Skip to main content
SpringerLink
Log in

Computational Identification of Ventricular Arrhythmia Risk in Pediatric Myocarditis

  • Original Article
  • Published:
Pediatric Cardiology Aims and scope Submit manuscript

Abstract

Children with myocarditis have increased risk of ventricular tachycardia (VT) due to myocardial inflammation and remodeling. There is currently no accepted method for VT risk stratification in this population. We hypothesized that personalized models developed from cardiac late gadolinium enhancement magnetic resonance imaging (LGE-MRI) could determine VT risk in patients with myocarditis using a previously-validated protocol. Personalized three-dimensional computational cardiac models were reconstructed from LGE-MRI scans of 12 patients diagnosed with myocarditis. Four patients with clinical VT and eight patients without VT were included in this retrospective analysis. In each model, we incorporated a personalized spatial distribution of fibrosis and myocardial fiber orientations. Then, VT inducibility was assessed in each model by pacing rapidly from 26 sites distributed throughout both ventricles. Sustained reentrant VT was induced from multiple pacing sites in all patients with clinical VT. In the eight patients without clinical VT, we were unable to induce sustained reentry in our simulations using rapid ventricular pacing. Application of our non-invasive approach in children with myocarditis has the potential to correctly identify those at risk for developing VT.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Sagar S, Liu P, Copper L (2012) Myocarditis. Lancet 379:738–747

    Article  PubMed  Google Scholar 

  2. Dennert Robert C, Harry J, Heymans S (2008) Acute viral myocarditis. Eur Heart J 29(17):2073–2082

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Matsuura H, Ichida F, Saji T, Ogawa S, Waki K, Kaneko M, Tahara M, Soga T, Ono Y, Yasukochi S (2016) Clinical features of acute and fulminant myocarditis in Children—2nd nationwide survey by Japanese Society of Pediatric Cardiology and Cardiac Surgery. Circ J 80(11):2362–2368

    Article  PubMed  Google Scholar 

  4. Butts RJ, Boyle GJ, Deshpande SR, Gambetta K, Knecht KR, Prada-Ruiz CA, Richmond ME, West SC, Lal AK (2017) Characteristics of clinically diagnosed pediatric myocarditis in a contemporary multi-center cohort. Pediatr Cardiol 38(6):1175–1182

    Article  PubMed  Google Scholar 

  5. Sankar J, Khalil S, Jeeva Sankar M, Kumar D, Dubey N (2011) Short-term outcomes of acute fulminant myocarditis in Children. Pediatr Cardiol 32(7):885–890

    Article  PubMed  Google Scholar 

  6. Teele SA, Allan CK, Laussen PC, Newburger JW, Gauvreau K, Thiagarajan RR (2011) Management and outcomes in pediatric patients presenting with acute fulminant myocarditis. Pediatrics 158(4):638–643.e1

    Article  Google Scholar 

  7. Saji T, Matsuura H, Hasegawa K, Nishikawa T, Yamamoto E, Ohki H, Yasukochi S, Arakaki Y, Joo K, Nakazawa M (2012) Comparison of the clinical presentation, treatment, and outcome of fulminant and acute myocarditis in children. Circ J 76(5):1222–1228

    Article  PubMed  Google Scholar 

  8. Feldman AM, McNamara D (2000) Myocarditis. NEJM 343:1388–1398

    Article  CAS  PubMed  Google Scholar 

  9. Li L, Zhang Y, Burke A, Xue A, Zhao Z, Fowler D, Shen Y, Li L (2017) Demographic, clinical and pathological features of sudden deaths due to myocarditis: results from a statewide population-based autopsy study. Forensic Sci Int 272:81–86

    Article  PubMed  Google Scholar 

  10. Doolan A, Langlois N, Semsarian C (2004) Causes of sudden cardiac death in young Australians. Med J Aust 180:110–112

    PubMed  Google Scholar 

  11. Cooper L, Keren A, Sliwa K, Matsumori A, Mensah GA (2014) The global burden of myocarditis: part 1: a systematic literature review for the global burden of diseases, injuries, and risk factors 2010 study. Glob Heart 9:121–129

    Article  PubMed  Google Scholar 

  12. Anderson BR, Silver ES, Richmond ME, Lieberman L (2014) Usefulness of arrhythmias as predictors of death and resource utilization in children with myocarditis. J Am Coll Cardiol 144(9):1400–1405

    Article  Google Scholar 

  13. Te AL, Wu T, Lin Y, Chen YY, Chung FP, Chang SL et al (2017) Increased risk of ventricular tachycardia and cardiovascular death in patients with myocarditis during the long-term follow up. Medicine 96:e6633

    Article  PubMed  PubMed Central  Google Scholar 

  14. Xu HF, Ding YJ, Shen YW, Xue AM, Xu HM, Luo CL, Li BX, Liu YL, Zhao ZQ (2012) MicroRNA-1 represses Cx43 expression in viral myocarditis. Mol Cell Biochem 361(1–2):141–148

    Article  CAS  Google Scholar 

  15. Tse G, Yeo JM, Chan YW, Lai ETH, Yan BP (2016) What is the arrhythmic substrate in viral myocarditis? Insights from clinical and animal studies. Front Physiol 7:308

    PubMed  PubMed Central  Google Scholar 

  16. Steinke K, Sachse F, Ettischer N, Strutz-Seebohm N, Henrion U, Rohrbeck M et al (2013) Coxsackievirus B3 modulates cardiac ion channels. FASEB J 27(10):4108–4121

    Article  CAS  PubMed  Google Scholar 

  17. Park H, Park H, Lee D, Oh S, Lim J, Hwang H et al (2014) Increased phosphorylation of Ca2+ handling proteins as a proarrhythmic mechanism in myocarditis. Circ J 78(9):2292–2301

    Article  CAS  PubMed  Google Scholar 

  18. Zhang A, Zhang H, Wu S (2010) Immunomodulation by atorvastatin upregulates expression gap junction proteins in coxsackievirus B3 (CVB3)-induced myocarditis. Inflamm Res 59(4):255–262

    Article  CAS  PubMed  Google Scholar 

  19. Veeraraghavan R, Salama MD, Poelzing S (2012) Interstitial volume modulates the conduction velocity-gap junction relationship. Am J Physiol Heart Circ Physiol 302:278–286

    Article  CAS  Google Scholar 

  20. Andreoletti L, Venteo L, Douche-Aorik F, Canas F et al (2007) Active coxsackieviral B infection is associated with disruption of dystrophin in endomyocardial tissue of patients who died suddenly of acute myocardial infarction. J Am Coll Cardiol 50:2207–2214

    Article  CAS  PubMed  Google Scholar 

  21. Dello Russo A, Casella M, Pieroni M et al (2012) Drug-refractory ventricular tachycardias after myocarditis: endocardial and epicardial radiofrequency ablation. Circ Arrhythm Electrophysiol 5:492–498

    Article  PubMed  Google Scholar 

  22. Wakisaka Y, Niwano S, Niwano H, Saito J, Yoshida T, Hirasawa S et al (2004) Structural and electrical remodeling in rat acute myocarditis and subsequent heart failure. Cardiovasc Res 63:689–699

    Article  CAS  PubMed  Google Scholar 

  23. Pieroni M, Smaldone C, Bellocci F, Cihakova D (2011) Myocarditis presenting with ventricular arrhythmias: role of electroanatomical mapping-guided endomyocardial biopsy in differential diagnosis. Myocarditis InTech, Rijeka

    Google Scholar 

  24. Bayer JD, Lalani GG, Vigmond EJ, Narayan SM, Trayanova NA (2016) Mechanisms linking electrical alternans and clinical ventricular arrhythmia in human heart failure. Heart Rhythm 13:1922–1931

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Friedrich MG, Sechtem U, Schulz-Menger J, Holmvang G, Alakija P, Cooper LT et al (2009) Cardiovascular magnetic resonance in myocarditis: a JACC white paper. J Am Coll Cardiol 53(17):1475–1487

    Article  PubMed  PubMed Central  Google Scholar 

  26. Hales-Kharazmi A, Hirsch N, Kelleman M, Slesnick T, Deshpande SR (2017) Utility of cardiac MRI in paediatric myocarditis. Cardiol Young 14:1–9

    Google Scholar 

  27. Lurz P, Luecke C, Eitel I, Fhrenbach F, Frank C, Grothoff M et al (2016) Comprehensive cardiac magnetic resonance imaging in patients with suspected myocarditis the MyoRacerTrial. J Am Coll Cardiol 67(15):1800–1811

    Article  PubMed  Google Scholar 

  28. Mavrogeni S, Bratis K, Georgakopoulos D, Karanasios E, Kolovou G, Pavlides G, Papadopoulos G (2012) Evaluation of myocarditis in a pediatric population using cardiovascular magnetic resonance and endomyocardial biopsy. Int J Cardiol 160(3):192–195

    Article  PubMed  Google Scholar 

  29. Brighenti M, Donti A, Giulia Gagliardi M, Maschietto N, Marini D, Lombardi M et al (2016) Endomyocardial biopsy safety and clinical yield in pediatric myocarditis: an Italian perspective. Catheter Cardiovasc Interv 87(4):762–767

    Article  PubMed  Google Scholar 

  30. Banka P, Robinson JD, Uppu SC, Harris MA, Hasbani K, Lai WW et al (2015) Cardiovascular magnetic resonance techniques and findings in children with myocarditis: a multicenter retrospective study. J Cardiovasc Magn Reson 7(1):96

    Article  Google Scholar 

  31. Sachdeva S, Song X, Dham N et al (2015) Analysis of clinical parameters and cardiac magnetic resonance imaging as predictors of outcome in pediatric myocarditis. J Am Coll Cardiol 115(4):499–504

    Article  Google Scholar 

  32. Satoh H, Sano M, Suwa K et al (2014) Distribution of late gadolinium enhancement in various types of cardiomyopathies: significance in differential diagnosis, clinical features and prognosis. World J Cardiol 6:585–601

    Article  PubMed  PubMed Central  Google Scholar 

  33. Mavrogeni S, Petrou E, Kolovou G, Theodorakis G, Iliodromitis E (2013) Prediction of ventricular arrhythmias using cardiovascular magnetic resonance. Eur Heart J Cardiovasc Imaging 14(6):518–525

    Article  PubMed  Google Scholar 

  34. Eichhorn C, Murthy VL, Agarwal V, Kaneko K, Cuddy S, Aghayev A et al (2017) Prognostic value of cardiac magnetic resonance tissue characterization in risk stratifying patients with suspected myocarditis. J Am Coll Cardiol 70(16):1964–1976

    Article  PubMed  PubMed Central  Google Scholar 

  35. Grün S, Schumm J, Greulich S, Wagner A, Schneider S, Bruder O et al (2012) Long-term follow-up of biopsy proven viral myocarditis: predictors of mortality and incomplete recovery. J Am Coll Cardiol 59(18):1604–1615

    Article  PubMed  Google Scholar 

  36. Teele SA, Allan CK, Laussen PC et al (2011) Management and outcomes in pediatric patients presenting with acute fulminant myocarditis. Pediatrics 158(4):638–643

    Article  Google Scholar 

  37. Miyake CY, Teele SA, Chen L et al (2014) In-hospital arrhythmia development and outcomes in pediatric patients with acute myocarditis. J Am Coll Cardiol 133(3):535–540

    Article  Google Scholar 

  38. Casadonte JR, Mazwi ML, Gambetta KE, Palac HL, McBride ME, Eltayeb OM et al (2017) Risk factors for cardiac arrest or mechanical circulatory support in children with fulminant myocarditis. Pediatr Cardiol 38(1):128–134

    Article  PubMed  Google Scholar 

  39. Puelz C, Acosta S, Riviere B, Penny DJ, Brady KM, Rusin CG (2017) A computational study of the Fontan circulation with fenestration or hepatic vein exclusion. Comput Biol Med 89:405–418

    Article  PubMed  PubMed Central  Google Scholar 

  40. Ni MW, Prather RO, Rodriguez G, Quinn R, Divo E, Fogel M et al (2018) Computational investigation of a self-powered fontan circulation. Cardiovasc Eng Tecnol. https://doi.org/10.1007/s13239-018-0342-5

    Article  Google Scholar 

  41. Slesnick TC (2017) Role of computational modelling in planning and executing interventional procedures for congenital heart disease. Can J Cardiol 33:1159–1170

    Article  PubMed  Google Scholar 

  42. Arevalo HJ, Vadakkumpadan F, Guallar E, Jebb A, Malamas P, Wu KC, Trayanova NA (2016) Arrhythmia risk stratification of patients after myocardial infarction using personalized heart models. Nat Commun 7:11437

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Deng D, Arevalo HJ, Prakosa A, Callans DJ, Trayanova NA (2016) A feasibility study of arrhythmia risk prediction in patients with myocardial infarction and preserved ejection fraction. Europace 18(suppl 4):iv60–iv66. https://doi.org/10.1093/europace/euw351

    Article  PubMed  PubMed Central  Google Scholar 

  44. Trayanova NA, Pashakhanloo F, Wu KC, Halperin HR (2017) Imaging-based simulations for predicting sudden death and guiding ventricular tachycardia ablation. Circ Arrhythm Electrophysiol 10(7):e004743. https://doi.org/10.1161/CIRCEP.117.004743

    Article  PubMed  PubMed Central  Google Scholar 

  45. Schmidt A, Azevedo CF, Cheng A, Gupta SN, Bluemke DA, Foo TK et al (2007) Infarct tissue heterogeneity by magnetic resonance imaging identifies enhanced cardiac arrhythmia susceptibility in patients with left ventricular dysfunction. Circulation 115:2006–2014

    Article  PubMed  PubMed Central  Google Scholar 

  46. Prakosa A, Malamas P, Zhang S, Pashakhanloo F, Arevalo H, Herzka DA et al (2014) Methodology for image-based reconstruction of ventricular geometry for patient-specific modeling of cardiac electrophysiology. Prog Biophys Mol Biol 115:226–234

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Ukwatta E, Arevalo H, Li K, Yuan J, Qiu W, Malamas P et al (2016) Myocardial infarct segmentation from magnetic resonance images for personalized modeling of cardiac electrophysiology. IEEE Trans Med Imaging 35(6):1408–1419

    Article  PubMed  Google Scholar 

  48. Deng DD, Arevalo H, Pashakhanloo F, Prakosa A, Ashikaga H, McVeigh E et al (2015) Accuracy of prediction of infarct-related circuits from image-based models reconstructed form low and high resolution MRI. Front Physiol 6:282. https://doi.org/10.3389/fphys.2015.00282

    Article  PubMed  PubMed Central  Google Scholar 

  49. Prassl A, Kickinger F, Ahammer H, Grau V, Schneider JE, Hofer E et al (2009) Automatically generated, anatomically accurate meshes for cardiac electrophysiology problems. IEEE Trans Biomed 56(5):1318–1330

    Article  Google Scholar 

  50. Bayer JD, Blake RC, Plank G, Trayanova NA (2012) A novel rule-based algorithm for assigning myocardial fiber orientation to computational heart models. Ann Biomed Eng 40(10):2243–2254

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. ten Tusscher KHWJ, Noble D, Noble PJ, Panfilov AV (2004) A model for human ventricular tissue. Am J Physiol Heart Circ Physiol 286(4):H1573–H1589

    Article  PubMed  Google Scholar 

  52. Vigmond EJ, Huhges M, Plank G, Leon LJ (2003) Computational tools for modeling electrical activity in cardiac tissue. J Electocardiol 3:69–74

    Article  Google Scholar 

  53. Vigmond EJ, dos Santos RW, Prassl AJ, Deo M, Plank G (2007) Solvers for the cardiac biodomain equations. Prog Biophys Mol Biol 96(1–3):3–18

    PubMed  PubMed Central  Google Scholar 

  54. Prakosa A, Arevalo HJ, Deng D, Boyle PM, Nikolov PP, Ashikaga H et al (2018) Personalized virtual-heart technology for guiding the ablation of infarct-related ventricular tachycardia. Nat Biomed Eng 2:732–740

    Article  PubMed  PubMed Central  Google Scholar 

  55. Cerqueira MD, Weissman NJ, Dilsizian V, Jacobs AK, Kaul S, Laskey WK et al (2002) 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 105:539–542

    Article  PubMed  Google Scholar 

  56. Vadakkumpadan F, Trayanova N, Wu KC (2014) Image-based left ventricular shape analysis for sudden cardiac death risk stratification. Heart Rhythm 11(10):1693–1700

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Funding

National Institutes of Health Pioneer Award (DP1-HL123271) to N.A.T., a grant from the Leducq Foundation to N.A.T., and National Institutes of Health T32 Grant (T32-HL-125239-3) to M.J.C.

Author information

Authors and Affiliations

  1. Divison of Pediatric Cardiology, Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Mark J. Cartoski & Philip J. Spevak

  2. Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA

    Plamen P. Nikolov, Adityo Prakosa, Patrick M. Boyle & Natalia A. Trayanova

  3. Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Natalia A. Trayanova

Authors
  1. Mark J. Cartoski
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Plamen P. Nikolov
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Adityo Prakosa
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Patrick M. Boyle
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Philip J. Spevak
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Natalia A. Trayanova
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mark J. Cartoski.

Ethics declarations

Conflict of interest

N.A.T. holds partial ownership of CardioSolv Ablation Technologies LLC. The other authors declare that they have no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Online Resource 1

Video showing three-dimensional pattern of simulated reentrant ventricular tachycardia in patient 1 following endocardial pacing (AVI 42040 KB)

Online Resource 2

Video showing three-dimensional pattern of simulated reentrant ventricular tachycardia in patient 4 following endocardial pacing (AVI 47839 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cartoski, M.J., Nikolov, P.P., Prakosa, A. et al. Computational Identification of Ventricular Arrhythmia Risk in Pediatric Myocarditis. Pediatr Cardiol 40, 857–864 (2019). https://doi.org/10.1007/s00246-019-02082-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00246-019-02082-7

Keywords

Search

Navigation