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

Joo Myung LeeGilwoo ChoiBon-Kwon KooDoyeon HwangJonghanne ParkJinlong ZhangKyung-Jin KimYaliang TongHyun Jin KimLeo GradyJoon-Hyung DohChang-Wook NamEun-Seok ShinYoung-Seok ChoSu-Yeon ChoiEun Ju ChunJin-Ho ChoiBjarne L. NørgaardEvald H. ChristiansenKoen NiemenHiromasa OtakeMartin PenickaBernard de BruyneTakashi KuboTakashi AkasakaJagat NarulaPamela S. DouglasCharles A. TaylorHyo-Soo Kim
Dept. of Internal Medicine (내과학)
Issue Date
JACC : Cardiovascular Imaging, Vol.12(6) : 1032-1043, 2019
OBJECTIVES The authors investigated the utility of noninvasive hemodynamic assessment in the identification of highrisk 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 [OFFRCT], wall shear stress [WSS], and axial plaque stress) were analyzed using computational fluid dynamics. The best cut-off values for FFRCT, OFFRCT, 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 þ APC, and model 3: model 2 þ 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 OFFRCT, 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 ¼ 0.014) and reclassification abilities (category-free net reclassification index 0.287; p ¼ 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 ¼ 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. (Exploring the Mechanism of Plaque Rupture in Acute Coronary Syndrome Using Coronary CT Angiography and Computational Fluid Dynamic
acute coronary syndromeadverse plaque characteristicsaxial plaque stresscomputational fluid dynamicscoronary computed tomography angiographycoronary plaquewall shear stress
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