Advertisement

The comparison of short-term prognostic value of T1 mapping with feature tracking by cardiovascular magnetic resonance in patients with severe dilated cardiomyopathy

  • Rui Chen
  • Jingjing Wang
  • Zhicheng Du
  • Yu-Hsiang Juan
  • Carmen Wing-Sze Chan
  • Hongwen Fei
  • Jiajun Xie
  • Wanjia Wu
  • Yulei Zhu
  • Liwen Li
  • Jinxiu Meng
  • Shulin Wu
  • Changhong Liang
  • Zhuliang Yu
  • Hui LiuEmail author
Original Paper

Abstract

To evaluate and compare the prognostic value of T1 mapping with feature tracking cardiovascular magnetic resonance (FT-CMR) imaging in patients with severe dilated cardiomyopathy (DCM) during short-term follow-up. A total of 46 patients with severe DCM (LVEF < 35%) underwent 3.0-T CMR with T1 mapping and FT-CMR analysis. The study end-point was defined as a combination of cardiac death, heart transplantation, and hospitalization due to cardiovascular events. The significance of the risk factors was mainly evaluated by univariate and multivariate Cox model analyses. During the median follow-up of 13 months (interquartile range 7–17 months), two patients died of heart failure, one received a heart transplantation, and six were hospitalized for heart failure. In the univariate analysis, extracellular volume fraction (ECV) showed significant predictive association with cardiovascular events (hazard ratio [HR] 1.35; 95% confidence interval [CI] 1.13–1.62; P = 0.001). No strain parameters in FT-CMR differed significantly between patients with or without events (all P > 0.05). In the multivariate analyses, ECV was the sole independent predictor of cardiovascular events (HR, 1.48; 95% CI 1.13–1.94; P = 0.005). The area under the curve of the time-dependent receiver operating characteristic in leave-one-out cross-validation (all > 0.70) further confirmed the predictive significance of ECV. In patients with severe DCM, ECV was not only a strong independent predictor of adverse cardiovascular events but also provided prognostic value prior to strain parameters of the FT-CMR in the short term.

Keywords

Cardiovascular magnetic resonance Short-term prognostic values T1 mapping Feature tracking Severe dilated cardiomyopathy 

Abbreviations

DCM

Dilated cardiomyopathy

CMR

Cardiovascular magnetic resonance

LVEF

Left ventricular ejection fraction

LVEDVI

Left ventricular end-diastolic volume index

LGE

Late gadolinium enhancement

ECV

Extracellular volume fraction

FT-CMR

Feature tracking cardiovascular magnetic resonance

STE

Speckle tracking echocardiography

GLS

Global longitudinal strain

GCS

Global circumferential strain

IQR

Interquartile range

ROI

Region of interest

ROC

Receiver operating characteristic

AUC

Area under the curve

Notes

Acknowledgements

The study received funding from the National Natural Science Foundation of China (No. 81771799), Guangdong Provincial Science and Technology Planning Project (No. 2014A020212676), and Science and Technology Program of Guangzhou, China (No. 201707010306).

Author contributions

RC drafted the manuscript; JW, JX and WW acquired the data; YHJ, CWSC, HF, LL, JM, SW, CL revised the manuscript; RC, ZD, YZ and ZY provided the analysis method. HL provided the conception and design of the study.

Compliance with ethical standards

Conflict of interest

The authors declare no conflicts of interest.

Supplementary material

10554_2018_1444_MOESM1_ESM.docx (1.8 mb)
Supplementary material 1 (DOCX 1845 KB)

References

  1. 1.
    Jefferies JL, Towbin JA (2010) Dilated cardiomyopathy. Lancet 375:752–762.  https://doi.org/10.1016/s0140-6736(09)62023-7 CrossRefGoogle Scholar
  2. 2.
    Felker GM, Thompson RE, Hare JM et al (2000) Underlying causes and long-term survival in patients with initially unexplained cardiomyopathy. N Engl J Med 342(15):1077–1084.  https://doi.org/10.1056/NEJM200004133421502 CrossRefGoogle Scholar
  3. 3.
    McMurray JJ, Adamopoulos S, Anker SD, Auricchio A, Bohm M, Dickstein K et al (2012) ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure 2012: the Task Force for the Diagnosis and Treatment of Acute and Chronic Heart Failure 2012 of the European Society of Cardiology. Developed in collaboration with the Heart Failure Association (HFA) of the ESC. Eur Heart J 33:1787–1847.  https://doi.org/10.1093/eurjhf/hfs105 CrossRefGoogle Scholar
  4. 4.
    Dec GW, Fuster V (1994) Idiopathic dilated cardiomyopathy. N Engl J Med 331:1564–1575.  https://doi.org/10.1056/NEJM199412083312307 CrossRefGoogle Scholar
  5. 5.
    Gulati A, Jabbour A, Ismail TF, Guha K, Khwaja J, Raza S et al (2013) Association of fibrosis with mortality and sudden cardiac death in patients with nonischemic dilated cardiomyopathy. JAMA 309:896–908.  https://doi.org/10.1001/jama.2013.1363 CrossRefGoogle Scholar
  6. 6.
    Halliday BP, Cleland JGF, Goldberger JJ, Prasad SK (2017) Personalizing risk stratification for sudden death in dilated cardiomyopathy: the past, present, and future. Circulation 136:215–231.  https://doi.org/10.1161/CIRCULATIONAHA.116.027134 CrossRefGoogle Scholar
  7. 7.
    Taylor RJ, Moody WE, Umar F, Edwards NC, Taylor TJ, Stegemann B et al (2015) Myocardial strain measurement with feature-tracking cardiovascular magnetic resonance: normal values. Eur Heart J 16:871–881.  https://doi.org/10.1093/ehjci/jev006 Google Scholar
  8. 8.
    Nagueh SF, Smiseth OA, Appleton CP, Byrd BF 3rd, Dokainish H, Edvardsen T et al (2016) Recommendations for the evaluation of left ventricular diastolic function by echocardiography: an update from the American Society of Echocardiography and the European Association of cardiovascular imaging. Eur Heart J 17:1321–1360.  https://doi.org/10.1093/ehjci/jew082 Google Scholar
  9. 9.
    Buss SJ, Breuninger K, Lehrke S, Voss A, Galuschky C, Lossnitzer D et al (2015) Assessment of myocardial deformation with cardiac magnetic resonance strain imaging improves risk stratification in patients with dilated cardiomyopathy. Eur Heart J 16:307–315.  https://doi.org/10.1093/ehjci/jeu181 Google Scholar
  10. 10.
    Park SM, Kim YH, Ahn CM, Hong SJ, Lim DS, Shim WJ (2011) Relationship between ultrasonic tissue characterization and myocardial deformation for prediction of left ventricular reverse remodelling in non-ischaemic dilated cardiomyopathy. Eur J Echocardiogr 12:887–894.  https://doi.org/10.1093/ejechocard/jer177 CrossRefGoogle Scholar
  11. 11.
    Moon JCMD, Kellman P, Piechnik SK, Robson MD, Ugander M, Gatehouse PD, Arai AE, Friedrich MG, Neubauer S, Schulz-Menger J, Schelbert EB (2013) Myocardial T1 mapping and extracellular volume quantification: a society for Cardiovascular Magnetic Resonance (SCMR) and CMR Working Group of the European Society of Cardiology consensus statement. J Cardiovasc Magn Reson 15:92.  https://doi.org/10.1186/1532-429X-15-92 CrossRefGoogle Scholar
  12. 12.
    aus dem Siepen F, Buss SJ, Messroghli D, Andre F, Lossnitzer D, Seitz S, Keller M et al (2014) T1 mapping in dilated cardiomyopathy with cardiac magnetic resonance: quantification of diffuse myocardial fibrosis and comparison with endomyocardial biopsy. Eur Heart J 16:210–216.  https://doi.org/10.1093/ehjci/jeu183 Google Scholar
  13. 13.
    Puntmann VO, Carr-White G, Jabbour A, Yu CY, Gebker R, Kelle S et al (2016) T1-mapping and outcome in nonischemic cardiomyopathy: all-cause mortality and heart failure. JACC Cardiovasc Imaging 9:40–50.  https://doi.org/10.1016/j.jcmg.2015.12.001 CrossRefGoogle Scholar
  14. 14.
    Maceira A, Prasad S, Khan M, Pennell D (2006) Normalized left ventricular systolic and diastolic function by steady state free precession cardiovascular magnetic resonance. J Cardiovasc Magn Reson 8:417–426.  https://doi.org/10.1080/10976640600572889 CrossRefGoogle Scholar
  15. 15.
    Heagerty PJ, Lumley T, Pepe MS (2000) Time-dependent ROC curves for censored survival data and a diagnostic marker. Biometrics 56:337–344.  https://doi.org/10.1111/j.0006-341X.2000.00337.x CrossRefGoogle Scholar
  16. 16.
    Wong TC, Piehler K, Meier CG, Testa SM, Klock AM, Aneizi AA et al (2012) Association between extracellular matrix expansion quantified by cardiovascular magnetic resonance and short-term mortality. Circulation 126:1206–1216.  https://doi.org/10.1161/circulationaha.111.089409 CrossRefGoogle Scholar
  17. 17.
    Barison A, Grigoratos C, Todiere G, Aquaro GD (2015) Myocardial interstitial remodelling in non-ischaemic dilated cardiomyopathy: insights from cardiovascular magnetic resonance. Heart Fail Rev 20:731–749.  https://doi.org/10.1007/s10741-015-9509-4 CrossRefGoogle Scholar
  18. 18.
    Miller CA, Naish JH, Bishop P, Coutts G, Clark D, Zhao S et al (2013) Comprehensive validation of cardiovascular magnetic resonance techniques for the assessment of myocardial extracellular volume. Circ Cardiovasc Imaging 6:373–383.  https://doi.org/10.1161/CIRCIMAGING.112.000192 CrossRefGoogle Scholar
  19. 19.
    Hong YJ, Park CH, Kim YJ, Hur J, Lee H-J, Hong SR et al (2015) Extracellular volume fraction in dilated cardiomyopathy patients without obvious late gadolinium enhancement: comparison with healthy control subjects. Int J Cardiovasc Imaging 31:115–122.  https://doi.org/10.1007/s10554-015-0595-0 CrossRefGoogle Scholar
  20. 20.
    Kawel NNM, Zavodni A, Jones J, Liu S, Sibley CT, Bluemke DA (2012) T1 mapping of the myocardium: intra-individual assessment of the effect of field strength, cardiac cycle and variation by myocardial region. J Cardiovasc Magn Reson Imaging 14:27.  https://doi.org/10.1186/1532-429X-14-27 CrossRefGoogle Scholar
  21. 21.
    Aleksova A, Sabbadini G, Merlo M, Pinamonti B, Barbati G, Zecchin M et al (2009) Natural history of dilated cardiomyopathy: from asymptomatic left ventricular dysfunction to heart failure—a subgroup analysis from the Trieste Cardiomyopathy Registry. J Cardiovasc Med 10:699–705.  https://doi.org/10.2459/JCM.0b013e32832bba35 CrossRefGoogle Scholar
  22. 22.
    Arenja NRJ, Fritz T, Andre F, Siepen F, Mueller-Hennessen MGE, Katus HA, Friedrich MG, Buss SJ (2017) Diagnostic and prognostic value of long-axis strain and myocardial contraction fraction using standard cardiovascular MR imaging in patients with nonischemic dilated cardiomyopathies. Radiology 283:681–691.  https://doi.org/10.1148/radiol.2016161184 CrossRefGoogle Scholar
  23. 23.
    Götte MJW, Germans T, Rüssel IK, Zwanenburg JJM, Marcus JT, van Rossum AC et al (2006) Myocardial strain and torsion quantified by cardiovascular magnetic resonance tissue tagging. J Am Coll Cardiol 48:2002–2011.  https://doi.org/10.1016/j.jacc.2006.07.048 CrossRefGoogle Scholar
  24. 24.
    Claus P, Omar AM, Pedrizzetti G, Sengupta PP, Nagel E (2015) Tissue tracking technology for assessing cardiac mechanics: principles, normal values, and clinical applications. JACC Cardiovasc Imaging 8:1444–1460.  https://doi.org/10.1016/j.jcmg.2015.11.001 CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  • Rui Chen
    • 1
    • 2
  • Jingjing Wang
    • 1
    • 2
  • Zhicheng Du
    • 3
  • Yu-Hsiang Juan
    • 4
  • Carmen Wing-Sze Chan
    • 5
  • Hongwen Fei
    • 6
  • Jiajun Xie
    • 7
  • Wanjia Wu
    • 2
  • Yulei Zhu
    • 1
    • 2
  • Liwen Li
    • 6
  • Jinxiu Meng
    • 6
  • Shulin Wu
    • 6
  • Changhong Liang
    • 1
    • 2
  • Zhuliang Yu
    • 1
    • 8
  • Hui Liu
    • 1
    • 2
    • 6
    Email author
  1. 1.School of MedicineSouth China University of TechnologyGuangzhouChina
  2. 2.Department of Radiology, Guangdong General HospitalGuangdong Academy of Medical SciencesGuangzhouChina
  3. 3.Department of Medical Statistics and Epidemiology, Health Information Research Center, Guangdong Key Laboratory of Medicine, School of Public HealthSun Yat-sen UniversityGuangzhouChina
  4. 4.Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, LinkouChang Gung UniversityTaoyuanTaiwan
  5. 5.Division of Cardiology, Department of Medicine, The University of Hong KongQueen Mary HospitalHong KongChina
  6. 6.Department of Cardiology, Guangdong General HospitalGuangdong Academy of Medical SciencesGuangzhouChina
  7. 7.Department of Radiology, The Second Affiliated HospitalSouth China University of TechnologyGuangzhouChina
  8. 8.College of Automation Science and TechnologySouth China University of TechnologyGuangzhouChina

Personalised recommendations