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Int J Biomed Data Min. 2015 Dec;4(1). pii: 113. Epub 2015 Jul 30.

ePhenotyping for Abdominal Aortic Aneurysm in the Electronic Medical Records and Genomics (eMERGE) Network: Algorithm Development and Konstanz Information Miner Workflow.

Author information

1
The Sigfried and Janet Weis Center for Research, Geisinger Health System, Danville, PA, USA.
2
Department of Vascular and Endovascular Surgery, Geisinger Health System, Danville, PA, USA.
3
Division of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, USA.
4
Divisions of General Internal Medicine and Preventive Medicine, and the Center for Genetic Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
5
Group Health Research Institute, Group Health Cooperative, Seattle, WA, USA.
6
Patient-Centered Research, Aurora Research Institute™, Aurora Sinai Medical Center, Milwaukee, WI, USA.
7
Department of Health Sciences Research, Mayo Clinic, Rochester, MN USA.
8
Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA.
9
Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, WI, USA.
10
The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
11
Essentia Institute of Rural Health, Duluth, MN, USA.
12
Departments of Medicine (Medical Genetics) and Genome Sciences, University of Washington, Seattle, WA, USA.
13
Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
14
The Sigfried and Janet Weis Center for Research, Geisinger Health System, Danville, PA, USA; Department of Surgery, Temple University School of Medicine, Philadelphia, PA, USA.

Abstract

BACKGROUND AND OBJECTIVE:

We designed an algorithm to identify abdominal aortic aneurysm cases and controls from electronic health records to be shared and executed within the "electronic Medical Records and Genomics" (eMERGE) Network.

MATERIALS AND METHODS:

Structured Query Language, was used to script the algorithm utilizing "Current Procedural Terminology" and "International Classification of Diseases" codes, with demographic and encounter data to classify individuals as case, control, or excluded. The algorithm was validated using blinded manual chart review at three eMERGE Network sites and one non-eMERGE Network site. Validation comprised evaluation of an equal number of predicted cases and controls selected at random from the algorithm predictions. After validation at the three eMERGE Network sites, the remaining eMERGE Network sites performed verification only. Finally, the algorithm was implemented as a workflow in the Konstanz Information Miner, which represented the logic graphically while retaining intermediate data for inspection at each node. The algorithm was configured to be independent of specific access to data and was exportable (without data) to other sites.

RESULTS:

The algorithm demonstrated positive predictive values (PPV) of 92.8% (CI: 86.8-96.7) and 100% (CI: 97.0-100) for cases and controls, respectively. It performed well also outside the eMERGE Network. Implementation of the transportable executable algorithm as a Konstanz Information Miner workflow required much less effort than implementation from pseudo code, and ensured that the logic was as intended.

DISCUSSION AND CONCLUSION:

This ePhenotyping algorithm identifies abdominal aortic aneurysm cases and controls from the electronic health record with high case and control PPV necessary for research purposes, can be disseminated easily, and applied to high-throughput genetic and other studies.

KEYWORDS:

Aortic aneurysm; Case-Control study; Computing methodologies; Electronic health records; Electronic medical record; ICD-9; KNIME

PMID:
27054044
PMCID:
PMC4820287

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