Clinical Research Directory

Risk Analysis of Intracranial Aneurysm Rupture Through Social Determinants of Health

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.

Epithelioid Haemangioendothelioma Observational Study

The EHE observational study was developed to obtain a high number of datas such as clinical presentation, natural history, and treatment outcomes, cto identificate cytokines and hormones as biomarkers and generate patient-derived preclinical models as a tool to assess the activity of anticancer agents and validate novel therapeutic targets

GUARD-PH: Guided Use of AI-ECG for Risk Detection of PH in Surgery (GUARD-PH Trial)

This study is a prospective, open-label, randomized controlled trial designed to evaluate a new artificial intelligence (AI) tool for heart monitoring. Researchers will use an AI-enabled electrocardiography (ECG) system to screen patients before they undergo surgery. The main goal is to determine if this AI system can accurately detect pulmonary hypertension and related heart diseases in the preoperative setting. The study is being conducted at Taipei Veterans General Hospital.

Exercise Prescription in Cardiac Rehabilitation Mediated by Autonomic Function

Cardiac rehabilitation (CR) is an essential secondary prevention component in the treatment of cardiovascular diseases and one of the most cost- effective clinical interventions. Exercise training (ET) in CR programs (CRP) has unequivocal benefits in the reduction of cardiovascular adverse events, by decreasing the overactivated sympathetic tone. This ET added value can be measured by variables that express autonomic control using indirect (standard) or direct (experimental) methodologies. Direct autonomic assessment (ex. Microneurography) is accurate but unusable in daily practice, whereas standard indirect autonomic assessment using clinical parameters is imprecise, resulting in underprescription to safeguard patient safety, with less benefit to the patients. In this project, we aim to apply Machine Learning models to a set of indirect and direct variables, to make a multivariate correlation analysis and so define a normalization factor for exercise prescription.

Prehabilitation for Ovarian Cancer Patients

Individuals with ovarian cancer have very poor survival rates. This is because the cancer is not usually detected until it has reached advanced stages. How long an individual survives also is determined by the cancer treatment they receive. Although there are best treatment practices to improve survival, some women have other conditions that limit treatment options. One such condition seen in as many as 50% of women with advanced ovarian cancer is frailty (an age-related decline in function and health). This is a major concern as doctors will often have to change how the cancer is treated based on the patient being frail. For example, patients living with frailty are less likely to have their full tumor removed during surgery. They are also more likely to have complications with surgery, stay in the hospital longer, and recover less well from surgery overall. Patients living with frailty also are more likely to experience delays in their chemotherapy starting, receive lower doses of chemotherapy and/or receive fewer cycles of chemotherapy. These changes in treatment may decrease how long a patient survives after diagnosis. Thus, research is needed to explore strategies to decrease frailty in patients who require treatment for advanced ovarian cancer. An option gaining more attention is physical exercise (e.g. walking, repeatedly rising from a chair). Exercise performed before surgery, which is called prehabilitation, can improve how well a patient recovers after surgery and increase how long they survive. Research has shown that prehabilitation is very beneficial for patients undergoing surgery for heart disease. However, it is not clear whether prehabilitation works for those with advanced ovarian cancer that are going to have surgery. Therefore, the investigators want to explore how a 4+ week exercise program performed while waiting for surgery for advanced ovarian cancer changes frailty and how a patient recovers after surgery. The investigators will specifically look whether the exercise program: 1) reduces how frail a patient is before surgery; 2) improves how well the patient recovers after surgery; and 3) affects the patient's chemotherapy treatment plan. This study will provide important information about the ability of prehabilitation exercise to improve surgical and treatment outcomes in women with advanced ovarian cancer. Overall, it is believed that exercise has the potential to improve the survival of advanced ovarian cancer patients.