An interventional study of Autonomic response in AAOCA in AAOCA, ACAOS and Anomalous Aortic Origin of the Coronary Artery (AAOCA), sponsored by IRCCS Policlinico S. Donato. Recruiting at 1 site in Italy. Open to participants aged 6 Years and older. Per ClinicalTrials.gov, last updated 2026-04-15.
Sponsored by IRCCS Policlinico S. Donato · Not applicable, Interventional, and Diagnostic
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.
The project aims to create a web-based platform that allows the uploading Computed Tomography Angiography (CTA) images, particularly cardio CTA, with contrast medium in anonymized form.
The CTA images will be processed by a neural network developed by the project, which will be able to segment CTA automatically, identify the presence or not of the anomalous coronary origin, and retrieve geometrical measurements of the anatomy of interest. The anatomical and geometrical measurements, automatically made by artificial intelligence, will be integrated with clinical data and computational simulations (Finite Element Structural Analysis) to understand the potential site of dynamic coronary compression under simulated stress conditions.
The final output of the platform will be a report that will integrate clinical data and geometrical and anatomical information to estimate the hazard risk of sudden cardiac deaths or major adverse ischemic events.
Exclusion Criteria:
The prospective phase aims to validate the entire model developed during the retrospective phase and to evaluate the role of autonomic response in subjects with Anomalous Aortic Origin of a Coronary Artery (AAOCA). The prospective recruitment of the cohort of AAOCA patients for autonomic assessment and validation will span the entire duration of the study. Our objective is to recruit 38 patients with AAOCA to obtain consistent and uniform data from at least 32 participants. Patients in this cohort will undergo an active standing test to elicit an autonomic response, and the results will be compared with reference normal values. During this examination, the following data will be collected: continuous ECG, non-invasive blood pressure, and respiratory measurements in both supine and prone positions.
Diagnostic Test: Autonomic response in AAOCA
Autonomic regulation sub-analysis: Autonomic control will be evaluated in a population prospectively recruited. Thirty-eight subjects with Anomalous Aortic Origin of a Coronary Artery (AAOCA) will undergo an active standing test. For this prospective sample, demographic and clinical data, as well as DICOM images from previously conducted diagnostic CT angiographies (CTAs) for AAOCA, will also be collected. These data will be utilized to assess the final functionality of the online platform before its public launch. The patients will be subjected to the active standing test to elicit an autonomic response, with results compared to reference normal values. During this examination, the following data will be collected: continuous ECG, non-invasive blood pressure, and respiratory measurements in both supine and prone positions.
Analysis of Autonomic Test Data
Beat-to-beat series will be extracted from recorded signals to derive indices related to autonomic, cardiovascular, cerebrovascular, and peripheral microcirculation control during REST and STAND phases. The cardiac period will be defined as the interval between consecutive R peaks (RR- msec) in the ECG, with systolic (SAP - mmHg) and diastolic blood pressure (DAP - mmHg) calculated as the maximum and minimum pressures between these peaks. Random sequences of 250 beats will be selected from each recording and manually verified for corrections. Ectopic beats will be adjusted using cubic spline interpolation. Indices of cardiovascular control will be derived from time-domain variability measures, and spectral density will be estimated using a parametric autoregressive approach. Analyses will be conducted using software developed in Matlab and C++.
Time frame: two years
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IRCCS Policlinico S. Donato