Trustworthy Artificial Intelligence for Improvement of Stroke Outcomes

NCT06710028 | Recruiting

• 1 other identifier: 7067
• Study Type: observational
• Target: 1,000
• Locations: 3 countries
• Sites: 3

Brief 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. 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

Outcome Measures

Primary Outcomes (1)

• 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.

  24 months

Secondary Outcomes (2)

• AI model's accuracy in predicting long-term functional outcomes

  24 months

• AI model's accuracy in predicting stroke associated risks

  24 months

Interventions

NORA DEVICE

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).

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

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).

You may qualify if:

• 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

Sponsors & Collaborators

• Fondazione Policlinico Universitario Agostino Gemelli IRCCS: lead

Study Sites (3)

KATHOLIEKE UNIVERSITEIT LEUVEN (KU Leuven)

Leuven, 3000, Belgium

NOT YET RECRUITING

Fondazione Policlinico Universitario A. Gemelli IRCCS, UOC Neurologia

Rome, Lazio, 00168, Italy

RECRUITING

Hospital Vall D'Hebron- Institut de Recerca (Vhir)

Barcelona, 08035, Spain

RECRUITING

MeSH Terms

Conditions

Stroke; Ischemic Stroke

Condition Hierarchy (Ancestors)

Cerebrovascular Disorders; Brain Diseases; Central Nervous System Diseases; Nervous System Diseases; Vascular Diseases; Cardiovascular Diseases

Study Officials

• Pietro Caliandro, MD

  Fondazione Policlinico Universitario A. Gemelli, IRCCS

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Pietro Caliandro

CONTACT

+390630154338; pietro.caliandro@policlinicogemelli.it

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: November 26, 2024
• First Posted: November 29, 2024
• Study Start: December 18, 2024
• Primary Completion (Estimated): November 30, 2026
• Study Completion (Estimated): December 31, 2026
• Last Updated: February 25, 2025

Record last verified: 2024-11

Locations

BE (1); IT (1); ES (1)