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

被引：38

作者：

Bielinski, Suzette J. ^{[1
]}

Pathak, Jyotishman ^{[1
]}

Carrell, David S. ^{[2
]}

Takahashi, Paul Y. ^{[3
]}

Olson, Janet E. ^{[1
]}

Larson, Nicholas B. ^{[1
]}

Liu, Hongfang ^{[1
]}

Sohn, Sunghwan ^{[1
]}

Wells, Quinn S. ^{[4
]}

Denny, Joshua C. ^{[5
]}

Rasmussen-Torvik, Laura J. ^{[6
]}

Pacheco, Jennifer Allen ^{[7
]}

Jackson, Kathryn L. ^{[8
]}

Lesnick, Timothy G. ^{[1
]}

Gullerud, Rachel E. ^{[1
]}

Decker, Paul A. ^{[1
]}

Pereira, Naveen L. ^{[9
]}

Ryu, Euijung ^{[1
]}

Dart, Richard A. ^{[10
]}

Peissig, Peggy ^{[11
]}

Linneman, James G. ^{[11
]}

Jarvik, Gail P. ^{[12
]}

Larson, Eric B. ^{[2
]}

Bock, Jonathan A. ^{[13
]}

Tromp, Gerard C. ^{[13
]}

de Andrade, Mariza ^{[1
]}

Roger, Veronique L. ^{[1
,9
]}

机构：

[1] Mayo Clin, Dept Hlth Sci Res, Rochester, MN 55905 USA

[2] Grp Hlth Res Inst, Seattle, WA 98101 USA

[3] Mayo Clin, Dept Med, Div Primary Care Internal Med, Rochester, MN 55905 USA

[4] Vanderbilt Univ, Dept Med, Nashville, TN 37232 USA

[5] Vanderbilt Univ, Dept Biomed Informat & Med, Nashville, TN 37232 USA

[6] Northwestern Univ, Feinberg Sch Med, Dept Prevent Med, Chicago, IL 60611 USA

[7] Northwestern Univ, Feinberg Sch Med, Ctr Genet Med, Chicago, IL 60611 USA

[8] Northwestern Univ, Feinberg Sch Med, Ctr Healthcare Studies, Chicago, IL 60611 USA

[9] Mayo Clin, Div Cardiovasc Dis, Rochester, MN 55905 USA

[10] Marshfield Clin Res Fdn, Ctr Human Genet, Marshfield, WI 54449 USA

[11] Marshfield Clin Res Fdn, Biomed Informat Res Ctr, Marshfield, WI 54449 USA

[12] Univ Washington, Dept Med Med Genet & Genome Sci, Seattle, WA 98195 USA

[13] Sigfried & Janet Weis Ctr Res, Geisinger Hlth Syst, Danville, PA 17822 USA

来源：

JOURNAL OF CARDIOVASCULAR TRANSLATIONAL RESEARCH | 2015年 / 8卷 / 08期

基金：

美国国家卫生研究院;

关键词：

Heart failure; Ventricular ejection fraction; Electronic medical records; Natural language processing; Phenotyping; CLINICAL-RESEARCH; NATIONAL-HEART; WORKING GROUP; IDENTIFICATION; DISEASE; TRENDS; STATEMENT; COMMUNITY; SURVIVAL; VALIDITY;

D O I：

10.1007/s12265-015-9644-2

中图分类号：

R5 [内科学];

学科分类号：

1002 ; 100201 ;

摘要：

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.

引用

页码：475 / 483

页数：9

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