Trustworthy Artificial Intelligence for Improvement of Stroke Outcomes (TRUSTroke)

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.

Study Overview

• Status: Recruiting
• Conditions: Stroke; Stroke, Ischemic; Stroke, Acute
• Intervention / Treatment: Device: NORA

• Study Type: Observational
• Enrollment (Estimated): 1000

Contacts and Locations

• Study Contact: Name: Pietro Caliandro; Phone Number: +390630154338; Email: pietro.caliandro@policlinicogemelli.it

Study Locations

• Belgium
  • Leuven, Belgium, 3000
    • Not yet recruiting
    • KATHOLIEKE UNIVERSITEIT LEUVEN (KU Leuven)
    • Principal Investigator: Robin Lemmens
    • Contact: Robin Lemmens; Email: robin.lemmens@uzleuven.be
• Italy
  • Lazio
    • Roma, Lazio, Italy, 00168
      • Recruiting
      • Fondazione Policlinico Universitario A. Gemelli IRCCS, UOC Neurologia
      • Contact: Pietro Caliandro; Phone Number: +390630151; Email: pietro.caliandro@policlinicogemelli.it
      • Principal Investigator: Pietro Caliandro
• Spain
  • Barcelona, Spain, 08035
    • Recruiting
    • Hospital Vall D'Hebron- Institut de Recerca (Vhir)
    • Contact: Carlos Molina; Email: carlosav.molina@vallhebron.cat
    • Principal Investigator: Carlos Molina

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• 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).

Description

Inclusion Criteria:

1. Subject is 18 years of age or older
2. Diagnosis of acute ischemic stroke
3. Signature of the informed consent form by the patient or a next of kin

Exclusion Criteria:

• No exclusion criteria are contemplated for this study.

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
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.	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.	24 months

Collaborators and Investigators

• Sponsor: Fondazione Policlinico Universitario Agostino Gemelli IRCCS
• Investigators: Principal Investigator: Pietro Caliandro, MD, Fondazione Policlinico Universitario A. Gemelli, IRCCS

Study record dates

Study Major Dates

• Study Start (Actual): December 18, 2024
• Primary Completion (Estimated): November 30, 2026
• Study Completion (Estimated): December 31, 2026

Study Registration Dates

• First Submitted: November 26, 2024
• First Submitted That Met QC Criteria: November 26, 2024
• First Posted (Actual): November 29, 2024

Study Record Updates

• Last Update Posted (Actual): March 25, 2025
• Last Update Submitted That Met QC Criteria: February 21, 2025
• Last Verified: November 1, 2024

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Cerebrovascular Disorders; Brain Diseases; Central Nervous System Diseases; Nervous System Diseases; Vascular Diseases; Cardiovascular Diseases; Ischemic Stroke; Stroke
• Other Study ID Numbers: 7067

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No