Stroke, AcuteTRUSTrokeNCT06710028
Trustworthy Artificial Intelligence for Improvement of Stroke Outcomes
An observational study of Stroke, Acute and Stroke, Ischemic, run by Fondazione Policlinico Universitario Agostino Gemelli IRCCS at 3 sites in 3 countries.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Stroke, Acute, Stroke, Ischemic, Stroke
- What is being tested?
- NORA
- Type of treatment
- Pill or oral medicine, Medical device
- How many people
- 1,000 participants
- Where
- 3 sitesin 3 countries
- Study dates
- 18 Dec 2024to 31 Dec 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: All consecutive ischemic stroke patients admitted to the participating sites, who are older than 18 and who signed the informed consent (either signed by the patient himself or a next of kin).
Who may be able to join
- 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
Who cannot join
- No exclusion criteria are contemplated for this study.
Trial locations and contacts
3 sites in 3 countries.
3 sites in 3 countries
Italy1 site
Fondazione Policlinico Universitario A. Gemelli IRCCS, UOC Neurologia
Rome, Lazio, 00168
About this study
Official title: Trustworthy Artificial Intelligence for Improvement of Stroke Outcomes. Phase II Prospective Study for AI Models Optimization
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.
- Conditions
- Stroke, Acute, Stroke, Ischemic, Stroke
- Being tested
- NORA
- Medical subject headings
- Stroke, Ischemic Stroke
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
NORADevice
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 control of cardiovascular risk factors, with the the goal of preventing new episodes. Stroke patients have actively participated in the development of NORA, its use is simple and intuitive, and there are no age restrictions for its use. Through NORA patients will receive questionnaires to evaluate their clinical outcomes after stroke (Patient Reported Outcome Measures- PROMs and Patient Reported Experience Measures- PREMs).
What is being measured
The primary outcome is the main question the study is designed to answer.
AI model's accuracy in predicting short-term functional stroke outcomes
To evaluate the accuracy of the developed AI models in predicting functional outcomes of stroke patients, such as National Institute of Health Stroke Scale (NIHSS, 0-42) and modified Rankin Scale (mRS, 0-6) at hospital discharge (short-term outcome).
Specifically, metrics such as Area Under the ROC Curve (AUROC) for classification tasks and R² for regression tasks will be evaluated, both for machine learning approaches such as Random Forest and XGBoost, and deep learning approaches, such as neural networks.
Measured: 24 months
Secondary outcomes (2)
AI model's accuracy in predicting long-term functional outcomes
To evaluate the accuracy of the developed AI models in predicting National Institute of Health Stroke Scale (NIHSS, 0-42) and modified Rankin Scale (mRS, 0-6) at 3, 6 and 12 months after discharge. Moreover, other functional outcomes will be evaluated, such as Patient Reported Outcome Measures (PROMs) and Patient Reported Experience Measures (PREMs).
Specifically, metrics such as Area Under the ROC Curve (AUROC) for classification tasks and R² for regression tasks will be evaluated, both for machine learning approaches such as Random Forest and XGBoost, and deep learning approaches, such as neural networks.
Measured: 24 months
AI model's accuracy in predicting stroke associated risks
To evaluate the accuracy of AI models in predicting the probability of early supported discharge (1 week after the event), the probabilty of unplanned hospital readmission (at 30 days) and the personalized risk of stroke recurrence at 3 and at 12 months.
Specifically, metrics such as Area Under the ROC Curve (AUROC) for classification tasks and R² for regression tasks will be evaluated, both for machine learning approaches such as Random Forest and XGBoost, and deep learning approaches, such as neural networks.
Measured: 24 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted29 Nov 2024
- Study start18 Dec 2024
- Primary completion30 Nov 2026 (estimated)
- Study completion31 Dec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Stroke, AcuteStudies of the same condition, open and under way.
- More trials run by Fondazione Policlinico Universitario Agostino Gemelli IRCCSThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 1,000 participants (estimated)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Pietro Caliandro, MD, Principal investigator, Fondazione Policlinico Universitario Agostino Gemelli IRCCS
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06710028
- Sponsor's protocol ID
- 7067
- Acronym
- TRUSTroke
- Status last verified
- Sep 2026
- First submitted
- 26 Nov 2024
- Last updated
- 2 Sep 2026
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 2 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Trustworthy Artificial Intelligence for Improvement of Stroke Outcomes. Phase II Prospective Study for AI Models Optimization
- ClinicalTrials.gov ID
- NCT06710028
- Acronym / sponsor ID
- TRUSTroke / 7067
- Conditions
- Stroke, Acute, Stroke, Ischemic, Stroke
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 1,000 participants (estimated)
- Lead sponsor
- Fondazione Policlinico Universitario Agostino Gemelli IRCCSUniversity, hospital or other
- Study start
- 18 Dec 2024 (actual)
- Primary completion
- 30 Nov 2026 (estimated)
- Study completion
- 31 Dec 2026 (estimated)
- First posted
- 29 Nov 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- All consecutive ischemic stroke patients admitted to the participating sites, who are older than 18 and who signed the informed consent (either signed by the patient himself or a next of kin).
Key inclusion criteria (3 listed)
- 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
Key exclusion criteria (1 listed)
- No exclusion criteria are contemplated for this study.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| NORANORA 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 control of cardiovascular risk factors, with the the goal of preventing new episodes. Stroke patients have actively participated in the development of NORA, its use is simple and intuitive, and there are no age restrictions for its use. Through NORA patients will receive questionnaires to evaluate their clinical outcomes after stroke (Patient Reported Outcome Measures- PROMs and Patient Reported Experience Measures- PREMs). | Device | – |
Primary outcome
AI model's accuracy in predicting short-term functional stroke outcomes
Time frame: 24 months
To evaluate the accuracy of the developed AI models in predicting functional outcomes of stroke patients, such as National Institute of Health Stroke Scale (NIHSS, 0-42) and modified Rankin Scale (mRS, 0-6) at hospital discharge (short-term outcome). Specifically, metrics such as Area Under the ROC Curve (AUROC) for classification tasks and R² for regression tasks will be evaluated, both for machine learning approaches such as Random Forest and XGBoost, and deep learning approaches, such as neural networks.
2 secondary outcomes
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
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 2 Sep 2026.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Pietro Caliandro, MD principal investigator
Fondazione Policlinico Universitario Agostino Gemelli IRCCS
3 sites in 3 countries: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Stroke, Acute and Stroke, Ischemic, run by Fondazione Policlinico Universitario Agostino Gemelli IRCCS at 3 sites in 3 countries.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: under way, not looking for participants, as of the sponsor's last update on 2 Sep 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 3 sites in Belgium, Italy, Spain. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
AI model's accuracy in predicting short-term functional stroke outcomes (time frame: 24 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 2 Sep 2026.