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A novel cardiac magnetic resonance–based personalized risk stratification model in dilated cardiomyopathy: a prospective study

  • Cardiac
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Abstract

Objectives

To explore individual weight of cardiac magnetic resonance (CMR) metrics to predict mid-term outcomes in patients with dilated cardiomyopathy (DCM), and develop a risk algorithm for mid-term outcome based on CMR biomarkers.

Materials and methods

Patients with DCM who underwent CMR imaging were prospectively enrolled in this study. The primary endpoint was a composite of heart failure (HF) death, sudden cardiac death (SCD), aborted SCD, and heart transplantation.

Results

A total of 407 patients (age 48.1 ± 13.8 years, 331 men) were included in the final analysis. During a median follow-up of 21.7 months, 63 patients reached the primary endpoint. NYHA class III/IV (HR = 2.347 [1.073–5.133], p = 0.033), left ventricular ejection fraction (HR = 0.940 [0.909–0.973], p < 0.001), late gadolinium enhancement (LGE) > 0.9% and ≤ 6.6% (HR = 3.559 [1.020–12.412], p = 0.046), LGE > 6.6% (HR = 6.028 [1.814–20.038], p = 0.003), and mean extracellular volume (ECV) fraction ≥ 32.8% (HR = 5.922 [2.566–13.665], p < 0.001) had a significant prognostic association with the primary endpoints (C-statistic: 0.853 [0.810–0.896]). Competing risk regression analyses showed that patients with mean ECV fraction ≥ 32.8%, LGE ≥ 5.9%, global circumferential strain ≥ − 5.6%, or global longitudinal strain ≥ − 7.3% had significantly shorter event-free survival due to HF death and heart transplantation. Patients with mean ECV fraction ≥ 32.8% and LGE ≥ 5.9% had significantly shorter event-free survival due to SCD or aborted SCD.

Conclusion

ECV fraction may be the best independently risk factor for the mid-term outcomes in patients with DCM, surpassing LVEF and LGE. LGE has a better prognostic value than other CMR metrics for SCD and aborted SCD. The risk stratification model we developed may be a promising non-invasive tool for decision-making and prognosis.

Clinical relevance statement

“One-stop” assessment of cardiac function and myocardial characterization using cardiac magnetic resonance might improve risk stratification of patients with DCM. In this prospective study, we propose a novel risk algorithm in DCM including NYHA functional class, LVEF, LGE, and ECV.

Key Points

• The present study explores individual weight of CMR metrics for predicting mid-term outcomes in dilated cardiomyopathy.

• We have developed a novel risk algorithm for dilated cardiomyopathy that includes cardiac functional class, ejection fraction, late gadolinium enhancement, and extracellular volume fraction.

• Personalized risk model derived by CMR contributes to clinical assessment and individual decision-making.

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Abbreviations

AUC:

Area under the curve

CMR:

Cardiac magnetic resonance

DCM:

Dilated cardiomyopathy

ECV:

Extracellular volume

EF:

Ejection fraction

GCS:

Global circumferential strain

GLS:

Global longitudinal strain

GRS:

Global radial strain

HF:

Heart failure

HRs:

Hazard ratios

ICD:

Implantable cardioverter-defibrillator

IDI:

Integrated discrimination improvement

LGE:

Late gadolinium enhancement

LV:

Left ventricular

MOLLI:

Modified Look–Locker inversion recovery

NRI:

Net reclassification index

NYHA:

New York Heart Association

ROC:

Receiver operating characteristic

SCD:

Sudden cardiac death

References

  1. Puntmann VO, Carr-White G, Jabbour A et al (2016) T1-map** and outcome in nonischemic cardiomyopathy: all-cause mortality and heart failure. JACC Cardiovasc Imaging 9:40–50

    Article  PubMed  Google Scholar 

  2. Li S, Zhou D, Sirajuddin A et al (2022) T1 map** and extracellular volume fraction in dilated cardiomyopathy: a prognosis study. JACC Cardiovasc Imaging 15:578–590

    Article  PubMed  Google Scholar 

  3. 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

    Article  PubMed  PubMed Central  Google Scholar 

  4. Heidenreich PA, Bozkurt B, Aguilar D et al (2022) 2022 AHA/ACC/HFSA guideline for the management of heart failure: a report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. J Am Coll Cardiol 79:e263–e421

    Article  PubMed  Google Scholar 

  5. Køber L, Thune JJ, Nielsen JC et al (2016) Defibrillator implantation in patients with nonischemic systolic heart failure. N Engl J Med 375:1221–30

    Article  PubMed  Google Scholar 

  6. Bardy GH, Lee KL, Mark DB et al (2005) Amiodarone or an implantable cardioverter-defibrillator for congestive heart failure. N Engl J Med 352:225–37

    Article  CAS  PubMed  Google Scholar 

  7. Kadish A, Dyer A, Daubert JP et al (2004) Prophylactic defibrillator implantation in patients with nonischemic dilated cardiomyopathy. N Engl J Med 350:2151–8

    Article  CAS  PubMed  Google Scholar 

  8. Canepa M, Fonseca C, Chioncel O et al (2018) Performance of prognostic risk scores in chronic heart failure patients enrolled in the European Society of Cardiology Heart Failure Long-Term Registry. JACC Heart Fail 6:452–462

    Article  PubMed  Google Scholar 

  9. Pocock SJ, Ariti CA, McMurray JJ et al (2013) Predicting survival in heart failure: a risk score based on 39 372 patients from 30 studies. Eur Heart J 34:1404–13

    Article  PubMed  Google Scholar 

  10. Pocock SJ, Wang D, Pfeffer MA et al (2006) Predictors of mortality and morbidity in patients with chronic heart failure. Eur Heart J 27:65–75

    Article  PubMed  Google Scholar 

  11. Levy WC, Mozaffarian D, Linker DT et al (2006) The Seattle Heart Failure Model: prediction of survival in heart failure. Circulation 113:1424–33

    Article  PubMed  Google Scholar 

  12. Li Y, Xu Y, Li W et al (2023) Cardiac MRI to predict sudden cardiac death risk in dilated cardiomyopathy. Radiology 307:e222552

  13. Di Marco A, Brown PF, Bradley J et al (2021) Improved risk stratification for ventricular arrhythmias and sudden death in patients with nonischemic dilated cardiomyopathy. J Am Coll Cardiol 77:2890–2905

    Article  PubMed  Google Scholar 

  14. Beltrami CA, Finato N, Rocco M et al (1995) The cellular basis of dilated cardiomyopathy in humans. J Mol Cell Cardiol 27:291–305

    Article  CAS  PubMed  Google Scholar 

  15. Di Marco A, Anguera I, Schmitt M et al (2017) Late gadolinium enhancement and the risk for ventricular arrhythmias or sudden death in dilated cardiomyopathy: systematic review and meta-analysis. JACC Heart Fail 5:28–38

    Article  PubMed  Google Scholar 

  16. Muser D, Nucifora G, Muser D et al (2021) Prognostic value of nonischemic ringlike left ventricular scar in patients with apparently idiopathic nonsustained ventricular arrhythmias. Circulation 143:1359–1373

    Article  CAS  PubMed  Google Scholar 

  17. Chen W, Qian W, Zhang X et al (2021) Ring-like late gadolinium enhancement for predicting ventricular tachyarrhythmias in non-ischaemic dilated cardiomyopathy. Eur Heart J Cardiovasc Imaging 22:1130–1138

    Article  PubMed  Google Scholar 

  18. Radenkovic D, Weingartner S, Ricketts L, Moon JC, Captur G (2017) T-1 map** in cardiac MRI. Heart Fail Rev 22:415–430

    Article  PubMed  PubMed Central  Google Scholar 

  19. Pinto YM, Elliott PM, Arbustini E et al (2016) Proposal for a revised definition of dilated cardiomyopathy, hypokinetic non-dilated cardiomyopathy, and its implications for clinical practice: a position statement of the ESC working group on myocardial and pericardial diseases. Eur Heart J 37:1850–8

    Article  PubMed  Google Scholar 

  20. Zhuang B, Li S, Xu J et al (2020) Age- and sex-specific reference values for atrial and ventricular structures in the validated normal Chinese population: a comprehensive measurement by cardiac MRI. J Magn Reson Imaging 52:1031–1043

    Article  PubMed  Google Scholar 

  21. Nishimura RA, Otto CM, Bonow RO et al (2017) 2017 AHA/ACC focused update of the 2014 AHA/ACC guideline for the management of patients with valvular heart disease: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. J Am Coll Cardiol 70:252–289

    Article  PubMed  Google Scholar 

  22. Hicks KA, Tcheng JE, Bozkurt B et al (2015) 2014 ACC/AHA key data elements and definitions for cardiovascular endpoint events in clinical trials: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Data Standards (Writing Committee to Develop Cardiovascular Endpoints Data Standards). J Am Coll Cardiol 66:403–69

    Article  PubMed  Google Scholar 

  23. Buxton AE, Calkins H, Callans DJ et al (2006) ACC/AHA/HRS 2006 key data elements and definitions for electrophysiological studies and procedures: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Data Standards (ACC/AHA/HRS Writing Committee to Develop Data Standards on Electrophysiology). J Am Coll Cardiol 48:2360–96

    Article  PubMed  Google Scholar 

  24. Kilic A, Mathier MA, Hickey GW et al (2021) Evolving trends in adult heart transplant with the 2018 Heart Allocation Policy Change. JAMA Cardiol 6:159–167

    Article  PubMed  Google Scholar 

  25. Greenland S (1989) Modeling and variable selection in epidemiologic analysis. Am J Public Health 79:340–9

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Hong H, Liu J, Zhu AX (2020) Modeling landslide susceptibility using LogitBoost alternating decision trees and forest by penalizing attributes with the bagging ensemble. Sci Total Environ 718:137231

    Article  CAS  PubMed  Google Scholar 

  27. Alba AC, Agoritsas T, Walsh M et al (2017) Discrimination and calibration of clinical prediction models: users’ guides to the medical literature. Jama 318:1377–1384

    Article  PubMed  Google Scholar 

  28. Kerr KF, Brown MD, Zhu K, Janes H (2016) assessing the clinical impact of risk prediction models with decision curves: guidance for correct interpretation and appropriate use. J Clin Oncol 34:2534–40

    Article  PubMed  PubMed Central  Google Scholar 

  29. Pencina MJ, D’Agostino RB Sr, D’Agostino RB Jr, Vasan RS (2008) Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med 27:157–772

    Article  PubMed  Google Scholar 

  30. ausdemSiepen F, Buss SJ, Messroghli D et al (2015) T1 map** in dilated cardiomyopathy with cardiac magnetic resonance: quantification of diffuse myocardial fibrosis and comparison with endomyocardial biopsy. Eur Heart J Cardiovasc Imaging 16:210–6

    Article  Google Scholar 

  31. Li S, Wang Y, Yang W et al (2022) Cardiac MRI risk stratification for dilated cardiomyopathy with left ventricular ejection fraction of 35% or higher. Radiology 306:e213059

  32. Ochs A, Riffel J, Ochs MM et al (2021) Myocardial mechanics in dilated cardiomyopathy: prognostic value of left ventricular torsion and strain. J Cardiovasc Magn Reson 23:136

    Article  PubMed  PubMed Central  Google Scholar 

  33. Pi SH, Kim SM, Choi JO et al (2018) Prognostic value of myocardial strain and late gadolinium enhancement on cardiovascular magnetic resonance imaging in patients with idiopathic dilated cardiomyopathy with moderate to severely reduced ejection fraction. J Cardiovasc Magn Reson 20:36

    Article  PubMed  PubMed Central  Google Scholar 

  34. Kitagawa T, Tatsugami F, Yokomachi K et al (2022) Native myocardial T1 value in predicting 1-year outcomes in patients with nonischemic dilated cardiomyopathy experiencing recent heart failure. Int Heart J 63:531–540

    Article  CAS  PubMed  Google Scholar 

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Funding

This study has received funding by the CAMS Innovation Fund for Medical Sciences (CIFMS) (2022-I2M-C&T-B-052), Construction Research Project of Key Laboratory (Cultivation) of Chinese Academy of Medical Sciences (2019PT310025), National Natural Science Foundation of China (Grant Nos. 81971588), and High-level research projects of the National Health Commission (2022-GSP-QZ-5).

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Author notes

  1. Di Zhou and Leyi Zhu contributed equally.

Authors and Affiliations

  1. Department of Magnetic Resonance Imaging, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 167 Beilishi Road, Bei**g, 100037, China

    Di Zhou, Leyi Zhu, Baiyan Zhuang, Jian He, **g Xu, Wen**g Yang, Yining Wang, Shuang Li, Shihua Zhao & Minjie Lu

  2. Department of Echocardiography, Fuwai Hospital and National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Bei**g, 100037, China

    Weichun Wu

  3. Department of Nuclear Medicine, Fuwai Hospital and National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Bei**g, 100037, China

    **aoxin Sun

  4. Saint James School of Medicine, Park Ridge, IL, 60068, USA

    Piyush Sharma

  5. Russell H. Morgan Department of Radiology and Radiological Sciences, Division of MR Research, The Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA

    Guanshu Liu

  6. National Heart, Lung and Blood Institute (NHLBI), National, Institutes of Health (NIH), Bethesda, MD, 20892, USA

    Arlene Sirajuddin & Andrew Arai

  7. Key Laboratory of Cardiovascular Imaging (Cultivation), Chinese Academy of Medical Sciences, Bei**g, China

    Minjie Lu

Authors
  1. Di Zhou
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Corresponding author

Correspondence to Minjie Lu.

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Guarantor

The scientific guarantor of this publication is Minjie Lu.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

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

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

No study subjects or cohorts have been previously reported.

Methodology

• prospective

• observational study

• performed at one institution

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Zhou, D., Zhu, L., Wu, W. et al. A novel cardiac magnetic resonance–based personalized risk stratification model in dilated cardiomyopathy: a prospective study. Eur Radiol 34, 4053–4064 (2024). https://doi.org/10.1007/s00330-023-10415-7

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