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Fractional flow reserve derived from CCTA may have a prognostic role in myocardial bridging

  • Cardiac
  • Published:
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Abstract

Purpose

To evaluate the feasibility of fractional flow reserve (cFFR) derivation from coronary CT angiography (CCTA) in patients with myocardial bridging (MB), its relationship with MB anatomical features, and clinical relevance.

Methods

This retrospective study included 120 patients with MB of the left anterior descending artery (LAD) and 41 controls. MB location, length, depth, muscle index, instance, and stenosis rate were measured. cFFR values were compared between superficial MB (≤ 2 mm), deep MB (> 2 mm), and control groups. Factors associated with abnormal cFFR values (≤ 0.80) were analyzed.

Results

MB patients demonstrated lower cFFR values in MB and distal segments than controls (all p < 0.05). A significant cFFR difference was only found in the MB segment during systole between superficial (0.94, 0.90–0.96) and deep MB (0.91, 0.83–0.95) (p = 0.018). Abnormal cFFR values were found in 69 (57.5%) MB patients (29 [49.2%] superficial vs. 40 [65.6%] deep; p = 0.069). MB length (OR = 1.06, 95% CI 1.03–1.10; p = 0.001) and systolic stenosis (OR = 1.04, 95% CI 1.01–1.07; p = 0.021) were the main predictors for abnormal cFFR, with an area under the curve of 0.774 (95% CI 0.689–0.858; p < 0.001). MB patients with abnormal cFFR reported more typical angina (18.8% vs 3.9%, p = 0.023) than patients with normal values.

Conclusion

MB patients showed lower cFFR values than controls. Abnormal cFFR values have a positive association with symptoms of typical angina. MB length and systolic stenosis demonstrate moderate predictive value for an abnormal cFFR value.

Key Points

• MB patients showed lower cFFR values than controls.

• Abnormal cFFR values have a positive association with typical angina symptoms.

• MB length and systolic stenosis demonstrate moderate predictive value for an abnormal cFFR value .

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Abbreviations

AUC:

Area under the curve

CAD:

Coronary artery disease

CCTA:

Coronary computed tomography angiography

CFD:

Computational fluid dynamics

cFFR:

CCTA-derived fractional flow reserve

DML:

Deep-machine-learning

LAD:

Left anterior descending coronary artery

MB:

Myocardial bridging

OR:

Odds ratio

ROC:

Receiver operating characteristic

References

  1. Forsdahl SH, Rogers IS, Schnittger I et al (2017) Myocardial bridges on coronary computed tomography angiography—correlation with intravascular ultrasound and fractional flow reserve. Circ J 81:1894–1900

    Article  PubMed  Google Scholar 

  2. Nakanishi R, Rajani R, Ishikawa Y, Ishii T, Berman DS (2012) Myocardial bridging on coronary CTA: an innocent bystander or a culprit in myocardial infarction? J Cardiovasc Comput Tomogr 6:3–13

    Article  PubMed  Google Scholar 

  3. Dimitriu-Leen AC, van Rosendael AR, Smit JM et al (2017) Long-term prognosis of patients with intramural course of coronary arteries assessed with CT angiography. JACC Cardiovasc Imaging 10:1451–1458

    Article  PubMed  Google Scholar 

  4. Rihal C, Ammash N (2017) Intramural course of coronary arteries: a bridge too far no more. JACC Cardiovasc Imaging 10:1459–1460

    Article  PubMed  Google Scholar 

  5. Rubinshtein R, Gaspar T, Lewis BS, Prasad A, Peled N, Halon DA (2013) Long-term prognosis and outcome in patients with a chest pain syndrome and myocardial bridging: a 64-slice coronary computed tomography angiography study. Eur Heart J Cardiovasc Imaging 14:579–585

    Article  PubMed  Google Scholar 

  6. Li Y, Yu M, Zhang J, Li M, Lu Z, Wei M (2017) Non-invasive imaging of myocardial bridge by coronary computed tomography angiography: the value of transluminal attenuation gradient to predict significant dynamic compression. Eur Radiol 27:1971–1979

    Article  PubMed  Google Scholar 

  7. Corban MT, Hung OY, Eshtehardi P et al (2014) Myocardial bridging: contemporary understanding of pathophysiology with implications for diagnostic and therapeutic strategies. J Am Coll Cardiol 63:2346–2355

    Article  PubMed  PubMed Central  Google Scholar 

  8. Tarantini G, Migliore F, Cademartiri F, Fraccaro C, Iliceto S (2016) Left anterior descending artery myocardial bridging: a clinical approach. J Am Coll Cardiol 68:2887–2899

    Article  PubMed  Google Scholar 

  9. Wang Y, Lv B, Chen J et al (2013) Intramural coronary arterial course is associated with coronary arterial stenosis and prognosis of major cardiac events. Arterioscler Thromb Vasc Biol 33:439–444

    Article  CAS  PubMed  Google Scholar 

  10. Ishikawa Y, Akasaka Y, Suzuki K et al (2009) Anatomic properties of myocardial bridge predisposing to myocardial infarction. Circulation 120:376–383

    Article  Google Scholar 

  11. Leschka S, Koepfli P, Husmann L et al (2008) Myocardial bridging: depiction rate and morphology at CT coronary angiography—comparison with conventional coronary angiography. Radiology 246:754–762

    Article  PubMed  Google Scholar 

  12. Kim PJ, Hur G, Kim SY et al (2009) Frequency of myocardial bridges and dynamic compression of epicardial coronary arteries: a comparison between computed tomography and invasive coronary angiography. Circulation 119:1408–1416

    Article  Google Scholar 

  13. Konen E, Goitein O, Sternik L, Eshet Y, Shemesh J, Di Segni E (2007) The prevalence and anatomical patterns of intramuscular coronary arteries: a coronary computed tomography angiographic study. J Am Coll Cardiol 49:587–593

    Article  PubMed  Google Scholar 

  14. Gould KL, Johnson NP (2015) Myocardial bridges: lessons in clinical coronary pathophysiology. JACC Cardiovasc Imaging 8:705–709

    Article  PubMed  Google Scholar 

  15. Tarantini G, Barioli A, Nai Fovino L et al (2018) Unmasking myocardial bridge–related ischemia by intracoronary functional evaluation. Circ Cardiovasc Interv 11:e006247

    Article  PubMed  Google Scholar 

  16. Kurata A, Coenen A, Lubbers MM et al (2017) The effect of blood pressure on non-invasive fractional flow reserve derived from coronary computed tomography angiography. Eur Radiol 27:1416–1423

    Article  PubMed  Google Scholar 

  17. Lee HJ, Hong YJ, Kim HY et al (2012) Anomalous origin of the right coronary artery from the left coronary sinus with an interarterial course: subtypes and clinical importance. Radiology 262:101–108

    Article  PubMed  Google Scholar 

  18. Liu SH, Yang Q, Chen JH, Wang XM, Wang M, Liu C (2010) Myocardial bridging on dual-source computed tomography: degree of systolic compression of mural coronary artery correlating with length and depth of the myocardial bridge. Clin Imaging 34:83–88

    Article  PubMed  Google Scholar 

  19. Zhang LJ, Wang Y, Schoepf UJ et al (2016) Image quality, radiation dose, and diagnostic accuracy of prospectively ECG-triggered high-pitch coronary CT angiography at 70 kVp in a clinical setting: comparison with invasive coronary angiography. Eur Radiol 26:797–806

    Article  PubMed  Google Scholar 

  20. Duguay TM, Tesche C, Vliegenthart R et al (2017) 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. Am J Cardiol 120:1260–1266

    Article  PubMed  Google Scholar 

  21. Solecki M, Kruk M, Demkow M et al (2017) What is the optimal anatomic location for coronary artery pressure measurement at CT-derived FFR? J Cardiovasc Comput Tomogr 11:397–403

    Article  PubMed  Google Scholar 

  22. Itu L, Rapaka S, Passerini T et al (2016) A machine-learning approach for computation of fractional flow reserve from coronary computed tomography. J Appl Physiol (1985) 121:42–52

    Article  Google Scholar 

  23. Tesche C, De Cecco CN, Baumann S et al (2018) Coronary CT angiography-derived fractional flow reserve: machine learning algorithm versus computational fluid dynamics modeling. Radiology 288:64–72

    Article  PubMed  Google Scholar 

  24. Kruk M, Wardziak Ł, Demkow M et al (2016) Workstation-based calculation of CTA-based FFR for intermediate stenosis. JACC Cardiovasc Imaging 9:690–699

    Article  PubMed  Google Scholar 

  25. Tonino PA, De Bruyne B, Pijls NH et al (2009) Fractional flow reserve versus angiography for guiding percutaneous coronary intervention. N Engl J Med 360:213–224

    Article  CAS  Google Scholar 

  26. Collet C, Miyazaki Y, Ryan N et al (2018) Fractional flow reserve derived from computed tomographic angiography in patients with multivessel CAD. J Am Coll Cardiol 71:2756–2769

    Article  PubMed  Google Scholar 

  27. Tesche C, De Cecco CN, Albrecht MH et al (2017) Coronary CT angiography-derived fractional flow reserve. Radiology 285:17–33

    Article  PubMed  Google Scholar 

  28. DeLong ER, DeLong DM, Clarke-Pearson DL (1998) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44:837–845

    Article  Google Scholar 

  29. Escaned J, Cortés J, Flores A et al (2003) Importance of diastolic fractional flow reserve and dobutamine challenge in physiologic assessment of myocardial bridging. J Am Coll Cardiol 42:226–233

    Article  PubMed  Google Scholar 

  30. Halliburton SS, Abbara S, Chen MY et al (2011) SCCT guidelines on radiation dose and dose optimization strategies in cardiovascular CT. J Cardiovasc Comput Tomogr 5:198–224

    Article  PubMed  PubMed Central  Google Scholar 

  31. Agrawal H, Molossi S, Alam M et al (2017) Anomalous coronary arteries and myocardial bridges: risk stratification in children using novel cardiac catheterization techniques. Pediatr Cardiol 38:624–630

    Article  PubMed  Google Scholar 

  32. Takx RAP, Celeng C, Schoepf UJ (2018) CT myocardial perfusion imaging: ready for prime time? Eur Radiol 28:1253–1256

    Article  PubMed  Google Scholar 

  33. Dey D, Gaur S, Ovrehus KA et al (2018) Integrated prediction of lesion-specific ischaemia from quantitative coronary CT angiography using machine learning: a multicentre study. Eur Radiol 28:2655–2664

    Article  PubMed  PubMed Central  Google Scholar 

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Funding

This study has received funding by The National Key Research and Development Program of China (2017YFC0113400 for L.J.Z.).

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Authors and Affiliations

  1. Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China

    Fan Zhou, Chun Xiang Tang, U. Joseph Schoepf, Chang Sheng Zhou, Meng Jie Lu, Guang Ming Lu & Long Jiang Zhang

  2. Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, MSC 226, 25 Courtenay Dr, Charleston, SC, 29425, USA

    U. Joseph Schoepf, Christian Tesche, Maximilian J. Bauer & Brian E. Jacobs

  3. Department of Cardiology and Intensive Care Medicine, Heart Center Munich-Bogenhausen, Munich, Germany

    Christian Tesche

  4. Siemens Healthcare Ltd., No. 278 ZhouZhu Road, Shanghai, 201318, China

    Jing Yan

Authors
  1. Fan Zhou
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  2. Chun Xiang Tang
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  3. U. Joseph Schoepf
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  4. Christian Tesche
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  5. Maximilian J. Bauer
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  6. Brian E. Jacobs
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  7. Chang Sheng Zhou
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  8. Jing Yan
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  9. Meng Jie Lu
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  10. Guang Ming Lu
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  11. Long Jiang Zhang
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Corresponding author

Correspondence to Long Jiang Zhang.

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Guarantor

The scientific guarantor of this publication is Long Jiang Zhang.

Conflict of interest

UJS is a consultant for and/or receives research support from Astellas, Bayer, General Electric, Guerbet, HeartFlow, and Siemens Healthineers. The other authors have no conflicts of interest to disclose.

Statistics and biometry

Meng Jie Lu has significant statistical expertise.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• observational

• performed at one institution

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Zhou, F., Tang, C.X., Schoepf, U.J. et al. Fractional flow reserve derived from CCTA may have a prognostic role in myocardial bridging. Eur Radiol 29, 3017–3026 (2019). https://doi.org/10.1007/s00330-018-5811-6

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  • DOI: https://doi.org/10.1007/s00330-018-5811-6

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