacovigilance to Clinical Care Optimization.

作者信息

Celi Leo Anthony, Moseley Edward, Moses Christopher, Ryan Padhraig, Somai Melek, Stone David, Tang Kai-Ou

机构信息

Institute for Medical Engineering and Science, Massachusetts Institute of Technology , Cambridge, Massachusetts.

Division of Vaccine Research, Beth Israel Deaconess Medical Center , Boston, Massachusetts.

出版信息

Big Data. 2014 Sep 1;2(3):134-141. doi: 10.1089/big.2014.0008.

Abstract

In order to ensure the continued, safe administration of pharmaceuticals, particularly those agents that have been recently introduced into the market, there is a need for improved surveillance after product release. This is particularly so because drugs are used by a variety of patients whose particular characteristics may not have been fully captured in the original market approval studies. Even well-conducted, randomized controlled trials are likely to have excluded a large proportion of individuals because of any number of issues. The digitization of medical care, which yields rich and accessible drug data amenable to analytic techniques, provides an opportunity to capture the required information via observational studies. We propose the development of an open, accessible database containing properly de-identified data, to provide the substrate for the required improvement in pharmacovigilance. A range of stakeholders could use this to identify delayed and low-frequency adverse events. Moreover, its power as a research tool could extend to the detection of complex interactions, potential novel uses, and subtle subpopulation effects. This far-reaching potential is demonstrated by our experience with the open Multi-parameter Intelligent Monitoring in Intensive Care (MIMIC) intensive care unit database. The new database could also inform the development of objective, robust clinical practice guidelines. Careful systematization and deliberate standardization of a fully digitized pharmacovigilance process is likely to save both time and resources for healthcare in general.

摘要

为确保药品持续、安全地使用,尤其是那些最近刚上市的药物,产品上市后需要加强监测。情况尤其如此,因为各类患者使用药物,而这些患者的特定特征在最初的上市批准研究中可能未被充分掌握。即便设计良好的随机对照试验,也可能因诸多问题而排除了很大一部分个体。医疗数字化产生了丰富且易于获取的药物数据,适合采用分析技术,这为通过观察性研究获取所需信息提供了契机。我们提议开发一个开放、可访问的数据库,其中包含经过适当去识别化处理的数据,为药物警戒所需的改进提供基础。一系列利益相关者可利用该数据库识别延迟和低频不良事件。此外,其作为研究工具的作用可扩展至检测复杂相互作用、潜在新用途以及细微的亚组效应。我们在开放的重症监护多参数智能监测(MIMIC)重症监护病房数据库方面的经验证明了这种深远的潜力。新数据库还可为客观、稳健的临床实践指南的制定提供参考。对完全数字化的药物警戒流程进行仔细的系统化和刻意的标准化,总体上可能会为医疗保健节省时间和资源。

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