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Medical & Biological Engineering & Computing

, Volume 56, Issue 3, pp 491–504 | Cite as

Sensitivity analysis of ventricular activation and electrocardiogram in tailored models of heart-failure patients

  • C. SánchezEmail author
  • G. D’Ambrosio
  • F. Maffessanti
  • E. G. Caiani
  • F. W. Prinzen
  • R. Krause
  • A. Auricchio
  • M. Potse
Original Article

Abstract

Cardiac resynchronization therapy is not effective in a variable proportion of heart failure patients. An accurate knowledge of each patient’s electroanatomical features could be helpful to determine the most appropriate treatment. The goal of this study was to analyze and quantify the sensitivity of left ventricular (LV) activation and the electrocardiogram (ECG) to changes in 39 parameters used to tune realistic anatomical-electrophysiological models of the heart. Electrical activity in the ventricles was simulated using a reaction-diffusion equation. To simulate cellular electrophysiology, the Ten Tusscher-Panfilov 2006 model was used. Intracardiac electrograms and 12-lead ECGs were computed by solving the bidomain equation. Parameters showing the highest sensitivity values were similar in the six patients studied. QRS complex and LV activation times were modulated by the sodium current, the cell surface-to-volume ratio in the LV, and tissue conductivities. The T-wave was modulated by the calcium and rectifier-potassium currents, and the cell surface-to-volume ratio in both ventricles. We conclude that homogeneous changes in ionic currents entail similar effects in all ECG leads, whereas the effects of changes in tissue properties show larger inter-lead variability. The effects of parameter variations are highly consistent between patients and most of the model tuning could be performed with only ~10 parameters.

Keywords

Computer simulation ECG morphology Heart failure Left bundle branch block Patient-specific model Sensitivity analysis 

Notes

Acknowledgments

This work was supported by a grant from the Swiss National Supercomputing Centre (CSCS) under project ID s598.

Supplementary material

11517_2017_1696_MOESM1_ESM.pdf (81 kb)
(PDF 81.2 KB)

References

  1. 1.
    Aguado-Sierra J, Krishnamurthy A, Villongco C, Chuang J, Howard E, Gonzales MJ, Omens J, Krummen DE, Narayan S, Kerckhoffs RCP, McCulloch AD (2011) Patient-specific modeling of dyssynchronous heart failure: a case study. Prog Biophys Mol Biol 107(1):147–155CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Auricchio A, Fantoni C, Regoli F, Carbucicchio C, Goette A, Geller C, Kloss M, Klein H (2004) Characterization of left ventricular activation in patients with heart failure and left bundle-branch block. Circulation 109(9):1133–1139CrossRefPubMedGoogle Scholar
  3. 3.
    Bayés de Luna A, Batchvarov VN, Malik M (2006) The morphology of the electrocardiogram. In: Camm AJ, Luscher TF, Serruys PW (eds) The ESC textbook of cardiovascular medicine. Blackwell Publishers, OxfordGoogle Scholar
  4. 4.
    Boulakia M, Cazeau S, Fernández MA, Gerbeau JF, Zemzemi N (2010) Mathematical modeling of electrocardiograms: a numerical study. Ann Biomed Eng 38(3):1071–1097CrossRefPubMedGoogle Scholar
  5. 5.
    Bradley CP, Pullan AJ, Hunter PJ (2000) Effects of material properties and geometry on electrocardiographic forward simulations. Ann Biomed Eng 28(7):721–741CrossRefPubMedGoogle Scholar
  6. 6.
    Brignole M, Auricchio A, Baron-Esquivias G, Bordachar P, Boriani G, Breithardt OA, Cleland J, Deharo JC, Delgado V, Elliott PM, Gorenek B, Israel CW, Leclercq C, Linde C, Mont L, Padeletti L, Sutton R, Vardas PE, Zamorano JL, Achenbach S, Baumgartner H, Bax JJ, Bueno H, Dean V, Deaton C, Erol C, Fagard R, Ferrari R, Hasdai D, Hoes AW, Kirchhof P, Knuuti J, Kolh P, Lancellotti P, Linhart A, Nihoyannopoulos P, Piepoli MF, Ponikowski P, Sirnes PA, Tamargo JL, Tendera M, Torbicki A, Wijns W, Windecker S, Blomstrom-Lundqvist C, Badano LP, Aliyev F, Bänsch D, Bsata W, Buser P, Charron P, Daubert JC, Dobreanu D, Faerestrand S, Le Heuzey JY, Mavrakis H, McDonagh T, Merino JL, Nawar MM, Nielsen JC, Pieske B, Poposka L, Ruschitzka F, Van Gelder IC, Wilson CM (2013) 2013 ESC Guidelines on cardiac pacing and cardiac resynchronization therapy. Eur Heart J 34:2281–2329CrossRefPubMedGoogle Scholar
  7. 7.
    Britton OJ, Bueno-Orovio A, Van Ammel K, Lu HR, Towart R, Gallacher DJ, Rodriguez B (2013) Experimentally calibrated population of models predicts and explains intersubject variability in cardiac cellular electrophysiology. Proc Natl Acad Sci U S A 110(23):E2098–2105CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Burton BM, Erem B, Potter K, Rosen P, Johnson CR, Brooks DH, Macleod RS (2013) Uncertainty visualization in forward and inverse cardiac models. Comput Cardiol 40:57–60Google Scholar
  9. 9.
    Buzzard GT, Xiu D (2011) Variance-based global sensitivity analysis via sparse-grid interpolation and cubature. Commun Comput Phys 9(03):542–567CrossRefGoogle Scholar
  10. 10.
    Carro J, Rodríguez JF, Laguna P, Pueyo E (2011) A human ventricular cell model for investigation of cardiac arrhythmias under hyperkalaemic conditions. Philos Trans R Soc A 369(1954):4205–4232CrossRefGoogle Scholar
  11. 11.
    Chang ETY, Strong M, Clayton RH (2015) Bayesian sensitivity analysis of a cardiac cell model using a gaussian process emulator. PLoS ONE 10(6):e0130252CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Conti CA, Votta E, Corsi C, De Marchi D, Tarroni G, Stevanella M, Lombardi M, Parodi O, Caiani EG, Redaelli A (2011) Left ventricular modelling: a quantitative functional assessment tool based on cardiac magnetic resonance imaging. Interface Focus 1(3):384–395CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Elshrif MM, Shi P, Cherry EM (2014) Electrophysiological properties under heart failure conditions in a human ventricular cell: a modeling study. In: Conference proceedings: annual international conference of the IEEE engineering in medicine and biology society 2014, pp 4324–4329Google Scholar
  14. 14.
    Fahy GJ, Pinski SL, Miller DP, McCabe N, Pye C, Walsh MJ, Robinson K (1996) Natural history of isolated bundle branch block. Am J Cardiol 77(14):1185–1190CrossRefPubMedGoogle Scholar
  15. 15.
    Geerts L, Kerckhoffs R, Bovendeerd P, Arts T (2003) Towards patient specific models of cardiac mechanics: a sensitivity study. In: Proceedings of the 2nd international conference on functional imaging and modeling of the heart. Springer, Berlin, FIMH’03, pp 81–90Google Scholar
  16. 16.
    Gradman AH, Alfayoumi F (2006) From left ventricular hypertrophy to congestive heart failure: management of hypertensive heart disease. Prog Cardiovasc Dis 48(5):326–341CrossRefPubMedGoogle Scholar
  17. 17.
    Grandi E, Pandit SV, Voigt N, Workman AJ, Dobrev D, Jalife J, Bers DM (2011) Human atrial action potential and Ca2 + model: sinus rhythm and chronic atrial fibrillation. Circ Res 109(9):1055–1066CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Kalayciyan R, Keller DUJ, Seemann G, Dössel O (2009) Creation of a realistic endocardial stimulation profile for the visible man dataset. In: Dössel O, Schlegel WC (eds) World congress on medical physics and biomedical engineering, 2009, Munich, Germany, no. 25/4 in IFMBE Proceedings. Springer, Berlin, pp 934–937Google Scholar
  19. 19.
    Keller D, Weber F, Seemann G, Dössel O (2010) Ranking the influence of tissue conductivities on forward-calculated ECGs. IEEE Trans Biomed Eng 57(7):1568–1576CrossRefPubMedGoogle Scholar
  20. 20.
    Kharche S, Lüdtke N, Panzeri S, Zhang H (2009) A global sensitivity index for biophysically detailed cardiac cell models: a computational approach. In: Ayache N, Delingette H, Sermesant M (eds) Functional imaging and modeling of the heart. Lecture Notes in Computer Science. Springer, Berlin, pp 366–375Google Scholar
  21. 21.
    Kohl P, Camelliti P, Burton FL, Smith GL (2005) Electrical coupling of fibroblasts and myocytes: relevance for cardiac propagation. J Electrocardiol 38(4, Supplement):45–50CrossRefPubMedGoogle Scholar
  22. 22.
    Krause D, Potse M, Dickopf T, Krause R, Auricchio A, Prinzen F (2012) Poster: hybrid parallelization of a realistic heart model. In: Keller R, Kramer D, Weiss J-P (eds) Facing the multicore - challenge II; Aspects of new paradigms and technologies in parallel computing. Lecture Notes in Computer Science, vol 7174Google Scholar
  23. 23.
    Krueger MW, Rhode K, Weber FM, Keller DUJ, Caulfield D, Seemann G, Knowles BR, Razavi R, Dössel O (2010) Patient-specific volumetric atrial models with electrophysiological components: a comparison of simulations and measurements. In: Biomedizinische Technik/Biomedical Engineering, vol 55 (Suppl. 1)Google Scholar
  24. 24.
    Levy D, Larson MG, Vasan RS, Kannel WB, Ho KK (1996) The progression from hypertension to congestive heart failure. JAMA 275(20):1557–1562CrossRefPubMedGoogle Scholar
  25. 25.
    MacLeod RS, Stinstra JG, Lew S, Whitaker RT, Swenson DJ, Cole MJ, Krüger J, Brooks DH, Johnson CR (2009) Subject-specific, multiscale simulation of electrophysiology: a software pipeline for image-based models and application examples. Phil Trans R Soc A 367(1896):2293–2310CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Mincholé A, Pueyo E, Rodríguez JF, Zacur E, Doblaré M, Laguna P (2011) Quantification of restitution dispersion from the dynamic changes of the T-wave peak to end, measured at the surface ECG. IEEE Trans Biomed Eng 58(5):1172–1182CrossRefPubMedGoogle Scholar
  27. 27.
    Myerburg RJ, Gelband H, Nilsson K, Castellanos A, Morales AR, Bassett AL (1978) The role of canine superficial ventricular muscle fibers in endocardial impulse distribution. Circ Res 42(1):27–35CrossRefPubMedGoogle Scholar
  28. 28.
    Neal ML, Kerckhoffs R (2010) Current progress in patient-specific modeling. Brief Bioinform 11(1):111–126CrossRefPubMedGoogle Scholar
  29. 29.
    Nguyên UC, Potse M, Regoli F, Caputo ML, Conte G, Murzilli R, Muzzarelli S, Moccetti T, Caiani EG, Prinzen FW, Krause R, Auricchio A (2015) An in-silico analysis of the effect of heart position and orientation on the ECG morphology and vectorcardiogram parameters in patients with heart failure and intraventricular conduction defects. J Electrocardiol 48(4):617–625CrossRefPubMedGoogle Scholar
  30. 30.
    O’Hara T, Virág L, Varró A, Rudy Y (2011) Simulation of the undiseased human cardiac ventricular action potential: model formulation and experimental validation. PLoS Comput Biol 7(5):e1002061CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Opthof T, Remme CA, Jorge E, Noriega F, Wiegerinck RF, Tasiam A, Beekman L, Alvarez-Garcia J, Munoz-Guijosa C, Coronel R, Cinca J (2017) Cardiac activation-repolarization patterns and ion channel expression mapping in intact isolated normal human hearts. Heart Rhythm 14(2):265–272CrossRefPubMedGoogle Scholar
  32. 32.
    Pitzalis MV, Iacoviello M, Romito R, Guida P, De Tommasi E, Luzzi G, Anaclerio M, Forleo C, Rizzon P (2005) Ventricular asynchrony predicts a better outcome in patients with chronic heart failure receiving cardiac resynchronization therapy. J Am Coll Cardiol 45(1):65–69CrossRefPubMedGoogle Scholar
  33. 33.
    Potse M, Dubé B, Richer J, Vinet A, Gulrajani R (2006) A comparison of monodomain and bidomain reaction-diffusion models for action potential propagation in the human heart. IEEE Trans Biomed Eng 53(12):2425–2435CrossRefPubMedGoogle Scholar
  34. 34.
    Potse M, Dubé B, Vinet A (2009) Cardiac anisotropy in boundary-element models for the electrocardiogram. Med Biol Eng Comput 47(7):719–729CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Potse M, Krause D, Kroon W, Murzilli R, Muzzarelli S, Regoli F, Caiani E, Prinzen FW, Krause R, Auricchio A (2014) Patient-specific modelling of cardiac electrophysiology in heart-failure patients. Europace 16(suppl 4):iv56–iv61CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Pueyo E, Husti Z, Hornyik T, Baczkó I, Laguna P, Varró A, Rodríguez B (2010) Mechanisms of ventricular rate adaptation as a predictor of arrhythmic risk. Am J Physiol Heart Circ Physiol 298(5):H1577–1587CrossRefPubMedGoogle Scholar
  37. 37.
    Pueyo E, Corrias A, Virág L, Jost N, Szél T, Varró A, Szentandrássy N, Nánási PP, Burrage K, Rodríguez B (2011) A multiscale investigation of repolarization variability and its role in cardiac arrhythmogenesis. Biophys J 101(12):2892–2902CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Rodriguez LM, Timmermans C, Nabar A, Beatty G, Wellens HJ (2003) Variable patterns of septal activation in patients with left bundle branch block and heart failure. J Cardiovasc Electrophysiol 14(2):135–141CrossRefPubMedGoogle Scholar
  39. 39.
    Roger VL, Go AS, Lloyd-Jones DM, Adams RJ, Berry JD, Brown TM, Carnethon MR, Dai S, de Simone G, Ford ES, Fox CS, Fullerton HJ, Gillespie C, Greenlund KJ, Hailpern SM, Heit JA, Ho PM, Howard V J, Kissela BM, Kittner SJ, Lackland DT, Lichtman JH, Lisabeth LD, Makuc DM, Marcus GM, Marelli A, Matchar D B, McDermott MM, Meigs JB, Moy CS, Mozaffarian D, Mussolino ME, Nichol G, Paynter NP, Rosamond WD, Sorlie P D, Stafford RS, Turan TN, Turner MB, Wong ND, Wylie-Rosett J, American Heart Association Statistics Committee and Stroke Statistics Subcommittee (2011) Heart disease and stroke statistics–2011 update: a report from the American Heart Association. Circulation 123(4):e18–e209CrossRefPubMedGoogle Scholar
  40. 40.
    Romero L, Pueyo E, Fink M, Rodríguez B (2009) Impact of ionic current variability on human ventricular cellular electrophysiology. Am J Physiol Heart Circ Physiol 297(4):H1436—1445CrossRefGoogle Scholar
  41. 41.
    Sahli Costabal F, Hurtado DE, Kuhl E (2016) Generating Purkinje networks in the human heart. J Biomech 49(12):2455–2465CrossRefPubMedGoogle Scholar
  42. 42.
    Sánchez C, Corrias A, Bueno-Orovio A, Davies M, Swinton J, Jacobson I, Laguna P, Pueyo E, Rodríguez B (2012) The Na + /K + pump is an important modulator of refractoriness and rotor dynamics in human atrial tissue. Am J Physiol Heart Circ Physiol 302(5):H1146–1159CrossRefPubMedGoogle Scholar
  43. 43.
    Sánchez C, Bueno-Orovio A, Wettwer E, Loose S, Simon J, Ravens U, Pueyo E, Rodriguez B (2014) Inter-subject variability in human atrial action potential in sinus rhythm versus chronic atrial fibrillation. PLoS ONE 9(8):e105897CrossRefPubMedPubMedCentralGoogle Scholar
  44. 44.
    Sarkar AX, Sobie EA (2011) Quantification of repolarization reserve to understand interpatient variability in the response to proarrhythmic drugs: a computational analysis. Heart Rhythm 8(11):1749–1755CrossRefPubMedPubMedCentralGoogle Scholar
  45. 45.
    Sarkar AX, Christini DJ, Sobie EA (2012) Exploiting mathematical models to illuminate electrophysiological variability between individuals. J Physiol 590(Pt 11):2555–2567CrossRefPubMedPubMedCentralGoogle Scholar
  46. 46.
    Sobie EA, Sarkar AX (2011) Regression methods for parameter sensitivity analysis: applications to cardiac arrhythmia mechanisms. In: Annual international conference of the IEEE EMBS 2011, pp 4657–4660Google Scholar
  47. 47.
    Strauss DG, Selvester RH, Lima JAC, Arheden H, Miller JM, Gerstenblith G, Marbán E, Weiss RG, Tomaselli GF, Wagner GS, Wu KC (2008) ECG quantification of myocardial scar in cardiomyopathy patients with or without conduction defects correlation with cardiac magnetic resonance and arrhythmogenesis. Circ Arrhythm Electrophysiol 1(5):327–336CrossRefPubMedPubMedCentralGoogle Scholar
  48. 48.
    Strauss DG, Selvester RH, Wagner GS (2011) Defining left bundle branch block in the era of cardiac resynchronization therapy. Am J Cardiol 107(6):927–934CrossRefPubMedGoogle Scholar
  49. 49.
    Surawicz B, Childers R, Deal BJ, Gettes LS, Bailey JJ, Gorgels A, Hancock EW, Josephson M, Kligfield P, Kors JA, Macfarlane P, Mason JW, Mirvis DM, Okin P, Pahlm O, Rautaharju PM, van Herpen G, Wagner GS, Wellens H, American Heart Association Electrocardiography and Arrhythmias Committee, Council on Clinical Cardiology, American College of Cardiology Foundation, Heart Rhythm Society (2009) AHA/ACCF/HRS recommendations for the standardization and interpretation of the electrocardiogram: part III: intraventricular conduction disturbances: a scientific statement from the American Heart Association Electrocardiography and Arrhythmias Committee, Council on Clinical Cardiology; the American College of Cardiology Foundation; and the Heart Rhythm Society. Endorsed by the International Society for Computerized Electrocardiology. J Am Coll Cardiol 53(11):976–981CrossRefPubMedGoogle Scholar
  50. 50.
    Sweeney MO, Prinzen FW (2008) Ventricular pump function and pacing; physiological and clinical integration. Circ Arrhythm Electrophysiol 1(2):127–139CrossRefPubMedGoogle Scholar
  51. 51.
    Swenson D, Levine J, Fu Z, Tate J, MacLeod R (2010) The effect of non-conformal finite element boundaries on electrical monodomain and Bidomain simulations. In: 2010 Computing in Cardiology, pp 97–100Google Scholar
  52. 52.
    Tomaselli GF, Marbán E (1999) Electrophysiological remodeling in hypertrophy and heart failure. Cardiovasc Res 42(2):270–283CrossRefPubMedGoogle Scholar
  53. 53.
    Tondel K, Vik JO, Martens H, Indahl UG, Smith N, Omholt SW (2013) Hierarchical multivariate regression-based sensitivity analysis reveals complex parameter interaction patterns in dynamic models. Chemom Intell Lab 120:25–41CrossRefGoogle Scholar
  54. 54.
    Trenor B, Cardona K, Gomez JF, Rajamani S, Ferrero JM, Belardinelli L, Saiz J (2012) Simulation and mechanistic investigation of the arrhythmogenic role of the late sodium current in human heart failure. PloS One 7(3):e32659CrossRefPubMedPubMedCentralGoogle Scholar
  55. 55.
    ten Tusscher KHWJ, Panfilov AV (2006) Alternans and spiral breakup in a human ventricular tissue model. Am J Physiol Heart Circ Physiol 291(3):H1088–1100CrossRefPubMedGoogle Scholar
  56. 56.
    Wang L, Chitiboi T, Meine H, Günther M, Hahn HK (2016) Principles and methods for automatic and semi-automatic tissue segmentation in MRI data. Magn Reson Mater Phys 29(2):95–110CrossRefGoogle Scholar
  57. 57.
    Zareba W, Klein H, Cygankiewicz I, Hall WJ, McNitt S, Brown M, Cannom D, Daubert JP, Eldar M, Gold MR, Goldberger JJ, Goldenberg I, Lichstein E, Pitschner H, Rashtian M, Solomon S, Viskin S, Wang P, Moss AJ, Investigators MADIT-CRT (2011) Effectiveness of cardiac resynchronization therapy by QRS morphology in the multicenter automatic defibrillator implantation trial-cardiac resynchronization therapy (MADIT-CRT). Circulation 123(10):1061–1072CrossRefPubMedGoogle Scholar

Copyright information

© International Federation for Medical and Biological Engineering 2017

Authors and Affiliations

  1. 1.Center for Computational Medicine in Cardiology (CCMC), Institute of Computational ScienceUniversità della Svizzera italianaLuganoSwitzerland
  2. 2.General Military Academy of Zaragoza (AGM)Defense University Centre (CUD)ZaragozaSpain
  3. 3.Division of CardiologyCardiocentro TicinoLuganoSwitzerland
  4. 4.Electronics, Information, and Bioengineering DepartmentPolitecnico di MilanoMilanItaly
  5. 5.Cardiovascular Research Institute Maastricht (CARIM)Maastricht UniversityMaastrichtThe Netherlands
  6. 6.IHU LIRYCUniversité de BordeauxPessacFrance
  7. 7.Inria Bordeaux Sud-OuestTalenceFrance
  8. 8.Present address: Biosignal Interpretation and Computational Simulation Group (BSICoS), Engineering Research Institute of Aragon (I3A)University of ZaragozaZaragozaSpain

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