Analyse du Risque de Rupture Des AnévrysMes Intracrâniens au Travers Des Déterminants Sociaux de Santé
Trial ID: NCT06866210
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-10-15
Completion date: 2026-12-31
Sponsor: Nantes University Hospital
Conditions: Social Determinant of Health; Intracranial Aneurysms; Subarachnoid Hemorrhage; Treatment Outcomes; Natural Languages Processing; Observational Study; Aneurysm, Ruptured
Locations: Nantes Hospital
Countries: France
Enrollment: 1800
Age range: 18 Years - None
Sex: ALL

Summary:
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.

Eligibility criteria:
Inclusion Criteria:

* Patients aged 18 years or older at the time of their first cerebral imaging report mentioning an intracranial aneurysm (IA);
* Patients with a confirmed saccular IA based on imaging;
* Presence of at least one document referencing social determinants of health, lifestyle, habits, or exposure.

Exclusion Criteria:

* Patients who refuse the reuse of their health data;
* Patients diagnosed with dominant autosomal polycystic liver-kidney disease, sickle cell disease, mycotic aneurysm, fusiform aneurysm, blister-like aneurysm, Marfan syndrome, or Ehlers-Danlos syndrome.

Outcomes:
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)