Trustworthy Artificial Intelligence for Improvement of Stroke Outcomes
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: NORA.
- Who it may be relevant to
- Registry conditions: Stroke, Acute, Stroke, Ischemic, Stroke. Basic parameters: from 18 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Belgium, Italy, Spain
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
Unsure about the terms? Read our patient guide →
Official title
Trustworthy Artificial Intelligence for Improvement of Stroke Outcomes. Phase II Prospective Study for AI Models Optimization
Overview
Stroke is a leading cause of death and disability worldwide. The clinical validation of explainable and interpretable Artificial Intelligence (AI) solutions to assist a timely, personalised management of the acute phase of stroke, would have a major impact since it can greatly reduce the disability levels of patients. Also, the prediction of long-term outcomes is a crucial factor as it may determine critical decisions such as the discharge destination for the patient. Moreover, compliance with guideline-based secondary stroke prevention has been demonstrated to reduce stroke recurrence, but currently, only 40% of patients are adherent to preventive treatments 3 months after stroke. Therefore, patients´ outcomes can improve with proper patient communication and engagement packages. AI may have a dramatic impact on stroke patient journey, improving predictions, resulting in a better choice of secondary stroke strategies, as well as using evidence-based information to promote better adherence to treatment and reduction of vascular risk factors. The aim of this multicentre observational prospective study is to develop and validate AI-based tools to predict short and long-term outcomes in ischemic stroke patients. Specifically, this study aims to demonstrate the accuracy of AI models in predicting the functional outcome of ischaemic stroke patients as measured by the National Institutes of health Stroke Scale (NIHSS, 0-42) and the modified Rankin Scale (mRS, 0-6) scores at hospital discharge and at 3, 6 and 12 months after discharge. Prospective ischemic stroke patients from 3 Large European centres will be recruited. The training and testing of local AI models will be performed using hospitalization data, collected during the standard of care procedures for stroke patient pathways, and outpatient monitored data from a remote home-care system (NORA app) during the follow-up after discharge. These local models will then be integrated into a federated learning system, where only a global AI model, derived from combined insights of all local models, is shared across participating hospitals. The individual local models and the original data are not shared, ensuring data privacy and security. The accuracy and performance of prospectively optimized AI models in predicting clinical outcomes over a 12-month follow-up period will be evaluated and compared to the actual outcomes of the patients.
Interventions
- Device NORA
NORA app will be downloaded on the patient's mobile device, tablet or computer for clinical monitoring after discharge from the hospital at 3, 6 and 12 months after stroke. At the time of discharge, the patient will be provided with all the information and training necessary for its use. This application has been clinically validated in stroke patients, demonstrating to improve communication between professionals and patients. It improves the adherence of patients to prescribed therapy and their
Primary outcome measures
- AI model's accuracy in predicting short-term functional stroke outcomes [Time frame: 24 months]
Secondary outcome measures (2)
- AI model's accuracy in predicting long-term functional outcomes [Time frame: 24 months]
- AI model's accuracy in predicting stroke associated risks [Time frame: 24 months]
Eligibility criteria
Inclusion criteria
- Subject is 18 years of age or older
- Diagnosis of acute ischemic stroke
- Signature of the informed consent form by the patient or a next of kin
Exclusion criteria
- No exclusion criteria are contemplated for this study.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Cohort
Study locations
Belgium · 1 center
- KATHOLIEKE UNIVERSITEIT LEUVEN (KU Leuven) — Leuven
Italy · 1 center
- Fondazione Policlinico Universitario A. Gemelli IRCCS, UOC Neurologia — Rome
Spain · 1 center
- Hospital Vall D'Hebron- Institut de Recerca (Vhir) — Barcelona
Identifiers
NCT: NCT06710028 · 7067