The Value of CT-FFR in Predicting Major Adverse Cardiovascular Events in Patients with Obstructive Coronary Artery Disease

doi:10.12114/j.issn.1007-9572.2024.0112

Abstract

Abstract:

Background

At present, FFR is the ＂gold standard＂ index for evaluating the function and physiology of coronary blood flow. CT-fractional flow reserve (CT-FFR) reflects the hemodynamic changes of coronary artery lesions, it has higher diagnostic and differential ability in distinguishing lesion-specific ischemia compare with FFR.

Objective

To evaluate the value of CT-FFR in predicting the incidence of MACE in patients with stable chest pain caused by coronary artery obstruction.

Methods

This study included 116 patients who underwent coronary CT angiography (CCTA) examination for stable chest pain in the General Hospital of Ningxia Medical University from January 2017 to June 2021, and were followed up for a median of 2 (0, 25) months. The subjects were divided into MACE group (n=55) and non-MACE group (n=61) according to whether occurred major adverse cardiovascular events (MACE) during the follow-up period. The differences of the degree of coronary artery stenosis and CT-FFR between the two groups were compared, and the patients were classified according to the median of degree of coronary artery stenosis and CT-FFR, and compared the total incidence of MACE and the incidence of MACE within 3 months, 3 to 6 months and after 6 months. Spearman rank correlation analysis was used to analyze the correlation between the degree of coronary artery stenosis and CT-FFR, and multivariate Logistic regression analysis was used to explore the influencing factors of MACE. The ROC curves of stenosis degree, CT-FFR and the combination of the two index on the occurrence of MACE in patients with stable chest pain caused by coronary artery obstruction were drawn, and compared the predictive performance of different indexes according to the area under ROC curve (AUC) .

Results

The median degree of coronary lumen stenosis in 116 patients was 70% (60%, 80%), and the median CT-FFR was 0.79 (0.74, 0.85). The degree of coronary lumen stenosis in the MACE group was higher than that in the non-MACE group (Z=-4.41, P<0.001), and CT-FFR was lower (Z=-5.54, P<0.001). The incidence of MACE in patients with coronary artery stenosis between 70% and 90% was higher than that in patients with coronary artery stenosis between 50% and 69% (χ²=19.221, P<0.001). The incidence of MACE in CT-FFR≤0.8 patients was higher than that in CT-FFR>0.8 (χ²=30.025, P<0.001). The incidence of MACE in patients with different degrees of coronary artery stenosis combined with different CT-FFR was significantly different (χ²=37.789, P<0.001). The incidence of MACE was higher in patients with stenosis between 70% and 90% within 3 months than that in patients with stenosis between 50% and 69%, and the incidence of MACE in patients with CT-FFR≤0.8 within 3 months was higher than that in patients with CT-FFR>0.8. The incidence of MACE in patients with stenosis between 70% and 90% and CT-FFR≤0.8 within 3 months of follow-up was higher than that in other categories (P<0.05). Spearman rank correlation analysis showed that the degree of coronary lumen stenosis was negatively correlated with CT-FFR (r_s=-0.532 6, P<0.001). Multivariate Logistic regression analysis showed that the degree of coronary lumen stenosis between 70% and 90% (OR=3.085, 95%CI=1.147-8.298, P=0.026), CT-FFR≤0.8 (OR=6.527, 95%CI=2.560-16.641, P<0.001) were risk factors for MACE. The value of coronary lumen stenosis combined with CT-FFR in predicting MACE was higher (AUC=0.812, 95%CI=0.731-0.892, P<0.001) .

Conclusion

The degree of coronary artery stenosis between 70% and 90%, CT-FFR≤0.8 may be the risk factors of MACE in patients. Compared with the degree of stenosis, CT-FFR has a beneficial value in predicting the incidence of MACE in patients with stable chest pain caused by coronary artery obstruction, and the prediction performance is better when the degree of stenosis is combined with CT-FFR.

Key words: Coronary artery disease, Major adverse cardiovascular events, CT-fractional flow reserve, Coronary stenosis, Forecasting

摘要：

背景

目前，血流储备分数（FFR）是评估冠状动脉血流的功能和生理学的金标准，与之相比，CT衍生的血流储备分数（CT-FFR）反映冠状动脉病变处血流动力学改变，以及在区分病变特异性缺血方面，均有较高的诊断性能和鉴别能力。

目的

评价CT-FFR对冠状动脉梗阻性稳定性胸痛患者发生MACE的预测价值。

方法

本研究纳入2017年1月—2021年6月在宁夏医科大学总医院因稳定性胸痛行冠状动脉CT血管造影（CCTA）检查的患者116例为研究对象，中位随访时间2（0，25）个月。按照随访期内是否发生主要不良心血管事件（MACE）将研究对象分为MACE组（55例）和非MACE组（61例）。比较两组间冠状动脉管腔狭窄程度和CT-FFR之间差异性；再分别根据狭窄程度及CT-FFR中位数将患者分类，比较不同分类患者MACE总发生率和随访<3个月、3~6个月、>6个月MACE的发生率。采用Spearman秩相关分析探讨冠状动脉管腔狭窄程度与CT-FFR之间的相关性；采用多因素Logistic回归分析探讨患者发生MACE的影响因素；绘制狭窄程度、CT-FFR及二者结合后预测冠状动脉梗阻性稳定性胸痛患者发生MACE的受试者工作特征（ROC）曲线，并依据ROC曲线下面积（AUC）比较不同指标的预测性能。

结果

116例患者冠状动脉管腔狭窄程度中位数为70%（60%，80%），中位CT-FFR为0.79（0.74，0.85）。MACE组患者冠状动脉管腔狭窄程度高于非MACE组（Z=-4.41，P<0.001），CT-FFR低于非MACE组（Z=-5.54，P<0.001）。冠状动脉管腔狭窄程度70%~90%患者MACE发生率高于50%~69%患者（χ²=19.221，P<0.001）；CT-FFR≤0.8患者MACE发生率高于CT-FFR>0.8患者（χ²=30.025，P<0.001）；不同冠状动脉管腔狭窄程度联合不同CT-FFR患者MACE发生率比较，差异有统计学意义（χ²=37.789，P<0.001）。冠状动脉管腔狭窄程度70%~90%患者随访时间<3个月MACE发生率高于50%~69%患者，CT-FFR≤0.8患者随访时间<3个月MACE发生率高于CT-FFR>0.8患者，狭窄程度70%~90%+CT-FFR≤0.8的患者随访时间<3个月MACE发生率高于其他分类（P<0.05）。Spearman秩相关分析结果显示，冠状动脉管腔狭窄程度与CT-FFR呈负相关（r_s=-0.532 6，P<0.001）。多因素Logistic回归分析结果显示，冠状动脉管腔狭窄程度70%~90%（OR=3.085，95%CI=1.147~8.298，P=0.026）、CT-FFR≤0.8（OR=6.527，95%CI=2.560~16.641，P<0.001）是患者发生MACE的危险因素。冠状动脉管腔狭窄程度联合CT-FFR预测患者发生MACE的价值更高（AUC=0.812，95%CI=0.731~0.892，P<0.001）。

结论

冠状动脉管腔狭窄程度70%~90%、CT-FFR≤0.8可能是患者发生MACE的危险因素。与狭窄程度相比，CT-FFR对预测冠状动脉阻塞性稳定性胸痛患者发生MACE具有增益价值，狭窄程度与CT-FFR结合后的预测性能更佳。

关键词: 冠状动脉疾病, 主要不良心血管事件, CT衍生血流储备分数, 冠状动脉狭窄, 预测

CLC Number:

R 541.4

WANG Rui,OUYANG Lina,WU Qian, et al. The Value of CT-FFR in Predicting Major Adverse Cardiovascular Events in Patients with Obstructive Coronary Artery Disease[J]. Chinese General Practice, 2025, 28(06): 713-719. DOI: 10.12114/j.issn.1007-9572.2024.0112.
王瑞,欧阳丽娜,吴倩等. CT衍生的血流储备分数对梗阻性冠状动脉疾病患者主要不良心血管事件的预测价值研究[J]. 中国全科医学, 2025, 28(06): 713-719. DOI: 10.12114/j.issn.1007-9572.2024.0112.

Figures/Tables 7

References 31

[1]	VADUGANATHAN M，MENSAH G A，TURCO J V，et al. The global burden of CardiovascularDiseases and risk[J]. J Am Coll Cardiol，2022，80（25）：2361-2371. DOI：10.1016/j.jacc.2022.11.005.
[2]	BARI 2D Study Group，FRYE R L，AUGUST P，et al. A randomized trial of therapies for type 2 diabetes and coronary artery disease[J]. N Engl J Med，2009，360（24）：2503-2515. DOI：10.1056/NEJMoa0805796.
[3]	SERRUYS P W，MORICE M C，KAPPETEIN A P，et al. Percutaneous coronary intervention versus coronary-artery bypass grafting for severe coronary artery disease[J]. N Engl J Med，2009，360（10）：961-972. DOI：10.1056/NEJMoa0804626.
[4]	TONINO P A，FEARON W F，BRUYNE B D，et al. Angiographic versus functional severity of coronary artery stenoses in the FAME study fractional flow reserve versus angiography in multivessel evaluation[J]. J Am Coll Cardiol，2010，55（25）：2816-2821. DOI：10.1016/j.jacc.2009.11.096.
[5]	ANTONOPOULOS A S，ANGELOPOULOS A，TSIOUFIS K，et al. Cardiovascular risk stratification by coronary computed tomography angiography imaging：current state-of-the-art[J]. Eur J Prev Cardiol，2022，29（4）：608-624. DOI：10.1093/eurjpc/zwab067.
[6]	DANAD I，SZYMONIFKA J，TWISK J W R，et al. Diagnostic performance of cardiac imaging methods to diagnose ischaemia-causing coronary artery disease when directly compared with fractional flow reserve as a reference standard：a meta-analysis[J]. Eur Heart J，2017，38（13）：991-998. DOI：10.1093/eurheartj/ehw095.
[7]	YUAN M Y，WU H，LI R X，et al. The value of quantified plaque analysis by dual-source coronary CT angiography to detect vulnerable plaques：a comparison study with intravascular ultrasound[J]. Quant Imaging Med Surg，2020，10（3）：668-677. DOI：10.21037/qims.2020.01.13.
[8]	KAY F U，CANAN A，ABBARA S. Future directions in coronary CT angiography：CT-fractional flow reserve，plaque vulnerability，and quantitative plaque assessment[J]. Korean Circ J，2020，50（3）：185-202. DOI：10.4070/kcj.2019.0315.
[9]	MIN J K，LEIPSIC J，PENCINA M J，et al. Diagnostic accuracy of fractional flow reserve from anatomic CT angiography[J]. JAMA，2012，308（12）：1237-1245. DOI：10.1001/2012.jama.11274.
[10]	BAUMANN S，RENKER M，HETJENS S，et al. Comparison of coronary computed tomography angiography-derived vs invasive fractional flow reserve assessment：meta-analysis with subgroup evaluation of intermediate stenosis[J]. Acad Radiol，2016，23（11）：1402-1411. DOI：10.1016/j.acra.2016.07.007.
[11]	WU W，PAN D R，FOIN N，et al. Noninvasive fractional flow reserve derived from coronary computed tomography angiography for identification of ischemic lesions：a systematic review and meta-analysis[J]. Sci Rep，2016，6：29409. DOI：10.1038/srep29409.
[12]	PATEL M R，NØRGAARD B L，FAIRBAIRN T A，et al. 1-year impact on medical practice and clinical outcomes of FFRCT：the ADVANCE registry[J]. JACC Cardiovasc Imaging，2020，13（1 Pt 1）：97-105. DOI：10.1016/j.jcmg.2019.03.003.
[13]	CURY R C，LEIPSIC J，ABBARA S，et al. CAD-RADS^TM 2.0-2022 coronary artery disease - reporting and data system an expert consensus document of the Society of Cardiovascular Computed Tomography（SCCT），the American College of Cardiology（ACC），the American College of Radiology（ACR）and the North America Society of Cardiovascular Imaging （NASCI）[J]. Radiol Cardiothorac Imaging，2022，4（5）：e220183. DOI：10.1148/ryct.220183.
[14]	LI Y，QIU H，HOU Z H，et al. Additional value of deep learning computed tomographic angiography-based fractional flow reserve in detecting coronary stenosis and predicting outcomes[J]. Acta Radiol，2022，63（1）：133-140. DOI：10.1177/0284185120983977.
[15]	GAO Z F，WANG X，SUN S H，et al. Learning physical properties in complex visual scenes：an intelligent machine for perceiving blood flow dynamics from static CT angiography imaging[J]. Neural Netw，2020，123：82-93. DOI：10.1016/j.neunet.2019.11.017.
[16]	TESCHE C，DE CECCO C N，BAUMANN S，et al. Coronary CT angiography-derived fractional flow reserve：machine learning algorithm versus computational fluid dynamics modeling[J]. Radiology，2018，288（1）：64-72. DOI：10.1148/radiol.2018171291.
[17]	BENTZON J F，OTSUKA F，VIRMANI R，et al. Mechanisms of plaque formation and rupture[J]. Circ Res，2014，114（12）：1852-1866. DOI：10.1161/CIRCRESAHA.114.302721.
[18]	KOSKINAS K C，UGHI G J，WINDECKER S，et al. Intracoronary imaging of coronary atherosclerosis：validation for diagnosis，prognosis and treatment[J]. Eur Heart J，2016，37（6）：524-35a-c. DOI：10.1093/eurheartj/ehv642.
[19]	VAN DEN HOOGEN I J，DE GRAAF M A，ROOS C J，et al. Prognostic value of coronary computed tomography angiography in diabetic patients without chest pain syndrome[J]. J Nucl Cardiol，2016，23（1）：24-36. DOI：10.1007/s12350-015-0213-5.
[20]	CHAIKRIANGKRAI K，VELANKAR P，SCHUTT R，et al. Additive prognostic value of coronary artery calcium score over coronary computed tomographic angiography stenosis assessment in symptomatic patients without known coronary artery disease[J]. Am J Cardiol，2015，115（6）：738-744. DOI：10.1016/j.amjcard.2014.12.032.
[21]	LEE K Y，HWANG B H，KIM T H，et al. Computed tomography angiography images of coronary artery stenosis provide a better prediction of risk than traditional risk factors in asymptomatic individuals with type 2 diabetes：a long-term study of clinical outcomes[J]. Diabetes Care，2017，40（9）：1241-1248. DOI：10.2337/dc16-1844.
[22]	PARK G M，LEE S W，CHO Y R，et al. Coronary computed tomographic angiographic findings in asymptomatic patients with type 2 diabetes mellitus[J]. Am J Cardiol，2014，113（5）：765-771. DOI：10.1016/j.amjcard.2013.11.028.
[23]	HOFFMANN U，FERENCIK M，UDELSON J E，et al. Prognostic value of noninvasive cardiovascular testing in patients with stable chest pain：insights from the PROMISE trial（prospective multicenter imaging study for evaluation of chest pain）[J]. Circulation，2017，135（24）：2320-2332. DOI：10.1161/CIRCULATIONAHA.116.024360.
[24]	NØRGAARD B L，TERKELSEN C J，MATHIASSEN O N，et al. Coronary CT angiographic and flow reserve-guided management of patients with stable ischemic heart disease[J]. J Am Coll Cardiol，2018，72（18）：2123-2134. DOI：10.1016/j.jacc.2018.07.043.
[25]	IHDAYHID A R，NORGAARD B L，GAUR S，et al. Prognostic value and risk continuum of noninvasive fractional flow reserve derived from coronary CT angiography[J]. Radiology，2019，292（2）：343-351. DOI：10.1148/radiol.2019182264.
[26]	COENEN A，LUBBERS M M，KURATA A，et al. Fractional flow reserve computed from noninvasive CT angiography data：diagnostic performance of an on-site clinician-operated computational fluid dynamics algorithm[J]. Radiology，2015，274（3）：674-683. DOI：10.1148/radiol.14140992.
[27]	YANG D H，KIM Y H，ROH J H，et al. Diagnostic performance of on-site CT-derived fractional flow reserve versus CT perfusion[J]. Eur Heart J Cardiovasc Imaging，2017，18（4）：432-440. DOI：10.1093/ehjci/jew094.
[28]	GAUR S，ØVREHUS K A，DEY D，et al. Coronary plaque quantification and fractional flow reserve by coronary computed tomography angiography identify ischaemia-causing lesions[J]. Eur Heart J，2016，37（15）：1220-1227. DOI：10.1093/eurheartj/ehv690.
[29]	KNUUTI J，BALLO H，JUAREZ-OROZCO L E，et al. The performance of non-invasive tests to rule-in and rule-out significant coronary artery stenosis in patients with stable angina：a meta-analysis focused on post-test disease probability[J]. Eur Heart J，2018，39（35）：3322-3330. DOI：10.1093/eurheartj/ehy267.
[30]	TONINO P A，FEARON W F，BRUYNE B D，et al. Angiographic versus functional severity of coronary artery stenoses in the FAME study fractional flow reserve versus angiography in multivessel evaluation[J]. J Am Coll Cardiol，2010，55（25）：2816-2821. DOI：10.1016/j.jacc.2009.11.096.
[31]	VON KNEBEL DOEBERITZ P L，DE CECCO C N，SCHOEPF U J，et al. Impact of coronary computerized tomography angiography-derived plaque quantification and machine-learning computerized tomography fractional flow reserve on adverse cardiac outcome[J]. Am J Cardiol，2019，124（9）：1340-1348. DOI：10.1016/j.amjcard.2019.07.061.

组别	例数	性别[例（%）]		年龄（岁）	糖尿病[例（%）]	高血压[例（%）]	吸烟史[例（%）]	冠心病史[例（%）]	血脂异常[例（%）]
组别	例数	男	女	年龄（岁）	糖尿病[例（%）]	高血压[例（%）]	吸烟史[例（%）]	冠心病史[例（%）]	血脂异常[例（%）]
MACE组	55	40（72.7）	15（27.3）	64.0±8.8	18（32.7）	42（76.3）	29（52.8）	30（54.5）	26（47.2）
非MACE组	61	37（60.7）	24（39.3）	63.5±7.8	10（16.4）	26（42.6）	18（29.5）	33（54.1）	19（31.1）
χ²（t）值		0.369		-0.736^a	0.842	1.758	0.783	1.495	0.025
P值		0.54		0.46	0.35	0.18	0.37	0.47	0.87

组别	例数	性别[例（%）]		年龄（岁）	糖尿病[例（%）]	高血压[例（%）]	吸烟史[例（%）]	冠心病史[例（%）]	血脂异常[例（%）]
组别	例数	男	女	年龄（岁）	糖尿病[例（%）]	高血压[例（%）]	吸烟史[例（%）]	冠心病史[例（%）]	血脂异常[例（%）]
MACE组	55	40（72.7）	15（27.3）	64.0±8.8	18（32.7）	42（76.3）	29（52.8）	30（54.5）	26（47.2）
非MACE组	61	37（60.7）	24（39.3）	63.5±7.8	10（16.4）	26（42.6）	18（29.5）	33（54.1）	19（31.1）
χ²（t）值		0.369		-0.736^a	0.842	1.758	0.783	1.495	0.025
P值		0.54		0.46	0.35	0.18	0.37	0.47	0.87

项目	例数	<3个月			3~6个月			>6个月
项目	例数	MACE发生率	χ²值	P值	MACE发生率	χ²值	P值	MACE发生率	χ²值	P值
狭窄程度			21.741	<0.001		0.931	0.335		0.086	0.769
50%~69%	43	0			0			9（20.9）
70%~90%	73	28（38.4）			1（1.4）			17（23.3）
CT-FFR			21.193	<0.001		1.196	0.274		2.678	0.102
>0.8	52	2（3.8）			0			8（15.4）
≤0.8	64	26（40.6）			1（1.6）			18（28.1）
狭窄程度+CT-FFR			34.298	<0.001		2.320	1.000		3.081	0.389
50%~69%+>0.8	33	0			0			6（18.2）
50%~69%+≤0.8	10	0			0			3（30.0）
70%~90%+>0.8	19	2（10.5）			0			2（10.5）
70%~90%+≤0.8	54	26（48.1）			1（1.8）			15（27.8）

项目	例数	<3个月			3~6个月			>6个月
项目	例数	MACE发生率	χ²值	P值	MACE发生率	χ²值	P值	MACE发生率	χ²值	P值
狭窄程度			21.741	<0.001		0.931	0.335		0.086	0.769
50%~69%	43	0			0			9（20.9）
70%~90%	73	28（38.4）			1（1.4）			17（23.3）
CT-FFR			21.193	<0.001		1.196	0.274		2.678	0.102
>0.8	52	2（3.8）			0			8（15.4）
≤0.8	64	26（40.6）			1（1.6）			18（28.1）
狭窄程度+CT-FFR			34.298	<0.001		2.320	1.000		3.081	0.389
50%~69%+>0.8	33	0			0			6（18.2）
50%~69%+≤0.8	10	0			0			3（30.0）
70%~90%+>0.8	19	2（10.5）			0			2（10.5）
70%~90%+≤0.8	54	26（48.1）			1（1.8）			15（27.8）

变量	B	SE	Wald χ²值	P值	OR（95%CI）
狭窄程度70%~90%（以50%~69%为参考）	1.127	0.505	4.981	<0.026	3.085（1.147~8.298）
CT-FFR≤0.8（以>0.8为参考）	1.876	0.478	15.430	<0.001	6.527（2.560~16.641）