山东大学学报 (医学版) ›› 2023, Vol. 61 ›› Issue (1): 27-31.doi: 10.6040/j.issn.1671-7554.0.2022.0708
张润知,顾慧,李亚妮,杨世锋,高艳,王箬芃,王锡明
ZHANG Runzhi, GU Hui, LI Yani, YANG Shifeng, GAO Yan, WANG Ruopeng, WANG Ximing
摘要: 目的 基于第三代双源冠状动脉CT血管成像(CCTA)图像获得冠状动脉周围脂肪衰减指数(FAI),探讨FAI与高危斑块及冠状动脉狭窄程度的关联。 方法 回顾性分析2020年1月至2021年6月于山东第一医科大学附属省立医院行CCTA检查的449例患者的临床资料。根据是否存在CT高危斑块,将患者分为高危斑块组(n=226)、非高危斑块组(n=223)。基于CCTA图像,测量右冠状动脉周围FAI,并进行两组间比较。利用受试者工作特征曲线(ROC)评估FAI及FAI联合狭窄程度对CT高危斑块的诊断价值。根据冠状动脉狭窄程度进一步将高危斑块组分为3组:组1(1%~49%)、组2(50%~99%)、组3(100%),计算各组间FAI值。 结果 高危斑块组与非高危斑块组的FAI值分别为(-81.54±7.46)HU、(-90.12±7.23)HU,差异具有统计学意义(P<0.001)。ROC结果显示,FAI及FAI联合狭窄程度诊断CT高危斑块的曲线下面积(AUC)分别为0.800、0.849,灵敏度分别为79.65%、80.53%,特异度分别为74.44%、79.37%。FAI诊断CT高危斑块的最佳阈值为-86.5 HU。高危斑块组中,不同狭窄程度组1~3 FAI值分别为:(-84.76±8.23)HU、(-80.41±6.59)HU、(-77.07±4.50)HU,差异具有统计学意义(P<0.001)。 结论 FAI作为新型影像标记物可监测冠状动脉炎症水平,其值越高,提示斑块风险越大。
中图分类号:
| [1] 中国心血管健康与疾病报告编写组. 中国心血管健康与疾病报告2020概要 [J]. 中国循环杂志, 2021, 36(6): 521-545. The writing committee of the report on cardiovascular health and diseases in China. Report on cardiovascular health and diseases burden in China: an updated summary of 2020 [J]. Chinese Circulation Journal, 2021, 36(6): 521-545. [2] Akoumianakis I, Antoniades C. The interplay between adipose tissue and the cardiovascular system: Is fat always bad? [J]. Cardiovasc Res, 2017, 113(9): 999-1008. [3] Elnabawi YA, Oikonomou EK, Dey AK, et al. Association of biologic therapy with coronary inflammation in patients with psoriasis as assessed by perivascular fat attenuation index [J]. JAMA Cardiol, 2019, 4(9): 885-891. [4] Dai X, Deng J, Yu M, et al. Perivascular fat attenuation index and high-risk plaque features evaluated by coronary ct angiography: Relationship with serum inflammatory marker level [J]. Int J Cardiovasc Imaging, 2020, 36(4): 723-730. [5] Antonopoulos AS, Sanna F, Sabharwal N, et al. Detecting human coronary inflammation by imaging perivascular fat [J]. Sci Transl Med, 2017, 9(398):eaal2658. doi: 10.1126/scitranslmed.aal2658. [6] Oikonomou EK, Marwan M, Desai MY, et al. Non-invasive detection of coronary inflammation using computed tomography and prediction of residual cardiovascular risk(the crisp ct study): a post-hoc analysis of prospective outcome data [J]. Lancet, 2018, 392(10151): 929-939. [7] Goeller M, Rahman Ihdayhid A, Cadet S, et al. Pericoronary adipose tissue and quantitative global non-calcified plaque characteristics from CT angiography do not differ in matched south Asian, east Asian and European-origin Caucasian patients with stable chest pain [J]. Eur J Radiol, 2020, 125: 108874. doi: 10.1016/j.ejrad.2020.108874. [8] 赵娜, 侯志辉, 安云强, 等. 基于冠状动脉CT血管成像的冠状动脉粥样硬化斑块量化特征及易损性的门诊队列研究 [J]. 中华放射学杂志, 2020, 54(5): 467-473. ZHAO Na, HOU Zhihui, AN Yunqiang, et al. Analysis of quantitative characteristics and vulnerability of coronary atherosclerotic plaques with distribution of age: based on an out-patient cohort study of coronary CT angiography [J]. Chinese Circulation Journal, 2020, 54(5): 467-473. [9] Small GR, Chow B. CT imaging of the vulnerable plaque [J]. Curr Treat Options Cardiovasc Med, 2017, 19(12): 92. doi: 10.1007/s11936-017-0592-9. [10] 周茜洋, 唐春香, 张龙江, 等. 冠状动脉周围脂肪影像学的研究进展[J]. 中华放射学杂志, 2021, 55(3): 320-323. ZHOU Qianyang, TANG Chunxiang, ZHANG Longjiang, et al. Progress of imaging pericoronary adipose tissue [J]. Chinese Circulation Journal, 2021, 55(3): 320-323. [11] Tanaka K, Fukuda D, Sata M. Roles of epicardial adipose tissue in the pathogenesis of coronary atherosclerosis- an update on recent findings [J]. Circ J, 2020, 85(1): 2-8. [12] Mushenkova NV, Summerhill VI, Zhang D, et al. Current advances in the diagnostic imaging of atherosclerosis: insights into the pathophysiology of vulnerable plaque [J]. Int J Mol Sci, 2020, 21(8): 2992. doi: 10.3390/ijms21082992. [13] Chen YD, Fang WY, Chen JY, et al. Chinese expert consensus on the non-invasive imaging examination pathways of stable coronary artery disease [J]. J Geriatr Cardiol, 2018, 15(1): 30-40. [14] Si N, Shi K, Li N, et al. Identification of patients with acute myocardial infarction based on coronary CT angiography: the value of pericoronary adipose tissue radiomics[J]. Eur Radiol, 2022. doi: 10.1007/s00330-022-08812-5. [15] Yan H, Zhao N, Geng W, et al. Pericoronary fat attenuation index and coronary plaque quantified from coronary computed tomography angiography identify ischemia-causing lesions [J]. Int J Cardiol, 2022, 357: 8-13. doi:10.1016/j.ijcard.2022.03.033. [16] Pergola V, Cabrelle G, Mattesi G, et al. Added value of CCTA-derived features to predict MACEs in stable patients undergoing coronary computed tomography [J]. Diagnostics(Basel), 2022, 12(6). doi: 10.3390/diagnostics12061446. [17] Sun JT, Sheng XC, Feng Q, et al. Pericoronary fat attenuation index is associated with vulnerable plaque components and local immune-inflammatory activation in patients with non-ST elevation acute coronary syndrome [J]. J Am Heart Assoc, 2022, 11(2): e022879. doi: 10.1161/JAHA.121.022879. [18] Goeller M, Achenbach S, Cadet S, et al. Pericoronary adipose tissue computed tomography attenuation and high-risk plaque characteristics in acute coronary syndrome compared with stable coronary artery disease [J]. JAMA Cardiol, 2018, 3(9): 858-863. [19] Goeller M, Tamarappoo BK, Kwan AC, et al. Relationship between changes in pericoronary adipose tissue attenuation and coronary plaque burden quantified from coronary computed tomography angiography [J]. Eur Heart J Cardiovasc Imaging, 2019, 20(6): 636-643. [20] Zhu X, Chen X, Ma S, et al. Dual-layer spectral detector CT to study the correlation between pericoronary adipose tissue and coronary artery stenosis [J]. J Cardiothorac Surg, 2021, 16(1): 325. doi: 10.1186/s13019-021-01709-2. [21] Wen D, Xu Z, An R. et al. Predicting haemodynamic significance of coronary stenosis with radiomics-based pericoronary adipose tissue characteristics [J]. Clin Radiol, 2022, 77: e154-e161. doi: 10.1016/j.crad.2021.10.019. [22] Wen D, Li J, Ren J, et al. Pericoronary adipose tissue CT attenuation and volume: Diagnostic performance for hemodynamically significant stenosis in patients with suspected coronary artery disease [J]. Eur J Radiol, 2021, 140: 109740. doi: 10.1016/j.ejrad.2021.109740. |
| [1] | 杨宝珠,黄书苑,于鑫鑫,邓艳,韩鹏熙,刘晓龙,王锡明. 基于CCTA的血管周围脂肪衰减指数对冠状动脉慢性全闭塞病变再通的预测价值[J]. 山东大学学报 (医学版), 2024, 62(10): 98-105. |
| [2] | 张华,王培源,常娜,许天旗,袁宪顺,王锡明. 不同心动周期冠状动脉CT- FFR差异及其影响因素[J]. 山东大学学报 (医学版), 2023, 61(7): 55-62. |
| [3] | 赵古月,尚靳,侯阳. 人工智能在冠状动脉CT血管成像的应用进展[J]. 山东大学学报 (医学版), 2023, 61(12): 30-35. |
| [4] | 许天旗,常娜,张帅,李莎,矫秉轩,于鑫鑫,王锡明. 非酒精性脂肪肝识别基于CTA颈动脉高危斑块[J]. 山东大学学报 (医学版), 2023, 61(12): 36-43. |
|
||