LESIM, ISPED, University Bordeaux Segalen, Bordeaux, France.
J Am Med Inform Assoc. 2013 Jan 1;20(1):184-92. doi: 10.1136/amiajnl-2012-000933. Epub 2012 Sep 6.
Data from electronic healthcare records (EHR) can be used to monitor drug safety, but in order to compare and pool data from different EHR databases, the extraction of potential adverse events must be harmonized. In this paper, we describe the procedure used for harmonizing the extraction from eight European EHR databases of five events of interest deemed to be important in pharmacovigilance: acute myocardial infarction (AMI); acute renal failure (ARF); anaphylactic shock (AS); bullous eruption (BE); and rhabdomyolysis (RHABD).
The participating databases comprise general practitioners' medical records and claims for hospitalization and other healthcare services. Clinical information is collected using four different disease terminologies and free text in two different languages. The Unified Medical Language System was used to identify concepts and corresponding codes in each terminology. A common database model was used to share and pool data and verify the semantic basis of the event extraction queries. Feedback from the database holders was obtained at various stages to refine the extraction queries.
Standardized and age specific incidence rates (IRs) were calculated to facilitate benchmarking and harmonization of event data extraction across the databases. This was an iterative process.
The study population comprised overall 19 647 445 individuals with a follow-up of 59 929 690 person-years (PYs). Age adjusted IRs for the five events of interest across the databases were as follows: (1) AMI: 60-148/100 000 PYs; (2) ARF: 3-49/100 000 PYs; (3) AS: 2-12/100 000 PYs; (4) BE: 2-17/100 000 PYs; and (5) RHABD: 0.1-8/100 000 PYs.
The iterative harmonization process enabled a more homogeneous identification of events across differently structured databases using different coding based algorithms. This workflow can facilitate transparent and reproducible event extractions and understanding of differences between databases.
电子健康记录（EHR）中的数据可用于监测药物安全性，但为了比较和汇总来自不同 EHR 数据库的数据，必须协调潜在不良事件的提取。本文介绍了从八个欧洲 EHR 数据库中提取五个被认为在药物警戒中很重要的感兴趣事件的过程：急性心肌梗死（AMI）；急性肾衰竭（ARF）；过敏性休克（AS）；大疱性皮疹（BE）；横纹肌溶解症（RHABD）。
参与的数据库包括全科医生的病历以及住院和其他医疗服务的索赔。临床信息使用四种不同的疾病术语和两种不同语言的自由文本收集。统一医学语言系统用于识别每个术语中的概念和相应代码。使用通用数据库模型共享和汇总数据，并验证事件提取查询的语义基础。在各个阶段从数据库持有者那里获得反馈，以改进提取查询。
计算标准化和年龄特异性发病率（IR），以促进数据库之间的事件数据提取的基准测试和协调。这是一个迭代过程。
研究人群包括 19647445 人，随访 59929690 人年（PY）。数据库中五个感兴趣事件的年龄调整发病率（IR）如下：（1）AMI：60-148/100000PY；（2）ARF：3-49/100000PY；（3）AS：2-12/100000PY；（4）BE：2-17/100000PY；（5）RHABD：0.1-8/100000PY。
迭代协调过程使使用不同编码算法和不同结构数据库能够更一致地识别事件。该工作流程可以促进透明且可重复的事件提取，并了解数据库之间的差异。
