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| Name | Class |
|---|---|
| Inje University Ilsan Paik Hospital | OTHER |
| St. Mary's hostpital | UNKNOWN |
| Odense University Hospital | OTHER |
| University of Milan |
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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Culprit | Plaques which is related with acute coronary syndrome |
| |
| Non-culprit | Plaques which is not related with acute coronary syndrome |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Coronary CT angiography | Diagnostic Test | 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. |
| Measure | Description | Time Frame |
|---|---|---|
| discrimination index of prediction model | discrimination index of prediction model | 1 months - 3 years |
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Inclusion Criteria:
Patients who presented with ACS* and underwent invasive coronary angiography with identifiable culprit lesion
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.
Time limit of CCTA: 1 months ~ 3 years prior to the event.
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
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Patients who presented with acute coronary syndrome (acute myocardial infarction or unstable angina) and had undergone CCTA from 1 months to 3 years prior to the event.
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| Name | Affiliation | Role |
|---|---|---|
| Bon-Kwon Koo, MD,PhD | Seoul National University Hospital | Study Chair |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Seoul National University Hospital | Seoul | South Korea |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 26655874 | Background | Koskinas KC, Ughi GJ, Windecker S, Tearney GJ, Raber L. Intracoronary imaging of coronary atherosclerosis: validation for diagnosis, prognosis and treatment. Eur Heart J. 2016 Feb 7;37(6):524-35a-c. doi: 10.1093/eurheartj/ehv642. Epub 2015 Dec 11. | |
| 21247313 | Background | Stone GW, Maehara A, Lansky AJ, de Bruyne B, Cristea E, Mintz GS, Mehran R, McPherson J, Farhat N, Marso SP, Parise H, Templin B, White R, Zhang Z, Serruys PW; PROSPECT Investigators. A prospective natural-history study of coronary atherosclerosis. N Engl J Med. 2011 Jan 20;364(3):226-35. doi: 10.1056/NEJMoa1002358. |
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| OTHER |
| Imperial College London | OTHER |
| Aarhus University Hospital | OTHER |
| Semmelweis University | OTHER |
| Oxford University Hospitals NHS Trust | OTHER |
| Emory University | OTHER |
| Ehime University Graduate School of Medicine | OTHER |
| Gifu Heart Center | OTHER |
| Wakayama Medical University | OTHER |
| Keimyung University Dongsan Medical Center | OTHER |
| Seoul National University Bundang Hospital | OTHER |
| Seoul National University Hospital Healthcare System Gangnam Center | UNKNOWN |
| Chosun University Hospital | OTHER |
| Chungnam National University Hospital | OTHER |
| Monzino Cardiology Center | UNKNOWN |
| OLV Hospital | UNKNOWN |
| Monash Heart | UNKNOWN |
| University of British Columbia | OTHER |
| MOUNT SINAI HOSPITAL | OTHER |
| Tokyo Medical University Hachioji Medical Center | UNKNOWN |
| Tokai University | OTHER |
| St. Luke's International Hospital | UNKNOWN |
| Aichi Medical University | OTHER |
| Toyohashi Heart Center | OTHER |
| Kobe University Hospital | UNKNOWN |
| National Cerebral and Cardiovascular Center, Japan | OTHER |
| Shin Koga Hospital | UNKNOWN |
| Saiseikai Kumamoto Hospital | UNKNOWN |
| Tsuchiura Kyodo Hospital | UNKNOWN |
| Tokyo Medical Dental University | UNKNOWN |
| Loyola University | OTHER |
| Leiden University | OTHER |
| Weil Cornell Medical College | UNKNOWN |
| West Penn Allegheny Health System | OTHER |
| Ulsan Hospital | UNKNOWN |
| Ulsan University Hospital | OTHER |
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|
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| 33248965 | Background | Yang S, Koo BK, Hoshino M, Lee JM, Murai T, Park J, Zhang J, Hwang D, Shin ES, Doh JH, Nam CW, Wang J, Chen S, Tanaka N, Matsuo H, Akasaka T, Choi G, Petersen K, Chang HJ, Kakuta T, Narula J. CT Angiographic and Plaque Predictors of Functionally Significant Coronary Disease and Outcome Using Machine Learning. JACC Cardiovasc Imaging. 2021 Mar;14(3):629-641. doi: 10.1016/j.jcmg.2020.08.025. Epub 2020 Nov 25. |
| 9249923 | Background | Obuchowski NA, McClish DK. Sample size determination for diagnostic accuracy studies involving binormal ROC curve indices. Stat Med. 1997 Jul 15;16(13):1529-42. doi: 10.1002/(sici)1097-0258(19970715)16:133.0.co;2-h. |
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| 42412030 | Derived | 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, Norgaard BL, Andreini D, Maurovich-Horvat P, Merkely B, Penicka M, de Bruyne B, Ihdayhid A, Ko B, Tzimas G, 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, Leipsic J, Koo BK. Association of Hemodynamic Disease Severity and Distribution With Risk of Future Acute Coronary Syndrome. JACC Cardiovasc Imaging. 2026 Jul 6:S1936-878X(26)00274-3. doi: 10.1016/j.jcmg.2026.05.012. Online ahead of print. |
| 41371781 | Derived | 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, Norgaard 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. Anatomical vs Physiological Lesion Characteristics in Prediction of Acute Coronary Syndrome. JACC Cardiovasc Interv. 2025 Dec 8;18(23):2833-2845. doi: 10.1016/j.jcin.2025.09.006. |
| 40272335 | Derived | 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, Norgaard 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. Prognostic Time Frame of Plaque and Hemodynamic Characteristics and Integrative Risk Prediction for Acute Coronary Syndrome. JACC Cardiovasc Imaging. 2025 Jul;18(7):784-795. doi: 10.1016/j.jcmg.2025.02.003. Epub 2025 Apr 23. |
| 38752951 | Derived | 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, Norgaard 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. Artificial Intelligence-Enabled Quantitative Coronary Plaque and Hemodynamic Analysis for Predicting Acute Coronary Syndrome. JACC Cardiovasc Imaging. 2024 Sep;17(9):1062-1076. doi: 10.1016/j.jcmg.2024.03.015. Epub 2024 May 15. |
| ID | Term |
|---|---|
| D000789 | Angina, Unstable |
| D054058 | Acute Coronary Syndrome |
| ID | Term |
|---|---|
| D000787 | Angina Pectoris |
| D017202 | Myocardial Ischemia |
| D006331 | Heart Diseases |
| D002318 | Cardiovascular Diseases |
| D014652 | Vascular Diseases |
| D002637 | Chest Pain |
| D010146 | Pain |
| D009461 | Neurologic Manifestations |
| D012816 | Signs and Symptoms |
| D013568 | Pathological Conditions, Signs and Symptoms |
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