Identification of High-Risk Plaques Destined to Cause Acute Coronary Syndrome Using Coronary Computed Tomographic Angiography and Computational Fluid Dynamics

Joo Myung Lee, Gilwoo Choi, Bon Kwon Koo, Doyeon Hwang, Jonghanne Park, Jinlong Zhang, Kyung Jin Kim, Yaliang Tong, Hyun Jin Kim, Leo Grady, Joon Hyung Doh, Chang Wook Nam, Eun Seok Shin, Young Seok Cho, Su Yeon Choi, Eun Ju Chun, Jin Ho Choi, Bjarne L. Nørgaard, Evald H. Christiansen, Koen NiemenHiromasa Otake, Martin Penicka, Bernard de Bruyne, Takashi Kubo, Takashi Akasaka, Jagat Narula, Pamela S. Douglas, Charles A. Taylor, Hyo Soo Kim

Research output: Contribution to journalArticlepeer-review

209 Scopus citations


Objectives: The authors investigated the utility of noninvasive hemodynamic assessment in the identification of high-risk plaques that caused subsequent acute coronary syndrome (ACS). Background: ACS is a critical event that impacts the prognosis of patients with coronary artery disease. However, the role of hemodynamic factors in the development of ACS is not well-known. Methods: Seventy-two patients with clearly documented ACS and available coronary computed tomographic angiography (CTA)acquired between 1 month and 2 years before the development of ACS were included. In 66 culprit and 150 nonculprit lesions as a case-control design, the presence of adverse plaque characteristics (APC)was assessed and hemodynamic parameters (fractional flow reserve derived by coronary computed tomographic angiography [FFRCT], change in FFRCT across the lesion [△FFRCT], wall shear stress [WSS], and axial plaque stress)were analyzed using computational fluid dynamics. The best cut-off values for FFRCT, △FFRCT, WSS, and axial plaque stress were used to define the presence of adverse hemodynamic characteristics (AHC). The incremental discriminant and reclassification abilities for ACS prediction were compared among 3 models (model 1: percent diameter stenosis [%DS]and lesion length, model 2: model 1 + APC, and model 3: model 2 + AHC). Results: The culprit lesions showed higher %DS (55.5 ± 15.4% vs. 43.1 ± 15.0%; p < 0.001)and higher prevalence of APC (80.3% vs. 42.0%; p < 0.001)than nonculprit lesions. Regarding hemodynamic parameters, culprit lesions showed lower FFRCT and higher △FFRCT, WSS, and axial plaque stress than nonculprit lesions (all p values <0.01). Among the 3 models, model 3, which included hemodynamic parameters, showed the highest c-index, and better discrimination (concordance statistic [c-index]0.789 vs. 0.747; p = 0.014)and reclassification abilities (category-free net reclassification index 0.287; p = 0.047; relative integrated discrimination improvement 0.368; p < 0.001)than model 2. Lesions with both APC and AHC showed significantly higher risk of the culprit for subsequent ACS than those with no APC/AHC (hazard ratio: 11.75; 95% confidence interval: 2.85 to 48.51; p = 0.001)and with either APC or AHC (hazard ratio: 3.22; 95% confidence interval: 1.86 to 5.55; p < 0.001). Conclusions: Noninvasive hemodynamic assessment enhanced the identification of high-risk plaques that subsequently caused ACS. The integration of noninvasive hemodynamic assessments may improve the identification of culprit lesions for future ACS.

Original languageEnglish
Pages (from-to)1032-1043
Number of pages12
JournalJACC: Cardiovascular Imaging
Issue number6
StatePublished - Jun 2019


  • acute coronary syndrome
  • adverse plaque characteristics
  • axial plaque stress
  • computational fluid dynamics
  • coronary computed tomography angiography
  • coronary plaque
  • wall shear stress


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