Developing Trustworthy Artificial Intelligence (AI)-Driven Tools to Predict Vascular Disease Risk and Progression

Recruiting

• Conditions: Peripheral Arterial Disease; Aneurysm Abdominal

• Registration Number: NCT06206369
• Lead Sponsor: Amsterdam UMC, location VUmc

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.

Study Timeline

• Study Start: 2023-10-31
• Status Verified: 2024-01
• Study First Submitted: 2024-01-04
• Study First Submitted QC: 2024-01-04
• Last Update Submitted: 2024-01-04
• Study First Posted: 2024-01-16
• Last Update Posted: 2024-01-16
• Primary Completion: 2029-05-01
• Study Completion: 2029-05-01

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 11000

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.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Development of disease progression prediction algorithms	3 years	The primary goal of this retrospective study is to develop and train algorithms to predict disease progression and risk of cardiovascular events in AAA and PAD patients by leveraging multi-parametric data from 5000 AAA (\>1000 in AUMC) and 6000 PAD (\>1000 in AUMC) patients from existing cohorts and biobanks.

Secondary Outcome Measures

Name	Time	Method
Internal validation of disease progression prediction algorithms	3 years	The secondary objective will be the internal validation of the developed algorithms using data from retrospective cohorts.

Trial Locations

Locations (6)

Hospital District of Helsinki and Uusimaa (HUS); 🇫🇮 Helsinki, Finland

Asklepios kliniken hamburg; 🇩🇪 Hamburg, Germany

Amsterdam UMC; 🇳🇱 Amsterdam, Netherlands

University Hospital Center of São João; 🇵🇹 Porto, Portugal

University Clinical Centre of Serbia; 🇷🇸 Belgrade, Serbia

Oxford University Hospitals; 🇬🇧 Oxford, United Kingdom