AAOCA, ACAOS, Anomalous Aortic Origin of the Coronary Artery (AAOCA), Anomalous Coronary Artery Arising From the Opposite Sinus, Anomalous Coronary Artery Origin, Anomalous Coronary Artery With Aortic Origin and Course Between the Great Arteries, Coronary Anomaly, Myocardial Ischemia, Myocardial Ischemia, Angina Pectoris, Sudden Cardiac Death
Conditions
Keywords
AAOCA, CT, IMAGE SEGMENTATION, ACAOS
Brief summary
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.
Detailed description
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.
Interventions
DIAGNOSTIC_TESTAutonomic 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.
Sponsors
IRCCS Policlinico S. Donato
University of Pavia
University of Naples
The Mediterranean Institute for Transplantation and Advanced Specialized Therapies
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
DIAGNOSTIC
Masking
NONE
Intervention model description
The prospective part involves the enrollment of patients with AAOCA at the San Donato Polyclinic in order to validate the entire system developed in the retrospective part and to assess the role of autonomic response in individuals with AAOCA. A total of 38 subjects with AAOCA will be recruited and will undergo an active standing test. For this prospective sample, demographic, clinical data, and DICOM files from diagnostic CT angiographies for AAOCA (previously performed for medical reasons) will also be collected to be used as a validation set for the created tool. The patients in this cohort will undergo an active standing test to evoke an autonomic response. During this examination, the following data will be collected: continuous ECG, non-invasive blood pressure (BP), and respiration in both supine and prone positions. The retrospective part includes four main tasks: development of U-NET and AI, FEA simulation, risk prediction model, and creation of the online platform.
Eligibility
Sex/Gender
ALL
Age
6 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* Adult and pediatric patients (age \> 6 years) with anomalous coronary origin from the aorta (AAOCA). * Patients in spontaneous sinus rhythm. * Signed informed consent.
Exclusion criteria
* Patients with acute and chronic inflammatory conditions such as chronic liver disease, chronic kidney failure (creatinine \> 1.5 mg/dl), and thyroid disorders. * Patients with arrhythmias, absence of sinus rhythm. * Contraindications to autonomic testing. * Patients with a known allergy to materials in recording devices. * Female patients who are pregnant.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Analysis of Autonomic Test Data | two years | 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++. |
Countries
Italy
Outcome results
None listed