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

Suzette J. Bielinski; Jyotishman Pathak; David S. Carrell; Paul Y. Takahashi; Janet E. Olson; Nicholas B. Larson; Hongfang Liu; Sunghwan Sohn; Quinn S. Wells; Joshua C. Denny; Laura J. Rasmussen-Torvik; Jennifer Allen Pacheco; Kathryn L. Jackson; Timothy G. Lesnick; Rachel E. Gullerud; Paul A. Decker; Naveen L. Pereira; Euijung Ryu; Richard A. Dart; Peggy Peissig; James G. Linneman; Gail P. Jarvik; Eric B. Larson; Jonathan A. Bock; Gerard C. Tromp; Mariza de Andrade; Véronique L. Roger

doi:10.1007/s12265-015-9644-2

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

Suzette J. Bielinski, Jyotishman Pathak, David S. Carrell, Paul Y. Takahashi, Janet E. Olson, Nicholas B. Larson, Hongfang Liu, Sunghwan Sohn, Quinn S. Wells, Joshua C. Denny, Laura J. Rasmussen-Torvik, Jennifer Allen Pacheco, Kathryn L. Jackson, Timothy G. Lesnick, Rachel E. Gullerud, Paul A. Decker, Naveen L. Pereira, Euijung Ryu, Richard A. Dart, Peggy PeissigJames G. Linneman, Gail P. Jarvik, Eric B. Larson, Jonathan A. Bock, Gerard C. Tromp, Mariza de Andrade, Véronique L. Roger

Research output: Contribution to journal › Article › peer-review

18 Scopus citations

Abstract

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.

Original language	English (US)
Pages (from-to)	475-483
Number of pages	9
Journal	Journal of cardiovascular translational research
Volume	8
Issue number	8
DOIs	https://doi.org/10.1007/s12265-015-9644-2
State	Published - Nov 1 2015

Keywords

Electronic medical records
Heart failure
Natural language processing
Phenotyping
Ventricular ejection fraction

ASJC Scopus subject areas

Molecular Medicine
Genetics
Pharmaceutical Science
Cardiology and Cardiovascular Medicine
Genetics(clinical)

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10.1007/s12265-015-9644-2

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Bielinski, S. J., Pathak, J., Carrell, D. S., Takahashi, P. Y., Olson, J. E., Larson, N. B., Liu, H., Sohn, S., Wells, Q. S., Denny, J. C., Rasmussen-Torvik, L. J., Pacheco, J. A., Jackson, K. L., Lesnick, T. G., Gullerud, R. E., Decker, P. A., Pereira, N. L., Ryu, E., Dart, R. A., ... Roger, V. L. (2015). 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. Journal of cardiovascular translational research, 8(8), 475-483. https://doi.org/10.1007/s12265-015-9644-2

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. / Bielinski, Suzette J.; Pathak, Jyotishman; Carrell, David S. et al.
In: Journal of cardiovascular translational research, Vol. 8, No. 8, 01.11.2015, p. 475-483.

Research output: Contribution to journal › Article › peer-review

Bielinski, SJ, Pathak, J, Carrell, DS, Takahashi, PY , Olson, JE , Larson, NB , Liu, H , Sohn, S, Wells, QS, Denny, JC, Rasmussen-Torvik, LJ, Pacheco, JA, Jackson, KL, Lesnick, TG, Gullerud, RE, Decker, PA, Pereira, NL , Ryu, E, Dart, RA, Peissig, P, Linneman, JG, Jarvik, GP, Larson, EB, Bock, JA, Tromp, GC, de Andrade, M & Roger, VL 2015, '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', Journal of cardiovascular translational research, vol. 8, no. 8, pp. 475-483. https://doi.org/10.1007/s12265-015-9644-2

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title = "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",

abstract = "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.",

keywords = "Electronic medical records, Heart failure, Natural language processing, Phenotyping, Ventricular ejection fraction",

author = "Bielinski, {Suzette J.} and Jyotishman Pathak and Carrell, {David S.} and Takahashi, {Paul Y.} and Olson, {Janet E.} and Larson, {Nicholas B.} and Hongfang Liu and Sunghwan Sohn and Wells, {Quinn S.} and Denny, {Joshua C.} and Rasmussen-Torvik, {Laura J.} and Pacheco, {Jennifer Allen} and Jackson, {Kathryn L.} and Lesnick, {Timothy G.} and Gullerud, {Rachel E.} and Decker, {Paul A.} and Pereira, {Naveen L.} and Euijung Ryu and Dart, {Richard A.} and Peggy Peissig and Linneman, {James G.} and Jarvik, {Gail P.} and Larson, {Eric B.} and Bock, {Jonathan A.} and Tromp, {Gerard C.} and {de Andrade}, Mariza and Roger, {V{\'e}ronique L.}",

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AU - Pathak, Jyotishman

AU - Carrell, David S.

AU - Takahashi, Paul Y.

AU - Olson, Janet E.

AU - Larson, Nicholas B.

AU - Liu, Hongfang

AU - Sohn, Sunghwan

AU - Wells, Quinn S.

AU - Denny, Joshua C.

AU - Rasmussen-Torvik, Laura J.

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AU - Pereira, Naveen L.

AU - Ryu, Euijung

AU - Dart, Richard A.

AU - Peissig, Peggy

AU - Linneman, James G.

AU - Jarvik, Gail P.

AU - Larson, Eric B.

AU - Bock, Jonathan A.

AU - Tromp, Gerard C.

AU - de Andrade, Mariza

AU - Roger, Véronique L.

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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

Abstract

Keywords

ASJC Scopus subject areas

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