The development of a data dictionary with clinical variables for artificial intelligence-driven tools in research on abdominal aortic aneurysms and peripheral arterial disease
Year: 2025
Type: article
Abstract: Aims: Patients with abdominal aortic aneurysms and peripheral arterial disease (arterial vascular diseases) carry a high disease burden and are likely to experience cardiovascular events. Novel strategies using artificial intelligence could identify which patients with arterial vascular diseases are at high risk of cardiovascular disease progression. Structured data dictionaries are needed to ensure high-quality, unbiased, and ethically sound data input for artificial intelligence models. The aim of this study was to obtain expert consensus-based data dictionaries that adhere to applicable ethical guidelines to support research on arterial vascular diseases. Methods and results: The data dictionaries were created through a modified Delphi approach to achieve consensus among key opinion leaders in the cardiovascular field. First, data requirements were defined and variable longlists were created per disease through a literature review. Secondly, written feedback rounds were held. Lastly, face-to-face meetings were held to establish consensus on the final data dictionaries. During the whole process, ethical and legal experts on trustworthy artificial intelligence were involved to ensure adherence to corresponding guidelines and laws. The aneurysm data dictionary contains 312 variables, and the peripheral arterial disease data dictionary contains 325 variables. A total of 16 clinical experts were involved in the creation, including 12 vascular surgeons, two vascular medicine specialists, one cardiologist, and one gastroenterology surgeon and digital health expert. Conclusion: Two expert consensus-based data dictionaries for use in clinical and artificial intelligence research on arterial vascular diseases were created, developed for application in research on predicting disease progression and cardiovascular risk.
Authors: Lotte Rijken, Sabrina L M Zwetsloot, Catelijne Muller, Marlies P Schijven, Vincent Jongkind, Kak Khee Yeung, Igor Koncar, Ivan Tomic, Marina Dias-Neto, Katarzyna D Bera, Riikka Tulamo, Maarit Venermo, Mirjami Laivuori, Christian-Alexander Behrendt, Stefan P M Smorenburg, Corrette Ploem, Roosmarie Jessen, Bert-Jan H van den Born, Ronak Delewi, Jelmer Wolterink, Noeska Smit, Ivana Išgum, Henk A Marquering, Fabio Catarinella, Fabien Lareyre, Juliette Raffort, Maja Živković, Tamara Djuric, Aleksandra Stankovic, VASCUL-AID collaborators, Rupert Bauersachs, Manar Khashram
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,725
Funders: Helsingin Yliopisto, Amsterdam University Medical Centers, University of Oxford, European Commission, Universiteit van Amsterdam, Vrije Universiteit Amsterdam, University of Twente, Centre National de la Recherche Scientifique, Universitetet i Bergen, Universidade do Porto, Haukeland Universitetssjukehus, HORIZON EUROPE Framework Programme, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centers, NIH Clinical Center, Justice Programme, HORIZON EUROPE Health
OpenAlex ID: W4413343622