Observational study using AI tools to study stroke care and outcomes
Follows adults with suspected stroke or mini-stroke, using routine hospital data to train AI tools.
Plain-language title generated from the registry record
Registry title: AI-Based Tools for Stroke Diagnosis, Treatment and Outcome Prediction: a Multicentre Prospective Observational Study
Key facts
- Where
- Hamburg, GermanySee the site
Where in Germany
- Universitätsklinikum Hamburg-EppendorfHamburg, GermanyNot yet recruiting
Questions to ask the study team
- How often do I need to come in, and for how long each time?
- Are travel costs paid?
- Can I keep my own doctor?
- What happens if I want to stop?
Trial details
- Who runs it
- Hospital Universitari Vall d'Hebron Research Institute
- Trial code
- NCT07924371
- Record updated
- 9 November 2026
- Original description
Stroke is a leading cause of death and adult disability. Stroke care is still limited by fragmented care pathways, non-standardised diagnostic work-up and follow-up, and local, non-interoperable data systems across the pre-hospital, in-hospital and post-hospital settings.Read full original descriptionShow less
Unleashing a CoMprehensive, Holistic and Patient Centric Stroke Management for a Better, Rapid, AdvancEd and PersonaLised Stroke Diagnosis, TreAtment and Outcome Prediction (UMBRELLA) - Prospective Clinical Study
Stroke is a leading cause of death and adult disability. Stroke care is still limited by fragmented care pathways, non-standardised diagnostic work-up and follow-up, and local, non-interoperable data systems across the pre-hospital, in-hospital and post-hospital settings.
UMBRELLA-P is a multicentre, prospective, observational study in 7 university hospitals in 6 European countries (Spain, Italy, Belgium, the Netherlands, Germany and Switzerland). Adults with clinically suspected acute stroke or transient ischemic attack (TIA) are managed according to standard of care. Routine clinical, imaging, laboratory, ECG and patient-reported data are collected, pseudonymised, stored on a dedicated server at each hospital and harmonised to the OMOP Common Data Model.
The harmonised data form a federated real-world data platform (U-Platform). On this platform, artificial intelligence (AI) models are trained and validated through federated learning, so that no patient data leave the participating sites. The models aim to predict short- and long-term outcomes, reduce time to treatment, and support diagnosis, triage, discharge, follow-up and secondary prevention. Five clinical sub-studies address pre-hospital triage, fast track to reperfusion, emergent intracerebral hemorrhage (ICH) management, TIA/minor stroke, and cardiac evaluation with blood biomarkers.
The study is observational. The outputs of the AI models are used for research only and will not influence clinical decisions. Participants are followed for up to 12 months.
Inclusion: Age 18 years or older; Clinical suspicion of acute stroke (including TIA); Written informed consent from the patient or, when not possible, from next of kin; deferred consent is accepted because of the acute setting; Additional inclusion criteria by sub-study:; WP2 and WP3: suspected stroke according to ambulance personnel or confirmed LVO stroke, AND symptom onset (or last seen well, if unwitnessed) less than 24 hours earlier; WP4: intracerebral hemorrhage, regardless of sex, risk factors or etiology; WP5: NIHSS below 7 AND premorbid mRS below 4; WP6: acute ischemic stroke, with or without atrial fibrillation.
Exclusion: None.