Clinical Research Directory

Creation of a Decision Aid for Coronary Anomalies

The coronary arteries supply blood to the heart muscle. Typically, the left coronary artery comes from the left side of the aorta and the right coronary artery comes from the right side. In some cases the coronary artery comes from the wrong side of the aorta. This is known as anomalous aortic origin of a coronary artery (AAOCA). In AAOCA, the major concern is the risk of sudden cardiac death (SCD). The risk of is significantly higher in left AAOCA (L-AAOCA) compared to right AAOCA (R-AAOCA). With the increased risk in L-AAOCA, surgery is recommended to "normalize" the coronary artery position. R-AAOCA has a low absolute risk of SCD. But the risk is higher than the general population. Patients, families, and clinicians must weigh the risks of surgery with the risks of observation. This leads to stress and anxiety around making the management choice. There is no "right" management choice. Shared decision making (SDM) is a strategy of including patient values, preferences, and risk tolerance in medical choices. SDM is particularly useful in settings where there is no clear correct management choice. Decision aids support SDM. No decision aid exists in R-AAOCA. This proposal will create a decision aid and collect pilot data of its implementation. We hypothesize that the use of an aid in R-AAOCA will improve SDM, comfort in the choice, and quality of life. We will engage patients, families, and clinicians to understand their needs to make management choices. This will inform the development of the aid. We will gather feedback on the aid from stakeholders and will revise it. The aid will include data and methods for patients to identify their preferences. When the aid is optimized, we will run a pilot study to evaluate its impact compared to not using the aid. We will evaluate SDM, comfort in the choice made, and quality of life at that time, at 3 months and at 6 months. The pilot data will be used to inform a larger study of the aid. This proposal can be an example how to design decision aids for other congenital heart conditions. This aligns with the AHA's mission of improving lifelong health of the whole person. By improving SDM , patients can feel more confident in their choice and relieve anxiety from the diagnosis. Overall, this proposal supports a shift to patient-centered care with a focus on improving meaningful lifelong outcomes.

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.