Journal of Shandong University (Health Sciences) ›› 2023, Vol. 61 ›› Issue (7): 55-62.doi: 10.6040/j.issn.1671-7554.0.2023.0322

• 临床医学 • Previous Articles    

Differences in CT- FFR of coronary arteries with different cardiac cycles and influencing factors

ZHANG Hua1, WANG Peiyuan2, CHANG Na3, XU Tianqi4, YUAN Xianshun4, WANG Ximing4   

  1. 1. Department of Interventional Radiology, Heze Municipal Hospital, Heze 274000, Shandong, China;
    2. Department of Medical Radiology, Yantai Affiliated Hospital of Binzhou Medical University, Yantai 264100, Shandong, China;
    3. Jinan Vocational College of Nursing, Jinan 250102, Shandong, China;
    4. Department of Medical Radiology, Shandong Provincial Hospital, Jinan 250021, Shandong, China
  • Published:2023-07-04

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Abstract: Objective To investigate the differences in the measurement of coronary computed tomography fractional flow reserve(CT-FFR)in diastole and systole and the influencing factors. Methods This study retrospectively included 159 patients with clinically suspected coronary artery disease who underwent coronary computed tomography angiography(CTA)during Jan. 2019 and Dec. 2020 which showed a single plaque in the vessel. Coronary CTA post-processing was performed in both systolic and diastolic phases, and CT-FFR-S and CT-FFR-D were calculated. The relationships between CT-FFR and ΔCT-FFR and heart rate, coronary stenosis degree and plaque type were evaluated. Paired-samples t-test was used to compare the differences between diastolic and systolic CT-FFR. Results A total of 176 vessels from the 159 patients(58.2±11.0 years, 61.0% men)were successfully analyzed. Overall, CT-FFR-D was lower than CT-FFR-S(0.74±0.18 vs 0.76±0.16, P=0.019), whereas ΔCT-FFR-D was higher than ΔCT-FFR-S(0.21±0.17 vs 0.19±0.15, P=0.003). Heart rate and degree of coronary artery stenosis were contributing factors to the differences in CT-FFR and ΔCT-FFR between different phases. In patients with heart rate <80, the differences between CT-FFR and ΔCT-FFR in diastole and systole were statistically significant(P<0.05). In addition, the differences between CT-FFR and ΔCT-FFR in diastole and systole were statistically significant(P<0.05)in obstructive lesions(≥50% stenosis). However, plaque type had no significant effects on CT-FFR and ΔCT-FFR in different phases(P>0.05). Conclusion There are differences in CT-FFR and ΔCT-FFR measured in systole and diastole, and the difference in ΔCT-FFR is more significant. The differences in CT-FFR measurements between different phases are affected by heart rate and coronary stenosis, but not by plaque type.

Key words: Coronary CT angiography, Fractional flow reserve, Diastolic phase, Systolic phase

CLC Number: 

  • R445.3
[1] Timmis A, Townsend N, Gale CP, et al. European society of cardiology: cardiovascular disease statistics 2019 [J]. European Heart Journal, 2020, 41(1): 12-85.
[2] Fihn SD, Gardin JM, Abrams J, et al. 2012 ACCF/AHA/ACP/AATS/PCNA/SCAI/STS Guideline for the diagnosis and management of patients with stable ischemic heart disease: executive summary: a report of the American college of cardiology Foundation/American heart association task force on practice guidelines, and the American college of physicians, American association for thoracic surgery, preventive cardiovascular nurses association, society for cardiovascular angiography and interventions, and society of thoracic surgeons [J]. Am Coll Cardiol, 2012, 60(24): 2564-2603.
[3] Padley SPG, Roditi G, Nicol ED, et al. Chest pain of recent onset: assessment and diagnosis(CG95). A step change in the requirement for cardiovascular CT [J]. Clin Radiol, 2017, 72(9): 751-753.
[4] Neumann FJ, Sousa-Uva M, Ahlsson A, et al. 2018 ESC/EACTS Guidelines on myocardial revascularization [J]. EuroIntervention, 2019, 14(14): 1435-1534.
[5] Baumann S, Hirt M, Rott C, et al. Comparison of machine learning computed tomography-based fractional flow reserve and coronary CT angiography-derived plaque characteristics with invasive resting full-cycle ratio [J]. J Clin Med, 2020, 9(3): 714.
[6] Nørgaard BL, Hjort J, Gaur S, et al. Clinical use of coronary CTA-derived FFR for decision-making in stable CAD [J]. JACC Cardiovasc Imaging, 2017, 10(5): 541-550.
[7] Lossnitzer D, Klenantz S, Andre F, et al. Stable patients with suspected myocardial ischemia: comparison of machine-learning computed tomography-based fractional flow reserve and stress perfusion cardiovascular magnetic resonance imaging to detect myocardial ischemia [J]. BMC Cardiovasc Disord, 2022, 22(1): 34.
[8] Tesche C, De Cecco CN, 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.
[9] Danad I, Raijmakers PG, Driessen RS, et al. Comparison of coronary CT angiography, SPECT, PET, and hybrid imaging for diagnosis of ischemic heart disease determined by fractional flow reserve [J]. JAMA Cardiol, 2017, 2(10): 1100-1107.
[10] Dempsey SCH, Lee TY, Samani A, So A. Effect of cardiac phase on cardiac output index derived from dynamic CT myocardial perfusion imaging [J]. Tomography, 2022, 8(2): 1129-1140.
[11] Rangelov BA, Young AL, Jacob J, et al. Thoracic imaging at exacerbation of chronic obstructive pulmonary disease: a systematic review [J]. Int J Chron Obstruct Pulmon Dis, 2020, 15: 1751-1787. doi:10.2147/COPD.S250746.
[12] Dai X, Hou Y, Tang C, et al. Long-term prognostic value of the serial changes of CT-derived fractional flow reserve and perivascular fat attenuation index [J]. Quant Imaging Med Surg, 2022, 12(1): 752-765.
[13] Cho YR, Ann SH, Won KB, et al. Association between insulin resistance, hyperglycemia, and coronary artery disease according to the presence of diabetes [J]. Sci Rep, 2019, 9(1): 6129.
[14] Itu L, Rapaka S, Passerini T, et al. A machine-learning approach for computation of fractional flow reserve from coronary computed tomography [J]. J Appl Physiol(1985), 2016, 121(1): 42-52.
[15] Duguay TM, Tesche C, Vliegenthart R, et al. Coronary computed tomographic angiography-derived fractional flow reserve based on machine learning for risk stratification of non-culprit coronary narrowings in patients with acute coronary syndrome [J]. Am J Cardiol, 2017, 120(8): 1260-1266.
[16] Solecki M, Kruk M, Demkow M, et al. What is the optimal anatomic location for coronary artery pressure measurement at CT-derived FFR? [J]. J Cardiovasc Comput Tomogr, 2017, 11(5): 397-403.
[17] Li Y, Qiu H, Hou Z, 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.
[18] Tang CX, Wang YN, Zhou F, et al. Diagnostic performance of fractional flow reserve derived from coronary CT angiography for detection of lesion-specific ischemia: a multi-center study and meta-analysis [J]. Eur J Radiol, 2019, 116: 90-97. doi: 10.1016/j.ejrad.2019.04.011.
[19] Li Y, Yu M, Dai X, et al. Detection of hemodynamically significant coronary stenosis: CT myocardial perfusion versus machine learning CT fractional flow reserve [J]. Radiology, 2019, 293(2): 305-314.
[20] Tang CX, Liu CY, Lu MJ, et al. CT FFR for ischemia-specific CAD with a new computational fluid dynamics algorithm: A Chinese multicenter study [J]. JACC Cardiovasc Imaging, 2020, 13(4): 980-990.
[21] 杨友常, 殷若涵, 汤晓强, 等. 冠状动脉CT血管造影结合机器学习算法预测心肌缺血的价值研究[J]. 临床放射学杂志, 2023, 42(2): 258-262. YANG Youchang, YIN Ruohan, TANG Xiaoqiang, et al. The value of coronary CT angiography combined with machine learning algorithm in predicting myocardial ischemia [J]. Journal of Clinical Radiology, 2023, 42(2): 258-262.
[22] Hu XH, Zheng WL, Wang D, et al. Accuracy of high-pitch prospectively ECG-triggering CT coronary angiography for assessment of stenosis in 103 patients: comparison with invasive coronary angiography [J]. Clin Radiol, 2012, 67(11): 1083-1088.
[23] Bersi MR, Khosravi R, Wujciak AJ, et al. Differential cell-matrix mechanoadaptations and inflammation drive regional propensities to aortic fibrosis, aneurysm or dissection in hypertension [J]. J R Soc Interface, 2017, 14(136): 20170327. doi: 10.1098/rsif.2017.0327.
[24] van Dijk SJ, Kooiker KB, Napierski NC, et al. Point mutations in the tri-helix bundle of the M-domain of cardiac myosin binding protein-C influence systolic duration and delay cardiac relaxation [J]. J Mol Cell Cardiol, 2018, 119: 116-124. doi: 10.1016/j.yjmcc.2018.05.001.
[25] Casadonte L, Verhoeff BJ, Piek JJ, et al. Influence of increased heart rate and aortic pressure on resting indices of functional coronary stenosis severity [J]. Basic Res Cardiol, 2017, 112(6): 61. doi: 10.1007/s00395-017-0651-0.
[26] de Waard GA, Broyd CJ, Cook CM, et al. Diastolic-systolic velocity ratio to detect coronary stenoses under physiological resting conditions: a mechanistic study [J]. Open Heart, 2019, 6(1): e000968. doi: 10.1136/openhrt-2018-000968.
[27] Shibutani H, Fujii K, Hashimoto K, et al. Additional decrease in fractional flow reserve in the left anterior descending artery after hand-grip exercise during pharmacological coronary hyperemia [J]. Heart Vessels, 2021, 36(11): 1611-1616.
[28] Adiputra Y, Chen SL. Clinical relevance of coronary fractional flow reserve: Art-of-state [J]. Chin Med J(Engl), 2015, 128(10): 1399-1406.
[29] Gu H, Gao Y, Wang H, et al. Difference of coronary stenosis severity between systolic and diastolic phases in quantitative CT angiography [J]. J Cardiovasc Comput Tomogr, 2017, 11(2): 105-110.
[30] Kulshreshtha A, Singh S, Ahmad M, et al. Simvastatin mediates inhibition of exosome synthesis, localization and secretion via multicomponent interventions [J]. Sci Rep, 2019, 9(1): 16373. doi: 10.1038/s41598-019-52765-7.
[31] Malota Z, Glowacki J, Sadowski W, et al. Numerical analysis of the impact of flow rate, heart rate, vessel geometry, and degree of stenosis on coronary hemodynamic indices [J]. BMC Cardiovasc Disord, 2018, 18(1): 132.
[32] Malota Z, Glowacki J, Sadowski W, Kostur M. Numerical analysis of the impact of flow rate, heart rate, vessel geometry, and degree of stenosis on coronary hemodynamic indices [J]. BMC Cardiovasc Disord, 2018, 18(1): 132. doi: 10.1186/s12872-018-0865-6.
[33] Christiansen MK, Jensen JM, Nørgaard BL, et al. Coronary plaque burden and adverse plaque characteristics are increased in healthy relatives of patients with early onset coronary artery disease [J]. JACC Cardiovasc Imaging, 2017, 10(10 Pt A): 1128-1135.
[34] Bauersachs R, Zeymer U, Brière JB, et al. Burden of coronary artery disease and peripheral artery disease: a literature review [J]. Cardiovasc Ther, 2019, 2019: 8295054. doi: 10.1155/2019/8295054.
[35] Nadjiri J, Hausleiter J, Jähnichen C, et al. Incremental prognostic value of quantitative plaque assessment in coronary CT angiography during 5 years of follow up [J]. J Cardiovasc Comput Tomogr, 2016, 10(2): 97-104.
[36] Baumann S, Hirt M, Rott C, et al. Comparison of machine learning computed tomography-based fractional flow reserve and coronary CT angiography-derived plaque characteristics with invasive resting full-cycle ratio [J]. J Clin Med, 2020, 9(3): 714. doi: 10.3390/jcm9030714.
[37] Ichikawa K, Miyoshi T, Osawa K, et al. High pericoronary adipose tissue attenuation on computed tomography angiography predicts cardiovascular events in patients with type 2 diabetes mellitus: post-hoc analysis from a prospective cohort study [J]. Cardiovasc Diabetol, 2022, 21(1): 44. doi: 10.1186/s12933-022-01478-9.
[38] Kawasaki T, Kidoh M, Kido T, et al. Evaluation of significant coronary artery disease based on CT fractional flow reserve and plaque characteristics using random forest analysis in machine learning [J]. Acad Radiol, 2020, 27(12): 1700-1708.
[39] Lee JM, Choi G, Koo BK, et al. Identification of high-risk plaques destined to cause acute coronary syndrome using coronary computed tomographic angiography and computational fluid dynamics [J]. JACC Cardiovasc Imaging, 2019, 12(6): 1032-1043.
[40] Liu T, Ji H, Jian X, Wang W, Fan Z. Novel nomogram for predicting coronary vulnerable plaque risk in patients with coronary artery disease [J]. Biomark Med, 2022, 16(16): 1139-1149.
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