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Cardiac ischemia—insights from computational models

Kardiale Ischämie – Erkenntnisse aus Computermodellen

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Herzschrittmachertherapie + Elektrophysiologie Aims and scope Submit manuscript

Abstract

Background

Complementary to clinical and experimental studies, computational cardiac modeling serves to obtain a comprehensive understanding of the cardiovascular system in order to analyze dysfunction, evaluate existing, and develop novel treatment strategies.

Objectives

We describe the basics of multiscale computational modeling of cardiac electrophysiology from the molecular ion channel to the whole body scale. By modeling cardiac ischemia, we illustrate how in silico experiments can contribute to our understanding of how the pathophysiological mechanisms translate into changes observed in diagnostic tools such as the electrocardiogram (ECG).

Materials and methods

Quantitative in silico modeling spans a wide range of scales from ion channel biophysics to ECG signals. For each of the scales, a set of mathematical equations describes electrophysiology in relation to the other scales. Integration of ischemia-induced changes is performed on the ion channel, single-cell, and tissue level. This approach allows us to study how effects simulated at molecular scales translate to changes in the ECG.

Results

Ischemia induces action potential shortening and conduction slowing. Hence, ischemic myocardium has distinct and significant effects on propagation and repolarization of excitation, depending on the intramural extent of the ischemic region. For transmural and subendocardial ischemic regions, ST segment elevation and depression, respectively, were observed, whereas intermediate ischemic regions were found to be electrically silent (NSTEMI).

Conclusions

In silico modeling contributes quantitative and mechanistic insight into fundamental ischemia-related arrhythmogenic mechanisms. In addition, computational modeling can help to translate experimental findings at the (sub-)cellular level to the organ and body context (e. g., ECG), thereby providing a thorough understanding of this routinely used diagnostic tool that may translate into optimized applications.

Zusammenfassung

Hintergrund

Das mechanistische Verständnis des Herz-Kreislauf-Systems ist von grundlegender Bedeutung, wenn man Fehlfunktionen verstehen, Behandlungsmöglichkeiten bewerten und neue Therapien entwickeln will. Die quantitative In-silico-Modellierung kann klinische und experimentelle Studien ergänzen.

Fragestellung

Wir beschreiben die Grundlagen einer computergestützten Multiskalenmodellierung der kardialen Elektrophysiologie und des Elektrokardiogramms (EKG) – von Ionenkanälen auf molekularer Ebene bis hin zur Ebene des Gesamtorganismus. Am Beispiel der Modellierung der kardialen Ischämie veranschaulichen wir, wie In-silico-Experimente zum Verständnis der Zusammenhänge zwischen fundamentalen pathophysiologischen Mechanismen und Diagnosewerkzeugen wie dem EKG beitragen können.

Material und Methoden

Die numerische Herzmodellierung integriert viele zeitlich-räumliche Skalen: Von der Ionenkanalbiophysik bis hin zu EKG-Signalen. Für jede der Skalen beschreiben mathematische Gleichungen die elektrophysiologische Funktion in Beziehung zu den anderen Skalen. Die Integration von ischämieinduzierten Veränderungen erfolgt auf Ionenkanal‑, Einzelzell- und Gewebeebene. Mit diesem Ansatz lässt sich untersuchen, wie sich aus simulierten Effekten auf molekularer Ebene Änderungen im simulierten EKG ergeben.

Ergebnisse

Aufgrund der Verkürzung des Aktionspotenzials und der Leitungsverlangsamung haben ischämische Bereiche unterschiedlicher transmuraler Ausdehnung einen deutlichen Effekt auf die Erregungsausbreitung und die Repolarisation. Eine ST-Segment-Hebung bzw. -Senkung zeigte sich für transmurale bzw. subendokardiale ischämische Regionen. Ischämische Regionen mittlerer Ausdehnung waren elektrisch unauffällig (NSTEMI).

Schlussfolgerung

Die In-silico-Modellierung kann quantitative und mechanistische Erkenntnisse zu fundamentalen ischämiebezogenen arrhythmogenen Mechanismen liefern. Darüber hinaus erlaubt die computergestützte Modellierung, experimentelle Ergebnisse von der (sub-)zellulären Ebene auf die Organ- und EKG-Ebene zu übertragen. Somit trägt sie zu einem tieferen Verständnis des routinemäßig eingesetzten EKGs und zu einer Optimierung dieses Werkzeugs bei.

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References

  1. Arevalo HJ, Boyle PM, Trayanova NA (2016) Computational rabbit models to investigate the initiation, perpetuation, and termination of ventricular arrhythmia. Prog Biophys Mol Biol 121:185–194

    Article  PubMed  PubMed Central  Google Scholar 

  2. Carmeliet E (1999) Cardiac ionic currents and acute ischemia: from channels to arrhythmias. Physiol Rev 79:917–1017

    Article  CAS  PubMed  Google Scholar 

  3. Clayton RH, Bernus O, Cherry EM et al (2011) Models of cardiac tissue electrophysiology: progress, challenges and open questions. Prog Biophys Mol Biol 104:22–48

    Article  CAS  PubMed  Google Scholar 

  4. Dutta S, Mincholé A, Quinn TA, Rodriguez B (2017) Electrophysiological properties of computational human ventricular cell action potential models under acute ischemic conditions. Prog Biophys Mol Biol 129:40–52. https://doi.org/10.1016/j.pbiomolbio.2017.02.007

    Article  PubMed  Google Scholar 

  5. Dutta S, Mincholé A, Zacur E et al (2016) Early afterdepolarizations promote transmural reentry in ischemic human ventricles with reduced repolarization reserve. Prog Biophys Mol Biol 120:236–248

    Article  PubMed  PubMed Central  Google Scholar 

  6. Fink M, Niederer SA, Cherry EM et al (2011) Cardiac cell modelling: observations from the heart of the cardiac physiome project. Prog Biophys Mol Biol 104:2–21

    Article  PubMed  Google Scholar 

  7. Foster DB (2007) Twelve-lead electrocardiography: theory and interpretation. Springer, Berlin

    Google Scholar 

  8. Gemmell P, Burrage K, Rodríguez B, Quinn TA (2016) Rabbit-specific computational modelling of ventricular cell electrophysiology: using populations of models to explore variability in the response to ischemia. Prog Biophys Mol Biol 121:169–184

    Article  PubMed  PubMed Central  Google Scholar 

  9. Hanna G, Trayanova N, Graham, Ukwatta E (2016) Evaluation of a T1 mapping technique for stratifying patient risk: A preliminary study using computer simulations of cardiac electrophysiology. In: 2016 IEEE EMBS Int. Student Conf. IEEE, S 1–4

  10. Hodgkin AL, Huxley AF (1952) A quantitative description of membrane current and its application to conduction and excitation in nerve. Bull Math Biol 117:500–544

    CAS  Google Scholar 

  11. Kahlmann W, Poremba E, Potyagaylo D et al (2017) Modelling of patient-specific Purkinje activation based on measured ECGs. Curr Dir Biomed Eng 3:171

    Google Scholar 

  12. Keller DUJ, Weiss DL, Dössel O, Seemann G (2012) Influence of IKs heterogeneities on the genesis of the T‑wave: a computational evaluation. IEEE Trans Biomed Eng 59:311–322

    Article  PubMed  Google Scholar 

  13. Li W, Kohl P, Trayanova N (2006) Myocardial ischemia lowers precordial thump efficacy: an inquiry into mechanisms using three-dimensional simulations. Heart Rhythm 3:179–186

    Article  PubMed  Google Scholar 

  14. Loewe A, Schulze WHW, Jiang Y et al (2011) Determination of optimal electrode positions of a wearable ECG monitoring system for detection of myocardial ischemia: A simulation study. Comput Cardiol (2010) 38:741–744

    Google Scholar 

  15. Loewe A, Schulze WHW, Jiang Y et al (2014) ECG-based detection of early myocardial ischemia in a computational model: impact of additional electrodes, optimal placement, and a new feature for ST deviation. Biomed Res Int 530352:1–11

    Google Scholar 

  16. Mayourian J, Cashman TJ, Ceholski DK et al (2017) Experimental and computational insight into human mesenchymal stem cell paracrine signaling and heterocellular coupling effects on cardiac contractility and arrhythmogenicity. Circ Res 121:411–423

    Article  CAS  PubMed  Google Scholar 

  17. Mayourian J, Savizky RM, Sobie EA, Costa KD (2016) Modeling electrophysiological coupling and fusion between human mesenchymal stem cells and cardiomyocytes. Plos Comput Biol 12:e1005014

    Article  PubMed  PubMed Central  Google Scholar 

  18. McDougal AD, Dewey CF (2017) Modeling oxygen requirements in ischemic cardiomyocytes. J Biol Chem 292:11760–11776

    Article  CAS  PubMed  Google Scholar 

  19. Potyagaylo D, Seemann G, Schulze WH, Dossel O (2015) Magnetocardiography did not uncover electrically silent ischemia in an in-silico study case. 2015 Comput. Cardiol. Conf. IEEE, pp 1145–1148

    Google Scholar 

  20. Quinn TA, Kohl P (2013) Combining wet and dry research: experience with model development for cardiac mechano-electric structure-function studies. Cardiovasc Res 97:601–611

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Schwab BC, Seemann G, Lasher RA et al (2013) Quantitative analysis of cardiac tissue including fibroblasts using three-dimensional confocal microscopy and image reconstruction: towards a basis for electrophysiological modeling. IEEE Trans Med Imaging 32:862–872

    Article  PubMed  PubMed Central  Google Scholar 

  22. Siogkas PK, Rigas G, Exarchos TP et al (2017) Computational estimation of the hemodynamic significance of coronary stenoses in arterial branches deriving from CCTA: A proof-of-concept study. Eng. Med. Biol. Soc. IEEE, pp 1348–1351

    Google Scholar 

  23. Stinstra JG, Shome S, Hopenfeld B, MacLeod RS (2005) Modelling passive cardiac conductivity during ischaemia. Med Biol Eng Comput 43:776–782

    Article  CAS  PubMed  Google Scholar 

  24. ten Tusscher KHWJ, Panfilov AV (2006) Alternans and spiral breakup in a human ventricular tissue model. Am J Physiol Heart Circ Physiol 291:H1088–H1100

    Article  PubMed  Google Scholar 

  25. Weiss DL, Ifland M, Sachse FB et al (2009) Modeling of cardiac ischemia in human myocytes and tissue including spatiotemporal electrophysiological variations. Biomed Tech (Berl) 54:107–125

    Article  Google Scholar 

  26. Wilhelms M, Dössel O, Seemann G (2010) Simulating the impact of the transmural extent of acute ischemia on the electrocardiogram. Comput Cardiol (2010) 37:13–16

    Google Scholar 

  27. Wilhelms M, Dössel O, Seemann G (2011) Comparing simulated electrocardiograms of different stages of acute cardiac ischemia. Lect Notes Comput Sci, vol. 6666., pp 11–19

    Google Scholar 

  28. Wilhelms M, Dössel O, Seemann G (2011) In silico investigation of electrically silent acute cardiac ischemia in the human ventricles. IEEE Trans Biomed Eng 58:2961–2964

    Article  PubMed  Google Scholar 

  29. Xie Y, Garfinkel A, Camelliti P et al (2009) Effects of fibroblast-myocyte coupling on cardiac conduction and vulnerability to reentry: a computational study. Heart Rhythm 6:1641–1649

    Article  PubMed  PubMed Central  Google Scholar 

  30. Xie Y, Garfinkel A, Weiss JN, Qu Z (2009) Cardiac alternans induced by fibroblast-myocyte coupling: mechanistic insights from computational models. Am J Physiol Heart Circ Physiol 297:H775–H784

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

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Correspondence to Gunnar Seemann.

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Conflict of interest

A. Loewe, E.M. Wülfers and G. Seemann declare that they have no competing interests.

This article does not contain any studies with human participants or animals performed by any of the authors. The ethical guidelines of the studies cited are provided within those studies.

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Loewe, A., Wülfers, E.M. & Seemann, G. Cardiac ischemia—insights from computational models. Herzschr Elektrophys 29, 48–56 (2018). https://doi.org/10.1007/s00399-017-0539-6

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