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Parametrization of an in-silico circulatory simulation by clinical datasets – towards prediction of ventricular function following assist device implantation

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Published/Copyright: March 6, 2017
Biomedical Engineering / Biomedizinische Technik
From the journal Biomedical Engineering / Biomedizinische Technik Volume 62 Issue 2

Abstract

Background:

Left ventricular assist device (LVAD) therapy has revolutionized the way end stage heart failure is treated today. Analysis of LVAD interaction with the whole cardiovascular system and its biological feedback loops is often conducted by means of computer models. Generating real time pressure volume loops (PV-loops) in patients, not using conductance catheters but routine diagnostics to feed an in-silico model could help to predict postoperative complications.

Methods:

Routinely obtained hemodynamic measurements to evaluate myocardial function prior to LVAD implantation like pressure readings in the aorta, the left atrium and the left ventricle and simultaneous three-dimensional (3D) echocardiography recordings were assessed to parametrize a reduced computational model of the cardiovascular system. An automatic parameter identification procedure has been developed.

Results:

The results constitute a patient-individual computational simulation model. An exemplary in-silico study focusing on the effect of different ventricular assist device (VAD) speeds has been conducted. Results allow for estimation of the resulting hemodynamic parameters and changes of the PV-loops.

Conclusion:

The model improves understanding and prediction of the interaction between pump and ventricles. Future modifications in exporting and merging routinely assessed real time hemodynamic patient data are necessary to investigate various clinical and pathological conditions of LVAD recipients.

Keywords: assist device; circulatory support; simulation
Corresponding author: Andreas Goetzenich, MD, PhD, Department of Thoracic and Cardiovascular Surgery, University Hospital RWTH Aachen, Pauwelsstr. 30, 52074 Aachen, Germany, Phone: +49 241 80 35556, Fax: +49 241 80 333 555 6
aAjay Moza and Jonas Gesenhues: These authors contributed equally to this work.

Acknowledgments

This work was supported by the German Research Foundation (DFG) within the Smart Life Suport 2.0 PathoMod project (PAK 183-2). The authors declare to have no conflicting interests.

References

[1] Atluri P, Goldstone AB, Fairman AS, et al. Predicting right ventricular failure in the modern, continuous flow left ventricular assist device era. Ann Thorac Surg 2013; 96: 857–863; discussion 863–4.10.1016/j.athoracsur.2013.03.099Search in Google Scholar PubMed PubMed Central

[2] Baan J, van der Velde ET, Steendijk P. Ventricular pressure-volume relations in vivo. Eur Heart J 1992; 13(Suppl E): 2–6.10.1093/eurheartj/13.suppl_E.2Search in Google Scholar PubMed

[3] Brunberg A, Heinke S, Spillner J, Autschbach R, Abel D, Leonhardt S. Modeling and simulation of the cardiovascular system: a review of applications, methods, and potentials. Biomed Tech (Berl) 2009; 54: 233–244.10.1515/BMT.2009.030Search in Google Scholar PubMed

[4] Buccheri S, Costanzo L, Tamburino C, Monte I. Reference values for real time three-dimensional echocardiography-derived left ventricular volumes and ejection fraction: review and meta-analysis of currently available studies. Echocardiography 2015; 32: 1841–1850.10.1111/echo.12972Search in Google Scholar PubMed

[5] Chung DC, Niranjan SC, Clark JW, et al. A dynamic model of ventricular interaction and pericardial influence. Am J Physiol-Heart Circ Physiol 1997; 272: H2942–62.10.1152/ajpheart.1997.272.6.H2942Search in Google Scholar PubMed

[6] Clark JE, Marber MS. Advancements in pressure-volume catheter technology—stress remodelling after infarction. Exp Physiol 2013; 98: 614–621.10.1113/expphysiol.2012.064733Search in Google Scholar PubMed

[7] Colacino FM, Moscato F, Piedimonte F, Arabia M, Danieli GA. Left ventricle load impedance control by apical VAD can help heart recovery and patient perfusion: a numerical study. ASAIO J 2007; 53: 263–277.10.1097/MAT.0b013e31805b7e39Search in Google Scholar PubMed

[8] Gesenhues J, Hein M, Albin T, Rossaint R, Abel D. Cardiac modeling: identification of subject specific left-ventricular isovolumic pressure curves. IFAC-PapersOnLine 2015; 48: 581–586.10.1016/j.ifacol.2015.10.204Search in Google Scholar

[9] Gesenhues J, Hein M, Ketelhut M, et al. Benefits of objectoriented models and ModeliChart: modern tools and methods for the interdisciplinary research on smart biomedical technology. Biomed Eng-Biomed Tech 2017; 62: 111–121Search in Google Scholar

[10] Kaufmann TA, Neidlin M, Büsen M, Sonntag SJ, Steinseifer U. Implementation of intrinsic lumped parameter modeling into computational fluid dynamics studies of cardiopulmonary bypass. J Biomech 2014; 47: 729–735.10.1016/j.jbiomech.2013.11.005Search in Google Scholar PubMed

[11] Kjørstad KE, Korvald C, Myrmel T. Pressure-volume-based single-beat estimations cannot predict left ventricular contractility in vivo. Am J Physiol Heart Circ Physiol2002; 282: H1739–H1750.10.1152/ajpheart.00638.2001Search in Google Scholar PubMed

[12] Kono A, Maughan WL, Sunagawa K, Hamilton K, Sagawa K, Weisfeldt ML. The use of left ventricular end-ejection pressure and peak pressure in the estimation of the end-systolic pressure-volume relationship. Circulation 1984; 70: 1057–65.10.1161/01.CIR.70.6.1057Search in Google Scholar

[13] Kormos RL, Teuteberg JJ, Pagani FD, et al. HeartMate II Clinical Investigators. Right ventricular failure in patients with the HeartMate II continuous-flow left ventricular assist device: incidence, risk factors, and effect on outcomes. J Thorac Cardiovasc Surg 2010; 139: 1316–1324.10.1016/j.jtcvs.2009.11.020Search in Google Scholar

[14] Leaning MS, Pullen HE, Carson ER, Finkelstein L. Modelling a complex biological system: the human cardiovascular system1. Methodology and model description. Trans Inst Meas Control 1983; 5: 71–86.10.1177/014233128300500202Search in Google Scholar

[15] Ramakrishna H, Feinglass N, Augoustides JG. Clinical update in cardiac imaging including echocardiography. J Cardiothorac Vasc Anesth 2010; 24: 371–378.10.1053/j.jvca.2009.12.018Search in Google Scholar

[16] Roger VL. Epidemiology of heart failure. Circ Res 2013; 113: 646–659.10.1161/CIRCRESAHA.113.300268Search in Google Scholar

[17] Saito S, Toda K, Nakamura T, et al. Should destination therapy with implantable left ventricular assist device replace heart transplantation? J Card Failure 2015; 21: S151.10.1016/j.cardfail.2015.08.032Search in Google Scholar

[18] Shimada YJ, Shiota T. A meta-analysis and investigation for the source of bias of left ventricular volumes and function by three-dimensional echocardiography in comparison with magnetic resonance imaging. Am J Cardiol 2011; 107: 126–138.10.1016/j.amjcard.2010.08.058Search in Google Scholar

[19] Spitzer VM, Whitlock DG. The visible human dataset: the anatomical platform for human simulation. Anatom Rec 1998; 253: 49–57.10.1002/(SICI)1097-0185(199804)253:2<49::AID-AR8>3.0.CO;2-9Search in Google Scholar

[20] Takeuchi M, Igarashi Y, Tomimoto S, et al. Single-beat estimation of the slope of the end-systolic pressure-volume relation in the human left ventricle. Circulation 1991; 83: 202–212.10.1161/01.CIR.83.1.202Search in Google Scholar

[21] ten Brinke EA, Klautz RJ, Tulner SA, et al. Clinical and functional effects of restrictive mitral annuloplasty at midterm follow-up in heart failure patients. Ann Thorac Surg 2010; 90: 1913–1920.10.1016/j.athoracsur.2010.08.010Search in Google Scholar

[22] Ursino M. Interaction between carotid baroregulation and the pulsating heart: a mathematical model. Am J Physiol 1998; 275: H1733–H1747.10.1152/ajpheart.1998.275.5.H1733Search in Google Scholar PubMed

[23] Vandenberghe S, Segers P, Steendijk P, et al. Modeling ventricular function during cardiac assist: does time-varying elastance work? ASAIO J 2006; 52: 4–8.10.1097/01.mat.0000196525.56523.b8Search in Google Scholar PubMed

[24] Westerhof N, Lankhaar J, Westerhof B. The arterial Windkessel. Med Biol Eng Comput 2009; 47.2: 131–141.10.1007/s11517-008-0359-2Search in Google Scholar PubMed

[25] Yu Y, Porter J. Mathematical modeling of ventricular suction induced by a rotary ventricular assist device. In: American Control Conference. Minneapolis, MN, USA, 2006.Search in Google Scholar

[26] Zimmermann W-H. Strip and dress the human heart. Circ Res 2016; 118: 12–13.10.1161/CIRCRESAHA.115.307938Search in Google Scholar PubMed

Received: 2016-3-31
Accepted: 2017-1-12
Published Online: 2017-3-6
Published in Print: 2017-4-1

©2017 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Editorial
  3. Smart life support reloaded: design and control of complex therapeutic devices
  4. Special Issue Articles
  5. Benefits of object-oriented models and ModeliChart: modern tools and methods for the interdisciplinary research on smart biomedical technology
  6. Parametrization of an in-silico circulatory simulation by clinical datasets – towards prediction of ventricular function following assist device implantation
  7. Design of a right ventricular mock circulation loop as a test bench for right ventricular assist devices
  8. A mock heart engineered with helical aramid fibers for in vitro cardiovascular device testing
  9. Comparison of novel physiological load-adaptive control strategies for ventricular assist devices
  10. High-frequency operation of a pulsatile VAD – a simulation study
  11. Convolutive blind source separation of surface EMG measurements of the respiratory muscles
  12. Determining the appropriate model complexity for patient-specific advice on mechanical ventilation
  13. Physiological closed-loop control of mechanical ventilation and extracorporeal membrane oxygenation
  14. Decentralized safety concept for closed-loop controlled intensive care
  15. Model-based glycaemic control: methodology and initial results from neonatal intensive care
https://doi.org/10.1515/bmt-2016-0078
Keywords for this article
assist device; circulatory support; simulation

Articles in the same Issue

  1. Frontmatter
  2. Editorial
  3. Smart life support reloaded: design and control of complex therapeutic devices
  4. Special Issue Articles
  5. Benefits of object-oriented models and ModeliChart: modern tools and methods for the interdisciplinary research on smart biomedical technology
  6. Parametrization of an in-silico circulatory simulation by clinical datasets – towards prediction of ventricular function following assist device implantation
  7. Design of a right ventricular mock circulation loop as a test bench for right ventricular assist devices
  8. A mock heart engineered with helical aramid fibers for in vitro cardiovascular device testing
  9. Comparison of novel physiological load-adaptive control strategies for ventricular assist devices
  10. High-frequency operation of a pulsatile VAD – a simulation study
  11. Convolutive blind source separation of surface EMG measurements of the respiratory muscles
  12. Determining the appropriate model complexity for patient-specific advice on mechanical ventilation
  13. Physiological closed-loop control of mechanical ventilation and extracorporeal membrane oxygenation
  14. Decentralized safety concept for closed-loop controlled intensive care
  15. Model-based glycaemic control: methodology and initial results from neonatal intensive care
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