Standardised and patient-centred artificial intelligence-driven research in vascular surgery

Abdominal aortic aneurysms (AAA) and peripheral arterial disease (PAD) are chronic diseases of the arterial vasculature. To date, there is no definitive cure for both diseases. Insight into novel risk factors for individual patients are direly needed to optimize personalized treatment. However, this is currently impeded by the quality of contemporary literature, more specifically by its heterogeneity and lack of patient involvement.
The aim of this thesis, therefore, is to improve the quality of research on patients with AAA and PAD by creating a more standardized and patient-centred approach. Standardization will contribute to consistent outcome reporting, facilitating comparison between studies and thereby enhancing evidence synthesis. Furthermore, studying outcomes valued by patients and healthcare workers ensures that research and clinical practice focus on outcomes that are meaningful to those directly involved.
A majority of this research was carried out in the context of the VASCUL-AID project, a multicentre consortium aimed at preventing cardiovascular disease progression in patients with AAA and PAD using artificial intelligence (AI). This has led to the creation of a core outcome set (COS) for clinical and AI research on patients with AAA and PAD, data dictionaries for use in AI-driven vascular surgery research, and an expert consensus-based knowledge base for endovascular lower limb treatment. This thesis concludes with a framework for researchers to apply to their own research design to improve standardization and patient-centredness. In doing so, quality of research may be improved in other fields of research, ultimately leading to more value-based research and healthcare.