Identifying People Living With or Those at Risk for HIV in a Nationally Sampled Electronic Health Record Repository
Eric Hurwitz1, Cara D Varley2, A Jerrod Anzolone3
1Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
JMIR Medical Informatics
|July 11, 2025
Summary
A new computational phenotyping method accurately identifies people living with HIV, pre-exposure prophylaxis (PrEP) users, and post-exposure prophylaxis (PEP) users in electronic health records (EHRs). This approach improves HIV research and care, especially during the COVID-19 pandemic.
Area of Science:
- Health Informatics
- Epidemiology
- Clinical Research
Background:
- Electronic health records (EHRs) are crucial for HIV research but require refined methods for accurate patient identification.
- Traditional diagnostic codes can lead to misclassification, especially with overlapping drug indications (e.g., ritonavir in HIV and COVID-19 treatments).
- The COVID-19 pandemic highlighted the need for robust phenotyping to understand its impact on people with HIV.
Purpose of the Study:
- To develop and validate a generalizable computational phenotyping method using granular EHR data.
- To accurately identify four distinct cohorts: people living with HIV, PrEP users, PEP users, and individuals not living with HIV.
- To describe COVID-19-related characteristics within these identified cohorts.
Main Methods:
- Utilized diagnostic codes, laboratory measurements, and drug concepts from the National Clinical Cohort Collaborative.
- Developed a computational phenotype with confidence levels for each of the four cohorts.
- Assessed phenotyping precision through randomly sampled, blinded clinician annotation.
Main Results:
- Successfully identified 132,664 people living with HIV, 36,088 PrEP users, 4,120 PEP users, and over 20 million individuals not living with HIV.
- The most effective identification strategy for people living with HIV involved combinations of medical conditions, laboratory measurements, and drug exposures (56.4%).
- People living with HIV showed higher rates of COVID-19-related hospitalization (3.5%), COVID-19 mortality (0.6%), and all-cause mortality (1.6%) compared to other cohorts.
Conclusions:
- An advanced phenotyping algorithm using granular EHR data can effectively identify key HIV-related cohorts.
- The developed method offers a robust and transferable approach for future EHR-based phenotyping studies.
- Findings underscore the disproportionate impact of COVID-19 on people living with HIV and the utility of EHR data in public health surveillance.
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