Clinical trial NCT06206369: Developing Trustworthy Artificial Intelligence (AI)-Driven Tools to Predict Vascular Disease Risk and Progression
Official title: Developing Trustworthy Artificial Intelligence (AI)-Driven Tools to Predict Vascular Disease Risk and Progression
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Aneurysm Abdominal
Peripheral Arterial Disease
Keywords: Artificial Intelligence, Prediction models, Image analysis, Abdominal Aortic Aneurysms, Peripheral Arterial Disease
Lead sponsor: Amsterdam UMC, location VUmc
Sponsor class: OTHER
Collaborators: name: Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)
name: Technical University of Twente
name: Universidade do Porto
name: Centre Hospitalier Universitaire de Nice
name: Stichting Allai
name: Faculty of Medicine, University of Belgrade
name: Brightfish Be
name: Hospital District of Helsinki and Uusimaa
name: University of Bergen
name: Asklepios Kliniken Hamburg GmbH
name: University of Oxford
name: VINČA INSTITUTE OF NUCLEAR SCIENCES Belgrado
First posted: 2024-01-16
Enrollment: 11000
Sex: ALL
Minimum age (years): 40
Maximum age (years): 90
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Males and females between 40 and 90 years of age with an abdominal aortic aneurysm and/or peripheral arterial disease.

Brief summary: The VASCULAID-RETRO study, within the broader VASCULAID project, aims to create artificial intelligence (AI) algorithms that can predict cardiovascular events and the progression of abdominal aortic aneurysm (AAA) and peripheral arterial disease (PAD). The study plans to gather and analyze data from at least 5000 AAA and 6000 PAD patients, combining existing cohorts and retrospectively collected data. During this project, AI tools will be developed to perform automatic anatomical segmentation and analyses on multimodal imaging. AI prediction algorithms will be developed based on multisource data (imaging, medical history, -omics).

Detailed description: To date, it is unknown which abdominal aortic aneurysm (AAA) and peripheral arterial disease (PAD) patients will suffer cardiovascular events or in which patients the AAA or PAD will progress. In the VASCULAID project, the VASCULAID-RETRO study aims to leverage data from existing cohorts and retrospectively collected data to develop artificial intelligence (AI) algorithms able to evaluate the risk of cardiovascular events and extent of disease progression.

In order to build and train the algorithms for the predictions, we plan to retrospectively enroll at least 5000 AAA and 6000 PAD patients AI-tools will be applied to the patient data. Automatic anatomical segmentation on images and image analysis on US, CTA and MRI will be performed. Also, algorithms to predict cardiovascular events and AAA or PAD progression based on multi-source data analysis will be developed.

Patient data from European clinical consortium partners is available. This consortium has access to big cohorts with relevant data for the envisioned study that will be used to enrich the existing registries. These data will be used to refine the algorithms developed for the prediction of cardiovascular events and AAA/PAD progression.

Eligibility criteria: Inclusion Criteria:

* Males and females, 40-90 years old, with an AAA \>3cm. This includes patients with infrarenal, juxtarenal, suprarenal, iliac (defined as 1.5x its normal diameter) aneurysms, as well as mycotic aneurysms. Patients that have had interventions or ruptures will also be included
* Males and females, 40-90 years old, all PAD patients (Fontaine stages 1,2,3, and 4).

Exclusion Criteria:

* Patients with an ascending, thoracic, thoracoabdominal (type 1-3) aneurysm.

Primary outcomes:
measure: Development of disease progression prediction algorithms
timeFrame: 3 years
Secondary outcomes:
measure: Internal validation of disease progression prediction algorithms
timeFrame: 3 years
Other outcomes:
Locations:
city: Helsinki
country: Finland
facility: Hospital District of Helsinki and Uusimaa (HUS)
city: Hamburg
country: Germany
facility: Asklepios kliniken hamburg
city: Amsterdam
country: Netherlands
facility: Amsterdam UMC
city: Porto
country: Portugal
facility: University Hospital Center of São João
city: Belgrade
country: Serbia
facility: University Clinical Centre of Serbia
city: Oxford
country: United Kingdom
facility: Oxford University Hospitals
Study officials:
References: