Clinical trials

Stroke is the leading cause of acquired disability in adults and a major cause of mortality worldwide; in Spain, Andalusia shows the highest stroke-related mortality rate. Comprehensive Stroke Units (SU) are the gold-standard organizational model for acute stroke care; however, only a fraction of patients have direct access to an SU, particularly those not eligible for mechanical thrombectomy who are admitted to regional or district hospitals without on-site SU capacity. The Virtual Stroke Unit (VSU) concept extends specialized stroke care to non-SU hospitals by combining standardized in-hospital monitoring boxes with synchronous remote multidisciplinary assessment by a stroke neurologist and stroke nurse from a reference center, via the regional telemedicine platform (CATI). This prospective, multicenter, non-inferiority cohort study compares effectiveness, safety, and feasibility of VSU care versus conventional SU care in patients with acute ischemic or hemorrhagic stroke who are not candidates for mechanical thrombectomy. Recruitment targets 363 patients per arm (726 total). The primary outcome is death or dependency at 3 months (modified Rankin Scale 3-6) - the canonical measure of stroke-unit effectiveness - with functional independence (mRS 0-2), adherence to the stroke-unit care quality bundle, safety, mortality, recurrence, length of stay, satisfaction (TUQ/TSQ/TMPQ) and cost-effectiveness as secondary outcomes.

This retrospective observational study, part of the EU-funded CARAMEL project, aims to develop and validate personalized cardiovascular disease (CVD) risk assessment models specifically designed for menopausal and perimenopausal women (ages 40-60). The study leverages Real World Data (RWD) collected from multiple international clinical partners, including electronic health records (EHR), diagnostic imaging data, and signal data. The main objective is to improve the prediction of CVD precursors such as hypertension and dyslipidemia, as well as mid- and long-term risk of CVD events, through advanced artificial intelligence (AI) models. These models will be trained on multimodal data to capture complex, individualized risk trajectories that current risk calculators fail to address, particularly in women. Special focus is placed on under-researched, women-specific risk factors and their interactions with traditional predictors. The study includes several research objectives: (1) predicting the onset of hypertension and dyslipidemia using EHR data; (2) modeling the long-term risk of fatal and non-fatal cardiovascular events and disease trajectories; (3) identifying novel imaging biomarkers from routine screening tests such as mammography, DXA, ultrasound, and cardiac MRI; (4) developing multimodal prediction models combining imaging and clinical data; (5) creating automated AI tools for imaging biomarker extraction; and (6) using signal data from cardiac devices to predict disease progression and events. The study population consists of middle-aged women with retrospective data available across different health systems. The expected outcome is a validated set of stratified, personalized CVD risk models that can support targeted prevention strategies and enable more equitable, sex-specific care. This will contribute to reducing the burden of CVD in women and addressing critical gaps in early detection, clinical decision-making, and health policy. This project has received funding from the European Union's Horizon Europe Research and Innovation Programme under Grant Agreement No 101156210.

The development of Clinical significant PH (CSPH) is a major driver of complications. PPG is the strongest predictor of decompensation . The shortcomings of GPVH measurements and invasiveness has resulted in an area of unmet clinical need. Non-selective beta-blockers (NSBB) have demonstrated efficacy in reducing PH. However, this requires a second invasive procedure, and there is high interindividual variability in the hemodynamic response to NSBB. EUS-guided GPP measurement emerges as a more accurate, accessible and less invasive technique to potentially predict chronic hemodynamic response. The aim of this project is to assess the safety and efficacy of EUS-derived GPP measurement (EUS-GPP) in patients diagnosed with cirrhosis, to assess the acute hemodynamic response through EUS-derived GPP to intravenous propranolol administration in order to determine the patients who would benefit from NSBB treatment in primary prophylaxis to prevent decompensation using a strategy guided by EUS-GPP measurement.