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.
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Development and internal validation of AI-driven tools to predict high or low risk disease progression in AAA and PAD.
Timeframe: From baseline to 4 years follow-up.