Coronary Artery Disease, Coronary Computed Tomography Angiography
Conditions
Brief summary
Coronary artery disease (CAD) is among the leading cause of death and disability. Identification of patients at high risk of cardiovascular events is pivotal. However, current risk stratification based on imaging and known biomarkers is suboptimal. The objective of this proposal is to develop a multicriteria decision model for non-invasive assessment of vulnerable atherosclerotic patients and to evaluate its ability to predict the occurrence of an adverse event in intermediate-to-high risk patients with suspected or known CAD. The planned workflow includes a first step using a retrospective cohort of patients undergoing clinically indicated coronary angiography (CCTA) to develop an integrated application for automatic coronary artery segmentation, quantitative plaque analysis, biomechanics and fluid dynamics, based on machine learning, radiomics and computational analysis approaches and validated against the reference standard for each tool. The second step will apply this new methodology to a larger retrospective cohort of patients with the integration of genomic biomarker assessment to derive the most accurate risk stratification model to properly identify vulnerable patients and vulnerable plaques with respect to outcome. Finally, in the third step, the derived predictive model will be prospectively validated in an independent cohort of patients from an ongoing study (CTP-PRO study) to assess the robustness and accuracy of the proposed solution.
Interventions
None listed
Sponsors
Centro Cardiologico Monzino
Fondazione IRCCS Policlinico San Matteo di Pavia
Politecnico di Milano
Scientific Institute San Raffaele
Study design
Observational model
COHORT
Time perspective
OTHER
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* patients (age ≥ 18 years) with known or suspected CAD referred for clinically indicated diagnostic evaluation; * CCTA performed with state-of-the-art scanner technology, i.e., scanners with more than 64 slices.
Exclusion criteria
* performance of any non-invasive diagnostic test within 90 days before enrolment; * low-to-intermediate pre-test likelihood of CAD according to the updated Diamond-Forrester risk model score; * acute coronary syndrome; * evidence of clinical instability; * contraindication to contrast agent administration and/or impaired renal function; * inability to sustain a breath hold; * pregnancy; * cardiac arrhythmias;- presence of a pacemaker or implantable cardioverter defibrillator; * contraindications to the administration of sublingual nitrates, β-blockers or adenosine; * structural cardiomyopathy
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Creation of an automated integrative artificial intelligence (AI) approach for the stratification of CAD patients and assessment of vulnerable coronary plaques at risk of acute complications | January 2026 | The main aim of the project develop a multicriteria decision model for the automatic (AI-assisted) non-invasive assessment of vulnerable atherosclerotic patients and evaluate the ability of this model to predict the occurrence of adverse event in intermediate-to-high risk patients with suspected or known CAD. As adverse events, we will consider the annual rate of events, intended as death or hospitalization for revascularization (either CABG or PCI) |
| Quantitative assessment of the atherosclerotic burden and high risk plaque features | January 2026 | * Extent and severity of coronary atherosclerosis (Leaman Score, number of lesions); * Vulnerability indices: plaque burden (total plaque volume, plaque density), LAP, PR, NRS and SC; * Fluid dynamic indexes of the coronary artery such as the CT derived Fractional Flow Reserve |
Countries
Italy
Contacts
CONTACTGianluca Pontone
Outcome results
None listed