Exploring the Mechanism of Plaque Rupture in Acute Coronary Syndrome Using Coronary CT Angiography and Computational Fluid Dynamics II (EMERALD II) Study

Conditions

Acute Myocardial Infarction, Unstable Angina

Keywords

Acute myocardial infarction, Unstable angina, Acute coronary syndrome, Computational fluid dynamics, Coronary computed tomography angiography, Fractional flow reserve

Brief summary

The EMERALD II study is a multinational, multicenter, and retrospective study. ACS patients who underwent CCTA from 1 months to 3 years prior to the event will be retrospectively identified. Plaques in the non-culprit vessels will be regarded as a primary control group.

Detailed description

The mechanisms of plaque rupture are not fully understood. Hemodynamic forces, plaque vulnerability, and the interaction between these factors may cause plaque instability and subsequent acute coronary syndrome (ACS). Previously, the first-in-human study, EMERALD I, showed that the addition of hemodynamic parameters calculated noninvasively from coronary computed tomography (CCTA) using computational fluid dynamics (CFD) improved the ability to predict the risk of ACS compared with conventional approaches based on anatomical stenosis severity and adverse plaque characteristics. In addition to hemodynamic properties, quantified compositional plaque volumes such as fibrofatty and necrotic core volume (FFNC) or low-attenuation plaque burden (% plaque to vessel volume) have been proven to be robust prognostic indicators of ACS. While various hemodynamic and plaque features predictive of ACS have been introduced, the relative importance among them and the additive value of the risk model with the best features over the current diagnostic scheme of CCTA have not been proposed. In this regard, we designed the subsequent EMERALD II study to find the best hemodynamic and plaque features in prediction of ACS from comprehensive CCTA analysis, including per-lesion and per-vessel plaque quantification and hemodynamic analysis, and to investigate whether a comprehensive risk prediction model with them has an incremental value in a larger population.

Yang S, Chung JW, Park SH, Zhang J, Lee K, Hwang D, Lee KS, Na SH, Doh JH, Nam CW, Kim TH, Shin ES, Chun EJ, Choi SY, Kim HK, Hong YJ, Park HJ, Kim SY, Husic M, Lambrechtsen J, Jensen JM, Nørgaard BL, Andreini D, Maurovich-Horvat P, Merkely B, Penicka M, de Bruyne B, Ihdayhid A, Ko B, Tzimas G, Leipsic J, Sanz J, Rabbat MG, Katchi F, Shah M, Tanaka N, Nakazato R, Asano T, Terashima M, Takashima H, Amano T, Sobue Y, Matsuo H, Otake H, Kubo T, Takahata M, Akasaka T, Kido T, Mochizuki T, Yokoi H, Okonogi T, Kawasaki T, Nakao K, Sakamoto T, Yonetsu T, Kakuta T, Yamauchi Y, Taylor CA, Bax JJ, Shaw LJ, Stone PH, Narula J, Koo BK.

2025-12-01

10.1016/j.jcin.2025.09.006

Prognostic Time Frame of Plaque and Hemodynamic Characteristics and Integrative Risk Prediction for Acute Coronary Syndrome.

Yang S, Jung JW, Park SH, Zhang J, Lee K, Hwang D, Lee KS, Na SH, Doh JH, Nam CW, Kim TH, Shin ES, Chun EJ, Choi SY, Kim HK, Hong YJ, Park HJ, Kim SY, Husic M, Lambrechtsen J, Jensen JM, Nørgaard BL, Andreini D, Maurovich-Horvat P, Merkely B, Penicka M, de Bruyne B, Ihdayhid A, Ko B, Tzimas G, Leipsic J, Sanz J, Rabbat MG, Katchi F, Shah M, Tanaka N, Nakazato R, Asano T, Terashima M, Takashima H, Amano T, Sobue Y, Matsuo H, Otake H, Kubo T, Takahata M, Akasaka T, Kido T, Mochizuki T, Yokoi H, Okonogi T, Kawasaki T, Nakao K, Sakamoto T, Yonetsu T, Kakuta T, Yamauchi Y, Taylor CA, Bax JJ, Shaw LJ, Stone PH, Narula J, Koo BK.

2025-04-232 citations

10.1016/j.jcmg.2025.02.003

Artificial Intelligence-Enabled Quantitative Coronary Plaque and Hemodynamic Analysis for Predicting Acute Coronary Syndrome.

Koo BK, Yang S, Jung JW, Zhang J, Lee K, Hwang D, Lee KS, Doh JH, Nam CW, Kim TH, Shin ES, Chun EJ, Choi SY, Kim HK, Hong YJ, Park HJ, Kim SY, Husic M, Lambrechtsen J, Jensen JM, Nørgaard BL, Andreini D, Maurovich-Horvat P, Merkely B, Penicka M, de Bruyne B, Ihdayhid A, Ko B, Tzimas G, Leipsic J, Sanz J, Rabbat MG, Katchi F, Shah M, Tanaka N, Nakazato R, Asano T, Terashima M, Takashima H, Amano T, Sobue Y, Matsuo H, Otake H, Kubo T, Takahata M, Akasaka T, Kido T, Mochizuki T, Yokoi H, Okonogi T, Kawasaki T, Nakao K, Sakamoto T, Yonetsu T, Kakuta T, Yamauchi Y, Bax JJ, Shaw LJ, Stone PH, Narula J.

2024-05-1536 citations

10.1016/j.jcmg.2024.03.015

Interventions

DIAGNOSTIC_TESTCoronary CT angiography

Comprehensive CCTA analysis of all culprit and non-culprit lesions to obtain their per-lesion and per-vessel quantitative, qualitative plaque, and hemodynamic features is performed by the independent core laboratory (HeartFlow, Mountain View, CA, USA) blinded to patient characteristics and ICA findings. The current CCTA reporting variables, including % diameter stenosis, segment involvement score (SIS), and HRP features, are obtained for all lesions by another independent core laboratory (University of British Columbia, Vancouver, Canada) to construct a reference model. ICA and invasive imaging studies performed at the event of ACS are analyzed by the independent core laboratory (Samsung Medical Center, Seoul, Korea) to define the culprit lesion blinded to CCTA findings. Other independent experts match culprit and non-culprit lesion data between ICA and CCTA findings.

Study design

Observational model

CASE_CONTROL

Time perspective

OTHER

Eligibility

Sex/Gender

ALL

Healthy volunteers

No

Inclusion criteria

1. Patients who presented with ACS\* and underwent invasive coronary angiography with identifiable culprit lesion 2. The patients who underwent coronary CT angiography, regardless of the reason (for example, routine healthcare check-up, or evaluation for stable angina or atypical chest pain) prior to the acute event. 3. Time limit of CCTA: 1 months \ 3 years prior to the event. * Definition of ACS: A. The patients with acute myocardial infarction should have cardiac enzyme elevation and identified culprit lesion confirmed by invasive coronary angiography, IVUS, or OCT. B. The patients with unstable angina should have evidence of plaque rupture, which includes at least one of the following: (1) the presence of plaque rupture or haziness including thrombus at invasive coronary angiography, (2) angiographic stenosis ≥90%, or (3) the evidence of rupture confirmed by IVUS or OCT.

Exclusion criteria

for Patient enrollment 1. Patients with ACS without clear evidence of culprit lesion 2. Patients with stents in two or more vessel territories prior to CCTA 3. Poor quality of CCTA which is unsuitable for plaque and CFD analysis 4. Patients with ACS culprit lesion in a stented segment 5. Patients with previous history of coronary artery bypass graft surgery 6. Patients with revascularization after CCTA and before ACS event (\*Patients with elective PCI for 1 vessel within 3 month after CCTA can be enrolled. 7. Secondary ACS due to other general medical conditions, such as sepsis, arrhythmia, bleeding, etc. 8. Patients with unstable angina without evidence of plaque rupture Additional

Design outcomes

Primary

Measure	Time frame	Description
discrimination index of prediction model	1 months - 3 years	discrimination index of prediction model

Countries

South Korea

Outcome results

None listed