Risk Characterization of Non-culprit Vessels in Patients Undergoing Primary PCI for ST-elevation MI in Multivessel Disease

Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18-85
SponsorUniversity Hospital Southampton NHS Foundation Trust

About this trial

Most heart attacks occur because a clot forms in a coronary artery blocking blood flow. Without blood heart muscle dies. Untreated, clots can cause a specific type of heart attack -ST-elevation myocardial infarction (STEMI). STEMI patients are treated immediately by finding the blocked artery ("culprit" lesion) using a dye injected into the coronary arteries and then by unblocking the artery using balloons and stents. This procedure - primary angioplasty - is offered 24/7 and limits the size of heart attacks and saves lives.

Cardiologists know how to treat STEMI patients but it's less clear what to do about narrowings in other coronary arteries ("bystander" disease). This is important - if they're left alone some bystander lesions can cause future events including heart attacks or angina. Recent trials compared stenting ALL the bystander narrowings after primary angioplasty, with stenting none and showed some benefit from stenting all of them ("complete revascularisation").

However, complete revascularisation carries extra risk, putting patients through more complicated procedures and using up resource. A blanket strategy of complete revascularisation of ALL bystander narrowings in ALL STEMI patients is unlikely to be the correct answer as only a small minority of these patients have further events.

In PICNIC the investigators want to identify bystander narrowings most likely to cause a future event, and those unlikely to do so. The study can then test the hypothesis that only the high-risk bystander narrowings need stenting, and the others can be treated with tablets only. Investigators will study patients using specialised imaging techniques from coronary artery CT scans and levels of inflammation to see which narrowings cause future events and which do not. If this can be done, a case can be made to test complete revascularisation only in bystander narrowings that look high risk.

Eligibility criteria

This trial does not accept healthy volunteers

Qualifiers

Ability to provide written informed consent (post PPCI)

Age 18 years to 85 years

Presentation of acute STEMI within 12 hours of symptom on-set

Culprit artery PPCI

Disqualifiers

Cardiogenic shock

Decompensated heart failure requiring intubation, inotropes, or intra-aortic balloon counter pulsation

Refractory ventricular arrhythmia

Previous coronary artery bypass surgery (CABG)

Trial population

Patients presenting with ST elevation myocardial infarction who are found to have significant stenoses in the non-infarct related artery(s) defined as coronary stenosis \>50% by visual estimation

Trial design

Design model

Cohort

Time perspective

Prospective

Treatments tested in this trial

  • CT Coronary Angiography

    Diagnostic test

    CTCA for anatomical, physiological, plaque composition and inflammatory assessment of coronary arteries

Treatment groups

320 Participants
are divided into 1 treatment group
Group A: STEMI patients with multivessel disease1 intervention

Trial outcomes

Primary outcomes

1

Correlation between major adverse cardiac & cerebrovascular events (MACCE) and CTCA-derived anatomical, physiological, plaque & inflammatory characteristics and serum inflammatory markers at 2 years

Correlation between: 1. major adverse cardiac \& cerebrovascular events (MACCE) defined as the composite of all-cause mortality, cardiovascular death, cardiac arrest, acute coronary syndrome (unstable angina, NSTEMI, or STEMI), additional revascularization by CABG or PCI, rehospitalisation for angina, heart failure, stroke, ventricular or atrial fibrillation or tachyarrhythmia at 2 years \&: 2. Anatomical \& physiological characteristics of coronary vessels/lesions in the NIRA(s) as assessed by CTCA parameters (FFRCT + FAI + plaque characteristics)

Time frame
2 years

Secondary outcomes

1

Correlation between major adverse cardiac & cerebrovascular events (MACCE) and components of CTCA-derived anatomical, physiological, plaque & inflammatory characteristics and serum inflammatory markers at 1 year

Correlation between: 1. major adverse cardiac \& cerebrovascular events (MACCE) defined as the composite of all-cause mortality, cardiovascular death, cardiac arrest, acute coronary syndrome (unstable angina, NSTEMI, or STEMI), additional revascularization by CABG or PCI, rehospitalisation for angina, heart failure, stroke, ventricular or atrial fibrillation or tachyarrhythmia at 1 year \&: 2. Anatomical \& physiological characteristics of coronary vessels/lesions in the NIRA(s) as assessed by CTCA/FFRCT/FAI parameters between individual components of MACCE

Time frame
1 year
2

Correlation between anatomical, physiological & serum inflammatory markers and future adverse events at 3 years

Correlation between: 1. major adverse cardiac \& cerebrovascular events (MACCE) defined as the composite of all-cause mortality, cardiovascular death, cardiac arrest, acute coronary syndrome (unstable angina, NSTEMI, or STEMI), additional revascularization by CABG or PCI, rehospitalisation for angina, heart failure, stroke, ventricular or atrial fibrillation or tachyarrhythmia at 3 years \&: 2. Anatomical \& physiological characteristics of coronary vessels/lesions in the NIRA(s) as assessed by CTCA/FFRCT/FAI parameters between individual components of MACCE

Time frame
3 years
3

Development of a risk score to predict clinical events based upon individual-, vessel- & lesion-specific factors

To build a multidimensional AI-mediated deep learning model that uses the following data inputs (a) blood biomarkers, (b) CTCA lesion severity, (c) CTCA conventional plaque characteristics (e.g. spotty calcification etc); (d) FFRCT-derived plaque characteristic (ie adverse plaque and haemodynamic characteristics); (d) FAI-derived output; (e) clinical characteristics in order to correlate them with clinical events. The model will then be used to develop a risk score

Time frame
1 year
4

Development of a risk score to predict clinical events based upon individual-, vessel- & lesion-specific factors

To build a multidimensional AI-mediated deep learning model that uses the following data inputs (a) blood biomarkers, (b) CTCA lesion severity, (c) CTCA conventional plaque characteristics (e.g. spotty calcification etc); (d) FFRCT-derived plaque characteristic (ie adverse plaque and haemodynamic characteristics); (d) FAI-derived output; (e) clinical characteristics in order to correlate them with clinical events. The model will then be used to develop a risk score

Time frame
3 years

Other outcomes

Sponsors and contacts

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

University Hospital Southampton NHS Foundation Trust

Lead sponsor

Boston Scientific Corporation

Collaborator

HeartFlow, Inc.

Collaborator

Wessex Heartbeat

Collaborator

Caristo

Collaborator