Clinical trial NCT06186336: Feasibility of a Deep Learning-based Algorithm for Non-invasive Assessment of Vulnerable Coronary Plaque
Official title: Feasibility of a Deep Learning-based Algorithm for Non-invasive Assessment of Vulnerable Coronary Plaque
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Chronic Coronary Syndrome
Coronary Artery Disease
Stable Angina
Acute Coronary Syndrome
Keywords: Coronary CT Angiography
Invasive Coronary Angiography
Optical Coherence Tomography
Vulnerable Coronary Plaque
Lead sponsor: GE Healthcare
Sponsor class: INDUSTRY
Collaborators: name: Azienda Socio-Sanitaria Territoriale di Pavia
First posted: 2024-01-02
Enrollment: 200
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Subjects with chronic coronary syndrome or stable angina, as well as patients with signs of acute coronary syndrome who have been ordered a clinically indicated CCTA and will likely be referred to undergo an ICA combined with OCT will be enrolled in this study. Subjects with recent onset or growing angina may be included in the study. Subjects will be representative of the general population expected to undergo cardiac non-invasive and invasive imaging and treatment procedures as standard of care clinical practice.

The CCTA and ICA with OCT must be carried out within 10 days one from each other. Clinical decision for a PCI after the diagnostic procedures will not limit the enrollment. To preserve the aspect of the plaque, CCTA must be performed first.

Brief summary: The primary objective of this study is to assess the accuracy in terms of sensitivity, specificity, negative and positive predicted values of the DL-based algorithm with respect to correct identification of the plaque and associated vulnerability grade.

Detailed description: Data collected in this study will be used for technology development, scientific evaluation, education, and regulatory submissions for future products. This is a pre-market, open label, prospective, non-randomized clinical research study conducted at one site in Italy. The product being researched is the Deep Learning-based (DL) algorithm for non-invasive detection of vulnerable coronary plaque.

Eligibility criteria: Inclusion Criteria:

1. Patients referred for a clinically indicated CCTA and ICA with OCT imaging examinations;
2. Diagnosis of chronic coronary syndrome, known CAD, or stable angina; AND,
3. Patients with ACS that may undergo a CCTA and not refer directly to the Cath lab for revascularization procedures.

Exclusion Criteria:

1. Contradictions to contrast;
2. Contraindications for beta blocker;
3. BMI \>30;
4. High heart rate ≥75 BPM;
5. Atrial Fibrillation;
6. Arrythmia or irregular heartbeats;
7. Any prior coronary revascularization;
8. Presence of pacemaker or implantable cardioverter defibrillator; OR,
9. Patients with TAVI/TAVR.

Primary outcomes:
measure: Data Collection
timeFrame: Through study completion, an average of 1 year
Secondary outcomes:
measure: Accuracy of Tool
timeFrame: Through study completion, an average of 1 year
measure: Safety Events
timeFrame: Through study completion, an average of 1 year
Other outcomes:
Locations:
city: Pavia
country: Italy
facility: Ospedale Di Voghera, Azienda Socio-Sanitaria Territoriale di Pavia
state: Lombardy
Study officials:
affiliation: GE Healthcare
name: Paul Deak
role: STUDY_DIRECTOR
References: