Clinical Research Directory

Speed-up the Diagnosis and Evaluation of anoMalous Coronary ARTery From the Aorta

Anomalous aortic origin of the coronary arteries (AAOCA) is a rare congenital disease and one of the leading causes of sudden cardiac deaths (SCD) in young athletes but also has a lethal presentation in adult age with myocardial infarction, even if not related to obstructive coronary arteries. Unfortunately, diagnostic imaging techniques, invasive assessment, and provocative stress tests have shown low sensitivity and specificity in detecting inducible ischemia, and a multimodality assessment is then necessary. Innovative tools have been developed in the medical field using computer-based simulation, 3-dimensional reconstruction, machine learning, and artificial intelligence (AI). With the application of such new technologies, we aim to fill the gap of knowledge and the diagnostic limitation regarding risk stratification for most subjects with AAOCA. This work seeks to enhance, fasten, and personalize the clinical diagnosis of AAOCA by integrating anatomical measurements, clinical data, and biomechanical patient-specific features. The SMART study will set a system to automatically segment and classify coronary arteries with AAOCA from computerized tomography angiography (CTA) by artificial intelligence (AI). Segmentation will feed a 3D model of the aortic root and coronary artery for biomechanical assessment through finite element analysis (FEA). This will allow us to assess the location of possible coronary artery compression under an effort condition. These in-silico results, the anatomical features measured by AI, and the clinical data will be integrated into a risk model to estimate the hazard risk of adverse events such as SCD or myocardial infarction. This workflow will be framed in an IT system to allow a web-based remote diagnostic service. Thanks to the proposed multidisciplinary approach, SMART aims to overcome the current diagnostic limitations related to the reduced ability of functional stress tests to detect ischemia. Potentially helping in patient-specific risk stratification, SMART is also thought to provide a way to get a first diagnostic indication about AAOCA being accessible from any hospital, fostering the diffusion of peripheral territorial support to the diagnosis and treatment of such rare disease.

Registry for Invasive and Non-invasive Anatomical Assessment and Outcome of Coronary Artery Anomalies

An anomalous coronary artery from the opposite sinus of Valsalva (ACAOS) represents a congenital disorder with an anomalous location and/or course of the coronary vessel. The prevalence of ACAOS in the general population is around 1 % and they are mostly clinically insignificant and remain often undetected. However, some variants of ACAOS are associated with adverse cardiac events. The possible presence of an interarterial/intramural course is the primary cause for an oval proximal vessel shape and/or proximal vessel narrowing, which may lead under stress conditions to a "dynamic compression" of the vessel (compared to "fixed" stenosis in coronary artery disease). To mimic these conditions, dobutamine and volume challenge is used to invasively measure fractional flow reserve (FFR) during coronary angiography and is seen as the gold standard in assessing the hemodynamic relevance of ACAOS. We established a specialized interdisciplinary clinic for coronary artery anomalies including imaging specialists, invasive cardiologists and congenital heart disease surgeons as correct downstream testing and treatment decision is highly challenging in these patients. Thus, systematic collecting of all available diagnostic methods (invasive and non-invasive) is required to assess the optimal diagnostic procedure and treatment for these patients. Coronary computed tomography angiography (CCTA) is the method of choice to characterize the exact anatomy of ACAOS. However, how functional invasive FFR is associated with anatomical CCTA findings is unknown. Further, diagnostic accuracy of a novel independent research algorithm with computational fluid dynamics (ctFFR) as well as functional imaging (i.e. stress single photon emission computed tomography) in this specific setting is unknown. The presented project will help to understand the pathophysiology of CAAs with particular focus on ACAOS-IC and improve risk stratification based on non-invasive imaging.