A Robust e-Epidemiology Tool in Phenotyping Heart Failure with Differentiation for Preserved and Reduced Ejection Fraction: the Electronic Medical Records and Genomics (eMERGE) Network
A Robust e-Epidemiology Tool in Phenotyping Heart Failure with Differentiation for Preserved and Reduced Ejection Fraction: the Electronic Medical Records and Genomics (eMERGE) Network
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DOI：
10.1007/s12265-015-9644-2
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发表时间：
2015-11-01
影响因子：
3.4
通讯作者：
Roger, Veronique L.
中科院分区：
文献类型：
--
作者：
Bielinski, Suzette J.;Pathak, Jyotishman;Roger, Veronique L.
确定心力衰竭（HF）患者人群对于旨在制定有效减轻这种疾病负担的策略的研究工作至关重要。鉴于HF的综合征性质以及区分HF与射血分数保留或降低的需要，将电子病历（EMR）数据用于此目的具有挑战性。使用手动提取的病例的金标准队列，开发了基于结构化和非结构化数据的EMR驱动的表型算法来识别所有病例。在来自电子病历和基因组学（eMERGE）网络的两个队列中执行所得算法，阳性预测值> 95%。该算法被扩展为包括HF的三个分层定义（即，明确的、很可能的、可能的），以捕获整个人群中的HF病例，从而增加用于e-流行病学研究的算法效用。
Identifying populations of heart failure (HF) patients is paramount to research efforts aimed at developing strategies to effectively reduce the burden of this disease. The use of electronic medical record (EMR) data for this purpose is challenging given the syndromic nature of HF and the need to distinguish HF with preserved or reduced ejection fraction. Using a gold standard cohort of manually abstracted cases, an EMR-driven phenotype algorithm based on structured and unstructured data was developed to identify all the cases. The resulting algorithm was executed in two cohorts from the Electronic Medical Records and Genomics (eMERGE) Network with a positive predictive value of > 95 %. The algorithm was expanded to include three hierarchical definitions of HF (i.e., definite, probable, possible) based on the degree of confidence of the classification to capture HF cases in a whole population whereby increasing the algorithm utility for use in e-Epidemiologic research.