Intracranial aneurysms (IA), the most common intracranial arterial malformation, affect
approximately 3% of the global population. They are characterized by a dilation of the
vascular wall within the intracranial region, typically at a bifurcation. The most feared
complication of IA is rupture, which is associated with high morbidity and mortality.
About 30% of patients face death or severe disability, and even among those with no
visible sequelae, up to 50% suffer from invisible disabilities such as cognitive
problems. This explains the significant economic burden of ruptured IAs, estimated at
approximately £168 million annually in the UK.
One way to reduce this morbidity and societal burden is to identify patients at high risk
of rupture to determine those most likely to experience disease progression. Various
initiatives, such as the ELAPSS, PHASES, and UCAS scores, have been developed to assess
rupture risk in routine clinical practice. However, these scores largely rely on
well-known modifiable and non-modifiable risk factors, such as ethnicity, hypertension,
sex, age, smoking, and history of subarachnoid hemorrhage. Despite their utility, they
are insufficient for reliably stratifying rupture risk, underestimating it by
approximately 30%, which limits their impact in daily practice.
Recent research has identified emerging risk and protective factors, such as sleep apnea
syndrome, lipid control, and the use of antidiabetic, lipid-lowering, or antihypertensive
treatments. Therefore, it is necessary to revisit IA rupture risk by considering these
factors in relation to patients' living environments.
Social determinants of health (SDOH) play a critical role in this context. Defined as the
conditions of life that influence individual and population health, SDOH include broad
factors such as age, ethnicity, and gender, as well as addiction behaviors, income
levels, environmental exposure, and social status. They are estimated to contribute to
over 80% of the population's health outcomes. In vascular diseases, SDOH -such as low
education levels, limited income, social isolation, and low physical activity-are known
to impact disease severity beyond traditional risk factors. The WHO estimates that
vascular diseases are the leading global cause of mortality, with 17.7 million deaths
annually, half of which are preventable. Understanding and addressing SDOH is therefore
crucial for public health prevention efforts.
Initial studies on the impact of SDOH on IA rupture are limited, often focusing on
individual determinants. A more comprehensive approach to SDH is needed, especially given
the lack of consensus guidelines for IA management. Current therapeutic options, while
advanced, still carry perioperative morbidity. Multivariable modeling and weighted
composite scores can help address the complexity of clinical and environmental factors.
SDH data are routinely collected in medical records and increasingly digitized through
hospital information systems. Health data warehouses (HDWs), such as the one at Nantes
University Hospital, integrate structured and unstructured data from millions of
patients, enabling their reuse for research purposes. The advent of artificial
intelligence (AI) has made it possible to extract and analyze this data, revealing
insights from previously inaccessible information.
Natural language processing (NLP), a branch of AI, enables automated extraction of
information from textual medical records, unlocking the potential of unstructured data
within HDWs. State-of-the-art models, such as BERT, ChatGPT, Llama, and Mistral, can
process and analyze large volumes of text. However, the application of NLP to non-English
languages is constrained by a lack of annotated corpora, privacy concerns, and data
sovereignty regulations.
The ARAMISS project aims to investigate the interaction between SDH and known risk
factors for IA rupture, comparing patients with unruptured IAs to those with ruptured
IAs. Using SDH, the project will classify patients into risk groups and spatially analyze
patient data through APIs like DataGouv. Additionally, the project will focus on
developing FAIR (Findable, Accessible, Interoperable, Reusable) methodologies to share
its algorithms and training corpora with the biomedical community while maintaining
regulatory compliance.
