Intracranial aneurysms (IA) are arterial malformations affecting about 3% of the overall population. Rupture is the most severe complication, as it is associated with nearly 30% of death or severe disability. The available scores to assess rupture risk are mainly based on usual modifiable and non-modifiable risk factors from the literature, but they appear insufficient to predict rupture. Emerging factors, such as sleep apnea syndrome and the use of certain medications, seem to influence the risk of rupture. The study of social determinants of health (SDOH) is highly relevant, given numerous reports showing the impact of SDOH, in addition to vascular risk factors, on vascular diseases like ischemic stroke or myocardial infarction. It is therefore reasonable to study the interaction between rupture risk factors and SDOH on the rupture risk of IA. Several initiatives have been undertaken to assess rupture risk, but few have included SDH. Limitations were often raised, especially regarding data accessibility. However, it is now possible, thanks to artificial intelligence (AI) algorithms, particularly natural language processing (NLP), to reuse large-scale health data to address longstanding issues, such as those posed by SDH. The use of health data warehouses (HDWs) offers an opportunity to collect and analyze accurate, real-world data, particularly through AI and NLP to extract information from medical reports. However, various challenges limit the use of NLP models, notably the dominance of models trained on English medical texts and privacy-related legislative restrictions. Therefore, alongside leveraging these models for clinical research, it is essential to continue efforts to develop transparent French-language models that comply with legislation. Thus, the ARAMISS project proposes to study the interaction between SDH and known risk factors for IA rupture by comparing control populations and rupture cases. This study will be based on a certified health data warehouse (HDW) and an NLP algorithm previously developed by the team. In parallel, the project plans two FAIR-compliant knowledge-sharing approaches to disseminate the algorithm and training corpus to the broader community.
Age range
18 Years
Sex
ALL
See this in plain English?
AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.
Bring these to your next appointment. They're a starting point for a shared conversation — not a sign you qualify or a recommendation to enrol.
Generated to help you prepare — always confirm anything about your own eligibility and care with the study team and your doctor.
The trial coordinator is the person who runs the study day to day. These cover the practical side — logistics, costs, and what taking part would actually mean for your life. The study team confirms whether you meet the criteria; these are questions to ask, not a sign you qualify.
A starting point for the conversation — always confirm anything about your own eligibility, costs, and care with the study team and your doctor.
Study the interaction between social determinants of health (SDH) and individual factors associated with intracranial aneurysm (IA) rupture using data available in the Nantes University Hospital Data Warehouse (EDS)
Timeframe: 18 months