Korean Journal of Radiology

The Korean Society of Radiology (대한영상의학회)
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CT Fractional Flow Reserve for the Diagnosis of Myocardial Bridging-Related Ischemia: A Study Using Dynamic CT Myocardial Perfusion Imaging as a Reference Standard

  • Yarong Yu (Institute of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital) ;
  • Lihua Yu (Institute of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital) ;
  • Xu Dai (Institute of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital) ;
  • Jiayin Zhang (Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine)
  • Received : 2021.01.13
  • Accepted : 2021.07.26
  • Published : 2021.12.01
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Abstract

Objective: To investigate the diagnostic performance of CT fractional flow reserve (CT-FFR) for myocardial bridging-related ischemia using dynamic CT myocardial perfusion imaging (CT-MPI) as a reference standard. Materials and Methods: Dynamic CT-MPI and coronary CT angiography (CCTA) data obtained from 498 symptomatic patients were retrospectively reviewed. Seventy-five patients (mean age ± standard deviation, 62.7 ± 13.2 years; 48 males) who showed myocardial bridging in the left anterior descending artery without concomitant obstructive stenosis on the imaging were included. The change in CT-FFR across myocardial bridging (ΔCT-FFR, defined as the difference in CT-FFR values between the proximal and distal ends of the myocardial bridging) in different cardiac phases, as well as other anatomical parameters, were measured to evaluate their performance for diagnosing myocardial bridging-related myocardial ischemia using dynamic CT-MPI as the reference standard (myocardial blood flow < 100 mL/100 mL/min or myocardial blood flow ratio ≤ 0.8). Results: ΔCT-FFRsystolic (ΔCT-FFR calculated in the best systolic phase) was higher in patients with vs. without myocardial bridging-related myocardial ischemia (median [interquartile range], 0.12 [0.08-0.17] vs. 0.04 [0.01-0.07], p < 0.001), while CT-FFRsystolic (CT-FFR distal to the myocardial bridging calculated in the best systolic phase) was lower (0.85 [0.81-0.89] vs. 0.91 [0.88-0.96], p = 0.043). In contrast, ΔCT-FFRdiastolic (ΔCT-FFR calculated in the best diastolic phase) and CT-FFRdiastolic (CT-FFR distal to the myocardial bridging calculated in the best diastolic phase) did not differ significantly. Receiver operating characteristic curve analysis showed that ΔCT-FFRsystolic had largest area under the curve (0.822; 95% confidence interval, 0.717-0.901) for identifying myocardial bridging-related ischemia. ΔCT-FFRsystolic had the highest sensitivity (91.7%) and negative predictive value (NPV) (97.8%). ΔCT-FFRdiastolic had the highest specificity (85.7%) for diagnosing myocardial bridging-related ischemia. The positive predictive values of all CT-related parameters were low. Conclusion: ΔCT-FFRsystolic reliably excluded myocardial bridging-related ischemia with high sensitivity and NPV. Myocardial bridging showing positive CT-FFR results requires further evaluation.

Keywords

Acknowledgement

This study is supported by Medical Guidance Scientific Research Support Project of Shanghai Science and Technology Commission (Grant No. 19411965100) and Shanghai Municipal Education Commission-Gaofeng Clinical Medicine Grant Support (Grant No. 20161428).

References

  1. Kim PJ, Hur G, Kim SY, Namgung J, Hong SW, Kim YH, et al. Frequency of myocardial bridges and dynamic compression of epicardial coronary arteries: a comparison between computed tomography and invasive coronary angiography. Circulation 2009;119:1408-1416 
  2. Leschka S, Koepfli P, Husmann L, Plass A, Vachenauer R, Gaemperli O, et al. Myocardial bridging: depiction rate and morphology at CT coronary angiography--comparison with conventional coronary angiography. Radiology 2008;246:754-762 
  3. Yu M, Zhang Y, Li Y, Li M, Li W, Zhang J. Assessment of myocardial bridge by cardiac CT: intracoronary transluminal attenuation gradient derived from diastolic phase predicts systolic compression. Korean J Radiol 2017;18:655-663 
  4. Li Y, Yu M, Zhang J, Li M, Lu Z, Wei M. Non-invasive imaging of myocardial bridge by coronary computed tomography angiography: the value of transluminal attenuation gradient to predict significant dynamic compression. Eur Radiol 2017;27:1971-1979 
  5. Mohlenkamp S, Hort W, Ge J, Erbel R. Update on myocardial bridging. Circulation 2002;106:2616-2622 
  6. Ural E, Bildirici U, Celikyurt U, Kilic T, Sahin T, Acar E, et al. Long-term prognosis of non-interventionally followed patients with isolated myocardial bridge and severe systolic compression of the left anterior descending coronary artery. Clin Cardiol 2009;32:454-457 
  7. Kodama K, Morioka N, Hara Y, Shigematsu Y, Hamada M, Hiwada K. Coronary vasospasm at the site of myocardial bridge--report of two cases. Angiology 1998;49:659-663 
  8. Tio RA, Van Gelder IC, Boonstra PW, Crijns HJ. Myocardial bridging in a survivor of sudden cardiac near-death: role of intracoronary doppler flow measurements and angiography during dobutamine stress in the clinical evaluation. Heart 1997;77:280-282 
  9. Tarantini G, Migliore F, Cademartiri F, Fraccaro C, Iliceto S. Left anterior descending artery myocardial bridging: a clinical approach. J Am Coll Cardiol 2016;68:2887-2899 
  10. Zhou F, Wang YN, Schoepf UJ, Tesche C, Tang CX, Zhou CS, et al. Diagnostic performance of machine learning based CT-FFR in detecting ischemia in myocardial bridging and concomitant proximal atherosclerotic disease. Can J Cardiol 2019;35:1523-1533 
  11. Bamberg F, Marcus RP, Becker A, Hildebrandt K, Bauner K, Schwarz F, et al. Dynamic myocardial CT perfusion imaging for evaluation of myocardial ischemia as determined by MR imaging. JACC Cardiovasc Imaging 2014;7:267-277 
  12. Ho KT, Chua KC, Klotz E, Panknin C. Stress and rest dynamic myocardial perfusion imaging by evaluation of complete time-attenuation curves with dual-source CT. JACC Cardiovasc Imaging 2010;3:811-820 
  13. Yang J, Dou G, He B, Jin Q, Chen Z, Jing J, et al. Stress myocardial blood flow ratio by dynamic CT perfusion identifies hemodynamically significant CAD. JACC Cardiovasc Imaging 2020;13:966-976 
  14. Li Y, Yu M, Dai X, Lu Z, Shen C, Wang Y, et al. Detection of hemodynamically significant coronary stenosis: CT myocardial perfusion versus machine learning CT fractional flow reserve. Radiology 2019;293:305-314 
  15. Li Y, Dai X, Lu Z, Shen C, Zhang J. Diagnostic performance of quantitative, semi-quantitative, and visual analysis of dynamic CT myocardial perfusion imaging: a validation study with invasive fractional flow reserve. Eur Radiol 2021;31:525-534 
  16. Schwarz F, Hinkel R, Baloch E, Marcus RP, Hildebrandt K, Sandner TA, et al. Myocardial CT perfusion imaging in a large animal model: comparison of dynamic versus single-phase acquisitions. JACC Cardiovasc Imaging 2013;6:1229-1238 
  17. Hubbard L, Lipinski J, Ziemer B, Malkasian S, Sadeghi B, Javan H, et al. Comprehensive assessment of coronary artery disease by using first-pass analysis dynamic CT perfusion: validation in a swine model. Radiology 2018;286:93-102 
  18. Itu L, Rapaka S, Passerini T, Georgescu B, Schwemmer C, Schoebinger M, et al. A machine-learning approach for computation of fractional flow reserve from coronary computed tomography. J Appl Physiol (1985) 2016;121:42-52 
  19. Yu M, Dai X, Yu L, Lu Z, Shen C, Tao X, et al. Hemodynamic change of coronary atherosclerotic plaque after statin treatment: a serial follow-up study by computed tomography-derived fractional flow reserve. J Am Heart Assoc 2020;9:e015772 
  20. Bamberg F, Klotz E, Flohr T, Becker A, Becker CR, Schmidt B, et al. Dynamic myocardial stress perfusion imaging using fast dual-source CT with alternating table positions: initial experience. Eur Radiol 2010;20:1168-1173 
  21. Cerqueira MD, Weissman NJ, Dilsizian V, Jacobs AK, Kaul S, Laskey WK, et al. Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart. A statement for healthcare professionals from the Cardiac Imaging Committee of the Council on Clinical Cardiology of the American Heart Association. Circulation 2002;105:539-542 
  22. Tonino PA, Fearon WF, De Bruyne B, Oldroyd KG, Leesar MA, Ver Lee PN, et al. Angiographic versus functional severity of coronary artery stenoses in the FAME study fractional flow reserve versus angiography in multivessel evaluation. J Am Coll Cardiol 2010;55:2816-2821 
  23. Schwarz ER, Gupta R, Haager PK, vom Dahl J, Klues HG, Minartz J, et al. Myocardial bridging in absence of coronary artery disease: proposal of a new classification based on clinical-angiographic data and long-term follow-up. Cardiology 2009;112:13-21 
  24. Hakeem A, Cilingiroglu M, Leesar MA. Hemodynamic and intravascular ultrasound assessment of myocardial bridging: fractional flow reserve paradox with dobutamine versus adenosine. Catheter Cardiovasc Interv 2010;75:229-236 
  25. Escaned J, Cortes J, Flores A, Goicolea J, Alfonso F, Hernandez R, et al. Importance of diastolic fractional flow reserve and dobutamine challenge in physiologic assessment of myocardial bridging. J Am Coll Cardiol 2003;42:226-233 
  26. Sen S, Asrress KN, Nijjer S, Petraco R, Malik IS, Foale RA, et al. Diagnostic classification of the instantaneous wave-free ratio is equivalent to fractional flow reserve and is not improved with adenosine administration. Results of CLARIFY (Classification Accuracy of Pressure-Only Ratios Against Indices Using Flow Study). J Am Coll Cardiol 2013;61:1409-1420 
  27. Bourassa MG, Butnaru A, Lesperance J, Tardif JC. Symptomatic myocardial bridges: overview of ischemic mechanisms and current diagnostic and treatment strategies. J Am Coll Cardiol 2003;41:351-359 
  28. Gould KL, Johnson NP. Imaging coronary blood flow in AS: let the data talk, again. J Am Coll Cardiol 2016;67:1423-1426 
  29. Gould KL, Johnson NP. Myocardial bridges: lessons in clinical coronary pathophysiology. JACC Cardiovasc Imaging 2015;8:705-709 
  30. Li Z, Zhang J, Xu L, Yang W, Li G, Ding D, et al. Diagnostic accuracy of a fast computational approach to derive fractional flow reserve from coronary CT angiography. JACC Cardiovasc Imaging 2020;13:172-175 
  31. Yu M, Lu Z, Shen C, Yan J, Wang Y, Lu B, et al. The best predictor of ischemic coronary stenosis: subtended myocardial volume, machine learning-based FFRCT, or high-risk plaque features? Eur Radiol 2019;29:3647-3657 
  32. Uusitalo V, Saraste A, Pietila M, Kajander S, Bax JJ, Knuuti J. The functional effects of intramural course of coronary arteries and its relation to coronary atherosclerosis. JACC Cardiovasc Imaging 2015;8:697-704