How to Identify Potential Candidates for ...
Type de document :
Article dans une revue scientifique: Article original
DOI :
PMID :
ArXiv :
hal-03246402
URL permanente :
Titre :
How to Identify Potential Candidates for HIV Pre-Exposure Prophylaxis: An AI Algorithm Reusing Real-World Hospital Data
Auteur(s) :
Duthe, J. C. [Auteur]
Bouzille, G. [Auteur]
Sylvestre, E. [Auteur]
Chazard, Emmanuel [Auteur]
METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694
Arvieux, C. [Auteur]
Cuggia, M. [Auteur]
Bouzille, G. [Auteur]
Sylvestre, E. [Auteur]
Chazard, Emmanuel [Auteur]
METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694
Arvieux, C. [Auteur]
Cuggia, M. [Auteur]
Titre de la revue :
Studies in Health Technology and Informatics
Nom court de la revue :
Stud Health Technol Inform
Numéro :
281
Pagination :
p. 714-718
Date de publication :
2021
ISSN :
1879-8365
Discipline(s) HAL :
Sciences du Vivant [q-bio]
Résumé en anglais : [en]
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 ...
Lire la suite >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.Lire moins >
Lire la suite >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.Lire moins >
Langue :
Anglais
Audience :
Internationale
Vulgarisation :
Non
Établissement(s) :
Université de Lille
CHU Lille
CHU Lille
Date de dépôt :
2023-11-15T06:32:48Z
2024-02-08T12:22:08Z
2024-02-08T12:22:08Z
Fichiers
- SHTI-281-SHTI210265.pdf
- Version éditeur
- Accès libre
- Accéder au document