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Joseph A, Mullett C, Lilly C, et al. Coronary Artery Disease Phenotype Detection in an Academic Hospital System Setting. Appl Clin Inform. 2021;12(1):10-16doi: 10.1055/s-0040-1721012.
Joseph, A., Mullett, C., Lilly, C., Armistead, M., Cox, H. J., Denney, M., Varma, M., Rich, D., Adjeroh, D. A., Doretto, G., Neal, W., & Pyles, L. A. (2021). Coronary Artery Disease Phenotype Detection in an Academic Hospital System Setting. Applied clinical informatics, 12(1), 10-16. https://doi.org/10.1055/s-0040-1721012
Joseph, Amy, et al. "Coronary Artery Disease Phenotype Detection in an Academic Hospital System Setting." Applied clinical informatics vol. 12,1 (2021): 10-16. doi: https://doi.org/10.1055/s-0040-1721012
Joseph A, Mullett C, Lilly C, Armistead M, Cox HJ, Denney M, Varma M, Rich D, Adjeroh DA, Doretto G, Neal W, Pyles LA. Coronary Artery Disease Phenotype Detection in an Academic Hospital System Setting. Appl Clin Inform. 2021 Jan;12(1):10-16. doi: 10.1055/s-0040-1721012. Epub 2021 Jan 06. PMID: 33406541; PMCID: PMC7787710.
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Appl Clin Inform. 2021 Jan;12(1):10-16. doi: 10.1055/s-0040-1721012. Epub 2021 Jan 06.

Coronary Artery Disease Phenotype Detection in an Academic Hospital System Setting.

Applied clinical informatics

Amy Joseph, Charles Mullett, Christa Lilly, Matthew Armistead, Harold J Cox, Michael Denney, Misha Varma, David Rich, Donald A Adjeroh, Gianfranco Doretto, William Neal, Lee A Pyles

Affiliations

  1. Department of Pediatrics, School of Medicine, West Virginia University, Morgantown, West Virginia, United States.
  2. West Virginia Clinical and Translational Science Institute, West Virginia University, Morgantown, West Virginia, United States.
  3. Department of Biostatistics, School of Public Health, West Virginia University, Morgantown, West Virginia, United States.
  4. West Virginia University Hospital System, Morgantown, West Virginia, United States.
  5. Lane Department of Computer Science and Electrical Engineering, Benjamin M. Statler College of Engineering and Mineral Resources, West Virginia University, Morgantown, West Virginia, United States.

PMID: 33406541 PMCID: PMC7787710 DOI: 10.1055/s-0040-1721012

Abstract

BACKGROUND: The United States, and especially West Virginia, have a tremendous burden of coronary artery disease (CAD). Undiagnosed familial hypercholesterolemia (FH) is an important factor for CAD in the U.S. Identification of a CAD phenotype is an initial step to find families with FH.

OBJECTIVE: We hypothesized that a CAD phenotype detection algorithm that uses discrete data elements from electronic health records (EHRs) can be validated from EHR information housed in a data repository.

METHODS: We developed an algorithm to detect a CAD phenotype which searched through discrete data elements, such as diagnosis, problem lists, medical history, billing, and procedure (International Classification of Diseases [ICD]-9/10 and Current Procedural Terminology [CPT]) codes. The algorithm was applied to two cohorts of 500 patients, each with varying characteristics. The second (younger) cohort consisted of parents from a school child screening program. We then determined which patients had CAD by systematic, blinded review of EHRs. Following this, we revised the algorithm by refining the acceptable diagnoses and procedures. We ran the second algorithm on the same cohorts and determined the accuracy of the modification.

RESULTS: CAD phenotype Algorithm I was 89.6% accurate, 94.6% sensitive, and 85.6% specific for group 1. After revising the algorithm (denoted CAD Algorithm II) and applying it to the same groups 1 and 2, sensitivity 98.2%, specificity 87.8%, and accuracy 92.4; accuracy 93% for group 2. Group 1 F1 score was 92.4%. Specific ICD-10 and CPT codes such as "coronary angiography through a vein graft" were more useful than generic terms.

CONCLUSION: We have created an algorithm, CAD Algorithm II, that detects CAD on a large scale with high accuracy and sensitivity (recall). It has proven useful among varied patient populations. Use of this algorithm can extend to monitor a registry of patients in an EHR and/or to identify a group such as those with likely FH.

Thieme. All rights reserved.

Conflict of interest statement

None declared.

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