Aneurysm Abdominal, Peripheral Arterial Disease
Conditions
Keywords
Artificial Intelligence, Prediction models, Image analysis, Abdominal Aortic Aneurysms, 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.
Interventions
OTHERNo intervention, retrospective study
No intervention, retrospective study
Sponsors
Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)
Technical University of Twente
Universidade do Porto
Centre Hospitalier Universitaire de Nice
Stichting Allai
University of Belgrade
Brightfish Be
Hospital District of Helsinki and Uusimaa
University of Bergen
Asklepios Kliniken Hamburg GmbH
University of Oxford
VINČA INSTITUTE OF NUCLEAR SCIENCES Belgrado
Amsterdam UMC, location VUmc
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
40 Years to 90 Years
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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
| Measure | Time frame | Description |
|---|---|---|
| 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. |
Countries
Finland, Germany, Netherlands, Portugal, Serbia, United Kingdom
Contacts
Primary ContactKak Khee Yeung, MD, PhD
Backup ContactLotte Rijken, Msc.
Outcome results
None listed