The epidemiologic impact of hereditary angioedema (HAE) is difficult to quantify, due to misclassification in retrospective studies resulting from non-specific diagnostic coding. The aim of this study was to identify cohorts of patients with HAE-1/2 by evaluating structured and unstructured data in a US ambulatory electronic medical record (EMR) database. A retrospective feasibility study was performed using the GE Centricity EMR Database (2006–2017). Patients with ≥ 1 diagnosis code for HAE-1/2 (International Classification of Diseases, Ninth Revision, Clinical Modification 277.6 or International Classification of Diseases, Tenth Revision, Clinical Modification D84.1) and/or ≥ 1 physician note regarding HAE-1/2 and ≥ 6 months’ data before and after the earliest code or note (index date) were included. Two mutually exclusive cohorts were created: probable HAE (≥ 2 codes or ≥ 2 notes on separate days) and suspected HAE (only 1 code or note). The impact of manually reviewing physician notes on cohort formation was assessed, and demographic and clinical characteristics of the 2 final cohorts were described. Initially, 1691 patients were identified: 190 and 1501 in the probable and suspected HAE cohorts, respectively. After physician note review, the confirmed HAE cohort comprised 254 patients and the suspected HAE cohort decreased to 1299 patients; 138 patients were determined not to have HAE and were excluded. The overall false-positive rate for the initial algorithms was 8.2%. Across final cohorts, the median age was 50 years and > 60% of patients were female. HAE-specific prescriptions were identified for 31% and 2% of the confirmed and suspected HAE cohorts, respectively. Unstructured EMR data can provide valuable information for identifying patients with HAE-1/2. Further research is needed to develop algorithms for more representative HAE cohorts in retrospective studies.

中文翻译：

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利用非结构化数据在电子病历中识别遗传性血管性水肿患者

遗传性血管性水肿（HAE）的流行病学影响难以量化，这是由于非特异性诊断编码导致的回顾性研究中的错误分类。这项研究的目的是通过评估美国门诊电子病历（EMR）数据库中的结构化和非结构化数据来识别HAE-1 / 2患者。使用GE Centricity EMR数据库（2006–2017）进行了回顾性可行性研究。HAE-1 / 2的诊断代码≥1（国际疾病分类，第九修订版，临床修改277.6或国际疾病分类，第十修订版，临床修改D84.1）和/或关于HAE-1的医生注释≥1的患者包括最早的代码或注释（索引日期）前后的/ 2和≥6个月的数据。创建了两个互斥的队列：可能的HAE（单独的日期≥2个代码或≥2个注释）和可疑的HAE（仅1个代码或注释）。评估了人工检查医师笔记对队列形成的影响，并描述了最后两个队列的人口统计学和临床​​特征。最初，确定了1691例患者：可能和怀疑的HAE队列中分别为190例和1501例。经医生检查后，确诊的HAE队列包括254例患者，可疑的HAE队列减少到1299例。138名患者被确定没有HAE并被排除在外。初始算法的总假阳性率为8.2％。在最终队列中，中位年龄为50岁，> 60％的患者为女性。分别针对已确认和怀疑的HAE队列中的31％和2％确定了HAE特定处方。非结构化的EMR数据可为识别HAE-1 / 2的患者提供有价值的信息。需要开展进一步的研究来开发用于回顾性研究中更具代表性的HAE队列的算法。