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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

Journal of Cardiovascular Translational Research volume 8, pages 475–483 (2015)Cite this article

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.

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Fig. 1

Abbreviations

EF:

Ejection fraction

eMERGE:

Electronic Medical Records and Genomics

EMR:

Electronic medical record

HF:

Heart failure

HFpEF:

HF with preserved ejection fraction

HFrEF:

HF with reduced ejection fraction

ICD-9:

International Classification of Diseases, 9th Revision

NLP:

Natural language processing

PCIM:

Mayo Clinic Primary Care Practice

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Conflict of Interest

All authors declare that they have no competing interests.

Human Subjects/Informed Consent Statement

All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000. All research procedures were approved by the Institutional Review Committee of the Mayo Clinic, and the participants from each participating study provided written and informed consent for general research. The inclusion of Group Health participants for studies was approved by Group Health Cooperative Human Subjects Review Committee, Seattle, WA. No animal studies were carried out by the authors for this article.

Funding

The Mayo Clinic Biobank and the Mayo Genome Consortia is funded by the Mayo Clinic Center for Individualized Medicine. Additional funding for this work came from National Institutes of Health grants R01HL72435 (Heart Failure in the Community Cohort), R01AG034676 (The Rochester Epidemiology Project), R01GM102282 (Natural Language Processing for Clinical and Translational Research), the Electronic Medical Record and Genomics (eMERGE) Network U01 HG06379 (Mayo Clinic), U01HG006375 (Group Health/University of Washington); U01HG006382 (Geisinger Health System); U01HG006389 (Essentia Health & Marshfield Clinic Research Foundation); U01HG006388 (Northwestern University); HG004438 (Center for Inherited Disease Research, Johns Hopkins University); HG004424 (Broad Institute of Harvard & MIT); U01HG006378, U01HG006385 (Vanderbilt University); U01HG006380 (The Mt. Sinai Hospital); U01HG006828 (Cincinnati Children’s Hospital Medical Center/Harvard); U01HG006830 (Children’s Hospital of Philadelphia), NIA grant U01AG006781-25, Life Sciences Discovery Fund Grant #2065508, and additional support was provided by a State of Washington Life Sciences Discovery Fund award to the Northwest Institute of Genetic Medicine.

Author information

Affiliations

  1. Department of Health Sciences Research, Mayo Clinic, Rochester, MN, 55905, USA

    Suzette J. Bielinski, Jyotishman Pathak, Janet E. Olson, Nicholas B. Larson, Hongfang Liu, Sunghwan Sohn, Timothy G. Lesnick, Rachel E. Gullerud, Paul A. Decker, Euijung Ryu, Mariza de Andrade & Véronique L. Roger

  2. Group Health Research Institute, Seattle, WA, 98101, USA

    David S. Carrell & Eric B. Larson

  3. Department of Medicine, Division of Primary Care Internal Medicine, Mayo Clinic, Rochester, MN, 55905, USA

    Paul Y. Takahashi

  4. Department of Medicine, Vanderbilt University, Nashville, TN, 37232, USA

    Quinn S. Wells

  5. Departments of Biomedical Informatics and Medicine, Vanderbilt University, Nashville, TN, 37232, USA

    Joshua C. Denny

  6. Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA

    Laura J. Rasmussen-Torvik

  7. Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA

    Jennifer Allen Pacheco

  8. Center for Healthcare Studies, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA

    Kathryn L. Jackson

  9. Division of Cardiovascular Diseases, Mayo Clinic Rochester, Rochester, MN, 55905, USA

    Naveen L. Pereira & Véronique L. Roger

  10. Center for Human Genetics, Marshfield Clinic Research Foundation, Marshfield, WI, 54449, USA

    Richard A. Dart

  11. Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, WI, 54449, USA

    Peggy Peissig & James G. Linneman

  12. Departments of Medicine (Medical Genetics) and Genome Sciences, University of Washington, Seattle, WA, 98195, USA

    Gail P. Jarvik

  13. The Sigfried and Janet Weis Center for Research, Geisinger Health System, Danville, PA, 17822, USA

    Jonathan A. Bock & Gerard C. Tromp

Authors
  1. Suzette J. Bielinski
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  2. Jyotishman Pathak
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  3. David S. Carrell
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  4. Paul Y. Takahashi
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  5. Janet E. Olson
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  6. Nicholas B. Larson
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  7. Hongfang Liu
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  8. Sunghwan Sohn
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  9. Quinn S. Wells
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  10. Joshua C. Denny
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  11. Laura J. Rasmussen-Torvik
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  12. Jennifer Allen Pacheco
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  13. Kathryn L. Jackson
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  14. Timothy G. Lesnick
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  15. Rachel E. Gullerud
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  16. Paul A. Decker
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  17. Naveen L. Pereira
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  18. Euijung Ryu
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  19. Richard A. Dart
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  20. Peggy Peissig
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  21. James G. Linneman
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  22. Gail P. Jarvik
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  23. Eric B. Larson
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  24. Jonathan A. Bock
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  25. Gerard C. Tromp
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  26. Mariza de Andrade
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  27. Véronique L. Roger
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Corresponding author

Correspondence to Suzette J. Bielinski.

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Associate Editor Emanuele Barbato oversaw the review of this article

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Bielinski, S.J., Pathak, J., Carrell, D.S. et al. 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. J. of Cardiovasc. Trans. Res. 8, 475–483 (2015). https://doi.org/10.1007/s12265-015-9644-2

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Keywords

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