Computational mOdelliNg of myoCardial pERfusion to Improve ouTcome Prediction Based on cOronary Artery Stenosis and Atherosclerotic Plaque Burden Assessment by Computed Tomography

Chronic Coronary Syndrome Clinical Trial

— CONCERTO  

Official title:

Computational mOdelliNg of myoCardial pERfusion to Improve ouTcome Prediction Based on cOronary Artery Stenosis and Atherosclerotic Plaque Burden Assessment by Computed Tomography

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT06337461
• Other study ID #: CCM1930
• Status: Recruiting
• Start date: May 22, 2023
• Est. completion date: May 30, 2025

Study information

• Verified date: March 2024
• Source: Centro Cardiologico Monzino
• Contact: Gianluca Pontone, MD
• Phone: 0258002574
• Email: gianluca.pontone[@]cardiologicomonzino.it
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

Detection of coronary stenosis is of utmost importance in identifying vulnerable patients. The combined use of coronary computed tomography angiography at rest (CCTA) and stress myocardial computed tomography perfusion (stress-CTP) provides both anatomic and functional analysis of coronary artery disease (CAD) using a single imaging test. Stress-CTP evaluates myocardial perfusion by measuring myocardial blood flow (MBF) under pharmacologically induced stress conditions. The drawback is that stress-CTP requires additional scanning and administration of an intravenous stressor with an increase in radiation exposure and potential stressor-related side effects. The investigators recently patented a computational model that can reproduce MBF under stress conditions (Italian patent n. 102021000031475 Metodo implementato mediante computer per la simulazione del flusso sanguigno miocardico in condizioni di stress [Computational method for simulating myocardial blood flow in stress conditions], half owned by Centro Cardiologico Monzino, half by Politecnico di Milano).

On top of this, CCTA can characterize plaque type and identify adverse plaque characteristics. Moreover, biomechanics analysis allows the study of luminal stenosis and stress within the plaque. Finally, radiomics, extracting quantitative features from medical images to create big data and identify novel imaging biomarkers, can be applied to improve the diagnostic accuracy of coronary plaques.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 400
• Est. completion date: May 30, 2025
• Est. primary completion date: May 30, 2025
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

• Symptomatic patients with suspected CAD referred for nonemergent, clinically indicated non-invasive coronary angiography.

Exclusion Criteria:

• Low pre-test likelihood of CAD

• Prior myocardial infarction

• Previous history of revascularization

• Acute coronary syndrome

• Need for an emergent procedure

• Evidence of clinical instability

• Contraindication for contrast agent or impaired renal function

• Inability to sustain a breath-hold

• Pregnancy

• Atrial fibrillation or flutter

• BMI > 35kg/m2

• Presence of pm or ICD

• Contraindications to the administration of sublingual nitrates, betablockade, and adenosine

Related Conditions & MeSH terms

• Chronic Coronary Syndrome
• Coronary Stenosis
• Plaque, Atherosclerotic

Locations

Country	Name	City	State
Italy	Centro Cardiologico Monzino	Milan

Sponsors (3)

Lead Sponsor	Collaborator
Centro Cardiologico Monzino	Azienda Ospedaliero-Universitaria Consorziale Policlinico di Bari, Politecnico di Milano

Country where clinical trial is conducted

Italy, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Development of a computational model to predict MBF avoiding CT stress protocol	CONCERTO main aim is to predict MBF values from cardiac CT scans without stress protocol.; This aim will be achieved by improving an existing computational model. To achieve this goal, we first need to explore available information on the effect of stenosis on the pressure gradient across it and the division of flow in the coronary tree.; A second step will be a ML analysis on a reasonable number of patients with known myocardial perfusion (from CT-stress) to build a suitable neural network that can predict some general features of the model parameters. Building the neural network from the measured blood flow maps will allow us to include this information in our calibration procedure and also use it for future patients in whom stress CT will no longer be necessary.; Finally, a comprehensive, patient-specific model calibration strategy will be developed leveraging the results.; This will allow us to apply our tool to any patient as long as a standard CT acquisition and some pressure	May 2025
Secondary	Improvement of CAD risk assesment	The second Aim is to improve CAD risk assessment by integrating the information from the perfusion computation model with imaging and radiomics features	May 2025