CT血管造影多参数预测冠心病不良心脏事件的价值^*

Value of Multivariate Computed Tomography Angiography for Predicting Major Adverse Cardiac Events in Patients With Coronary Artery Heart Disease

摘要

目的 探究基于CT血管造影（computed tomography angiography, CTA）的血流储备分数（fractional flow reserve, FFR）、脂肪衰减指数（fat attenuation index, FAI）及斑块特征对冠心病（coronary artery heart disease, CAD）患者主要不良心脏事件（major adverse cardiac events, MACE）发生的预测价值。方法 回顾性分析我院205例CAD患者资料，所有患者均进行CTA检查，测量其FFR、FAI及斑块特征，随访患者36个月内MACE的发生情况，将患者分为MACE组（n=42）和非MACE组（n=163），比较两组临床资料、FFR、FAI及斑块特征，采用logistic回归分析影响CAD患者并发MACE的相关因素；比较不同冠状动脉狭窄程度患者FFR、FAI及斑块负荷（plaque burden, PB），采用Spearman相关分析FFR、FAI及PB与CAD患者冠状动脉狭窄程度的相关性；采用受试者工作特征（receiver operating characteristic, ROC）曲线分析FFR、FAI及PB对CAD患者MACE的预测价值。结果 MACE组体质量指数、两支及以上血管病变占比、重度狭窄占比、FAI、脂质斑块体积、非钙化斑块体积及PB均高于非MACE组（P<0.05），FFR低于非MACE组（P<0.05）。共线性诊断将方差膨胀因子<3的变量纳入多因素logistic回归分析，结果显示，多支病变〔比值比（odds ratio, OR）=2.188，95%置信区间（confidence interval, CI）：1.304～3.671）〕、冠状动脉重度狭窄（OR=2.918，95%CI：1.608～5.295）、FAI升高（OR=2.635，95%CI：1.510～4.598）、PB升高（OR=2.396，95%CI：1.382～4.157）是影响CAD患者发生MACE的危险因素（均P<0.05），FFR升高（OR=0.373，95%CI：0.221～0.630）为保护因素（P<0.05）。按冠状动脉狭窄程度分组，轻度组FFR高于中度组和重度组（P<0.05），FAI、PB低于中度组和重度组（P<0.05），中度组FFR高于重度组（P<0.05），FAI、PB低于重度组（P<0.05）；Spearman相关分析显示，冠状动脉狭窄程度与FAI（r=0.687，95%CI：0.598～0.760）及PB值（r=0.664，95%CI：0.571～0.741）呈正相关（P<0.05），与FFR（r=-0.712，95%CI：-0.782～-0.628）呈负相关（P<0.05）。ROC曲线分析显示，FFR、FAI及PB单独及联合检测预测CAD患者发生MACE的曲线下面积分别为0.831（95%CI：0.750～0.876）、0.790（95%CI：0.719～0.861）、0.724（95%CI：0.646～0.802）、0.927（95%CI：0.890～0.964），联合预测价值更高。结论 基于CTA的FFR、FAI及PB与CAD患者冠状动脉狭窄程度具有相关性，且联合检测对患者并发MACE具有较高的预测价值。

Abstract

Objective To investigate the predictive value of computed tomography angiography (CTA)-derived fractional flow reserve (FFR), fat attenuation index (FAI), and plaque characteristics for the occurrence of major adverse cardiac events (MACE) in patients with coronary artery heart disease (CAD). Methods A total of 205 CAD patients were retrospectively enrolled at our hospital, and their demographic and clinical data were analyzed accordingly. All patients underwent CTA, and their FFR, FAI and plaque characteristics were measured. According to the occurrence of MACE within a 36-month follow-up period, the patients were divided into a MACE group (n = 42) and a non-MACE group (n = 163). The clinical data, FFR, FAI, and plaque characteristics were compared between the 2 groups, and logistic regression analysis was performed to identify factors associated with MACE in CAD patients. FFR, FAI, and plaque burden (PB) were further compared among patients with different degrees of coronary artery stenosis, and Spearman correlation analysis was performed to evaluate the associations of FFR, FAI, and PB with the severity of coronary artery stenosis. Receiver operating characteristic (ROC) curves were plotted to assess the predictive value of FFR, FAI, and PB for MACE in CAD patients. Results Compared with those in the non-MACE group, the body mass index (BMI), the proportion of patients with vascular lesions involving 2 or more coronary arteries, the proportion of patients with severe coronary stenosis, FAI, lipid plaque volume, non-calcified plaque volume, and PB were significantly higher (all P < 0.05) in the MACE group, whereas FFR was significantly lower ( P < 0.05) in the MACE group. Variables with variance inflation factor (VIF) < 3 on collinearity diagnostics were included in multivariate logistic regression analysis. The results showed that multivessel lesion (odds ratio [OR], 2.188; 95% CI, 1.304-3.671), severe coronary artery stenosis (OR, 2.918; 95% CI, 1.608-5.295), increased FAI (OR, 2.635; 95% CI, 1.510-4.598), and increased PB (OR, 2.396; 95% CI, 1.382-4.157) were risk factors for MACE in CAD patients ( P < 0.05), while increased FFR (OR, 0.373; 95% CI, 0.221-0.630) was a protective factor ( P < 0.05). Stratified analysis by the severity of coronary artery stenosis showed that patients with mild stenosis had significantly higher FFR and significantly lower FAI and PB than those with moderate or severe stenosis (all P < 0.05), and that patients with moderate stenosis had significantly higher FFR and significantly lower FAI and PB than those with severe stenosis (all P < 0.05). Spearman correlation coefficient analysis revealed that the severity of coronary artery stenosis was positively correlated with FAI ( r = 0.687; 95% CI, 0.598-0.760) and PB (r = 0.664; 95% CI, 0.571-0.741) (P < 0.05), but negatively correlated with FFR ( r = -0.712; 95% CI, -0.782 to -0.628; P < 0.05). ROC curve analysis showed that the areas under the curve (AUCs) for predicting MACE in CAD patients using FFR, FAI, and PB individually were 0.831 (95% CI, 0.750-0.876), 0.790 (95% CI, 0.719-0.861), and 0.724 (95% CI, 0.646-0.802), respectively, whereas the combined model using the 3 parameters achieved an AUC of 0.927 (95% CI, 0.890-0.964), demonstrating superior predictive performance. Conclusion CTA-derived FFR, FAI, and PB are significantly correlated with the severity of coronary artery stenosis in patients with CAD. Combined assessment of these parameters demonstrates higher predictive value for the occurrence of MACE in patients with CAD.

关键词

冠心病 / CT血管造影 / 主要不良心脏事件 / 血流储备分数 / 脂肪衰减指数 / 斑块特征

Key words

Coronary artery heart disease / Computed tomography angiography / Major adverse cardiac events / Fractional flow reserve / Fat attenuation index / Plaque characteristics

参考文献

[1] Bawaskar P, Thomas N, Ismail K, et al. Nonischemic or dual cardiomyopathy in patients with coronary artery disease[J]. Circulation, 2024, 149(11): 807. DOI: 10.1161/CIRCULATIONAHA.123.067032.

[2] Peeters D, Woelders E, Jansen T, et al. Association between coronary artery spasm and atherosclerotic disease[J]. JACC Cardiovasc Imaging, 2025, 18(2): 226. DOI: 10.1016/j.jcmg.2024.05.024.

[3] Amin N, Devasia T, Kamath S U, et al. Association between Lipoprotein(a) concentration and adverse cardiac events in patients with coronary artery disease: An observational cohort study[J]. Indian Heart J, 2024, 76(3): 197. DOI: 10.1016/j.ihj.2024.06.001.

[4] Nurmohamed N S, Van Rosendael A R, Danad I, et al. Atherosclerosis evaluation and cardiovascular risk estimation using coronary computed tomography angiography[J]. Eur Heart J, 2024, 45(20): 1784. DOI: 10.1093/eurheartj/ehae190.

[5] Guo W, Zhao S, Xu H, et al. Comparison of machine learning-based CT fractional flow reserve with cardiac MR perfusion mapping for ischemia diagnosis in stable coronary artery disease[J]. Eur Radiol, 2024, 34(9): 5657. DOI: 10.1007/s00330-024-10650-6.

[6] Sun X, Zhu Y, Zhang N, et al. Prognostic value of serial coronary computed tomography angiography-derived perivascular fat-attenuation index and plaque volume in patients with suspected coronary artery disease[J]. Clin Radiol, 2024, 79(8): 602. DOI: 10.1016/j.crad.2024.04.007.

[7] 张华, 王文红, 乔璐, 等. 心脏超声联合颈动脉超声对冠心病诊断价值的临床研究[J]. 转化医学杂志, 2024, 13(9): 1336. DOI: 10.3639/j.issn.2095-3097.2024.09.007.

[8] Zhang H, Wang W H, Qiao L, et al. Clinical Study on the Diagnosis of Coronary Heart Disease by Cardiac Ultrasound Combined with Carotid Ultrasound[J]. Translational Medicine Journal, 2024, 13(9): 1336. DOI: 10.3639/j.issn.2095-3097.2024.09.007.

[9] Van Smeden M, Moons K G, De Groot J A, et al. Sample size for binary logistic prediction models: beyond events per variable criteria[J]. Stat Methods Med Res, 2019, 28(8): 2455. DOI: 10.1177/0962280218784726.

[10] 中华医学会, 中华医学会杂志社, 中华医学会全科医学分会, 等. 稳定性冠心病基层诊疗指南(2020年)[J]. 中华全科医师杂志, 2021, 20(3): 266. DOI: 10.3760/cma.j.cn114798-20210120-00079.

[11] Chinese Medical Association, Chinese Medical Association Press, Chinese Medical Association General Practice Branch, et al. Guideline for primary care of stable coronary artery disease(2020)[J]. Chin J Gen Pract, 2021, 20(3): 266. DOI: 10.3760/cma.j.cn114798-20210120-00079.

[12] 万书友, 杜灵艳, 郑琦. CTA与DSA评估冠心病冠状动脉狭窄程度的临床价值[J]. 医学影像学杂志, 2023, 33(1): 135. DOI: 10.20258/j.cnki.1006-9011.2023.01.032.

[13] Wan S Y, Du L Y, Zheng Q. Clinical value of CT angiography in assessing coronary stenosis and plaque status in coronary heart disease[J]. J Med Imaging, 2023, 33(1): 135. DOI: 10.20258/j.cnki.1006-9011.2023.01.032.

[14] 解福友, 刘艺超, 邱晓晖, 等. 基于人工智能的冠状动脉周围脂肪衰减指数联合CT血流储备分数预测冠心病患者不良心血管事件风险的价值[J]. 临床放射学杂志, 2024, 43(8): 1325. DOI: 10.13437/j.cnki.jcr.2024.08.018.

[15] Xie F Y, Liu Y C, Qiu X H, et al. The value of predicting the risk of adverse cardiovascular events in patients with coronary heart disease using artificial intelligence based pericoronal fat attenuation index combined with CT-FFR technology[J]. Journal of Clinical Radiology, 2024, 43(8): 1325. DOI: 10.13437/j.cnki.jcr.2024.08.018.

[16] 王玉兰, 刘晓楚, 贺文英, 等. 冠心病患者PCI术后近期预后的危险因素分析及预测模型构建[J]. 转化医学杂志, 2025, 14(1): 36. DOI: 10.3639/i.issn.2095-3097.2025.01.008.

[17] Wang Y L, Liu X C, He W Y, et al. Analysis of risk factors and construction of prediction model for short-term prognosis of patients with coronary heart disease after PCI[J]. Translational Medicine Journal, 2025, 14(1): 36. DOI: 10.3639/i.issn.2095-3097.2025.01.008.

[18] 雷国龙, 陈映烨, 骆周展, 等. 心脏超声造影联合CD137和胰岛素样生长因子结合蛋白6与冠心病患者终点事件分析[J]. 中华老年心脑血管病杂志, 2023, 25(10): 1038. DOI: 10.3969/j.issn.1009-0126.2023.10.008.

[19] Lei G L, Chen Y Y, Luo Z Z, et al. Relationship of contrast-enhanced echocardiography combined with serum CD137 and IGFBP-6 with endpoint events in patients with CHD[J]. Chin J Geriatr Heart Brain Vessel Dis, 2023, 25(10): 1038. DOI: 10.3969/j.issn.1009-0126.2023.10.008.

[20] Nurmohamed N S, Danad I, Jukema R A, et al. Development and validation of a quantitative coronary CT angiography model for diagnosis of vessel-specific coronary ischemia[J]. JACC Cardiovasc Imaging, 2024, 17(8): 894. DOI: 10.1016/j.jcmg.2024.01.007.

[21] Li Y, Na K, Qiu M, et al. Coronary CT angiography quantitative features and outcomes after complete revascularization in acute coronary syndrome: a post hoc analysis of a prospective PCI registry[J]. Acad Radiol, 2026, 33(4): 1522. DOI: 10.1016/j.acra.2025.12.007.

[22] Ma Z, Tu C, Zhang B, et al. A meta-analysis comparing the diagnostic performance of computed tomography-derived fractional flow reserve and coronary computed tomography angiography at different levels of coronary artery calcium score[J]. Eur Radiol, 2024, 34(9): 5621. DOI: 10.1007/s00330-024-10591-0.

[23] Sato Y, Motoyama S, Miyajima K, et al. Clinical outcomes based on coronary computed tomography-derived fractional flow reserve and plaque characterization[J]. JACC Cardiovasc Imaging, 2024, 17(3): 284. DOI: 10.1016/j.jcmg.2023.07.013.

[24] 杜天剑, 顾翔, 朱业. 基于冠状动脉CT血流储备分数预测冠心病价值的研究[J]. 中华老年心脑血管病杂志, 2024, 26(9): 991. DOI: 10.3969/j.issn.1009-0126.2024.09.002.

[25] Du T J, Gu X, Zhu Y. Predictive value of coronary computed tomography-derived fractional flow reserve for coronary artery disease[J]. Chin J Geriatr Heart Brain Vessel Dis, 2024, 26(9): 991. DOI: 10.3969/j.issn.1009-0126.2024.09.002.

[26] Jing M, Xi H, Yang J, et al. Relationship between pericoronary fat-attenuation values quantified by coronary computed tomography angiography and coronary artery disease severity[J]. Clin Radiol, 2024, 79(8): 1021. DOI: 10.1016/j.crad.2024.02.020.

[27] He W, Lu Y, Yin J, et al. Coronary computed tomography angiography-derived fat attenuation index predict future percutaneous coronary intervention[J]. Br J Radiol, 2024, 97(1163): 1782. DOI: 10.1093/bjr/tqae135.

[28] 朱铭, 冯长明, 郑琦, 等. 基于CTA探究冠周脂肪体积、FAI、PCATMA与冠心病患者冠脉狭窄、斑块性质及预后关系[J]. 中国CT和MRI杂志, 2024, 22(9): 74. DOI: 10.3969/j.issn.1672-5131.2024.09.024.

[29] Zhu M, Feng C M, Zheng Q, et al. Exploration on the relationship between pericoronal fat volume, FAI, PCATMA and coronary stenosis, plaque properties, prognosis in patients with coronary heart disease based on CTA[J]. Chin J CT MRI, 2024, 22(9): 74. DOI: 10.3969/j.issn.1672-5131.2024.09.024.

[30] 孙刚, 常艺桐, 徐娜, 等. 冠状动脉周围脂肪CT衰减指数联合CT-FFR对急性冠状动脉综合征患者MACE事件预测价值[J]. 中国CT和MRI杂志, 2024, 22(7): 91. DOI: 10.3969/j.issn.1672-5131.2024.07.028.

[31] Sun G, Chang Y T, Xu N, et al. Value of peripheral coronary fat CT attenuation index combined with CT-FFR in predicting MACE events in patients with acute coronary syndrome[J]. Chin J CT MRI, 2024, 22(7): 91. DOI: 10.3969/j.issn.1672-5131.2024.07.028.

[32] 陈瑞, 贾涵, 冯长静, 等. 基于冠状动脉CT血管成像的斑块进展参数在预测非阻塞性冠心病预后中的价值[J]. 中华放射学杂志, 2024, 58(12): 1414. DOI: 10.3760/cma.j.cn112149-20240117-00032.

[33] Chen R, Jia H, Feng C J, et al. The value of coronary artery plaque progression parameters based on coronary CT angiography in predicting prognosis of non-obstructive coronary artery disease[J]. Chinese J Radiol, 2024, 58(12): 1414. DOI: 10.3760/cma.j.cn112149-20240117-00032.

[34] 尹小花, 徐荣, 李春晨, 等. CT血流储备分数联合斑块特征预测冠心病患者发生主要不良心脏事件的价值[J]. 中国中西医结合影像学杂志, 2024, 22(6): 650. DOI: 10.3969/j.issn.1672-0512.2024.06.004.

[35] Yin X H, Xu R, Li C C, et al. Value of fractional flow reserve derived from coronary CT angiography combined with plaque features in predicting major adverse cardiac events in patients with coronary heart disease[J]. Chin Imaging J Integr Tradit West Med, 2024, 22(6): 650. DOI: 10.3969/j.issn.1672-0512.2024.06.004.

[36] 欧阳丽娜, 王晶晶, 张怀瑢, 等. CT血流储备分数联合斑块特征对主要不良心脏事件的预测价值[J]. 中国医学影像学杂志, 2023, 31(6): 596. DOI: 10.3969/j.issn.1005-5185.2023.06.007.

[37] Ouyang L N, Wang J J, Zhang H R, et al. Prediction of coronary CTA-derived fractional flow reserve and atherosclerosis plaque characteristics for major adverse cardiac events[J]. Chin J Med Imag, 2023, 31(6): 596. DOI: 10.3969/j.issn.1005-5185.2023.06.007.

[38] Machado M F, Felix N, Melo P H C, et al. Coronary computed tomography angiography versus invasive coronary angiography in stable chest pain: a meta-analysis of randomized controlled trials[J/OL]. Circ Cardiovasc Imaging, 2023, 16(11): e015800. https://doi.org/10.1161/CIRCIMAGING.123.015800.

基金资助

*四川省科技计划项目(2021YJ0258)