Assessing electronic health record phenotypes against gold-standard diagnostic criteria for diabetes mellitus
Assessing electronic health record phenotypes against gold-standard diagnostic criteria for diabetes mellitus
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DOI：
10.1093/jamia/ocw123
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发表时间：
2017-04-01
影响因子：
6.4
通讯作者：
Barton, Anna Beth
中科院分区：
文献类型：
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作者：
Spratt, Susan E.;Pereira, Katherine;Barton, Anna Beth
目的：通过临床专家的图表回顾，我们评估了8种基于电子健康记录(EHR)的糖尿病表型与美国糖尿病协会(ADA)金标准诊断标准的敏感性和特异性。材料和方法：我们确定了基于电子健康记录(EHR)的糖尿病表型定义，这些定义由不同的用户开发，包括学术医疗中心、联邦医疗保险、纽约市卫生局和药房福利经理。我们将这些定义应用于在Duke Health System Enterprise Data Warehouse中有记录且在5年内(2007-2011)至少就诊一次的173 503名患者的样本。在这些患者中，22679人(13%)符合一种或多种选定的糖尿病表型定义的标准。结果：基于EHR的2型糖尿病表型的敏感度(62-94%)和特异度(95-99%)根据成分标准和观察和测量的时机而不同。讨论和结论：使用基于EHR的表型定义的研究人员应该清楚地说明构成定义的特征、ADA标准的变化，以及不同的表型定义和成分如何影响所检索的患者群体和预期的应用。如果要实现利用电子病历数据改善个人和人群健康的承诺，仔细注意表型定义是至关重要的。
Objective: We assessed the sensitivity and specificity of 8 electronic health record (EHR)-based phenotypes for diabetes mellitus against gold-standard American Diabetes Association (ADA) diagnostic criteria via chart review by clinical experts.Materials and Methods: We identified EHR-based diabetes phenotype definitions that were developed for various purposes by a variety of users, including academic medical centers, Medicare, the New York City Health Department, and pharmacy benefit managers. We applied these definitions to a sample of 173 503 patients with records in the Duke Health System Enterprise Data Warehouse and at least 1 visit over a 5-year period (2007-2011). Of these patients, 22 679 (13%) met the criteria of 1 or more of the selected diabetes phenotype definitions. A statistically balanced sample of these patients was selected for chart review by clinical experts to determine the presence or absence of type 2 diabetes in the sample.Results: The sensitivity (62-94%) and specificity (95-99%) of EHR-based type 2 diabetes phenotypes (compared with the gold standard ADA criteria via chart review) varied depending on the component criteria and timing of observations and measurements.Discussion and Conclusions: Researchers using EHR-based phenotype definitions should clearly specify the characteristics that comprise the definition, variations of ADA criteria, and how different phenotype definitions and components impact the patient populations retrieved and the intended application. Careful attention to phenotype definitions is critical if the promise of leveraging EHR data to improve individual and population health is to be fulfilled.