Spratt Susan E, Pereira Katherine, Granger Bradi B, Batch Bryan C, Phelan Matthew, Pencina Michael, Miranda Marie Lynn, Boulware Ebony, Lucas Joseph E, Nelson Charlotte L, Neely Benjamin, Goldstein Benjamin A, Barth Pamela, Richesson Rachel L, Riley Isaretta L, Corsino Leonor, McPeek Hinz Eugenia R, Rusincovitch Shelley, Green Jennifer, Barton Anna Beth, Kelley Carly, Hyland Kristen, Tang Monica, Elliott Amanda, Ruel Ewa, Clark Alexander, Mabrey Melanie, Morrissey Kay Lyn, Rao Jyothi, Hong Beatrice, Pierre-Louis Marjorie, Kelly Katherine, Jelesoff Nicole
Department of Medicine, Duke University School of Medicine, Durham, NC, USA.
Duke University School of Nursing, Durham, NC, USA.
J Am Med Inform Assoc. 2017 Apr 1;24(e1):e121-e128. doi: 10.1093/jamia/ocw123.
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.
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.
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.
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.
我们通过临床专家的病历审查，评估了8种基于电子健康记录（EHR）的糖尿病表型相对于美国糖尿病协会（ADA）金标准诊断标准的敏感性和特异性。
我们确定了由包括学术医疗中心、医疗保险、纽约市卫生部门和药房福利管理人员在内的各种用户出于不同目的开发的基于EHR的糖尿病表型定义。我们将这些定义应用于杜克健康系统企业数据仓库中有记录且在5年期间（2007 - 2011年）至少有1次就诊的173503名患者的样本。在这些患者中，22679名（13%）符合1种或更多选定糖尿病表型定义的标准。从这些患者中选取了一个统计学上均衡的样本，由临床专家进行病历审查，以确定样本中2型糖尿病的存在与否。
基于EHR的2型糖尿病表型的敏感性（62 - 94%）和特异性（95 - 99%）（与通过病历审查的ADA金标准相比）因组成标准以及观察和测量的时间而异。
使用基于EHR的表型定义的研究人员应明确规定构成该定义的特征、ADA标准的变体，以及不同的表型定义和组成部分如何影响所检索的患者群体和预期应用。如果要实现利用EHR数据改善个人和群体健康的前景，仔细关注表型定义至关重要。
