Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

A Clinical Phenotyping Algorithm to Identify Cases of Chronic Obstructive Pulmonary Disease in Electronic Health Records

View ORCID ProfileVictoria L. Martucci, Nancy Liu, V. Eric Kerchberger, Travis J. Osterman, Eric Torstenson, Bradley Richmond, Melinda C. Aldrich
doi: https://doi.org/10.1101/716779
Victoria L. Martucci
1Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN
2Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Victoria L. Martucci
Nancy Liu
2Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
V. Eric Kerchberger
3Department of Medicine, Division of Allergy, Pulmonary, and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN
4Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Travis J. Osterman
4Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
5Department of Medicine, Division of Hematology and Oncology, Vanderbilt University Medical Center, Nashville, TN
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Eric Torstenson
1Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Bradley Richmond
3Department of Medicine, Division of Allergy, Pulmonary, and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN
6Department of Veteran Affairs Medical Center, Nashville, TN
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Melinda C. Aldrich
1Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN
2Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN
4Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
7Department of Medicine, Division of Epidemiology, Vanderbilt University Medical Center, Nashville, TN
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: melinda.aldrich@vumc.org
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

Abstract

Rationale Chronic obstructive pulmonary disease (COPD) is a leading cause of mortality in the United States. Electronic health records provide large-scale healthcare data for clinical research, but have been underutilized in COPD research due to challenges identifying these individuals, especially in the absence of pulmonary function testing data.

Objectives To develop an algorithm to electronically phenotype individuals with COPD at a large tertiary care center.

Methods We identified individuals over 45 years of age at last clinic visit within Vanderbilt University Medical Center electronic health records. We tested phenotyping algorithms using combinations of both structured and unstructured text and examined the clinical characteristics of the resulting case sets.

Measurement and Main Results A simple algorithm consisting of 3 International Classification of Disease codes for COPD achieved a sensitivity of 97.6%, a specificity of 76.0%, a positive predictive value of 57.1%, and a negative predictive value of 99.0%. A more complex algorithm consisting of both billing codes and a mention of oxygen on the problem list that achieved a positive predictive value of 86.5%. However, the association of known risk factors with chronic obstructive pulmonary disease was consistent in both algorithm sets, suggesting a simple code-only algorithm may suffice for many research applications.

Conclusions Simple code-only phenotyping algorithms for chronic obstructive pulmonary disease can identify case populations with epidemiologic and genetic profiles consistent with published literature. Implementation of this phenotyping algorithm will expand opportunities for clinical research and pragmatic trials for COPD.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted July 28, 2019.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
A Clinical Phenotyping Algorithm to Identify Cases of Chronic Obstructive Pulmonary Disease in Electronic Health Records
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
A Clinical Phenotyping Algorithm to Identify Cases of Chronic Obstructive Pulmonary Disease in Electronic Health Records
Victoria L. Martucci, Nancy Liu, V. Eric Kerchberger, Travis J. Osterman, Eric Torstenson, Bradley Richmond, Melinda C. Aldrich
bioRxiv 716779; doi: https://doi.org/10.1101/716779
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
A Clinical Phenotyping Algorithm to Identify Cases of Chronic Obstructive Pulmonary Disease in Electronic Health Records
Victoria L. Martucci, Nancy Liu, V. Eric Kerchberger, Travis J. Osterman, Eric Torstenson, Bradley Richmond, Melinda C. Aldrich
bioRxiv 716779; doi: https://doi.org/10.1101/716779

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (3683)
  • Biochemistry (7762)
  • Bioengineering (5658)
  • Bioinformatics (21219)
  • Biophysics (10544)
  • Cancer Biology (8151)
  • Cell Biology (11895)
  • Clinical Trials (138)
  • Developmental Biology (6727)
  • Ecology (10385)
  • Epidemiology (2065)
  • Evolutionary Biology (13833)
  • Genetics (9685)
  • Genomics (13047)
  • Immunology (8116)
  • Microbiology (19922)
  • Molecular Biology (7820)
  • Neuroscience (42930)
  • Paleontology (318)
  • Pathology (1276)
  • Pharmacology and Toxicology (2255)
  • Physiology (3346)
  • Plant Biology (7201)
  • Scientific Communication and Education (1309)
  • Synthetic Biology (1998)
  • Systems Biology (5526)
  • Zoology (1126)