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Identification of High-Risk Plaques Destined to Cause Acute Coronary Syndrome Using Coronary Computed Tomographic Angiography and Computational Fluid Dynamics.
JACC: Cardiovascular Imaging ( IF 14.0 ) Pub Date : 2018-03-14 , DOI: 10.1016/j.jcmg.2018.01.023
Joo Myung Lee 1 , Gilwoo Choi 2 , Bon-Kwon Koo 3 , Doyeon Hwang 4 , Jonghanne Park 4 , Jinlong Zhang 4 , Kyung-Jin Kim 4 , Yaliang Tong 5 , Hyun Jin Kim 2 , Leo Grady 2 , Joon-Hyung Doh 6 , Chang-Wook Nam 7 , Eun-Seok Shin 8 , Young-Seok Cho 9 , Su-Yeon Choi 10 , Eun Ju Chun 11 , Jin-Ho Choi 1 , Bjarne L Nørgaard 12 , Evald H Christiansen 12 , Koen Niemen 13 , Hiromasa Otake 14 , Martin Penicka 15 , Bernard de Bruyne 15 , Takashi Kubo 16 , Takashi Akasaka 16 , Jagat Narula 17 , Pamela S Douglas 18 , Charles A Taylor 19 , Hyo-Soo Kim 4
Affiliation  

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. (Exploring the Mechanism of Plaque Rupture in Acute Coronary Syndrome Using Coronary CT Angiography and Computational Fluid Dynamic [EMERALD]; NCT02374775).

中文翻译:

使用冠状动脉计算机断层扫描血管造影和计算流体动力学技术鉴定注定会导致急性冠状动脉综合征的高危斑块。

目的作者研究了无创血液动力学评估在识别引起继发性急性冠状动脉综合征(ACS)的高危斑块中的实用性。背景技术ACS是影响冠状动脉疾病患者预后的关键事件。然而,血液动力学因素在ACS发展中的作用尚不为人所知。方法纳入72例ACS明确记录并在ACS发生前1个月至2年之间获得的可用冠状动脉计算机断层血管造影(CTA)的患者。作为病例对照设计,在66个罪魁祸首和150个非罪犯的病变中,评估了不良菌斑特征(APC)的存在并评估了血流动力学参数(通过冠状动脉计算机断层血管造影[FFRCT]得出的血流储备分数,使用计算流体动力学分析了整个病灶的FFRCT变化[△FFRCT],壁切应力[WSS]和轴向斑块应力。FFRCT,△FFRCT,WSS和轴向斑块应力的最佳临界值用于定义不良血液动力学特征(AHC)的存在。在3种模型(模型1:直径狭窄百分比[%DS]和病变长度,模型2:模型1 + APC,模型3:模型2 + AHC)之间比较了ACS预测的增量判别和重新分类能力。结果罪魁祸首病变的%DS(55.5±15.4%vs. 43.1±15.0%; p <0.001)和APC患病率更高(80.3%vs. 42.0%; p <0.001)。就血液动力学参数而言,罪魁祸首病变的FFRCT较低,△FFRCT,WSS和轴向斑块应力均高于非罪犯病变(所有p值<0。01)。在这3种模型中,包括血液动力学参数的模型3显示出最高的c指数,并具有更好的辨别力(一致性统计[c-index] 0.789 vs. 0.747; p = 0.014)和重分类能力(无分类净重分类指数) 0.287; p = 0.047;相对综合辨别力改善0.368; p <0.001)比模型2高。同时具有APC和AHC的病变比不具有APC / AHC的病变的元凶高得多(危险比:11.75; 95) %置信区间:2.85至48.51; p = 0.001),并且具有APC或AHC(危险比:3.22; 95%置信区间:1.86至5.55; p <0.001)。结论无创血流动力学评估增强了随后导致ACS的高风险斑块的识别。整合无创血流动力学评估可能会改善对未来ACS的罪魁祸首的识别。(使用冠状动脉CT血管造影术和计算流体动力学方法研究急性冠状动脉综合征斑块破裂的机制[EMERALD]; NCT02374775)。
更新日期:2019-06-04
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