Development and validation of an electronic medical record (EMR)-based computed phenotype of HIV-1 infection

Devon W. Paul, Nigel B. Neely, Meredith Clement, Isaretta Riley, Mashael Al-Hegela, Matthew Phelan, Monica Kraft, David M. Murdoch, Joseph Lucas, John Bartlett, Mehri McKellar, Loretta G. Que

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


Background: Electronic medical record (EMR) computed algorithms allow investigators to screen thousands of patient records to identify specific disease cases. No computed algorithms have been developed to detect all cases of human immunodeficiency virus (HIV) infection using administrative, laboratory, and clinical documentation data outside of the Veterans Health Administration. We developed novel EMR-based algorithms for HIV detection and validated them in a cohort of subjects in the Duke University Health System (DUHS). Methods: We created 2 novel algorithms to identify HIV-infected subjects. Algorithm 1 used laboratory studies and medications to identify HIV-infected subjects, whereas Algorithm 2 used International Classification of Diseases, Ninth Revision (ICD-9) codes, medications, and laboratory testing. We applied the algorithms to a wellcharacterized cohort of patients and validated both against the gold standard of physician chart review. We determined sensitivity, specificity, and prevalence of HIV between 2007 and 2011 in patients seen at DUHS. Results: A total of 172 271 patients were detected with complete data; 1063 patients met algorithm criteria for HIV infection. In all, 970 individuals were identified by both algorithms, 78 by Algorithm 1 alone, and 15 by Algorithm 2 alone. The sensitivity and specificity of each algorithm were 78% and 99%, respectively, for Algorithm 1 and 77% and 100% for Algorithm 2. The estimated prevalence of HIV infection at DUHS between 2007 and 2011 was 0.6%. Conclusions: EMR-based phenotypes of HIV infection are capable of detecting cases of HIV-infected adults with good sensitivity and specificity. These algorithms have the potential to be adapted to other EMR systems, allowing for the creation of cohorts of patients across EMR systems.

Original languageEnglish (US)
Pages (from-to)150-157
Number of pages8
JournalJournal of the American Medical Informatics Association
Issue number2
StatePublished - Feb 1 2018


  • Diagnostic algorithm
  • Electronic medical record
  • HIV

ASJC Scopus subject areas

  • Health Informatics


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