Bouzillé Guillaume, Osmont Marie-Noëlle, Triquet Louise, Grabar Natalia, Rochefort-Morel Cécile, Chazard Emmanuel, Polard Elisabeth, Cuggia Marc
INSERM, Rennes, France.
Université de Rennes 1, LTSI, Rennes, France.
J Eval Clin Pract. 2018 Jun;24(3):536-544. doi: 10.1111/jep.12908. Epub 2018 Mar 13.
RATIONALE, AIMS, AND OBJECTIVES: The spontaneous reporting system currently used in pharmacovigilance is not sufficiently exhaustive to detect all adverse drug reactions (ADRs). With the widespread use of electronic health records, biomedical data collected during the clinical care process can be reused and analysed to better detect ADRs. The aim of this study was to assess whether querying a Clinical Data Warehouse (CDW) could increase the detection of drug-induced anaphylaxis.
All known cases of drug-induced anaphylaxis that occurred or required hospitalization at Rennes Academic Hospital in 2011 (n = 19) were retrieved from the French pharmacovigilance database, which contains all reported ADR events. Then, from the Rennes Academic Hospital CDW, a training set (all patients hospitalized in 2011) and a test set (all patients hospitalized in 2012) were extracted. The training set was used to define an optimized query, by building a set of keywords (based on the known cases) and exclusion criteria to search structured and unstructured data within the CDW in order to identify at least all known cases of drug-induced anaphylaxis for 2011. Then, the real performance of the optimized query was tested in the test set.
Using the optimized query, 59 cases of drug-induced anaphylaxis were identified among the 253 patient records extracted from the test set as possible anaphylaxis cases. Specifically, the optimal query identified 41 drug-induced anaphylaxis cases that were not detected by searching the French pharmacovigilance database but missed 7 cases detected only by spontaneous reporting.
We proposed an information retrieval-based method for detecting drug-induced anaphylaxis, by querying structured and unstructured data in a CDW. CDW queries are less specific than spontaneous reporting and Diagnosis-related Groups queries, although their sensitivity is much higher. CDW queries can facilitate monitoring by pharmacovigilance experts. Our method could be easily incorporated in the routine practice.
原理、目的和目标：目前药物警戒中使用的自发报告系统不够详尽，无法检测到所有药物不良反应（ADR）。随着电子健康记录的广泛使用，临床护理过程中收集的生物医学数据可被重新利用和分析，以更好地检测ADR。本研究的目的是评估查询临床数据仓库（CDW）是否能增加药物性过敏反应的检测率。
从包含所有报告的ADR事件的法国药物警戒数据库中检索出2011年在雷恩学术医院发生或需要住院治疗的所有已知药物性过敏反应病例（n = 19）。然后，从雷恩学术医院的CDW中提取一个训练集（2011年所有住院患者）和一个测试集（2012年所有住院患者）。训练集用于定义优化查询，通过构建一组关键词（基于已知病例）和排除标准来搜索CDW中的结构化和非结构化数据，以便识别出至少所有2011年已知的药物性过敏反应病例。然后，在测试集中测试优化查询的实际性能。
使用优化查询，在从测试集中提取的253份患者记录中，有59例被确定为可能的过敏反应病例，具体为药物性过敏反应。具体而言，优化查询识别出41例通过搜索法国药物警戒数据库未检测到的药物性过敏反应病例，但遗漏了仅通过自发报告检测到的7例病例。
我们提出了一种基于信息检索的方法来检测药物性过敏反应，即查询CDW中的结构化和非结构化数据。CDW查询不如自发报告和诊断相关组查询具体，但其灵敏度要高得多。CDW查询可便于药物警戒专家进行监测。我们的方法可轻松纳入日常实践。
