Introduction:
Peripheral arterial disease (PAD) and abdominal aortic aneurysm (AAA) are vascular conditions associated with significant morbidity and mortality, with 25% of AAA patients and 23% of PAD patients being at a high risk of developing cardiovascular disease. Cardiovascular risk management can reduce the risk of major adverse cardiovascular events in AAA patients from 43% to 14%. However, cardiovascular risk is not always adequately addressed in these patients. The VASCUL-AID-PRO study aims to deliver clinically relevant prediction models using artificial intelligence and machine learning of patient outcomes to enable personalized management of vascular disease.
Method:
VASCUL-AID-PRO is a multi-centre international prospective cross-sectional study aiming to include 500 AAA patients and 600 PAD patients across 6 European centres. The aim is to achieve a follow-up time up to 4 years.
The study will include individuals aged 40-90 with either an abdominal aortic aneurysm (infrarenal, juxtarenal, pararenal, or suprarenal abdominal aortic aneurysm) or Fontaine stage 2 peripheral arterial disease.
The VASCUL-AID-PRO study will gather a variety of data from all participants, including clinical data, blood and tissue samples, cardiovascular lab values, imaging data, electrocardiograms, data from wearables and quality of life.
This data gathered will be used to further develop the multi model prediction models being developed on 5000 AAA and 6000 PAD patients included in the currently ongoing VASCUL-AID-RETRO study, with the aim of providing a clinically relevant prediction models of disease progression and other cardiovascular disease for AAA and PAD patients.
Furthermore, the models developed in the VASCUL-AID studies will be internally validated using a subset of patients from the VASCUL-AID-PRO study.
Ethical considerations:
Ethical and legal considerations are paramount throughout the VASCUL-AID project. To address these concerns, an ELSI framework will be developed and integrated into all stages of the project. This framework will be continuously updated to ensure alignment with evolving ethical, legal, and social standards. This framework will specifically focus on patient safety, data handling, AI regulation and implementation, and potential biases associated with AI.
Males/females between 40 and 90 years of age.
Males and females with AAA at inclusion, any diameter >3 cm (or 1.5x larger than their non-dilated part of the aorta).
Include: infrarenal, juxtarenal, pararenal, suprarenal abdominal aortic aneurysms.
Insufficient schooling or sensorial deficits that interfere understanding informed consent.
Not able to use the VASCUL-AID mobile health app (that will be provided in Dutch, English, German, Portuguese, Serbian, Finish, Swedish languages).
Proven or highly suspected for infected, mycotic AAA
Previous AAA surgery or planned for an AAA surgery within 6 months
A total of 100 AAA patients and 100 PAD will be included per clinical center, with exeption for Asklepios Hamburg, they will solely include 100 PAD patients. This comes to a total of 1100 patients across 6 clinical centers in Europe.
Cohort
Prospective
Not listed
Development and internal validation of AI-driven tools to predict high or low risk disease progression in AAA and PAD.
The VASCUL-AID AI tools aim to predict AAA (rupture, mortality, cerebrovascular accident) and PAD (mortality, myocardial infarction, major amputation) progression, MACE and MALE. The internal validation is done by comparing the predicted disease progression, MACE, MALE and mortality rates with actual disease progression.
Accuracy of developed VASCUL-AID driven algorithms that classify AAA patients according to high or low risk disease progression.
Accuracy of developed VASCUL-AID AI-driven algorithms that classify AAA patients according to high or low risk disease progression and MACE. In addition, models will be developed on the other predefined outcomes from the core outcome set, these include: clinical success after an operation \[yes/no\], graft infection after an operation \[yes/no\], reintervention \[yes/no\], the AAA can be treated through endovascular treatment \[yes/no\], adherence to prescribed drugs \[yes/no\], secondary prevention, treatment options and complications.
Accuracy of developed VASCUL-AID driven algorithms that classify PAD patients according to high or low risk disease progression.
Accuracy of developed VASCUL-AID AI-driven algorithms that classify PAD patients according to high or low risk disease progression, MALE and MACE. In addition, models will be developed on the other predefined outcomes from the core outcome set, these include: acute limb ischemia \[yes/no\], thromboembolic complications \[yes/no\], disease progression \[yes/no\], pain-free walking distance \[meters\], physical activity, smoking cessation, secondary prevention, treatment options and complications.
Patient reported outcome measures for AAA and PAD.
Good communication by healthcare worker about diagnosis \[Questionnaires\].
Patient reported outcome measures for health-related quality of life.
Numeric (Pain) Rating Scale (0-10, no pain to most intense pain).
Click on the lead sponsor to view all of their trials.
Amsterdam UMC
Lead sponsor
Amsterdam UMC, location VUmc
Collaborator
Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)
Collaborator
Brightfish
Collaborator
Technical University of Twente
Collaborator
Universidade do Porto
Collaborator
Aveiro University
Collaborator
Centro Hospitalar De São João, E.P.E.
Collaborator
Stichting Allai
Collaborator
University of Belgrade
Collaborator
Hospital District of Helsinki and Uusimaa
Collaborator
VINČA INSTITUTE OF NUCLEAR SCIENCES Belgrado
Collaborator
University of Bergen
Collaborator
Asklepios Kliniken Hamburg GmbH
Collaborator
Centre Hospitalier Universitaire de Nice
Collaborator
University of Oxford
Collaborator
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