Health TrialsbyHealthPanel

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

ConditionsAAOCAACAOSAnomalous Aortic Origin of the Coronary Artery (AAOCA)Anomalous Coronary Artery Arising From the Opposite SinusAnomalous Coronary Artery OriginAnomalous Coronary Artery With Aortic Origin and Course Between the Great ArteriesCoronary AnomalySudden Cardiac DeathMyocardial IschemiaMyocardial Ischemia, Angina Pectoris
Trial statusRecruiting
Trial phaseNot applicable
Trial typeInterventional
Biological sexAll
Age6+
SponsorIRCCS Policlinico S. Donato

About this trial

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.

Eligibility criteria

This trial does not accept healthy volunteers

Qualifiers

Adult and pediatric patients (age > 6 years) with anomalous coronary origin from the aorta (AAOCA).

Patients in spontaneous sinus rhythm.

Signed informed consent.

Disqualifiers

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.

Trial design

Design model

Single group

Treatments tested in this trial

  • Autonomic response in AAOCA

    Diagnostic test

    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.

Treatment groups

38 Participants
are divided into 1 treatment group
Group A: Prospective studyExperimental treatment 1 intervention

Trial outcomes

Primary outcomes

1

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

Secondary outcomes

Other outcomes

Sponsors and contacts

Click on the lead sponsor to view all of their trials.

IRCCS Policlinico S. Donato

Lead sponsor

University of Pavia

Collaborator

University of Naples

Collaborator

The Mediterranean Institute for Transplantation and Advanced Specialized Therapies

Collaborator

Source ClinicalTrials.gov
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