How to Identify Potential Candidates for HIV Pre-Exposure Prophylaxis: An AI Algorithm Reusing Real-World Hospital Data
| Autor: | Emmanuelle Sylvestre, Marc Cuggia, Guillaume Bouzillé, Jean-Charles Duthe, Cedric Arvieux, Emmanuel Chazard |
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| Přispěvatelé: | Laboratoire Traitement du Signal et de l'Image (LTSI), Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM), CHU Pontchaillou [Rennes], CHU Lille, Evaluation des technologies de santé et des pratiques médicales - ULR 2694 (METRICS), Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de la Santé et de la Recherche Médicale (INSERM), Jonchère, Laurent |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Male
Anti-HIV Agents HIV prevention Human immunodeficiency virus (HIV) sexual health HIV Infections Hiv risk medicine.disease_cause Health informatics Pre-exposure prophylaxis (PrEP) Men who have sex with men Sexual and Gender Minorities Pre-exposure prophylaxis clinical informatics Humans Medicine Homosexuality Male Reproductive health [SDV.IB] Life Sciences [q-bio]/Bioengineering business.industry risk reduction practices University hospital Hospitals 3. Good health predictive analytics machine learning Automated algorithm Pre-Exposure Prophylaxis [SDV.IB]Life Sciences [q-bio]/Bioengineering France business Algorithm Algorithms |
| Zdroj: | Studies in Health Technology and Informatics Studies in Health Technology and Informatics, 2021, 281, pp.714-718. ⟨10.3233/SHTI210265⟩ Studies in Health Technology and Informatics, IOS Press, 2021, 281, pp.714-718. ⟨10.3233/SHTI210265⟩ MIE |
| ISSN: | 0926-9630 1879-8365 |
| DOI: | 10.3233/SHTI210265⟩ |
| Popis: | International audience; HIV Pre-Exposure Prophylaxis (PrEP) is effective in Men who have Sex with Men (MSM), and is reimbursed by the social security in France. Yet, PrEP is underused due to the difficulty to identify people at risk of HIV infection outside the "sexual health" care path. We developed and validated an automated algorithm that re-uses Electronic Health Record (EHR) data available in eHOP, the Clinical Data Warehouse of Rennes University Hospital (France). Using machine learning methods, we developed five models to predict incident HIV infections with 162 variables that might be exploited to predict HIV risk using EHR data. We divided patients aged 18 or more having at least one hospital admission between 2013 and 2019 in two groups: cases (patients with known HIV infection in the study period) and controls (patients without known HIV infection and no PrEP in the study period, but with at least one HIV risk factor). Among the 624,708 admissions, we selected 156 cases (incident HIV infection) and 761 controls. The best performing model for identifying incident HIV infections was the combined model (LASSO, Random Forest, and Generalized Linear Model): AUC = 0.88 (95% CI: 0.8143-0.9619), specificity = 0.887, and sensitivity = 0.733 using the test dataset. The algorithm seems to efficiently identify patients at risk of HIV infection. |
| Databáze: | OpenAIRE |
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