PRecisiOn medicine in CAD patients: artificial intelliGence for integRated gEnomic, functional and anatomical aSSessment of the coronary collateral circulation

The overarching objective of PROGRESS is to develop a tool for more accurate, reproducible and automated prediction of patients’ potential to develop coronary collateral circulation (CCC), which could be used for a more efficient CAD patient management. Our hypothesis is that patients’ potential to develop CCC is, in part, determined by genetics. Thus, uncovering the genetic risk variants holds the potential of predicting CCC formation. To overcome previous difficulties of CCC research, we harness Artificial Intelligence (AI)-based angiogram image and genetics analyses aiming to improve risk stratification and management of CAD patients, based on their CCC formation profile, followed by timely application of therapeutic approaches in order to stimulate CCC formation and thus improve survival rates of patients after diagnosis. We have collected well-powered CAD cohorts with genetic and imaging data. AI-based image analysis will aid in phenotyping CCC and also to generate post-hoc surrogate functional parameters (validated against a cohort of invasively phenotyped patients) in an unbiased fashion. This provides the basis for a genome-wide association study (GWAS) on CCC performed in large detection and validation cohorts.